Анализ сайта decode.mit.edu
Основное Готовность: 95%
Домен
decode.mit.edu
Состояние доменного имени
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Проверяем корректность доменного имени и наличие технических проблем на уровне домена.
Используйте для продвижения только домен второго уровня.
Отличный запоминающийся домен.
Ответ сервера
200 Успешный ответ
HTTP-код ответа и цепочка редиректов
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Код 200 — страница доступна. Коды 3xx — редиректы (цепочки замедляют загрузку и размывают ссылочный вес). Коды 4xx/5xx — ошибки, поисковик не сможет проиндексировать страницу.
Сервер настроен корректно.
Цепочка редиректов:
http://decode.mit.edu
302 Found
https://decode.mit.edu/
200 OK
Безопасность
Сайт безопасен
Использование HTTPS и SSL-сертификат
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HTTPS — обязательный стандарт. Google и Яндекс отдают предпочтение защищённым сайтам. Отсутствие SSL или просроченный сертификат ведут к предупреждениям в браузере и снижению позиций.
Не настроен HSTS (Strict-Transport-Security) — рекомендуется включить.
На сайте работает защищенный протокол ssl и сайт открывается по https.
Ssl-сертификат действителен до 09.01.2027 2:59:59.
HTTP автоматически перенаправляется на HTTPS.
Поздравляем! Сайт не содержится в реестре РКН.
Кодировка
utf-8
Кодировка символов страницы
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Стандарт — UTF-8. Неправильная кодировка вызывает нечитаемые символы и мешает поисковику корректно распознать текст страницы.
Указана кодировка на странице utf-8.
Язык
en
Атрибут lang в HTML-теге
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Атрибут lang (<html lang="ru">) сообщает поисковикам и браузерам, на каком языке написана страница. Помогает при ранжировании в региональном поиске.
Язык документа указан явно: en.
Скорость загрузки
~1,47сек
Время отклика сервера (TTFB)
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Time To First Byte — время до получения первого байта от сервера. Норма до 200 мс. Медленный отклик ухудшает пользовательский опыт и ранжирование: Яндекс и Google учитывают скорость страниц.
Скорость загрузки сайта 1,47сек превышает 1 секунду. Желательно улучшить работу сайта!
Объем документа
170Кб
Размер HTML-кода страницы
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Слишком большой HTML замедляет парсинг браузером и сканирование поисковым роботом. Рекомендуется не более 200 Кб.
Объем html-документа 170Кб оптимален.
Структура html-документа корректна.
Ресурсы
Ресурсы: 10
Внешние ресурсы страницы (CSS, JS, изображения)
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Количество и тип подключённых ресурсов влияют на скорость загрузки. Большое число запросов увеличивает время рендеринга страницы.
Кол-во файлов ресурсов 10 достаточно.
Показать полный список ресурсов
| Тип | Название | Значение |
|---|---|---|
| stylesheet | https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/css/bootstrap.min.css | |
| stylesheet | https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css | |
| stylesheet | /assets/css/main.css?v=20240807 | |
| stylesheet | /assets/css/projects.css?v=20240807 | |
| js | https://friconix.com/cdn/friconix.js | |
| js | https://code.jquery.com/jquery-3.6.0.min.js | |
| js | https://www.googletagmanager.com/gtag/js?id=UA-40679228-2 | |
| js | /assets/scripts/main.js | |
| js | https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/js/bootstrap.bundle.min.js | |
| js | /assets/scripts/abstract-toggle.js |
Серверные заголовки
Кол-во: 7
HTTP-заголовки ответа сервера
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Заголовки сервера передают браузеру и поисковику служебную информацию: кеширование, безопасность (CSP, HSTS), сжатие (gzip). Правильная настройка ускоряет загрузку и повышает защищённость.
Найдены серверные заголовки 7шт. Подробнее про серверные заголовки.
Показать полный список серверных заголовков
| Ключ | Значение |
|---|---|
| Date | Sat, 22 Aug 2026 14:09:00 GMT |
| Server | Apache |
| ETag | "2a979-658f71b73bc40" |
| Accept-Ranges | bytes |
| Vary | Accept-Encoding |
| Cache-Control | public, max-age=3600, s-maxage=3600 |
| Connection | close |
CMS
Drupal
Система управления сайтом (движок)
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CMS — это движок, на котором работает сайт (WordPress, 1C-Bitrix, Tilda и др.). Знание CMS помогает понять возможности SEO-оптимизации и подобрать подходящие инструменты. «Не определена» — вероятно, самописный сайт или нестандартная сборка.
Сайт работает на CMS Drupal.
Веб-сервер
Apache
Программное обеспечение сервера
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Веб-сервер — это ПО, которое отдаёт страницы посетителям (nginx, Apache, IIS, LiteSpeed и др.). Определяется по серверным заголовкам ответа (Server, X-Powered-By и т.п.). «Не определён» — сервер намеренно скрывает эти заголовки, это нормальная практика безопасности.
Сайт работает на веб-сервере Apache.
Мета-теги Готовность: 32%
Title
DeCoDe Lab
Заголовок страницы в браузере и поисковой выдаче
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Title — главный SEO-заголовок страницы. Влияет на CTR в поиске и ранжирование. Оптимальная длина: 50–70 символов. Ключевые слова — ближе к началу.
Необходимо увеличить число символов в title (текущее значение мало: 10, минимум: 25, оптимально: от 40 до 45)
Дублей словоформ в title не найдено.
Description
DeCoDe Lab at MIT MechE
Описание страницы в поисковой выдаче (сниппет)
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Meta Description — текст под заголовком в выдаче. Напрямую на позиции не влияет, но влияет на CTR. Оптимальная длина: 120–160 символов.
Необходимо увеличить число символов в description (текущее значение мало: 23, минимум: 80, оптимально: от 120 до 130)
Keywords
Список ключевых слов страницы (устаревший тег)
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Meta Keywords не учитывается Яндексом и Google для ранжирования с 2009–2012 годов. Заполнение не обязательно, но не вредит. Конкурент может использовать содержимое для анализа.
Установите мета-тег keywords!
Канонический Url
Указывает поисковику основную версию страницы
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Canonical (rel=canonical) предотвращает проблему дублей страниц. Должен точно совпадать с URL проверяемой страницы. Неправильный canonical может передать ссылочный вес на другую страницу.
Рекомендуем прописать канонический Url.
Robots
Ошибок нет
Директивы для поисковых роботов на уровне страницы
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Meta Robots управляет индексацией конкретной страницы: index/noindex — индексировать ли, follow/nofollow — следовать ли по ссылкам. Noindex полностью исключает страницу из поиска.
Meta-тег robots не указан. Страница свободна для индексации.
Адаптивность
width=device-width, initial-scale=1, shrink-to-fit=no
Настройка масштабирования на мобильных устройствах
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Тег viewport (<meta name="viewport">) сообщает браузеру, как масштабировать страницу на мобильных. Стандарт: width=device-width, initial-scale=1. Отсутствие — признак отсутствия мобильной версии.
Meta-тег viewport со значением-константой width=device-width задаёт ширину страницы в соответствии с размером экрана.
Meta-тег viewport со значением initial-scale=1.0 определяет масштаб 1:1, т.е. «не масштабировать».
Разметка OpenGraph
Не найдено
Мета-теги для красивых превью в соцсетях
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OpenGraph (og:title, og:description, og:image) управляет тем, как страница выглядит при репосте в социальных сетях и мессенджерах. Отсутствие OG-тегов — невзрачный превью при шеринге.
Разметка OpenGraph не задана. Страница не оптимизирована под социальные сети. Мета-теги с разметкой Og помогают социальным роботам лучше структурировать Ваш сайт.
Все мета-теги
Кол-во: 3
Полный список мета-тегов страницы
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Таблица всех meta-тегов, включая нестандартные. Позволяет найти опечатки, дубли и лишние теги.
Найдены мета-теги 3шт. Мета-теги не видимы для человека и предназначены для обмена информацией между веб-страницей и поисковыми системами, браузерами и другими веб-службами. С ними роботы 🤖 и устройства ведут себя более ожидаемо.
Показать полный список мета-тегов
| Тип | Название | Значение |
|---|---|---|
| name | viewport | width=device-width, initial-scale=1, shrink-to-fit=no |
| name | description | DeCoDe Lab at MIT MechE |
| name | author | Faez Ahmed |
Оптимизация Готовность: 42%
Структура
Ошибок нет
Семантические HTML-элементы страницы
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Проверяет наличие основных структурных элементов: nav, header, footer, main. Корректная семантическая структура помогает поисковику понять архитектуру страницы.
Структура документа корректна (теги <html> и <body> присутствуют по одному на документ).
Контент
Есть ошибки
Объём и качество текстового содержимого
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Анализирует объём полезного текста на странице. Слишком мало — страница может считаться малополезной. Слишком много — ухудшается читаемость и восприятие.
Слова из title 2 встречаются в тексте редко. Добавьте в контент страницы слова из тега <title>!
Абзацев с текстом 142 достаточно.
Среднее число слов в абзаце 28 достаточно.
Кол-во знаков контента 39715 на странице оптимально.
Кол-во слов 5616 на странице оптимально.
Заголовки
Ошибок нет
Иерархия заголовков H1–H6
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H1 должен быть один и содержать ключевой запрос. H2–H6 описывают подразделы. Пропуск уровней (H1 → H3) и несколько H1 — типичные ошибки, снижающие понятность страницы для поисковика.
На странице присутствуют заголовки <h2> 7. Это хорошо.
На странице присутствуют заголовки <h3> 5.
Тошнота
7,28
Насколько одно слово доминирует в тексте
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Классическая тошнота = √(частота самого повторяющегося слова). Норма до 7–8: текст воспринимается естественно. Выше — поисковик может счесть страницу переспамленной.
Тошнота превышает норму 5. Измените текст страницы!
Академич. тошнота
26,87%
Насколько текст перенасыщен ключевыми словами
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Академическая тошнота = (частота слова / общее количество слов) × 100%. Показывает долю конкретного слова в тексте. Норма 5–15%.
Академическая тошнота превышает норму 5-15%. Измените текст страницы!
Семантическое ядро
20
Наиболее часто встречающиеся слова на странице
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Топ слов по частоте использования. Показывает, какие слова доминируют в тексте с точки зрения поисковика.
Контент страницы содержит осмысленный текст и слова.
Показать список слов
| Слово | Кол-во | Частота |
|---|---|---|
| design | 53 | 0,94% |
| dataset | 50 | 0,89% |
| engineering | 45 | 0,80% |
| received | 31 | 0,55% |
| decode | 28 | 0,50% |
| optimization | 26 | 0,46% |
| research | 19 | 0,34% |
| models | 15 | 0,27% |
| august | 15 | 0,27% |
| student | 13 | 0,23% |
| fellowship | 13 | 0,23% |
| kristen | 13 | 0,23% |
| across | 12 | 0,21% |
| candidate | 12 | 0,21% |
| framework | 12 | 0,21% |
| aerodynamic | 12 | 0,21% |
| datasets | 11 | 0,20% |
| workshop | 11 | 0,20% |
| conference | 11 | 0,20% |
| abstract | 11 | 0,20% |
Индексация Готовность: 10%
Индексирование
Есть ошибки
Разрешено ли индексирование страницы
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Проверяет, не закрыта ли страница от индексации через robots.txt, meta robots или X-Robots-Tag. Страница, закрытая от индексации, не появится в поисковой выдаче.
Анкоров на странице 256 слишком много. Проведите ревизию и оптимизацию ссылок сайта.
Robots.txt
Найден корректный robots.txt
Файл управления сканированием сайта роботами
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Robots.txt указывает поисковым роботам, какие страницы сканировать, а какие — нет. Ошибки в файле могут случайно закрыть важные разделы от индексации.
Robots.txt настроен корректно. Размер файла: 174405 байт. Загружен за: 2сек.
Проверяемая страница не запрещена в robots.txt.
Robots.txt доступен по постоянному адресу
Цепочка редиректов для файла robots.txt:
http://decode.mit.edu/robots.txt
302 Found
https://decode.mit.edu/
200 OK
Показать содержимое robots.txt
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="description" content="DeCoDe Lab at MIT MechE">
<meta name="author" content="Faez Ahmed">
<link rel="favicon" href="/assets/images/favicon.png">
<link rel="icon" href="/assets/images/favicon.png">
<title>DeCoDe Lab</title>
<!-- Bootstrap core CSS -->
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.6.2/dist/css/bootstrap.min.css" integrity="sha256-+IZRbz1B6ee9mUx/ejmonK+ulIP5A5bLDd6v6NHqXnI=" crossorigin="anonymous">
<!-- integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous"> -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css">
<link href="/assets/css/main.css?v=20240807" rel="stylesheet">
<link href="/assets/css/projects.css?v=20240807" rel="stylesheet">
</head>
<body>
<style>
@import url('https://fonts.googleapis.com/css2?family=Roboto:ital,wght@0,100;0,300;0,400;0,500;0,700;0,900;1,100;1,300;1,400;1,500;1,700;1,900&display=swap');
</style>
<script defer src="https://friconix.com/cdn/friconix.js"> </script>
<script
src="https://code.jquery.com/jquery-3.6.0.min.js"
integrity="sha256-/xUj+3OJU5yExlq6GSYGSHk7tPXikynS7ogEvDej/m4="
crossorigin="anonymous"></script>
<div class="dc-header">
<div class="logo-decode">
<a href="/">
<img class="img-fluid" src="/assets/images/logo/decode_logo.png" alt="DeCoDE Lab">
</a>
</div>
<div class="logo-mit">
<img class="img-fluid" src="/assets/images/logo/mit_logo_std_rgb_black.png" alt="MIT">
</div>
</div>
<nav class="navbar navbar-expand-md dc-navbar">
<div class="container">
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarTogglerDemo01"
aria-controls="navbarTogglerDemo01" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div class="collapse navbar-collapse" id="navbarTogglerDemo01">
<ul class="navbar-nav w-100">
<li class="nav-item">
<a class="nav-link" href="/">Home</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/team/">Team</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/news/">News</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/publications/">Publications</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/projects/eng-datasets/">Datasets</a>
</li>
<li class="nav-item">
<a class="nav-link" href="/software/">Software</a>
</li>
<li class="nav-item dropdown">
<a class="nav-link dropdown-toggle" href="#" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Courses</a>
<div class="dropdown-menu">
<a class="dropdown-item" href="/courses/2155-fall-2025/">2.155 Fall 2025</a>
<a class="dropdown-item" href="/courses/2155-fall-2024/">2.155 Fall 2024</a>
<a class="dropdown-item" href="/courses/2155-fall-2023/">2.155 Fall 2023</a>
<a class="dropdown-item" href="/courses/2155-fall-2022/">2.S997 Fall 2022</a>
<a class="dropdown-item" href="/courses/2155-fall-2021/">2.S997 Fall 2021</a>
<a class="dropdown-item" href="https://meche.mit.edu/featured-classes/numerical-computation-mechanical-engineers" target="_blank">2.086 Spring 2025 <i class="fas fa-external-link-alt fa-xs"></i></a>
<a class="dropdown-item" href="https://meche.mit.edu/featured-classes/numerical-computation-mechanical-engineers" target="_blank">2.086 Spring 2024 <i class="fas fa-external-link-alt fa-xs"></i></a>
<a class="dropdown-item" href="https://meche.mit.edu/featured-classes/numerical-computation-mechanical-engineers" target="_blank">2.086 Spring 2022 <i class="fas fa-external-link-alt fa-xs"></i></a>
<a class="dropdown-item" href="https://web.mit.edu/2.s009/www/course_info/course_description.html" target="_blank">2.S009 Fall 2020 <i class="fas fa-external-link-alt fa-xs"></i></a>
</div>
</li>
<li class="nav-item dropdown">
<a class="nav-link dropdown-toggle" href="#" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Workshops</a>
<div class="dropdown-menu">
<a class="dropdown-item" href="/D2D-IDETC26/">D2D @ IDETC 2026</a>
<a class="dropdown-item" href="/D2D-IDETC25/">D2D @ IDETC 2025</a>
<a class="dropdown-item" href="/D2D-IDETC24/">D2D @ IDETC 2024</a>
<a class="dropdown-item" href="/D2D-IDETC23/">D2D @ IDETC 2023</a>
</div>
</li>
<li class="nav-item">
<a class="nav-link" href="/contact-us/">Contact</a>
</li>
</ul>
</div>
</div>
</nav>
<div class="row no-gutters">
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-40679228-2"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag() { dataLayer.push(arguments); }
gtag('js', new Date());
gtag('config', 'UA-40679228-2');
</script>
<div class="row no-gutters">
<div id="carouselHome" class="carousel slide main-car" data-ride="carousel">
<div class="carousel-inner overlay"></div>
<div class="carousel-inner">
<div class="carousel-item active">
<img class="d-block img-fluid" src="/assets/images/lab_photo_25.jpg" alt="DeCoDE Lab photo">
</div>
<div class="carousel-item">
<img class="d-block img-fluid" src="/assets/images/network2.png" alt="Research network">
</div>
<div class="carousel-item">
<img class="d-block img-fluid" src="/assets/images/new_mit.gif" alt="MIT campus">
</div>
</div>
<a class="carousel-control-prev" href="#carouselHome" role="button" data-slide="prev">
<span class="carousel-control-prev-icon" aria-hidden="true"></span>
<span class="sr-only">Previous</span>
</a>
<a class="carousel-control-next" href="#carouselHome" role="button" data-slide="next">
<span class="carousel-control-next-icon" aria-hidden="true"></span>
<span class="sr-only">Next</span>
</a>
</div>
</div>
<div class="container">
<div class="hugespacer"></div>
<div class="dc-about-section">
<h2 class="intro_title">About the Lab</h2>
<div class="dc-about-card">
<p>The Design Computation and Digital Engineering (DeCoDE) Lab at MIT Mechanical Engineering advances the science of <b>AI-driven design</b>. We envision a future where humans and AI design together to tackle the world's most pressing challenges. To realize this vision, we build engineering-native AI — models, datasets, and solvers that work the way engineering actually works, through CAD, geometry, simulation, and optimization, under real physical and manufacturing constraints — to enhance the design of complex systems and support human teams in creating better products.</p>
<p>Our goal is to build versatile approaches that automate and augment engineering design across scales, levels of complexity, and disciplines, framing design problems as generalizable machine learning and optimization tasks. Much of our current research is organized around three pillars of engineering-native AI: workflow-native models and agents that operate directly on CAD programs, geometry, and topology; transferable foundation-model priors that keep prediction and optimization reliable when data are scarce and evaluations expensive; and open infrastructure — large datasets, objective-aware benchmarks, and fast solvers — that helps the whole field explore, evaluate, and generate solutions cumulatively. Together, these threads build toward our long-term goal: a General Artificial Designer — a dependable AI collaborator for engineering.</p>
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<span>Our core values — <b>Integrity, Inclusivity, Collaboration, and Excellence</b> — guide us to conduct research with honesty and transparency, foster a diverse and welcoming environment, recognize the power of teamwork, and continually strive for the highest quality. We are advocates for reproducible and open-source science, sharing most of our research code and papers online.</span>
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<img src="assets/images/news/idetc2026.png" alt="IDETC 2026: Best Paper Award, Fourth D2D Workshop, and a Student Hackathon">
<span class="dc-news-date">August 2026</span>
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<h4 class="dc-news-headline">IDETC 2026: Best Paper Award, Fourth D2D Workshop, and a Student Hackathon</h4>
<p class="dc-news-desc">A big IDETC-CIE 2026 in Houston is coming up for the lab as we have three papers and seven additional presentations. Our FLARE paper with GE Vernova won both the CIE Best Conference Paper Award and the AI/ML Technical Committee Best Paper Award. We are also organizing the fourth "From Data to Design" workshop and a student hackathon with nTop and MIT Lincoln Laboratory.</p>
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<img src="assets/images/news/afosr.png" alt="Dr. Ahmed Received the AFOSR Young Investigator Award">
<span class="dc-news-date">July 2026</span>
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<h4 class="dc-news-headline">Dr. Ahmed Received the AFOSR Young Investigator Award</h4>
<p class="dc-news-desc">Dr. Ahmed received the Air Force Office of Scientific Research (AFOSR) Young Investigator Program award (selected in 2025, with the grant effective 2026).</p>
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<img src="assets/images/faez.png" alt="Dr. Ahmed Named Esther and Harold E. Edgerton Career Development Professor">
<span class="dc-news-date">July 2026</span>
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<h4 class="dc-news-headline">Dr. Ahmed Named Esther and Harold E. Edgerton Career Development Professor</h4>
<p class="dc-news-desc">Dr. Ahmed was appointed to the Esther and Harold E. Edgerton Career Development Professorship at MIT.</p>
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<img src="assets/images/news/icml_ghadi.jpeg" alt="Four Papers at ICML 2026, FIRE Named Spotlight, and GIT-BO at ICLR 2026">
<span class="dc-news-date">July 2026</span>
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<h4 class="dc-news-headline">Four Papers at ICML 2026, FIRE Named Spotlight, and GIT-BO at ICLR 2026</h4>
<p class="dc-news-desc">The lab presented four papers at ICML 2026, with FIRE selected as a Spotlight (top 2.2% of submissions). Earlier in April, GIT-BO was presented at ICLR 2026.</p>
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<img src="assets/images/news/ghadi_rosen.png" alt="Fellowships for Ghadi and Rosen">
<span class="dc-news-date">July 2026</span>
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<h4 class="dc-news-headline">Fellowships for Ghadi and Rosen</h4>
<p class="dc-news-desc">Congratulations to Ghadi Nehme on receiving the GE Vernova Fellowship and to Rosen Yu on receiving the MathWorks Fellowship!</p>
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<img src="assets/images/logo/decode_logo.png" alt="DeCoDE Lab Moved to a New Lab Space">
<span class="dc-news-date">July 2026</span>
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<h4 class="dc-news-headline">DeCoDE Lab Moved to a New Lab Space</h4>
<p class="dc-news-desc">On July 16, the DeCoDE Lab moved to its new home in Building 35, Room 35-332. Come visit us in our new space!</p>
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<img src="assets/images/papers/2026_Ribeiro_FLOAT.png" alt="Lab's AI-Designed Tower Adopted as Official IEA Wind 22-MW Reference Floating Tower">
<span class="dc-news-date">June 2026</span>
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<h4 class="dc-news-headline">Lab's AI-Designed Tower Adopted as Official IEA Wind 22-MW Reference Floating Tower</h4>
<p class="dc-news-desc">Our AI-driven tower redesign with UPorto was adopted as the official floating tower in the IEA Wind 22-MW reference turbine repository maintained by the National Laboratory of the Rockies and DTU. <a href="https://github.com/IEAWindSystems/IEA-22-280-RWT">See the repository.</a></p>
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<img src="assets/images/news/convo.jpeg" alt="Celebrating Our First Two Ph.D. Graduates and Six Master's Milestones">
<span class="dc-news-date">May 2026</span>
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<h4 class="dc-news-headline">Celebrating Our First Two Ph.D. Graduates and Six Master's Milestones</h4>
<p class="dc-news-desc">At Commencement, we celebrated the DeCoDE Lab's first two Ph.D. graduates, Lyle Regenwetter and Amin Heyrani Nobari. We were also proud to celebrate the MS thesis milestones of Era Syla, Brandon Man, Annie Doris, Nomi Yu, Nicholas Sung, and Aditya Palaparthi. Excited for what comes next for all of them!</p>
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<img src="assets/images/news/lyle_defense.jpg" alt="Lyle Regenwetter Defends His Ph.D. Joining UIUC as Tenure-Track Faculty in August">
<span class="dc-news-date">May 2026</span>
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<h4 class="dc-news-headline">Lyle Regenwetter Defends His Ph.D. Joining UIUC as Tenure-Track Faculty in August</h4>
<p class="dc-news-desc">Lyle Regenwetter successfully defended his Ph.D. and will be joining the University of Illinois Urbana-Champaign as an Assistant Professor. Congratulations, Dr. Regenwetter.</p>
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<img src="assets/images/news/design2026.JPG" alt="Dr. Ahmed Delivered a Keynote at DESIGN 2026">
<span class="dc-news-date">May 2026</span>
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<h4 class="dc-news-headline">Dr. Ahmed Delivered a Keynote at DESIGN 2026</h4>
<p class="dc-news-desc">Dr. Ahmed delivered a keynote at the DESIGN 2026 conference in Cavtat, Croatia.</p>
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<img src="assets/images/members/ghadi.jpg" alt="Ghadi Received MechE's Thomas Sheridan Award">
<span class="dc-news-date">April 2026</span>
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<h4 class="dc-news-headline">Ghadi Received MechE's Thomas Sheridan Award</h4>
<p class="dc-news-desc">Congratulations to Ghadi Nehme on receiving MIT MechE's Thomas Sheridan Award for creativity in human-machine integration!</p>
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<img src="assets/images/papers/2024_doris_designqa.png" alt="DesignQA Received the JCISE Editor's Award">
<span class="dc-news-date">March 2026</span>
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<h4 class="dc-news-headline">DesignQA Received the JCISE Editor's Award</h4>
<p class="dc-news-desc">Our DesignQA paper received the Editor's Award from the ASME Journal of Computing and Information Science in Engineering.</p>
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<img src="assets/images/members/kanghyun.jpg" alt="Dr. Kang-Hyun Lee Joined Soongsil University as Tenure-Track Faculty">
<span class="dc-news-date">February 2026</span>
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<h4 class="dc-news-headline">Dr. Kang-Hyun Lee Joined Soongsil University as Tenure-Track Faculty</h4>
<p class="dc-news-desc">DeCoDE alumnus Dr. Kang-Hyun Lee accepted a tenure-track faculty position at Soongsil University.</p>
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<img src="assets/images/news/amin_defense.jpg" alt="Dr. Amin Heyrani Nobari Becomes the Lab's First Ph.D. Graduate">
<span class="dc-news-date">January 2026</span>
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<h4 class="dc-news-headline">Dr. Amin Heyrani Nobari Becomes the Lab's First Ph.D. Graduate</h4>
<p class="dc-news-desc">Amin Heyrani Nobari successfully defended his thesis, becoming the first Ph.D. graduate of the DeCoDE Lab. He continues with the lab as a Postdoctoral Associate. Congratulations, Dr. Nobari!</p>
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<img src="assets/images/members/Ferdous.png" alt="Dr. Ferdous Alam Joined Georgia Tech as Tenure-Track Faculty">
<span class="dc-news-date">January 2026</span>
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<h4 class="dc-news-headline">Dr. Ferdous Alam Joined Georgia Tech as Tenure-Track Faculty</h4>
<p class="dc-news-desc">DeCoDE alumnus Dr. Md Ferdous Alam accepted a tenure-track faculty position at the Georgia Institute of Technology.</p>
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<img src="assets/images/news/fuji.png" alt="Dr. Ahmed Visited Toyota's Higashi-Fuji Technical Center">
<span class="dc-news-date">January 2026</span>
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<h4 class="dc-news-headline">Dr. Ahmed Visited Toyota's Higashi-Fuji Technical Center</h4>
<p class="dc-news-desc">Dr. Ahmed visited Toyota at the Higashi-Fuji Technical Center in Japan, discussing AI for engineering design with Toyota researchers.</p>
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<img src="assets/images/news/neurips2025.jpeg" alt="Four Lab Papers at NeurIPS 2025">
<span class="dc-news-date">December 2025</span>
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<h4 class="dc-news-headline">Four Lab Papers at NeurIPS 2025</h4>
<p class="dc-news-desc">The lab presented four papers at NeurIPS 2025, including Optimize Any Topology, VideoCAD, BikeBench, and Activation-Informed Merging. Read more in <a href="https://www.linkedin.com/posts/ahmedfaez_icml2026-decodelab-activity-7455969448303378433-wtex?">this post</a>.</p>
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<img src="assets/images/news/tata_visit.jpg" alt="Dr. Ahmed Visited Tata Steel, Tata Motors, and Tata Power facilities in India">
<span class="dc-news-date">December 2025</span>
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<h4 class="dc-news-headline">Dr. Ahmed Visited Tata Steel, Tata Motors, and Tata Power facilities in India</h4>
<p class="dc-news-desc">Dr. Ahmed visited the Tata Steel plant in Jamshedpur, along with visits to Tata Motors in Pune and Tata Power in Mumbai, exploring opportunities for AI in manufacturing and energy.</p>
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<img src="assets/images/news/amazon.png" alt="Amazon Research Award">
<span class="dc-news-date">October 2025</span>
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<h4 class="dc-news-headline">Amazon Research Award</h4>
<p class="dc-news-desc">Dr. Ahmed received an Amazon Research Award, supporting the lab's work with AWS resources.</p>
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<img src="assets/images/news/AI-EDSE-banner.png" alt="Dr. Ahmed Co-Organized the 2025 NSF Workshop on AI in Engineering Design and Systems Engineering">
<span class="dc-news-date">October 2025</span>
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<h4 class="dc-news-headline">Dr. Ahmed Co-Organized the 2025 NSF Workshop on AI in Engineering Design and Systems Engineering</h4>
<p class="dc-news-desc">Dr. Ahmed co-organized the 2025 NSF Workshop on Artificial Intelligence in Engineering Design and Systems Engineering (AI-EDSE), held October 16-17, 2025 at Purdue University, bringing together researchers to discuss the future of AI in engineering design.</p>
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<img src="assets/images/members/Francis.jpg" alt="Dr. Francis Ogoke Joined Carnegie Mellon as Tenure-Track Faculty">
<span class="dc-news-date">August 2025</span>
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<h4 class="dc-news-headline">Dr. Francis Ogoke Joined Carnegie Mellon as Tenure-Track Faculty</h4>
<p class="dc-news-desc">DeCoDE alumnus Dr. Francis Ogoke started a tenure-track faculty position at Carnegie Mellon University.</p>
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<img src="assets/images/members/cyril.jpg" alt="Dr. Cyril Picard Joined P-1 AI">
<span class="dc-news-date">August 2025</span>
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<h4 class="dc-news-headline">Dr. Cyril Picard Joined P-1 AI</h4>
<p class="dc-news-desc">DeCoDE alumnus Dr. Cyril Picard joined P-1 AI, a technology startup.</p>
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<img src="assets/images/news/dac_annie.jpg" alt="DAC Best Paper Award">
<span class="dc-news-date">August 2025</span>
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<h4 class="dc-news-headline">DAC Best Paper Award</h4>
<p class="dc-news-desc">CAD-Coder paper by Annie Doris and team received the "DAC Best Paper Award"</p>
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<img src="assets/images/news/dac_idetc25.jpg" alt="Dr. Ahmed Received DAC Young Investigator Award">
<span class="dc-news-date">August 2025</span>
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<h4 class="dc-news-headline">Dr. Ahmed Received DAC Young Investigator Award</h4>
<p class="dc-news-desc">Dr. Ahmed received the 2025 DAC Young Investigator Award at the ASME IDETC conference.</p>
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<img src="assets/images/news/keenan.jpeg" alt="Dr. Ahmed Received the Keenan Award for Innovation in Undergraduate Education">
<span class="dc-news-date">July 2025</span>
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<h4 class="dc-news-headline">Dr. Ahmed Received the Keenan Award for Innovation in Undergraduate Education</h4>
<p class="dc-news-desc">Dr. Ahmed received MIT MechE's Keenan Award for Innovation in Undergraduate Education.</p>
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<img src="assets/images/news/studentawards25.png" alt="Annie, Noah, and Amin Won MechE Student Awards">
<span class="dc-news-date">May 2025</span>
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<h4 class="dc-news-headline">Annie, Noah, and Amin Won MechE Student Awards</h4>
<p class="dc-news-desc">Congratulations to Annie Doris, Noah Bagazinski, and Amin Heyrani Nobari on receiving MIT MechE student awards!</p>
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<img src="assets/images/news/nsf.png" alt="Dr. Ahmed Received the NSF CAREER Award">
<span class="dc-news-date">April 2025</span>
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<h4 class="dc-news-headline">Dr. Ahmed Received the NSF CAREER Award</h4>
<p class="dc-news-desc">Dr. Ahmed received the National Science Foundation CAREER Award. <a href="https://news.mit.edu/2025/school-engineering-faculty-staff-receive-awards-0509">Read more at MIT News.</a></p>
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<img src="assets/images/news/nordanglia.jpg" alt=""Designing Solutions with AI" MIT Challenge Reaches 1,800+ Student Projects Worldwide">
<span class="dc-news-date">January 2025</span>
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<h4 class="dc-news-headline">"Designing Solutions with AI" MIT Challenge Reaches 1,800+ Student Projects Worldwide</h4>
<p class="dc-news-desc">Dr. Ahmed co-created "Designing Solutions with AI," an MIT Challenge delivered worldwide through Nord Anglia Education, drawing more than 1,800 submitted projects from about 50 schools in over 20 countries. <a href="https://youtu.be/i-oxlDK0VFc">Watch the launch video.</a></p>
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<img src="assets/images/papers/DrivaerNet++.gif" alt="DrivAerNet++ Published at NeurIPS 2024, Wins MIT Open Data Prize">
<span class="dc-news-date">December 2024</span>
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<h4 class="dc-news-headline">DrivAerNet++ Published at NeurIPS 2024, Wins MIT Open Data Prize</h4>
<p class="dc-news-desc">DrivAerNet++, our 39 TB dataset of 8,000 cars with high-fidelity CFD simulations, was published at NeurIPS 2024 and received an MIT Open Data Prize.</p>
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<img src="assets/images/news/toyota.png" alt="DeCoDE Lab Hosts Toyota Research Institute">
<span class="dc-news-date">November 2024</span>
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<h4 class="dc-news-headline">DeCoDE Lab Hosts Toyota Research Institute</h4>
<p class="dc-news-desc">The DeCoDE Lab hosted a workshop with visitors from Toyota Research and Toyota Japan.</p>
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<img src="assets/images/news/dtm_award.jpg" alt="Dr. Ahmed Received DTM Young Investigator Award">
<span class="dc-news-date">August 2024</span>
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<h4 class="dc-news-headline">Dr. Ahmed Received DTM Young Investigator Award</h4>
<p class="dc-news-desc">Dr. Ahmed received the 2024 DTM Young Investigator Award at the ASME IDETC conference.</p>
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<img src="assets/images/news/idetc2024.jpg" alt="DeCoDE Lab Presented Their Work at ASME IDETC 2024 Conference">
<span class="dc-news-date">August 2024</span>
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<h4 class="dc-news-headline">DeCoDE Lab Presented Their Work at ASME IDETC 2024 Conference</h4>
<p class="dc-news-desc">DeCoDE lab members attended and presented work at the ASME IDETC 2024 conference.</p>
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<img src="assets/images/news/d2d_2024.jpg" alt="Second 'From Data to Design' Workshop Conducted at IDETC 2024">
<span class="dc-news-date">August 2024</span>
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<h4 class="dc-news-headline">Second 'From Data to Design' Workshop Conducted at IDETC 2024</h4>
<p class="dc-news-desc">We conducted the second Data2Design workshop during IDETC 2024. We were grateful for the thoughtful discussions, insightful questions, and genuine engagement from all the attendees. Check out the webpage to see the program <a href="https://decode.mit.edu/D2D-IDETC24/">here.</a></p>
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<div class="dc-news-image">
<img src="assets/images/faez.png" alt="Dr. Ahmed Appointed Doherty Chair in Ocean Utilization">
<span class="dc-news-date">July 2024</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Dr. Ahmed Appointed Doherty Chair in Ocean Utilization</h4>
<p class="dc-news-desc">Dr. Ahmed was appointed to the Doherty Chair in Ocean Utilization at MIT.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/ibm_workshop.jpeg" alt="MIT DeCoDE and IBM LLM Workshop Co-Organized">
<span class="dc-news-date">May 2024</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">MIT DeCoDE and IBM LLM Workshop Co-Organized</h4>
<p class="dc-news-desc">Dr. Srivastava and Dr. Ahmed co-organized the first-ever InstructLab workshop on LLMs at the MIT IBM office.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/google.png" alt="Google Research Scholar Award">
<span class="dc-news-date">May 2024</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Google Research Scholar Award</h4>
<p class="dc-news-desc">Dr. Ahmed and Dr. Alam received the Google Research Scholar Award 2024 in Applied Science.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Lyle-Regenwetter_2.jpg" alt="Lyle Received the MIT MAD Design Fellowship">
<span class="dc-news-date">March 2024</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Lyle Received the MIT MAD Design Fellowship</h4>
<p class="dc-news-desc">Lyle Regenwetter was named a Design Fellow by the MIT Morningside Academy for Design (MAD).</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/kristen_washington.jpeg" alt="Kristen Presented at The National Academies of Sciences, Engineering, and Medicine">
<span class="dc-news-date">February 2024</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Kristen Presented at The National Academies of Sciences, Engineering, and Medicine</h4>
<p class="dc-news-desc">Kristen spoke about her research which she and her team published in the ASME Journal of Mechanical Design: ADVISE: Accelerating the Creation of Evidence Syntheses for Global Development Using Natural Language Processing-Supported Human-Artificial Intelligence Collaboration.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/rui.jpeg" alt="Rui Zhou Won Pillar AI Collective Fellowship">
<span class="dc-news-date">December 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Rui Zhou Won Pillar AI Collective Fellowship</h4>
<p class="dc-news-desc">Rui Zhou won the MIT Pillar AI Collective Fellowship.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/neurips23.jpeg" alt="DeCoDE Lab Presented Their Work at NeurIPS Conference">
<span class="dc-news-date">December 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">DeCoDE Lab Presented Their Work at NeurIPS Conference</h4>
<p class="dc-news-desc">Lyle, Amin, and DeCoDE alumni Giorgio and Binyang attended and presented their work at NeurIPS 2023.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/IGNITE.jpeg" alt="Kristen Edwards, Noah Bagazinski, and Rui Zhou Won MIT Ignite Competition">
<span class="dc-news-date">October 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Kristen Edwards, Noah Bagazinski, and Rui Zhou Won MIT Ignite Competition</h4>
<p class="dc-news-desc">Rui Zhou won the MIT Flagship award and Noah Bagazinski and Kristen Edwards won the runner-up prize at the MIT IGNITE Generative AI Entrepreneurship Competition.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Binyang.png" alt="Dr. Binyang Song Joined Virginia Tech as Tenure-Track Faculty">
<span class="dc-news-date">September 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Dr. Binyang Song Joined Virginia Tech as Tenure-Track Faculty</h4>
<p class="dc-news-desc">We announced that Dr. Binyang Song had accepted a tenure-track faculty position at Virginia Tech in the Department of Industrial and Systems Engineering. <a href="https://www.ise.vt.edu/">Learn more.</a></p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/faez.png" alt="Dr. Ahmed Named ABS Career Development Professor">
<span class="dc-news-date">July 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Dr. Ahmed Named ABS Career Development Professor</h4>
<p class="dc-news-desc">Dr. Ahmed was appointed to the American Bureau of Shipping (ABS) Career Development Professorship at MIT.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/new_2023.png" alt="DeCoDE Lab Welcomed New Graduate Students">
<span class="dc-news-date">September 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">DeCoDE Lab Welcomed New Graduate Students</h4>
<p class="dc-news-desc">We welcomed Annie, Nomi, Nicholas, Rosen, Brandon, and Kaira to the DeCoDE lab.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/20230820_120538.jpg" alt="First 'From Data to Design' Workshop Held at IDETC 2023">
<span class="dc-news-date">August 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">First 'From Data to Design' Workshop Held at IDETC 2023</h4>
<p class="dc-news-desc">We saw a large number of participants join our workshop during IDETC 2023. We were grateful for the thoughtful discussions, insightful questions, and genuine engagement from all the attendees. Check out the webpage to see the program <a href="https://decode.mit.edu/D2D-IDETC23/">here.</a></p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/NoahNDSEG.png" alt="Noah Won Exemplary Poster Presentation Award at NDSEG Fellowship Conference">
<span class="dc-news-date">August 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Noah Won Exemplary Poster Presentation Award at NDSEG Fellowship Conference</h4>
<p class="dc-news-desc">Noah Bagazinski won an award for Exemplary Poster Presentation in Naval Architecture and Ocean Engineering at the NDSEG Fellowship Conference in San Antonio, Texas.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Amin.png" alt="Amin Was Awarded The 2023 Mathworks Fellowship">
<span class="dc-news-date">July 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Amin Was Awarded The 2023 Mathworks Fellowship</h4>
<p class="dc-news-desc">Amin received the 2023 Mathworks Fellowship at MIT.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Kristen.jpeg" alt="Kristen Passed Quals">
<span class="dc-news-date">May 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Kristen Passed Quals</h4>
<p class="dc-news-desc">Kristen passed the MechE qualifying exams.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/instagram.png" alt="Instagram Page Launched by DeCoDE Lab">
<span class="dc-news-date">April 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Instagram Page Launched by DeCoDE Lab</h4>
<p class="dc-news-desc">DeCoDE lab launched its student-run Instagram page <a href="https://instagram.com/decodelabmit/">follow us.</a></p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/fun/northend_2023.png" alt="Celebrations Held by DeCoDE Lab">
<span class="dc-news-date">March 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Celebrations Held by DeCoDE Lab</h4>
<p class="dc-news-desc">DeCoDE lab celebrated IDETC conference submissions in North End.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/mere_2023.png" alt="Kristen and Noah Won Poster Awards">
<span class="dc-news-date">February 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Kristen and Noah Won Poster Awards</h4>
<p class="dc-news-desc">Kristen and Noah won awards for their posters at MERE 2023.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Noah-thumbnail.jpg" alt="Noah and Amin Passed Quals">
<span class="dc-news-date">February 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Noah and Amin Passed Quals</h4>
<p class="dc-news-desc">Noah and Amin passed MechE qualifying exams.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/aipex.jpeg" alt="CMU AiPEX-MIT DeCoDE Get-Together Held">
<span class="dc-news-date">January 2023</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">CMU AiPEX-MIT DeCoDE Get-Together Held</h4>
<p class="dc-news-desc">We thanked Dr. Conrad Tucker and the AiPEX lab team for visiting us and sharing their fantastic work.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Amin.jpg" alt="Amin Was Awarded The 2022 Mathworks Fellowship">
<span class="dc-news-date">August 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Amin Was Awarded The 2022 Mathworks Fellowship</h4>
<p class="dc-news-desc">Amin received the 2022 Mathworks Fellowship at MIT.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/noah_lyle_idetc2022.jpeg" alt="Noah and Lyle Won Third Place at IDETC Hackathon">
<span class="dc-news-date">August 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Noah and Lyle Won Third Place at IDETC Hackathon</h4>
<p class="dc-news-desc">Noah and Lyle won the third prize at two ASME IDETC Hackathons.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/faez_editorschoice_idetc2022.jpeg" alt="Dr. Ahmed's Work Was Featured">
<span class="dc-news-date">August 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Dr. Ahmed's Work Was Featured</h4>
<p class="dc-news-desc">Dr. Ahmed won the Honorable Mention award in the JMD editors’ choice award for their PaDGAN paper.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Kristen.jpeg" alt="Kristen Completed Her Master's Degree">
<span class="dc-news-date">August 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Kristen Completed Her Master's Degree</h4>
<p class="dc-news-desc">Kristen completed her master's degree in MechE.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/news/3M.png" alt="Dr. Ahmed Received 3M Award">
<span class="dc-news-date">July 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Dr. Ahmed Received 3M Award</h4>
<p class="dc-news-desc">Dr. Ahmed received the 3M Non-Tenured Faculty Award.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Noah-thumbnail.jpg" alt="Noah's Poster Received Award">
<span class="dc-news-date">June 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Noah's Poster Received Award</h4>
<p class="dc-news-desc">Noah won the ASME Student Poster Travel Award.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Lyle-Regenwetter_2.jpg" alt="Lyle Became DeCoDE's First PhD Candidate">
<span class="dc-news-date">May 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Lyle Became DeCoDE's First PhD Candidate</h4>
<p class="dc-news-desc">Lyle passed MechE qualifying exams and became DeCoDE lab's first Ph.D. candidate.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Amin-prof.jpeg" alt="Lyle And Amin Completed Their Master's Degrees">
<span class="dc-news-date">May 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Lyle And Amin Completed Their Master's Degrees</h4>
<p class="dc-news-desc">Lyle and Amin completed their master's degrees in MechE.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Lyle-Regenwetter_2.jpg" alt="Lyle Received Honorable Mention">
<span class="dc-news-date">April 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Lyle Received Honorable Mention</h4>
<p class="dc-news-desc">Lyle received honorable mention in the NSF GRFP award.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Kristen.jpeg" alt="Kristen Received NSF GRFP Award">
<span class="dc-news-date">April 2022</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Kristen Received NSF GRFP Award</h4>
<p class="dc-news-desc">Kristen received the NSF GRFP award.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/members/Amin-prof.jpeg" alt="Amin Was Awarded The 2021 Mathworks Fellowship">
<span class="dc-news-date">September 2021</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Amin Was Awarded The 2021 Mathworks Fellowship</h4>
<p class="dc-news-desc">Amin received the 2021 Mathworks Fellowship at MIT.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/faez-thumbnail.png" alt="Dr. Ahmed Received Award from The UMD Alumni Association">
<span class="dc-news-date">July 2021</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Dr. Ahmed Received Award from The UMD Alumni Association</h4>
<p class="dc-news-desc">Dr. Ahmed received the 2022 Alumni Excellence Research Award from the UMD Alumni Association.</p>
</div>
</div>
</div>
<div class="col-lg-4 col-md-6 col-12 dc-news-col dc-news-hidden">
<div class="dc-news-card">
<div class="dc-news-image">
<img src="assets/images/faez.png" alt="Dr. Ahmed Named d'Arbeloff Career Development Professor">
<span class="dc-news-date">July 2020</span>
</div>
<div class="dc-news-body">
<h4 class="dc-news-headline">Dr. Ahmed Named d'Arbeloff Career Development Professor</h4>
<p class="dc-news-desc">Dr. Ahmed was appointed to the d'Arbeloff Career Development Professorship at MIT upon joining the Department of Mechanical Engineering.</p>
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<h3 class="headline" id="publications">Selected Publications <a href="/publications/" class="dc-see-all">[See all]</a></h3>
<hr>
<div class="row">
<div class="pub-card">
<div class="pub-card-inner">
<div class="pub-thumbnail">
<img src="/assets/images/papers/flare.png" alt="FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition">
</div>
<div class="pub-content">
<h4 class="pub-title">FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition</h4>
<p class="pub-award"><i class="fas fa-trophy"></i> CIE Best Conference Paper & AI/ML TC Best Paper, IDETC 2026</p>
<p class="pub-authors">Thiamchaiboonthawee, K., Nehme, G., Telikicherla, R. M., Tian, J., Jayaraman, B., Chandan, V., Mariappan, D., Ahmed, F.</p>
<p class="pub-venue">In <i>IDETC 2026</i> (Accepted)</p>
<div class="pub-links">
<a href="https://arxiv.org/abs/2604.16649" class="pub-link-btn" target="_blank">
<i class="fas fa-external-link-alt"></i> Link
</a>
<a href="#" class="pub-link-btn pub-abstract-toggle">
<i class="fas fa-align-left"></i> Abstract <span class="arrow">▼</span>
</a>
</div>
<div class="pub-abstract">
<p>Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufactured part. Thermo-mechanical finite element simulations are widely used to estimate these effects, but their computational cost and the complexity of accurately capturing DED physics limit their use in design iteration and process optimization. This paper introduces FLARE (Field Prediction via Linear Affine Reconstruction in wEight-space), a data-efficient surrogate modeling framework for predicting post-cooling displacement fields in DED from geometric and process parameters. We develop a predefined-geometry DED simulation workflow using an open-source finite element framework and generate a dataset of simulations with varying geometry, laser power, and deposition velocity. Each simulation provides full-field displacement, stress, strain, and temperature data throughout the manufacturing process. FLARE encodes each simulation as an implicit neural field and regularizes the corresponding neural-network weights so that they follow the affine structure of the input parameter space. This enables prediction of unseen parameter combinations by reconstructing network weights through affine mixing of training examples. On this DED benchmark, the method shows improved accuracy compared to baseline methods in both in-distribution and extrapolation settings. Although the present study focuses on DED displacement prediction, the proposed affine weight-space reconstruction framework offers a promising approach for data-efficient surrogate modeling of physical fields.</p>
</div>
</div>
</div>
</div>
<div class="pub-card">
<div class="pub-card-inner">
<div class="pub-thumbnail">
<img src="/assets/images/papers/2025_Nobari_PGDTO.png" alt="PGD-TO: A Scalable Alternative to MMA Using Projected Gradient Descent for Multi-Constraint Topology Optimization">
</div>
<div class="pub-content">
<h4 class="pub-title">PGD-TO: A Scalable Alternative to MMA Using Projected Gradient Descent for Multi-Constraint Topology Optimization</h4>
<p class="pub-authors">Heyrani Nobari, A., Ahmed, F.</p>
<p class="pub-venue">In <i>Structural and Multidisciplinary Optimization (in press)</i></p>
<div class="pub-links">
<a href="https://arxiv.org/abs/2511.13905" class="pub-link-btn" target="_blank">
<i class="fas fa-external-link-alt"></i> Link
</a>
<a href="https://github.com/ahnobari/pyFANTOM" class="pub-link-btn" target="_blank">
<i class="fas fa-code"></i> Code
</a>
<a href="#" class="pub-link-btn pub-abstract-toggle">
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<p>Projected Gradient Descent (PGD) methods offer a simple and scalable approach to topology optimization (TO), yet they often struggle with nonlinear and multi-constraint problems due to the complexity of active-set detection. This paper introduces PGD-TO, a framework that reformulates the projection step into a regularized convex quadratic problem, eliminating the need for active-set search and ensuring well-posedness even when constraints are infeasible. The framework employs a semismooth Newton solver for general multi-constraint cases and a binary search projection for single or independent constraints, achieving fast and reliable convergence. It further integrates spectral step-size adaptation and nonlinear conjugate-gradient directions for improved stability and efficiency. We evaluate PGD-TO on four benchmark families representing the breadth of TO problems: (i) minimum compliance with a linear volume constraint, (ii) minimum volume under a nonlinear compliance constraint, (iii) multi-material minimum compliance with four independent volume constraints, and (iv) minimum compliance with coupled volume and center-of-mass constraints. Across these single- and multi-constraint, linear and nonlinear cases, PGD-TO achieves convergence and final compliance comparable to the Method of Moving Asymptotes (MMA) and Optimality Criteria (OC), while reducing per-iteration computation time by 10–43x on general problems and 115–312x when constraints are independent. Overall, PGD-TO establishes a fast, robust, and scalable alternative to MMA, advancing topology optimization toward practical large-scale, multi-constraint, and nonlinear design problems. Public code available at: https://github.com/ahnobari/pyFANTOM.</p>
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<img src="/assets/images/papers/2026_Edwards_judges_jmd.png" alt="AI Judges in Design: Toward Expert-Equivalent Design Evaluations With Vision-Language Models and In-Context Learning">
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<div class="pub-content">
<h4 class="pub-title">AI Judges in Design: Toward Expert-Equivalent Design Evaluations With Vision-Language Models and In-Context Learning</h4>
<p class="pub-authors">Edwards, K. M., Tehranchi, F., Miller, S. R., Ahmed, F.</p>
<p class="pub-venue">In <i>Journal of Mechanical Design 2026</i></p>
<div class="pub-links">
<a href="/assets/papers/2026_Edwards_judges_jmd.pdf" class="pub-link-btn" target="_blank">
<i class="fas fa-file-pdf"></i> PDF
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<a href="https://doi.org/10.1115/1.4071835" class="pub-link-btn" target="_blank">
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<p>The subjective evaluation of early-stage engineering designs, such as concept sketches, traditionally relies on human experts. However, expert evaluations are time-consuming, expensive, and sometimes inconsistent. Recent advances in vision-language models (VLMs) offer the potential to automate design assessments, but it is crucial to ensure that these artificial intelligence (AI) “judges” perform on par with human experts. This work introduces in-context learning (ICL)-enhanced VLM judges and a comprehensive statistical framework (including agreement, error, correlation, statistical difference checks, equivalence testing, and top-set overlap) to rigorously assess AI–expert equivalence. Across two case studies, we show that reasoning-enabled VLMs are the strongest-performing AI judges. They consistently outperform two-third trained novices across all metrics, and for measures such as uniqueness, creativity, and drawing quality, they approach expert-equivalent performance. In specific cases, they even exceed expert–expert agreement, attaining lower mean absolute error and higher rank correlations than the expert baseline. These findings suggest that, on certain statistical tests, AI judges are not only approaching expert–expert equivalence but in some cases surpassing it.</p>
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<img src="/assets/images/papers/2026_ghadi_cadfit.png" alt="CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization">
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<h4 class="pub-title">CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization</h4>
<p class="pub-authors">Nehme, G., Whalen, E., Ahmed, F.</p>
<p class="pub-venue">In <i>ICML 2026</i></p>
<div class="pub-links">
<a href="https://github.com/ghadinehme/CADFit" class="pub-link-btn">
<i class="fas fa-globe"></i> Project
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<a href="https://arxiv.org/abs/2605.01171" class="pub-link-btn" target="_blank">
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<p>Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets. We introduce CADFit, a hybrid optimization-based CAD reconstruction framework that recovers complex, editable CAD construction sequences from meshes by incrementally fitting and validating parametric operations using geometric feedback. Our approach is distinguished by formulating reconstruction as an IoU-driven optimization over structured CAD programs and supporting a rich set of operations, including extrusions, revolutions, fillets, and chamfers. Experiments on multiple CAD benchmarks show that CADFit outperforms state-of-the-art mesh-to-CAD methods in volumetric Intersection-over-Union and Chamfer Distance, while substantially reducing the Invalid Ratio of reconstructed CAD programs, particularly for complex designs. We further present a multimodal pipeline that enables end-to-end reconstruction of CAD construction sequences from images by combining image-based geometry reconstruction with CADFit. By enabling accurate reconstruction of higher-complexity CAD models, CADFit provides a practical foundation for generating richer datasets and advancing future learning-based approaches to CAD reverse engineering. The code is available at: https://github.com/ghadinehme/CADFit</p>
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<img src="/assets/images/papers/fire/fire_icon.png" alt="FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models">
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<h4 class="pub-title">FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models</h4>
<p class="pub-invited"><i class="fas fa-star"></i> Spotlight Paper: Top 2.2% of 23,918 Submissions</p>
<p class="pub-authors">Yu, R. T.-Y., Sung, N., Ahmed, F.</p>
<p class="pub-venue">In <i>ICML 2026</i></p>
<div class="pub-links">
<a href="/projects/fire/" class="pub-link-btn">
<i class="fas fa-globe"></i> Project
</a>
<a href="https://arxiv.org/abs/2601.22371" class="pub-link-btn" target="_blank">
<i class="fas fa-external-link-alt"></i> Link
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<a href="https://github.com/rosenyu304/FIRE" class="pub-link-btn" target="_blank">
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<p>Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance, where the commonly-used Gaussian-process (GP) surrogates struggle with cubic scaling costs and overfit to sparse high-fidelity observations, limiting efficiency and generalization in real-world applications. We introduce FIRE, a training-free MF framework that couples tabular foundation models (TFMs) to perform zero-shot in-context Bayesian inference via a high-fidelity correction model conditioned on the low-fidelity model's posterior predictive distributions. This cross-fidelity information transfer via distributional summaries captures heteroscedastic errors, enabling robust residual learning without model retraining. Across 31 benchmark problems spanning synthetic and real-world tasks (e.g., DrivAerNet, LCBench), FIRE delivers a stronger performance-time trade-off than seven state-of-the-art GP-based or deep learning MF regression methods, ranking highest in accuracy and uncertainty quantification with runtime advantages. Limitations include context window constraints and dependence on the quality of the pre-trained TFM's.</p>
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<img src="/assets/images/papers/2025_yu_gitbo.png" alt="GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models">
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<h4 class="pub-title">GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models</h4>
<p class="pub-authors">Yu, R. T.-Y., Picard, C., Ahmed, F.</p>
<p class="pub-venue">In <i>ICLR 2026</i></p>
<div class="pub-links">
<a href="https://arxiv.org/pdf/2505.20685" class="pub-link-btn" target="_blank">
<i class="fas fa-file-pdf"></i> PDF
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<a href="/projects/gitbo/" class="pub-link-btn">
<i class="fas fa-globe"></i> Project
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<a href="https://openreview.net/forum?id=9iTdKS4SRQ" class="pub-link-btn" target="_blank">
<i class="fas fa-external-link-alt"></i> Link
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<a href="https://github.com/rosenyu304/GITBO" class="pub-link-btn" target="_blank">
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<p>Bayesian optimization (BO) struggles in high dimensions, where Gaussian-process surrogates demand heavy retraining and brittle assumptions, slowing progress on real engineering and design problems. We introduce GIT-BO, a Gradient-Informed BO framework that couples TabPFN v2, a tabular foundation model (TFM) that performs zero-shot Bayesian inference in context, with an active-subspace mechanism computed from the model's own predictive-mean gradients. This aligns exploration to an intrinsic low-dimensional subspace via a Fisher-information estimate and selects queries with a UCB acquisition, requiring no online retraining. Across 60 problem variants spanning 20 benchmarks — nine scalable synthetic families and eleven real-world tasks (e.g., power systems, Rover, MOPTA08, Mazda) — up to 500 dimensions, GIT-BO delivers a better performance-time trade-off than state-of-the-art GP-based methods (SAASBO, TuRBO, Vanilla BO, BAxUS), ranking highest in performance and with runtime advantages that grow with dimensionality. Limitations include memory footprint and dependence on the capacity of the underlying TFM.</p>
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<img src="/assets/images/papers/2026_Edwards_INM.png" alt="Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities">
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<h4 class="pub-title">Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities</h4>
<p class="pub-authors">Edwards, K. M., Bauer, M., Jacquillat, C., Hart, A. J., Ahmed, F.</p>
<p class="pub-venue">MIT Initiative for New Manufacturing</p>
<div class="pub-links">
<a href="/assets/papers/2026_Edwards_Agentic_AI_in_Engineering_and_Manufacturing.pdf" class="pub-link-btn" target="_blank">
<i class="fas fa-file-pdf"></i> PDF
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<a href="https://arxiv.org/abs/2604.09633" class="pub-link-btn" target="_blank">
<i class="fas fa-external-link-alt"></i> Link
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<p>This work examines how AI, especially agentic systems, is being adopted in engineering and manufacturing workflows, what value it provides today, and what is needed for broader deployment. This is an exploratory and qualitative state-of-practice study grounded in over 30 interviews across four stakeholder groups (large enterprises, small/medium firms, AI developers, and CAD/CAM/CAE vendors). We find that near-term AI gains cluster around structured, repetitive work and data-intensive synthesis, while higher-value agentic gains come from orchestrating multi-step workflows across tools. Adoption is constrained less by model capability than by fragmented and machine-unfriendly data, stringent security and regulatory requirements, and limited API-accessible legacy toolchains. Reliability, verification, and auditability are central requirements for adoption, driving human-in-the-loop frameworks and governance aligned with existing engineering reviews. Beyond technical barriers there are also organizational ones: a persistent AI literacy gap, cultural heterogeneity, and governance structures that have not yet caught up with agentic capabilities. Together, the findings point to a staged progression of AI utility from low-consequence assistance toward higher-order automation, as trust, infrastructure, and verification mature. This highlights key breakthroughs needed, including integration with traditional engineering tools and data types, robust verification frameworks, and improved spatial and physical reasoning.</p>
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<img src="/assets/images/papers/2026_Nobari_OAT.png" alt="Optimize Any Topology: A Foundation Model for Shape-and Resolution-Free Structural Topology Optimization">
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<h4 class="pub-title">Optimize Any Topology: A Foundation Model for Shape-and Resolution-Free Structural Topology Optimization</h4>
<p class="pub-authors">Heyrani Nobari, A., Regenwetter, L., Picard, C., Han, L., Ahmed, F.</p>
<p class="pub-venue">In <i>NeurIPS 2025</i></p>
<div class="pub-links">
<a href="https://arxiv.org/pdf/2510.23667" class="pub-link-btn" target="_blank">
<i class="fas fa-file-pdf"></i> PDF
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<a href="https://openreview.net/forum?id=eD0lYGQKn0" class="pub-link-btn" target="_blank">
<i class="fas fa-external-link-alt"></i> Link
</a>
<a href="https://github.com/ahnobari/OptimizeAnyTopology" class="pub-link-btn" target="_blank">
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<p>Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning methods are limited to fixed square grids, a few hand-coded boundary conditions, and post-hoc optimization, preventing general deployment. We introduce Optimize Any Topology (OAT), a foundation-model framework that directly predicts minimum-compliance layouts for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines a resolution- and shape-agnostic autoencoder with an implicit neural-field decoder and a conditional latent-diffusion model trained on OpenTO, a new corpus of 2.2 million optimized structures covering 2 million unique boundary-condition configurations. On four public benchmarks and two challenging unseen tests, OAT lowers mean compliance up to 90% relative to the best prior models and delivers sub-1 second inference on a single GPU across resolutions from 64 x 64 to 256 x 256 and aspect ratios as high as 10:1. These results establish OAT as a general, fast, and resolution-free framework for physics-aware topology optimization and provide a large-scale dataset to spur further research in generative modeling for inverse design.</p>
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<img src="/assets/images/papers/fig_framework.jpg" alt="Offshore Wind Turbine Tower Design and Optimization: A Review and AI-Driven Future Directions">
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<h4 class="pub-title">Offshore Wind Turbine Tower Design and Optimization: A Review and AI-Driven Future Directions</h4>
<p class="pub-authors">Ribeiro, J. A., Ribeiro, B. A., Pimenta, F., Tavares, S. M. O., Zhang, J., Ahmed, F.</p>
<p class="pub-venue">In <i>Applied Energy 2025</i></p>
<div class="pub-links">
<a href="https://arxiv.org/pdf/2502.02594" class="pub-link-btn" target="_blank">
<i class="fas fa-file-pdf"></i> PDF
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<a href="https://doi.org/10.1016/j.apenergy.2025.126294" class="pub-link-btn" target="_blank">
<i class="fas fa-external-link-alt"></i> Link
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<p>Offshore wind energy leverages the high intensity and consistency of oceanic winds, playing a key role in the transition to renewable energy. As energy demands grow, larger turbines are required to optimize power generation and reduce the Levelized Cost of Energy (LCoE), which represents the average cost of electricity over a project’s lifetime. However, upscaling turbines introduces engineering challenges, particularly in the design of supporting structures, especially towers. These towers must support increased loads while maintaining structural integrity, cost-efficiency, and transportability, making them essential to offshore wind projects’ success. This paper presents a comprehensive review of the latest advancements, challenges, and future directions driven by Artificial Intelligence (AI) in the design optimization of Offshore Wind Turbine (OWT) structures, with a focus on towers. It provides an in-depth background on key areas such as design types, load types, analysis methods, design processes, monitoring systems, Digital Twin (DT), software, standards, reference turbines, economic factors, and optimization techniques. Additionally, it includes a state-of-the-art review of optimization studies related to tower design optimization, presenting a detailed examination of turbine, software, loads, optimization method, design variables and constraints, analysis, and findings, motivating future research to refine design approaches for effective turbine upscaling and improved efficiency. Lastly, the paper explores future directions where AI can revolutionize tower design optimization, enabling the development of efficient, scalable, and sustainable structures. By addressing the upscaling challenges and supporting the growth of renewable energy, this work contributes to shaping the future of offshore wind turbine towers and others supporting structures.</p>
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<img src="/assets/images/papers/2025_blendednet.png" alt="BlendedNet: A Blended Wing Body Aircraft Dataset and Surrogate Model for Aerodynamic Predictions">
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<h4 class="pub-title">BlendedNet: A Blended Wing Body Aircraft Dataset and Surrogate Model for Aerodynamic Predictions</h4>
<p class="pub-authors">Sung, N., Spreizer, S., Elrefaie, M., Samuel, K. M., Jones, M. C., Ahmed, F.</p>
<p class="pub-venue">In <i>IDETC 2025</i></p>
<div class="pub-links">
<a href="https://arxiv.org/pdf/2509.07209" class="pub-link-btn" target="_blank">
<i class="fas fa-file-pdf"></i> PDF
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<a href="https://doi.org/10.1115/DETC2025-168977" class="pub-link-btn" target="_blank">
<i class="fas fa-external-link-alt"></i> Link
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<a href="https://github.com/nicksungg/clarc_blended_wing_body" class="pub-link-btn" target="_blank">
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<p>BlendedNet is a publicly available aerodynamic dataset of 999 blended wing body (BWB) geometries. Each geometry is simulated across about nine flight conditions, yielding 8830 converged RANS cases with the Spalart-Allmaras model and 9 to 14 million cells per case. The dataset is generated by sampling geometric design parameters and flight conditions, and includes detailed pointwise surface quantities needed to study lift and drag. We also introduce an end-to-end surrogate framework for pointwise aerodynamic prediction. The pipeline first uses a permutation-invariant PointNet regressor to predict geometric parameters from sampled surface point clouds, then conditions a Feature-wise Linear Modulation (FiLM) network on the predicted parameters and flight conditions to predict pointwise coefficients Cp, Cfx, and Cfz. Experiments show low errors in surface predictions across diverse BWBs. BlendedNet addresses data scarcity for unconventional configurations and enables research on data-driven surrogate modeling for aerodynamic design.</p>
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<img src="/assets/images/papers/DrivaerNet++.gif" alt="DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks">
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<h4 class="pub-title">DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks</h4>
<p class="pub-award"><i class="fas fa-trophy"></i> MIT Open Data Prize, 2024</p>
<p class="pub-authors">Elrefaie, M., Morar, F., Dai, A., Ahmed, F.</p>
<p class="pub-venue">In <i>NeurIPS 2024</i></p>
<div class="pub-links">
<a href="https://arxiv.org/pdf/2406.09624" class="pub-link-btn" target="_blank">
<i class="fas fa-file-pdf"></i> PDF
</a>
<a href="https://github.com/Mohamedelrefaie/DrivAerNet" class="pub-link-btn">
<i class="fas fa-globe"></i> Project
</a>
<a href="https://openreview.net/forum?id=c4NnhBi4oM" class="pub-link-btn" target="_blank">
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<div class="pub-abstract">
<p>We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and es- tateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. Each entry in the dataset features detailed 3D meshes, parametric models, aerodynamic coefficients, and extensive flow and surface field data, along with segmented parts for car classification and point cloud data. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification. With more than 39 TB of publicly available engineering data, DrivAerNet++ fills a significant gap in available resources, providing high-quality, diverse data to enhance model training, promote generalization, and accelerate automotive design processes. Along with rigorous dataset validation, we also provide ML benchmarking results on the task of aerodynamic drag prediction, showcasing the breadth of applications supported by our dataset. This dataset is set to significantly impact automotive design and broader engineering disciplines by fostering innovation and improving the fidelity of aerodynamic evaluations.</p>
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<h3 class="headline">Announcements</h3>
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<div class="dc-announce-list">
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<div class="dc-announce-icon">
<i class="fas fa-bullhorn"></i>
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<div class="dc-announce-text">
<p>Prospective Ph.D. students - If you are interested in joining the DeCoDE lab, you can apply to the <a href="https://cse.mit.edu/admissions/phd/">Computational Science and Engineering Program</a> or to the <a href="http://meche.mit.edu/education/prospective-students/graduate/apply">Mechanical Engineering</a> department at MIT.</p>
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<div class="dc-announce-icon">
<i class="fas fa-bullhorn"></i>
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<div class="dc-announce-text">
<p>If you are interested in joining the DeCoDE lab, drop me an email with the following text in your subject line "Join DeCoDE:" followed by the position you are interested in (for example, a postdoc, intern, visiting student, etc.).</p>
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<h3 class="headline" id="datasets">Datasets <a href="/projects/eng-datasets/" class="dc-see-all"> [See all datasets]</a> </h3>
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<p class="dc-datasets-intro">Engineering design datasets from the DeCoDE lab. Please cite the corresponding paper when using a dataset.</p>
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<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="https://github.com/nicksungg/clarc_blended_wing_body">Blendednet</a> - A large-scale aerodynamic dataset of 999 blended wing body geometries. Each geometry is simulated across about nine flight conditions, yielding 8830 converged RANS cases with the Spalart-Allmaras model and 9 to 14 million cells per case.</span>
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<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="https://github.com/Mohamedelrefaie/DrivAerNet">DrivAerNet++</a> - A large-scale multimodal dataset of 8000 detailed 3D car meshes and aerodynamic performance data comprising of full 3D pressure, velocity fields, and wall-shear stresses, point clouds and parts annotation.</span>
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<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="https://github.com/Mohamedelrefaie/DrivAerNet">DrivAerNet dataset</a> - A dataset of 4000 detailed 3D car meshes and aerodynamic performance data comprising of full 3D pressure, velocity fields, and wall-shear stresses.</span>
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<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/vlms4design/">VLM dataset</a> - A dataset of 1000+ tasks to evaluate vision language models.</span>
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<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/dragprediction/">Car Drag Coefficient</a> - A dataset of 4,948 3D car meshes, their renderings, and their drag coefficients.</span>
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<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/links/">LINKS dataset</a> - A dataset of 100 million planar linkage mechanisms and 1.1 billion coupler curves obtained from kinematic simulations. The dataset also contains curated curves, 100 million negative samples, and a publicly available simulation software.</span>
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<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="https://github.com/cyrilpic/radcomp">Turbo-compressors dataset</a> - A dataset of 22 million turbo-compressors and their performance under different operating conditions.</span>
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<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="https://github.com/pcdgan/PcDGAN">Airfoil dataset </a> - A synthetic dataset of 48,503 airfoils and their aerodynamic performance computed using OpenFOAM.</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/topodiff/">Topodiff topology optimization dataset</a> - A dataset of 33,000 images corresponding to optimal topologies for diverse boundary conditions. The dataset also contains their physical fields, compliance values, and an additional 42000 non-optimal topologies.</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="https://github.com/noahbagz/ShipD">SHIP-D dataset</a> - A dataset of 30,000 ship hulls each with design and functional performance information, including parameterization, mesh, point-cloud, and image representations, as well as 32 hydrodynamic drag measures under different operating conditions.</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/dragprediction/">3D cars dataset</a> - A diverse dataset of 9,070 high-quality 3D car meshes labeled by drag coefficients computed from computational fluid dynamics simulations.</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/biked/">BIKED dataset</a> - A dataset of 4,500 community-designed bicycles in tabular and image format, along with images corresponding to different bike parts for each bicycle.</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/biked++/">BIKED++ dataset</a> - A dataset of 1.4 million bicycles represented in tabular and image format, along with CLIP embeddings of all designs.</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/framed/">FRAMED dataset</a> - A dataset of 4,500 bicycle frames and ten performance metrics obtained from structural simulations.</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/formfunction/">Aircraft dataset</a> - A dataset of lift and drag performance values of 4,045 3D aircraft models from Shapenet.</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="https://github.com/likeshine/sketch-text-multimodal-transfer-learning">Milk Frother dataset</a> - A multimodal dataset of 1,126 milk frother sketches and their text descriptions. The dataset is derived from a <a href="https://sites.psu.edu/creativitymetrics/"> milk frother dataset</a> collected at the <a href="https://www.engr.psu.edu/britelab/">Brite lab.</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="https://github.com/ahnobari/AutoSurf">Autosurf aircraft dataset</a> A dataset of 1,050 airplane models with segmentation labels, created using NASA's Open Vehicle Sketch Pad (OpenVSP).</span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-database dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/datasets/"> Other engineering datasets</a> A collection of datasets from the engineering design community, curated for our JMD review paper. Note that this list was made in 2022 and is not regularly updated.</span>
</div>
</div>
</div>
</div>
<h3 class="headline" style="margin-top: 1.5rem;">Projects</h3>
<hr>
<div class="row">
<div style="padding: 0 15px;">
<p class="dc-datasets-intro">Interactive project pages for our publications.</p>
<div class="dc-dataset-list">
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/fire/">FIRE</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/gitbo/">GIT-BO</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/carbench/">CarBench</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/bikebench/">Bike-Bench</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/NITO/">NITO</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/biked++/">BIKED++</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/ndgms/">Negative Data Generative Models</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/C_ShipGen/">C-ShipGen</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/vlms4design/">VLMs4Design</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/ShipGen/">ShipGen</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/trajectory-alignment-diffusion/">DOM-TA</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/pfns4ed/">PFNs for Engineering Design</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/dragprediction/">Car Drag Prediction</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/vehicleratings/">MMML Vehicle Rating Prediction</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/autosurf/">Autosurf</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/counterfactuals/">MCD</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/metrics/">Metrics</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/links/">LINKS</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/topodiff/">TOPODIFF</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/creativegan/">CreativeGAN</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/dtai/">Design Target Achievement Index</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/framed/">FRAMED</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/pcdgan/">PcDGAN</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/creativegan/">CreativeGAN</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/rangegan/">Range-GAN</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/biked/">BIKED</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/formfunction/">Form and Function</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/nlp-design-eval/">NLP in Design Evaluation</a></span>
</div>
<div class="dc-dataset-item">
<i class="fas fa-flask dc-dataset-icon"></i>
<span class="dc-dataset-text"><a href="/projects/eng-datasets/">Engineering Datasets & Benchmarks</a></span>
</div>
</div>
</div>
</div>
</div>
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<div class='col-12'>
<h2 class="intro_title" id="team">Principal Investigator </h2>
<div class="row">
<div class="col-12">
<div class="dc-pi-card">
<div class="dc-pi-inner">
<div class="dc-pi-photo">
<img src="/assets/images/faez.JPG" alt="Faez Ahmed">
</div>
<div class="dc-pi-info">
<h3 class="dc-pi-name">Faez Ahmed</h3>
<ul class="dc-pi-details">
<li>Associate Professor</li>
<li>Department of Mechanical Engineering</li>
<li>Massachusetts Institute of Technology</li>
<li>Email: faez at mit dot edu</li>
</ul>
<p class="dc-pi-bio">Prof. Faez Ahmed is an Associate Professor in the Department of Mechanical Engineering at MIT, where he directs the Design Computation and Digital Engineering Lab. His research interests lie at the intersection of Artificial Intelligence and engineering design, focusing particularly on first‑principle generative AI and optimization algorithms, multi‑modal representation learning, and engineering design methodology with human–AI design co‑pilots. Before joining MIT, Prof. Ahmed was a postdoctoral fellow at Northwestern University and earned his Ph.D. in Mechanical Engineering from the University of Maryland. He also spent several years in Australia’s railway and mining sector, leading data‑driven predictive‑maintenance initiatives. Prof. Ahmed has received the NSF CAREER Award, the AFOSR Young Investigator Award, the ASME DAC and DTM Young Investigator Awards, the Google Research Scholar and Amazon Research Awards, and MIT's Keenan Award for Innovation in Undergraduate Education. He holds the Esther and Harold E. Edgerton Career Development Professorship, having previously held the Doherty, ABS, and d'Arbeloff chairs, and serves as Associate Editor of Design Science and on the editorial boards of Computer-Aided Design and Machine Learning: Engineering.</p>
<div class="dc-pi-social">
<a href="https://github.com/decode-mit" title="GitHub"><i class="fab fa-github"></i></a>
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<a href="https://scholar.google.com/citations?user=5iElzo8AAAAJ" title="Google Scholar"><i class="fas fa-graduation-cap"></i></a>
<a href="https://orcid.org/0000-0002-5227-2628" title="ORCID"><i class="fas fa-info-circle"></i></a>
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</div>
</div>
</div>
<div class='halfspacer'></div>
<h2 class="intro_title"> Current Members <a href="/team/" class="dc-see-all"> [See all members]</a> </h2>
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<a href="mailto:hongrui@mit.edu" target="_blank" class="dc-member-link">
<div class="dc-member-avatar">
<img src="/assets/images/members/Hongrui.png" alt="Hongrui Chen">
</div>
</a>
<div class="dc-member-info">
<a href="mailto:hongrui@mit.edu" target="_blank" class="dc-member-name">Hongrui Chen</a>
<p class="dc-member-role">Postdoctoral Associate</p>
<a href="mailto:hongrui@mit.edu" class="dc-member-email" title="hongrui@mit.edu">
<i class="fas fa-envelope"></i>
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<a href="mailto:zhen_wei@mit.edu" target="_blank" class="dc-member-link">
<div class="dc-member-avatar">
<img src="/assets/images/members/zhen.jpg" alt="Zhen Wei">
</div>
</a>
<div class="dc-member-info">
<a href="mailto:zhen_wei@mit.edu" target="_blank" class="dc-member-name">Zhen Wei</a>
<p class="dc-member-role">Postdoctoral Associate</p>
<a href="mailto:zhen_wei@mit.edu" class="dc-member-email" title="zhen_wei@mit.edu">
<i class="fas fa-envelope"></i>
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<img src="/assets/images/members/peerasait.jpg" alt="Peerasait (Jeffrey) Prachaseree">
</div>
</a>
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<a href="mailto:pprachas@mit.edu" target="_blank" class="dc-member-name">Peerasait (Jeffrey) Prachaseree</a>
<p class="dc-member-role">Postdoctoral Associate</p>
<a href="mailto:pprachas@mit.edu" class="dc-member-email" title="pprachas@mit.edu">
<i class="fas fa-envelope"></i>
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<img src="/assets/images/members/Amin.png" alt="Amin Heyrani Nobari">
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<a href="mailto:ahnobari@mit.edu" target="_blank" class="dc-member-name">Amin Heyrani Nobari</a>
<p class="dc-member-role">Postdoctoral Associate</p>
<a href="mailto:ahnobari@mit.edu" class="dc-member-email" title="ahnobari@mit.edu">
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<img src="/assets/images/members/shrenik_zinage.png" alt="Shrenik Vijaykumar Zinage">
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<a href="mailto:shrenik@mit.edu" target="_blank" class="dc-member-name">Shrenik Vijaykumar Zinage</a>
<p class="dc-member-role">Postdoctoral Associate</p>
<a href="mailto:shrenik@mit.edu" class="dc-member-email" title="shrenik@mit.edu">
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<img src="/assets/images/members/Kristen.jpeg" alt="Kristen M. Edwards">
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<a href="https://www.linkedin.com/in/kristenmedwards" target="_blank" class="dc-member-name">Kristen M. Edwards</a>
<p class="dc-member-role">Ph.D. Candidate</p>
<a href="mailto:kme@mit.edu" class="dc-member-email" title="kme@mit.edu">
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<a href="mailto:noahbagz@mit.edu" target="_blank" class="dc-member-name">Noah Joseph Bagazinski</a>
<p class="dc-member-role">Ph.D. Candidate</p>
<a href="mailto:noahbagz@mit.edu" class="dc-member-email" title="noahbagz@mit.edu">
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<img src="/assets/images/members/Manideep.png" alt="Manideep Rebbagondla">
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<a href="mailto:manideep@mit.edu" target="_blank" class="dc-member-name">Manideep Rebbagondla</a>
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<a href="mailto:manideep@mit.edu" class="dc-member-email" title="manideep@mit.edu">
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<a href="mailto:ghadi@mit.edu" target="_blank" class="dc-member-name">Ghadi Nehme</a>
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<a href="mailto:ghadi@mit.edu" class="dc-member-email" title="ghadi@mit.edu">
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<a href="mailto:mwortham@mit.edu@mit.edu" target="_blank" class="dc-member-name">Myles Wortham</a>
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<a href="mailto:mwortham@mit.edu@mit.edu" class="dc-member-email" title="mwortham@mit.edu@mit.edu">
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<a href="mailto:rosenyu@mit.edu" target="_blank" class="dc-member-name">Rosen Yu</a>
<p class="dc-member-role">Ph.D. Candidate</p>
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<a href="mailto:adoris@mit.edu" target="_blank" class="dc-member-name">Annie Clare Doris</a>
<p class="dc-member-role">Ph.D. Candidate</p>
<a href="mailto:adoris@mit.edu" class="dc-member-email" title="adoris@mit.edu">
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<a href="mailto:nicksung@mit.edu" target="_blank" class="dc-member-name">Nicholas Wei Yong Sung</a>
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<a href="mailto:moatef@mit.edu" target="_blank" class="dc-member-name">Mohamed Elrefaie</a>
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<a href="mailto:jacob_ts@mit.edu" target="_blank" class="dc-member-name">Jacob Thomas Sony</a>
<p class="dc-member-role">Graduate Student</p>
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<a href="mailto:kt455@mit.edu" target="_blank" class="dc-member-name">Kittipong Thiamchaiboonthawee</a>
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<p>Fun fact: According to the Mathematics Genealogy Project, our academic ancestors include: <a href="https://en.wikipedia.org/wiki/Sim%C3%A9on_Denis_Poisson">Poisson</a>, <a href="https://en.wikipedia.org/wiki/Pierre-Simon_Laplace">Laplace</a>, <a href="https://en.wikipedia.org/wiki/Joseph-Louis_Lagrange">Lagrange</a>, <a href="https://en.wikipedia.org/wiki/Leonhard_Euler">Euler</a>, <a href="https://en.wikipedia.org/wiki/Jacob_Bernoulli">Bernoulli</a>, <a href="https://en.wikipedia.org/wiki/Gottfried_Wilhelm_Leibniz">Leibniz</a>, <a href="https://en.wikipedia.org/wiki/Nicolaus_Copernicus">Copernicus</a>, <a href="https://en.wikipedia.org/wiki/Nasir_al-Din_al-Tusi">Nasir al-Din al-Tusi</a>, and many more. Check out our <a href="assets/images/academic_tree_decode.png" target="_self">academic family tree.</a>
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| /projects/dragprediction/ |
3D cars dataset
|
|
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| /projects/biked/ |
BIKED dataset
|
|
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| /projects/biked++/ |
BIKED++ dataset
|
|
|
| /projects/framed/ |
FRAMED dataset
|
|
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| /projects/formfunction/ |
Aircraft dataset
|
|
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| /datasets/ |
Other engineering datasets
|
|
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| /projects/fire/ |
FIRE
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| /projects/gitbo/ |
GIT-BO
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| /projects/carbench/ |
CarBench
|
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| /projects/bikebench/ |
Bike-Bench
|
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| /projects/NITO/ |
NITO
|
|
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| /projects/biked++/ |
BIKED++
|
|
|
| /projects/ndgms/ |
Negative Data Generative Models
|
|
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| /projects/C_ShipGen/ |
C-ShipGen
|
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| /projects/vlms4design/ |
VLMs4Design
|
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| /projects/ShipGen/ |
ShipGen
|
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| /projects/trajectory-alignment-diffusion/ |
DOM-TA
|
|
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| /projects/pfns4ed/ |
PFNs for Engineering Design
|
|
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| /projects/dragprediction/ |
Car Drag Prediction
|
|
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| /projects/vehicleratings/ |
MMML Vehicle Rating Prediction
|
|
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| /projects/autosurf/ |
Autosurf
|
|
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| /projects/counterfactuals/ |
MCD
|
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| /projects/metrics/ |
Metrics
|
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| /projects/links/ |
LINKS
|
|
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| /projects/topodiff/ |
TOPODIFF
|
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| /projects/creativegan/ |
CreativeGAN
|
|
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| /projects/dtai/ |
Design Target Achievement Index
|
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| /projects/framed/ |
FRAMED
|
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| /projects/pcdgan/ |
PcDGAN
|
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| /projects/creativegan/ |
CreativeGAN
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| /projects/rangegan/ |
Range-GAN
|
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| /projects/biked/ |
BIKED
|
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| /projects/formfunction/ |
Form and Function
|
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| /projects/nlp-design-eval/ |
NLP in Design Evaluation
|
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| /projects/eng-datasets/ |
Engineering Datasets & Benchmarks
|
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| /team/ |
[See all members]
|
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| /assets/images/academic_tree_decode.png |
academic family tree.
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| /media/ |
[See all]
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| /videos/ |
[See all]
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|
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| / |
Home
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| /team/ |
Team
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| /news/ |
News
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| /publications/ |
Publications
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| /projects/eng-datasets/ |
Datasets & Benchmarks
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| /software/ |
Software
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| /media/ |
Media
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| /videos/ |
Videos
|
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| /contact-us/ |
Contact
|
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Внешние ссылки
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| Url | Анкор | Анализировать |
|---|---|---|
| meche.mit.edu |
2.086 Spring 2025 <i class="fas fa-external-link-alt fa-xs"></i>
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| meche.mit.edu |
2.086 Spring 2024 <i class="fas fa-external-link-alt fa-xs"></i>
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| meche.mit.edu |
2.086 Spring 2022 <i class="fas fa-external-link-alt fa-xs"></i>
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| web.mit.edu |
2.S009 Fall 2020 <i class="fas fa-external-link-alt fa-xs"></i>
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| github.com |
See the repository.
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| linkedin.com |
this post
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| news.mit.edu |
Read more at MIT News.
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| youtu.be |
Watch the launch video.
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| ise.vt.edu |
Learn more.
|
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| instagram.com |
follow us.
|
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| arxiv.org |
<i class="fas fa-external-link-alt"></i> Link
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| arxiv.org |
<i class="fas fa-external-link-alt"></i> Link
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| github.com |
<i class="fas fa-code"></i> Code
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| doi.org |
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| github.com |
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| arxiv.org |
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| arxiv.org |
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| github.com |
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| arxiv.org |
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| openreview.net |
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| github.com |
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| arxiv.org |
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| arxiv.org |
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| openreview.net |
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| github.com |
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| arxiv.org |
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| doi.org |
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| arxiv.org |
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| doi.org |
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| github.com |
<i class="fas fa-code"></i> Code
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| arxiv.org |
<i class="fas fa-file-pdf"></i> PDF
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| github.com |
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| openreview.net |
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| cse.mit.edu |
Computational Science and Engineering Program
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| meche.mit.edu |
Mechanical Engineering
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| github.com |
Blendednet
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| github.com |
DrivAerNet++
|
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| github.com |
DrivAerNet dataset
|
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| github.com |
Turbo-compressors dataset
|
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| github.com |
Airfoil dataset
|
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| github.com |
SHIP-D dataset
|
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| github.com |
Milk Frother dataset
|
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| sites.psu.edu |
milk frother dataset
|
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| engr.psu.edu |
Brite lab.
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| github.com |
Autosurf aircraft dataset
|
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| github.com |
<i class="fab fa-github"></i>
|
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| twitter.com |
<i class="fab fa-twitter"></i>
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| linkedin.com |
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| scholar.google.com |
<i class="fas fa-graduation-cap"></i>
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| orcid.org |
<i class="fas fa-info-circle"></i>
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| linkedin.com |
Kristen M. Edwards
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| en.wikipedia.org |
Poisson
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| en.wikipedia.org |
Laplace
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| en.wikipedia.org |
Lagrange
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| en.wikipedia.org |
Euler
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| en.wikipedia.org |
Bernoulli
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| en.wikipedia.org |
Leibniz
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| en.wikipedia.org |
Copernicus
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| en.wikipedia.org |
Nasir al-Din al-Tusi
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| accessibility.mit.edu |
Accessibility
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