Dr. Kayhan Batmanghelich

Boston University

Dr. Kayhan Batmanghelich is an Assistant Professor at the Department of Electrical and Computer Engineering at Boston University. Previously, he was an Assistant Professor at the Department of Biomedical Informatics with a secondary appointment at the School of Computing and Information at the University of Pittsburgh. Before that, he was a post-doc at Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, and worked with Prof. Polina Golland.

Dr. Kayhan has developed algorithms to analyze and understand medical images, genetic data, and other electrical health records, such as clinical reports. The main themes of research in his lab are about the main challenges of AI in healthcare: (1) Explainability, (2) Data Efficiency, (3) Multimodal Data Fusion, and Causality. His lab works on Alzheimer’s, Chronic Obstructive Pulmonary Disease (COPD), and Non-Alcoholic Fatty Liver Disease (NAFLD) projects. His research is supported by funding from NIH, NSF, and industry awards. Dr. Kayhan is the co-founder of MLxMed, a multi-campus online similar series focusing on ML methods specifically for healthcare applications. He is also the co-founder of READE.ai, a start-up about the Realtime Evaluation of Adverse Events during surgery.

Dr. Kun Zhang

Carnegie Mellon University

Dr. Kun Zhang is an associate professor at Carnegie Mellon University (CMU), and he is also a visiting professor in the machine learning department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He has been actively developing methods for automated causal discovery from various kinds of data and investigating machine learning problems including transfer learning, representation learning, and reinforcement learning from a causal perspective. He has been frequently serving as a senior area chair, area chair, or senior program committee member for major conferences in machine learning or artificial intelligence, including UAI, NeurIPS, ICML, IJCAI, AISTATS, and ICLR. He was a general & program co-chair of the first Conference on Causal Learning and Reasoning (CLeaR 2022), a program co-chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), and is a general co-chair of UAI 2023. He currently serves as an associate editor of JASA, JMLR, ACM Computing Surveys, etc.

Dr. Shereen Fouad

Aston University

Dr. Shereen Fouad a Senior Lecturer in AI and Machine Learning at Aston University’s Department of Applied AI and Robotics, Aston University. She is a senior fellow of Higher Education Academy and Programme Lead for the MSc AI for Health. She serves as an expert member of the British Standards Institution (BSI) in software systems for medical devices and as an Honorary Research Fellow at the University of Birmingham’s School of Dentistry. She specialize in responsible AI methods for healthcare, medical imaging analysis, and data analytics in business. Her research focuses on developing trustworthy AI solutions for healthcare, bridging knowledge gaps among practitioners, and fostering interdisciplinary collaboration.