Organizing Team

Dr. Kilian Pohl is the founder of the Computational Neuroscience Lab, and a Professor in Psychiatry & Behavioral Sciences and (by courtesy) in Electrical Engineering at Stanford University. He received his Ph.D. from the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology and was an Instructor at Harvard Medical School, a Research Scientist at IBM Research, an Assistant Professor at the University of Pennsylvania, and the Director for Biomedical Computing at SRI International. His research in medical image analysis has received several awards, including the 2007 Elsevier Medical Image Analysis Best Paper Award and 2022 Runner-up of the Elsevier Medical Image Analysis Best Paper Award. Dr. Pohl is currently the principal investigator of several NIH funded projects and a Senior Editor of Medical Image Analysis.

Dr. Wei Peng is a Staff Research Scientist at Stanford University. His research focuses on deep learning and its applications in medical imaging. He has published over 50 academic papers in top-tier AI-related journals and conferences, such as TPAMI, TMI, MeDIA, CVPR, ICCV, ICLR, AAAI, and MICCAI. He has received numerous academic honors, including the Best PhD Thesis Award in Artificial Intelligence from Finland, the IEEE Best Conference Paper Award, the ISMRM Magna Cum Laude Merit Award, the Runner-up in the ECCV Human Action Challenge, and the Champion in the IJCAI Action Recognition Challenge. Dr. Peng has long served as a program committee member and reviewer for top-tier academic conferences and journals in the fields of AI and medical imaging, including journals like Nature sub-journals (Nature Biomedical Engineering, Scientific Reports), TPAMI, IJCV, and the leading conferences in machine learning and computer vision.

Dr. Alan Wang is a Postdoctoral Scholar at Stanford University affiliated with Computer Science and Psychiatry and Behavioral Sciences, where he is advised by Professor Ehsan Adeli. He is also a Human-Centered AI (HAI) Fellow. Previously, he completed my PhD at Cornell University and Cornell Tech, where I was advised by Mert Sabuncu. Before that, he studied Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). His research interests are at the intersection of machine learning and medical imaging. In particular, he is interested in and has worked on developing deep learning algorithms for medical imaging and healthcare, with an emphasis on improving interpretability, robustness, and fairness of deep learning models in these contexts.

Dr. Ben Glocker is a Professor in Machine Learning for Imaging and Kheiron Medical Technologies, Imperial College London. He is also RAEng Research Chair in Safe Deployment of Medical Imaging AI. He is co-leading the Biomedical Image Analysis Group, lead the HeartFlow-Imperial Research Team and he is Head of ML Research at Kheiron. His research is at the intersection of medical imaging and artificial intelligence aiming to build safe and ethical computational tools for improving image-based detection and diagnosis of disease.

Dr. Ehsan Adeli is an Assistant Professor of Psychiatry and Behavioral Sciences at Stanford University and serves as the Director of the Stanford Translational AI (STAI) Lab, focusing on AI applications in Medicine and Mental Health. He also holds the position of Co-Director of the Stanford AGILE (Advancing technology for fraIlty and LongEvity) Consortium. His interdisciplinary research group maintains active collaborations with multiple labs and centers, including the Computational Neuroscience (CNS) Lab in Psychiatry and Behavioral Sciences and the Stanford Vision and Learning (SVL) Lab in Computer Science.

Dr. Qingyu Zhao is an Assistant Professor in the Department of Radiology at Weill Cornell Medicine. He works at the intersection of machine learning and translational research, focusing on neuroimaging applications. His research centers on machine-learning-based computational analysis of neuroimaging and neuropsychological data to explain brain-behavior relationships and determine biomedical phenotypes of neurological diseases. His work has received the K99 Pathway to Independence Award from the NIH, the NARSAD Young Investigator Award from the BBRF, and the Innovator Grant Award and Chairman’s Award for Advancing Science from Stanford Psychiatry. Before joining Cornell, he earned his Ph.D. in Computer Science from UNC Chapel Hill and was a faculty member in the Department of Psychiatry and Behavioral Sciences at Stanford School of Medicine. His broad interests lie in image analysis and statistical learning for the detection, diagnosis, and treatment of diseases.

Dr. Ulas Bagci is an Associate Professor (with tenure) at the Northwestern Univer- sity’s Radiology and Biomedical Engineering Department at Chicago. His research interests are artificial intelligence, machine learning and their applications in biomedical and clinical imaging. Dr. Bagci has more than 250 peer-reviewed articles on these topics. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health’s radiology and imaging sci- ences department, center for infectious disease imaging. Dr. Bagci holds two NIH R01 grants (as Principal Investigator) and serves as a steering committee member of AIR (artificial intelligence resource) at the NIH. Dr. Bagci also serves as an area chair for MICCAI for several years and he is an associate editor of top-tier journals in his fields such as IEEE Trans. on Medical Imaging, Medical Physics, and Medical Image Analysis. Prof. Bagci teaches machine learning, advanced deep learning methods, computer and robot vision, and medical imaging courses. He has several international and national recognitions including best paper and reviewer awards.

Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering De- partment and an Associate Member in the Computer Science Department at the University of British Columbia (UBC). She serves as a faculty member at the Vector Institute and holds an adjunct Assistant Professor position at Yale University’s School of Medicine. She has been rec- ognized with prestigious appointments as a Canada CIFAR AI Chair and a Canada Research Chair (Tier II) in Responsible AI. Her research focuses on machine learning and its applications to healthcare, with the goal of bridging the gap between AI research and practical applications through the development of next-generation trustworthy AI systems. 

Yixin Wang is a PhD candidate at Stanford University, working on applying AI to biomedical imaging analysis. Her current PhD research aims to improve the diagnosis and treatment of neurocognitive disorders using advanced machine learning techniques, from the highly-dimensional multi-modal neuroimaging data. Her work has earned multiple academic honors, including the Best Paper Award at MICCAI Machine Learning in Clinical Neuroimaging, Second place in MICCAI BraTS Challenge, Stanford BioX Travel Award, Wu Tsai Neurosciences MBCT PhD Fellowship, and ISMRM Travel Award.

Dr. Fabio De Sousa Ribeiro is Postdoc at Imperial College London, and he got his PhD in Computer Science, he working closely with Professor Ben Glocker.