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2024.ieeeicip.org
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ADNEC Centre
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2024.ieeeicip.org
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ICIP 2024
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nyuad.nyu.edu
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<p class="title is-4">Yi Fang</p>
<p class="subtitle is-6">Associate Professor, New York University</p>
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Dr. Yi Fang, is an Associate Professor of Electrical and Computer Engineering and an Affiliated Associate Professor of Computer Science at NYU and NYU Abu Dhabi, as well as a member of the Center for Artificial Intelligence and Robotics (CAIR) at NYU. After earning his doctorate from Purdue University with a focus on computer graphics and vision, he gained industry experience at Siemens and Riverain Technologies, and academic experience at Vanderbilt University. His research focuses on embodied AI, general-purpose robots, and humanoids, with applications spanning engineering, social science, medicine, and biology. Dr. Fang founded the NYU AIR Lab (Embodied AI and Robotics Lab), a leading center for research in robotics and AI. </div>
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mbzuai.ac.ae
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<p class="title is-4">Hisham Cholakkal</p>
<p class="subtitle is-6">Assistant Professor of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence</p>
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Dr. Hisham Cholakkal is an Assistant Professor at MBZUAI, having diverse experiences in fundamental research, teaching, and commercial product development across diverse industries. Prior to joining MBZUAI, he worked as a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI) in Abu Dhabi. Before his role at IIAI, he served as a Senior Technical Lead in the Computer Vision and Deep Learning Research team at Mercedes-Benz R&D in India. He has also worked at the Advanced Digital Science Center (ADSC) in Singapore and at the BEL Central Research Lab in India. Cholakkal's research interests include multimodal models, LLMs/VLMs, visual recognition, and AI in healthcare. His recent focus is on building multimodal conversational systems capable of reasoning and interacting seamlessly with humans in real time. He is also interested in the real-world applications of computer vision and machine learning algorithms in healthcare and remote sensing. Cholakkal's research has received several recognitions and funding, including the Google Research Award 2023 at MBZUAI, Meta Llama Impact Innovation Award 2024, MBZUAI Seed fund 2024, Weizmann Institute of Science - MBZUAI Joint Research Grant 2022-2025, etc. Cholakkal will serve as a General Chair at ACM Multimedia Asia 2026 and has previously acted as Area Chair for ECCV 2024 and BMVC 2024. He was the Primary Organizer of workshops at ICCV 2023, CVPR 2024, NeurIPS 2022, and ACCV 2022. Additionally, he serves as an Associate Editor for journals such as IET Computer Vision and is a program committee member for several top conferences, including CVPR, ICCV, NeurIPS, ICLR, and ECCV.
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me.udel.edu
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<p class="title is-4">Gregory S. Chirikjian</p>
<p class="subtitle is-6">Professor & Department Chair, University of Delaware</p>
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Dr. Gregory S. Chirikjian is the Willis F. Harrington Professor and Chair of the Mechanical Engineering Department at the University of Delaware. A distinguished roboticist and applied mathematician, he is known for his groundbreaking contributions to robotics, particularly in kinematics, motion planning, and the application of group theory to engineering. His research has advanced the understanding of hyper-redundant robots and stochastic methods on Lie groups, and he is actively involved in embodied AI, focusing on affordance-based reasoning to enhance robotic intelligence. Chirikjian's career is marked by numerous honors, including being named an NSF Young Investigator, a Presidential Faculty Fellow, and a Fellow of both IEEE and ASME. Before joining the University of Delaware in 2024, he held leadership roles at the National University of Singapore
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vcg.engr.ucr.edu
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<p class="title is-4">Amit K Roy Chowdhury</p>
<p class="subtitle is-6">Professor and Director of UC Riverside AI Research and Education Institute, University of California, Riverside</p>
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Dr. Amit Roy-Chowdhury received his PhD from the University of Maryland, College Park (UMCP) in 2002 and joined the University of California, Riverside (UCR) in 2004 where he is a Professor and UC Presidential Chair of Electrical and Computer Engineering, Cooperating Faculty in Computer Science and Engineering, and Co-Director of the UC Riverside AI Research and Education Institute. He leads the Video Computing Group at UCR, working on foundational principles of computer vision, image processing, and machine learning, with applications in cyber-physical, autonomous and intelligent systems. He has published over 250 papers in peer-reviewed journals and conferences and two monographs: Person Re-identification with Limited Supervision and Camera Networks: The Acquisition and Analysis of Videos Over Wide Areas. He is on the editorial boards of major journals and program committees of the main conferences in his area. He is a Fellow of the IEEE and IAPR, received the Doctoral Dissertation Advising/Mentoring Award from UCR, and the ECE Distinguished Alumni Award from UMCP.
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di.ens.fr
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<p class="title is-4">Ivan Laptev</p>
<p class="subtitle is-6">Professor, Mohamed bin Zayed University of Artificial Intelligence</p>
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Dr. Ivan Laptev obtained his master's degree in computer science at the Royal Institute of Technology (KTH) in Sweden in 1997 and then worked as a research assistant at the Technical University of Munich. In 2004, he earned his Ph.D. in computer science from KTH and pursued a postdoc position at the INRIA Vista team in France. He was appointed as INRIA Research Scientist in 2005 and then as INRIA Research Director in 2013. He has been with INRIA Paris since 2009, where he has led the WILLOW research team between 2021 and 2023. He has published more than 150 technical papers, most of which appeared in international journals and major peer-reviewed conferences of computer vision, machine learning and robotics. He has graduated 19 Ph.D. students who now pursue careers in industrial and academic research labs. He has also co-founded a computer vision company, VisionLabs, which has grown to 250 people. Laptev has been actively involved in the scientific community, serving as an associate editor of IJCV and TPAMI, and as a program chair for CVPR 2018, ICCV 2023 and ACCV 2024. He will also serve as a General Chair of ICCV 2029 bringing the international computer vision community to UAE. He has co-organized several tutorials, workshops and challenges at major computer vision conferences. He has also co-organized a series of INRIA summer schools on computer vision and machine learning (2010–2013) and Machines Can See summits (2017–2023). He received an ERC Starting Grant in 2012 and was awarded a Helmholtz prize for significant impact on computer vision in 2017.</div>
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ece.ualberta.ca
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<p class="title is-4">Li Cheng</p>
<p class="subtitle is-6">Associate Professor, University of Alberta</p>
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Dr. Li Cheng is an associate professor with the Department of Electrical and Computer Engineering, University of
Alberta. He also hold an adjunct position with A*STAR, Singapore, where he have led a group in Machine Learning
for Bioimage Analysis at the Bioinformatics Institute. Prior to joining University of Alberta in year 2018, he
worked at A*STAR, Singapore, TTI-Chicago, USA, and NICTA, Australia. He received my BSc degree in Computer
Science from Jilin University in 1996, M. Eng. degree from Nankai University in 1999, and PhD in Computing
Science from the University of Alberta in 2004. His research expertise is mainly on computer vision and machine
learning. He is a member of the Institute of Electrical and Electronics Engineers (IEEE), the Association for
Computing Machinery (ACM), and the Association for the Advancement of Artificial Intelligence (AAAI).</div>
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xiaojun.ai
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<p class="title is-4">Xiaojun Chang</p>
<p class="subtitle is-6">Professor, Australian Artificial Intelligence Institute (AAII) and Visiting Professor at Mohamed bin Zayed University of Artificial Intelligence </p>
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Dr. Xiaojun Chang is a Professor at the Australian Artificial Intelligence Institute (AAII) at UTS and a Visiting Professor at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He directs the Recognition, Learning, and Reasoning Lab (ReLER), focusing on AI, computer vision, multimedia, and machine learning, particularly for analyzing visual, acoustic, and textual signals in applications like video surveillance. Before joining UTS in 2022, Dr. Chang held positions at Carnegie Mellon University, Monash University, and RMIT. He has secured over $3 million in research funding and made significant contributions to video analysis and multimedia retrieval, including healthcare innovations. A Clarivate Analytics Highly Cited Researcher (2019-2023), his work, including an automatic report generation system for critically ill COVID-19 patients, has gained international recognition. His team has won prestigious global challenges, and he has published over 200 peer-reviewed papers. Committed to advancing AI for real-world applications, Dr. Chang regularly collaborates with industry to develop intelligent systems that benefit humanity.
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mbzuai.ac.ae
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<p class="title is-4">Zhiqiang Shen</p>
<p class="subtitle is-6">Assistant Professor, Mohamed bin Zayed University of Artificial Intelligence</p>
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</div>
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Dr. Zhiqiang Shen is an Assistant Professor of Machine Learning at MBZUAI, specializing in efficient deep learning, machine learning, and computer vision. His research focuses on developing deep learning methods for image recognition, object detection, and designing efficient architectures with parameter-efficient fine-tuning strategies. Prior to MBZUAI, Dr. Shen was an assistant research professor at Hong Kong University of Science and Technology (HKUST) and a postdoctoral researcher at CyLab, Carnegie Mellon University. His recent work includes low-bit neural networks, knowledge distillation, and efficient architectures for CNNs and transformers, with a focus on unsupervised learning and image understanding.
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dr.ntu.edu.sg
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<p class="title is-4">Weisi Lin</p>
<p class="subtitle is-6">Associate Dean (Research), College of Computing & Data Science, Nanyang Technological
University</p>
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Dr. Weisi Lin is a distinguished researcher and educator, holding a PhD in Computer Vision from King's College
London, as well as a BSc in Electronics and MSc in Digital Signal Processing from Sun Yat-Sen University, China.
He has held academic and research positions at institutions such as Sun Yat-Sen University, Bath University, and
the National University of Singapore, as well as leadership roles in Singapore's Institute for Infocomm
Research. With over 400 refereed publications, 16 patents, and contributions to international standards, Dr. Lin
has led more than 10 major projects in digital multimedia technology. His research focuses on
perception-inspired signal modeling, visual quality evaluation, video compression, and multimedia systems,
balancing academic theory with industrial application. </div>
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danxurgb.net
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<p class="title is-4">Dan Xu</p>
<p class="subtitle is-6">Assistant Professor, Department of Computer Science and Engineering, Hong Kong University of Sciences and Technology (HKUST)</p>
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Dr. Dan Xu, is an Assistant Professor in the Department of Computer Science and Engineering at HKUST, with a research focus on computer vision, multimedia, and machine learning. He was previously a Postdoctoral Research Fellow in the Visual Geometry Group at the University of Oxford, working under Prof. Andrea Vedaldi and Prof. Andrew Zisserman, and earned his PhD from the University of Trento under Prof. Nicu Sebe. Dr. Xu's research interests include deep learning, multi-modal and multi-task learning, with applications in 2D/3D perception, scene understanding, dense scene prediction, and large-scale 3D modeling, as well as human- and scene-centric generation and editing. </div>
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engineering.lehigh.edu
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<p class="title is-4">Maryam Rahnemoonfar</p>
<p class="subtitle is-6">Associate Professor, Director of Computer Vision and Remote Sensing Laboratory (Bina lab), Lehigh University </p>
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</div>
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Dr. Maryam Rahnemoonfar is a Tenured Associate Professor of Computer Science and Engineering at Lehigh University's P.C. Rossin College of Engineering and Applied Science, with a joint appointment in Civil and Environmental Engineering. She directs the Computer Vision and Remote Sensing Laboratory (Bina Lab), where her research spans Data Science for Sustainability, Deep Learning, Computer Vision, AI for Social Good, and Remote Sensing. Her work focuses on developing machine learning algorithms for heterogeneous sensors such as Radar, Sonar, and Multi-spectral. Dr. Rahnemoonfar has secured multiple prestigious awards, including the NSF HDR Institute Award and Amazon Machine Learning Award. Passionate about interdisciplinary research for environmental and humanitarian solutions, she has led numerous projects and served on the National Academy of Sciences' workshop on Antarctic research technologies. She earned her Ph.D. in Computer Science from the University of Salford, UK, and previously held academic positions at UMBC and Texas A&M University-Corpus Christi. </div>
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tianlong-chen.github.io
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<p class="title is-4">Tianlong Chen</p>
<p class="subtitle is-6">Assistant Professor, University of North Carolina at Chapel Hill</p>
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</div>
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Dr. Tianlong Chen received the Ph.D. degree in Electrical and Computer Engineering from University of Texas at Austin, TX,
USA, in 2023. He starts as an Assistant Professor of Computer Science at The University of North Carolina at Chapel Hill
in Fall 2024. Before that, he is a Postdoctoral Researcher at Massachusetts Institute of Technology (CSAIL@MIT), Harvard
(BMI@Harvard), and Broad Institute of MIT & Harvard in 2023-2024. His research focuses on building accurate, trustworthy, and efficient machine learning systems. He devotes his most
recent passion to various (A) important machine learning problems - sparsity, robustness, learning to optimize, graph
learning, and diffusion models; (B) interdisciplinary scientific challenges - bioengineering and quantum comptuing. He
received IBM Ph.D. Fellowship, Adobe Ph.D. Fellowship, Graduate Dean's Prestigious Fellowship, AdvML Rising Star, and
the Best Paper Award from the inaugural Learning on Graphs (LoG) Conference 2022.</div>
</div>
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cmsworkshops.com
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ICIP's guidelines
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Анализировать url
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cmsworkshops.com
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format
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urldefense.proofpoint.com
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<b>Link</b>
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creativecommons.org
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Creative
Commons Attribution-ShareAlike 4.0 International License
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github.com
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this website
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Анализировать url
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