Dr. Haibin Ling received the B.S. and M.S. degrees from Peking University in 1997 and 2000, respectively, and the Ph.D. degree from the University of Maryland, College Park, in 2006. From 2000 to 2001, he was an assistant researcher at Microsoft Research Asia. From 2006 to 2007, he worked as a postdoctoral scientist at the University of California Los Angeles. In 2007, he joined Siemens Corporate Research as a research scientist; then, from 2008 to 2019, he worked as an Assistant Professor and then Associate Professor at Temple University. In fall 2019, he joined Stony Brook University as a SUNY Empire Innovation Professor in the Department of Computer Science. His research interests include computer vision, augmented reality, medical image analysis, machine learning, and AI for science. He received Best Student Paper Award at ACM UIST (2003), Best Journal Paper Award at IEEE VR (2021), NSF CAREER Award (2014), Yahoo Faculty Research and Engagement Award (2019), and Amazon Machine Learning Research Award (2019). He serves or served as Associate Editors for IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), IEEE Trans. on Visualization and Computer Graphics (TVCG), Computer Vision and Image Understanding (CVIU), and Pattern Recognition (PR), and as Area Chairs various times for CVPR, ICCV, ECCV, ACM MM and WACV. He is a fellow of IEEE.

Dr. Rose T. Faghih is an associate professor of Biomedical Engineering at the New York University (NYU) where she directs the Computational Medicine Laboratory. She received a bachelor’s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from Massachusetts Institute of Technology (MIT). She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory at MIT as well as the Department of Anesthesia, Critical Care and Pain Medicine at the Massachusetts General Hospital. Dr. Faghih is the recipient of various awards including a 2024 IEEE Engineering Medicine and Biology Society (EMBS) Early Career Achievement Award, a 2023 National Institutes of Health (NIH) Maximizing Investigators’ Research Award for Early Stage Investigators, a 2020 National Science Foundation CAREER Award, a 2020 MIT Technology Review Innovator Under 35 award, and a 2016 IEEE-USA New Face of Engineering award. In 2020, she was featured by the IEEE Women in Engineering Magazine as a “Woman to Watch”. She is on the editorial board of PNAS Nexus by the National Academy of Sciences, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Neural Systems and Rehabilitation Engineering. Moreover, she is a senior member of IEEE and currently an IEEE Engineering in Medicine and Biology Society Administrative Committee Technical Representative. Dr. Faghih is the senior author of a Biomedical Engineering book titled Bayesian Filter Design for Computational Medicine published by Springer. Her research interests include wearable technologies, and medical cyber-physical systems, as well as neural and biomedical signal processing.

Dr. Mengyu Wang is a human and computer vision researcher with a faculty appointment in the Department of Ophthalmology of Schepens Eye Research Institute of Massachusetts Eye and Ear at Harvard Medical School. Dr. Wang has a diverse research background, including ophthalmology, radiology, computational mechanics, image processing, and artificial intelligence (AI). For human vision, Dr. Wang’s current research focuses on developing mathematical, mechanical, statistical, and AI models to enhance our knowledge and understanding of eye diseases and ultimately improve clinical treatment of eye diseases. For computer vision, Dr. Wang’s current research is pivoted on innovating various fairness-promoting AI techniques to improve the performance equity of various AI models, including classification models, segmentation models, cross-domain adaptation and generalization models, vision language models, generative models, and self-supervised foundation models.

Dr. Azhar Zam is an Associate Professor of Bioengineering. He holds a B.S. from The University of Indonesia, an M.Sc. from the University of Luebeck, Germany, and a Ph.D. from Friedrich-Alexander-University Erlangen-Nuremberg, Germany. Zam’s research interests focused on the development of smart devices for medical imaging, diagnostics, and monitoring using novel optical technologies, which include smart laser surgery, optical coherence tomography (OCT), photoacoustics, biomedical spectroscopy, AI-aided optical diagnostics and imaging, optical-based smart biosensors, and miniaturized systems. He is an associate editor for the Biophotonics section of Frontiers in Photonics and reviews editor for the Retina section of Frontiers in Ophthalmology. He has written over 85 peer-reviewed articles and book chapters, and books and holds more than several patents.

Dr. Taimur Hassan completed his Bachelors in Computer Engineering in 2013 with a specialization in Digital Systems and Signal Processing. Afterward, he completed his M.S. in Computer Engineering in 2015 with a specialization in Embedded Systems. He holds a Ph.D. degree from the National University of Sciences and Technology (NUST), where his Ph.D. dissertation is in Ophthalmic Image Analysis & Recognition. Dr. Taimur has around ten years of industrial, teaching, and research experience. Currently, he is working as an Assistant Professor of Computer Engineering in the Department of Electrical, Computer and Biomedical Engineering at Abu Dhabi University, United Arab Emirates. Prior to that, he worked as a Postdoctoral Fellow at the Center for Cyber-Physical Systems (C2PS), and the Khalifa University Center for Autonomous Robotic Systems (KUCARS), Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates.

Dr. Jiaya Jia is a Chair Professor of Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST) and director of Von Neumann Institute. He joined CUHK in 2004 as an assistant professor and was promoted to full professor in 2015. He moved to HKUST in 2024. He obtained his PhD degree in Computer Science jointly from HKUST and Microsoft Research in 2004. From March 2003 to August 2004, he was a visiting scholar at Microsoft. He conducted collaborative research at Adobe Research in 2007.

Dr. Muhammad Shafique received his Ph.D. degree in Computer Science from the Karlsruhe Institute of Technology (KIT), Germany, in 2011. Afterwards, he established and led a highly recognized research group at KIT for several years as well as conducted impactful collaborative R&D activities across the globe. Before KIT, he was with Streaming Networks Pvt. Ltd. where he was involved in research and development of video coding systems several years. In Oct.2016, he joined the Institute of Computer Engineering at the Faculty of Informatics, Technische Universität Wien (TU Wien), Vienna, Austria as a Full Professor of Computer Architecture and Robust, Energy-Efficient Technologies. Since Sep.2020, Dr. Shafique is with the New York University (NYU), where he is currently a Full Professor and the director of eBRAIN Lab at the NYU-Abu Dhabi in UAE, and a Global Network Professor at the Tandon School of Engineering, NYU-New York City in USA. He is also a Co-PI/Investigator in multiple NYUAD Centers, including Center of Artificial Intelligence and Robotics (CAIR), Center of Cyber Security (CCS), Center for InTeractIng urban nEtworkS (CITIES), and Center for Quantum and Topological Systems (CQTS). His research interests are in AI & machine learning hardware and system-level design, brain-inspired computing, EdgeAI, tinyML, machine learning security and privacy, quantum machine learning, cognitive autonomous systems, wearable healthcare, AI for healthcare/medical imaging, energy-efficient systems, robust computing, hardware security, emerging technologies, electronic design automation, FPGAs, MPSoCs, embedded systems, and quantum computing. His research has a special focus on cross-layer analysis, modeling, design, and optimization of computing and memory systems. The researched technologies and tools are deployed in application use cases from Internet-of-Things (IoT), Smart Cyber-Physical Systems (CPS), and ICT for Development (ICT4D) domains. Dr. Shafique has given several Keynotes, Invited Talks, and Tutorials at premier venues. He has served as the Associate Editor and Guest Editor of several prestigious IEEE and ACM journals. He has also served as the TPC Chair, General Chair, and Program Committee Member of several IEEE and ACM conferences. He is a senior member of the IEEE and IEEE Signal Processing Society (SPS), and a professional member of the ACM, SIGARCH, SIGDA, SIGBED, and HIPEAC. Dr. Shafique holds one U.S. patent, and has (co-)authored 7 Books, 20+ Book Chapters, 400+ papers in premier journals and conferences, and 100+ archive articles. Dr. Shafique received the prestigious 2015 ACM/SIGDA Outstanding New Faculty Award, the AI-2000 Chip Technology Most Influential Scholar Award in 2020, 2022 and 2023, the ATRC’s ASPIRE Award for Research Excellence in 2021, six gold medals in his educational career, and several best paper awards and nominations at prestigious conferences.

Dr. Mostafa Mobasher is an Assistant Professor of Civil and Urban Engineering at NYUAD. He holds Associated Affiliations with the Civil and Urban Engineering and the Mechanical and Aerospace Engineering departments at New York University, Tandon School of Engineering. Mobasher leads the Computational Solid Mechanics (CSM) Lab at NYUAD. The lab’s research is focused on modeling fracture and multi-physical response of manmade and natural materials across spatial and temporal scales. Mobasher’s research contributes to the development of a wide range of numerical models to address the needs for non-linear modeling of materials subjected to mechanical and multi-physical loading scenarios. More recently, the CSM lab’s research scope was expanded to explore the integration between the Machine Learning (ML) and Artificial Intelligence (AI) approaches along with well-established computational methods such as Finite Element Method (FEM). The outcomes of this research support various engineering applications including Infrastructure Integrity and Resilience, Energy, Automotive and Aerospace applications. Mobasher is also a Co-PI and the leader of the Energy Research Theme for Sand Hazards and Opportunities for Resilience, Energy, and Sustainability (SHORES), one of the NYUAD Research Institute Centers.