Put data and learning at the center of healthcare delivery. Learn from a collaborative group of computer scientists, health providers and social scientists working to improve population health through data analytics and data mining routinely collected in the process of patient care.
Put data and learning at the center of healthcare delivery. Learn from a collaborative group of computer scientists, health providers and social scientists working to improve population health through data analytics and data mining routinely collected in the process of patient care.
Research has been traditionally viewed as a purely academic undertaking, especially in limited-resource healthcare systems. Clinical trials, the hallmark of medical research, are expensive to perform, and take place primarily in countries which can afford them. Around the world, the blood pressure thresholds for hypertension, or the blood sugar targets for patients with diabetes, are established based on research performed in a handful of countries. There is an implicit assumption that the findings and validity of studies carried out in Western countries generalize to patients around the world. Big data collected in the process of patient care presents an opportunity to gain important insights into health and disease across diverse populations, including those that may not otherwise be represented in clinical trials.
Experience with R, Python and/or SQL is desirable but not required.
Louis Agha-Mir-Salim is a physician and postdoctoral researcher at the Institute of Medical Informatics at Charité - Universitätsmedizin Berlin. Before graduating from medical school at the University of Southampton, Louis completed a BSc in Medical Sciences with Management at Imperial College London while working for two digital health start-ups. As a former visiting student at the MIT Laboratory for Computational Physiology, he was involved in several projects on the analysis of critical care data. Before going into full-time research, Louis initially worked as a physician in anesthesiology and nephrology. Wanting to foster cross-disciplinary collaboration between data scientists and clinicians, he served as faculty member at numerous MIT Critical Data events worldwide.
Leo Anthony Celi is the principal investigator behind the Medical Information Mart for Intensive Care (MIMIC) and its derivatives (MIMIC-CXR, MIMIC-ED, MIMIC-ECHO, and MIMIC-ECG), serving close to 100,000 users worldwide and generating over 10,000 publications that have fundamentally shaped machine learning in healthcare. His group at MIT leads MIT Critical Data, a global consortium whose mission is to build local AI capacity and agency in healthcare through open data and software, foster community engagement through datathons and workshops, and advance equity through technology. His work pioneers process-based approaches to algorithmic fairness that move beyond traditional demographic metrics, developing culturally-aware human-AI systems that incorporate diverse knowledge traditions including indigenous epistemologies and faith perspectives. In partnership with hospitals, universities, and professional societies worldwide, Dr. Celi's team has organized over 100 health data science events across >40 countries, democratizing access to opportunities for growth and fostering interdisciplinary collaboration that actively addresses power dynamics in the global knowledge ecosystem.
Marie-Laure Charpignon is a postdoctoral fellow in computational health informatics at Kaiser Permanente Northern California, UC Berkeley, and Boston Children’s Hospital. She received her PhD in Social & Engineering Systems and Statistics from MIT. Her research focuses on the development and implementation of causal inference and network science approaches in public health and clinical medicine, including to repurpose existing drugs and prevent or delay the onset of neurodegenerative, psychiatric, and cardiovascular diseases. During her thesis, she has also partnered with Moderna and the Massachusetts Department of Public Health to evaluate alternative vaccine distribution strategies leveraging household structure and mobility data via agent-based model simulations. Her work has been recognized by the 2023 Rising Stars in Data Science and the 2024 Moderna Global Fellowship. She is a veteran mentor of health datathons organized by MIT Critical Data and has co-organized multiple events such as the ML4H symposium, the AI for Public Health workshop at ICLR, and epiDAMIK at KDD.
Kenneth Paik is a clinical informatician focused on quality improvement in healthcare through technology innovation, combining a multidisciplinary background in medicine, machine learning, business management, and technology strategy. As a Research Scientist at the MIT Laboratory for Computational Physiology his work encompasses the secondary analysis of clinical health data, taking a collaborative (academic and industry) and multidisciplinary (clinicians and data scientists) approach to build high-resolution datasets for the practical application of machine learning in healthcare. His global health work through MIT Sana leverages mobile technology innovation and digital health to bridge immense gaps in resources or infrastructure, including for example, pilot projects for NCD management mHealth for refugee and displaced populations in Syria and Lebanon.
Wesley Yeung is an interventional cardiologist and clinician scientist at the National University Heart Centre Singapore, leading research at the intersection of cardiovascular medicine, artificial intelligence, and large scale clinical data science. He directs multi institutional initiatives developing ECG based foundation models, multimodal electronic health record platforms, and real world AI deployment pipelines to advance precision cardiology and population health. His work spans translational research, digital health innovation, and education, with a focus on building clinically grounded AI systems that improve cardiovascular outcomes in diverse Asian populations.
Salamata Konate is a postdoctoral fellow at York University and a Connected Minds Fellow, working at the intersection of machine learning, interpretability, and fairness. Her research focuses on understanding and controlling generative and foundation models, with particular emphasis on diffusion models, semantic disentanglement, and bias mitigation. Her broader work explores explainability and interpretability in AI systems, especially in high-stakes domains such as healthcare.Through interdisciplinary collaborations across computer science and medicine, she aims to build interpretable, reliable, and ethically grounded AI systems that can be safely deployed in real-world settings.