Dr. Collin M. Stultz is the Nina T. and Robert H. Rubin Professor in Medical Engineering and Science, a Professor of Electrical Engineering and Computer Science, Co-Director of the Harvard-MIT Program in Health Sciences and Technology, a member of the Research Laboratory of Electronics (RLE), and an associate member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). He is also a practicing cardiologist at the Massachusetts General Hospital (MGH). Dr. Stultz received his undergraduate degree in Mathematics and Philosophy from Harvard University; a PhD in Biophysics from Harvard University; and a MD from Harvard Medical School. He did his internship, residency, and fellowship at the Brigham and Women’s Hospital in Boston. His scientific contributions have spanned multiple fields including computational chemistry, biophysics, and machine learning for cardiovascular risk stratification. He is a member of the American Society for Biochemistry and Molecular Biology and the Federation of American Societies for Experimental Biology and he is a past recipient of a National Science Foundation CAREER Award and a Burroughs Wellcome Fund Career Award in the Biomedical Sciences. Currently research in his group is focused on the development of machine learning tools that can guide clinical decision making.

Ridwan Alam, PhD

Ridwan Alam is a Visiting Scientist at the Research laboratory of Electronics (RLE) in the Massachusetts Institute of Technology (MIT), and a Research Fellow at the Cardiovascular Research Center (CVRC) in the Massachusetts General Hospital (MGH). He received his PhD in Electrical Engineering from the University of Virginia, after acquiring his MS in ECE from the University of Oklahoma and his BS in EEE from Bangladesh University of Engineering and Technology. Ridwan’s research contributions and interest span the broad area of signal processing and machine learning for health applications; including areas such as HealthAI, Smart and Connected Health, Biomedical Health Informatics, and Digital Health. He is a member of the Institute of Electrical and Electronics Engineers (IEEE), IEEE Engineering in Medicine and Biology Society (EMBS), and Association for Computing Machinery (ACM). His current research is focused on inferring physiological and behavioral health parameters from non-invasive sensing modalities toward designing systems for clinical decision making.

Teya SooHoo Bergamaschi

Teya is working towards her PhD in Electrical Engineering and Computer Science. Her focus is in the application of machine learning to clinical decision making and healthcare problems and she is currently applying contrastive learning techniques to echocardiograms. Prior to coming to MIT, Teya received her M.S.E. in Biomedical Engineering and her B.S. in Biomedical Engineering and Applied Mathematics from Johns Hopkins University. In her free time Teya enjoys all things outdoors and is an avid hiker and backpacker.

Payal Chandak

Payal is working towards her PhD in the Harvard-MIT Program in Health Sciences and Technology. She is passionate about building solutions that can improve the practice of medicine and empower patients to take charge of their health. She is working on applying gradient-based learning techniques to biomedical signals collected from sensors and wearable devices, especially in the domain of cardiac electrophysiology. Before starting at MIT, she received a BA in Computer Science and in Neuroscience from Columbia University.

Charles Spencer Comiter

Charles is a PhD student in MIT’s Electrical Engineering and Computer Science Department, where he is advised by Professors Jian Shu and Collin Stultz. He is broadly interested in developing deep learning methods for the in silico mining biological data that would be otherwise difficult to obtain experimentally. More specifically, he is interested in generating biomolecular data at single-cell granularity from imaging data. Before coming to MIT, he received a BS in Computer Science from Yale University while also taking significant coursework in music. He additionally worked as an engineer for Facebook’s Artificial Intelligence Department for one year. 

Daphne Esther Schlesinger

Daphne is working towards her PhD in the Medical Engineering and Medical Physics program at HST, with a focus in Computer Science. She is interested in developing interpretable clinical decision support models for applications in cardiovascular disease. Before starting at MIT, she received a BS in Biomedical Engineering and in Physics from Johns Hopkins University.

Aniruddh Raghu

Aniruddh is a PhD student in the Department of Electrical Engineering and Computer Science, co-advised by Prof. Collin Stultz and Prof. John Guttag. He is interested in applications of machine learning to healthcare problems, including causal inference in medicine and developing tools for clinical risk stratification. Before coming to MIT, Aniruddh completed his Bachelor’s and Masters degrees at the University of Cambridge in 2018.

Daniel O Prakah-Asante

Daniel Prakah-Asante is an undergraduate majoring in Computer Science. He is interested in applications of machine learning in clinical decision making and disease prediction.

Paige Stockwell

Paige Stockwell is a Clinical Research Coordinator assisting in trials principally investigated by Dr. Stultz and Dr. Aguirre through MGH. She is currently a Master of Public Health student at Boston University School of Public Health concentrating in epidemiology and biostatistics. She is interested in applications of biostatistics to the field of clinical research.

Megumi Masuda-Loos

megumima@mit.edu

Megumi is the administrative assistant for Prof. Collin Stultz. She provides administrative support to Prof. Collin Stultz and his students.

Wangzhi Dai

Wangzhi Dai is a graduate student in the Department of Electrical Engineering and Computer Science. He is primarily interested in developing computational tools to help clinical decision making. Currently he is working on risk stratification of patients with acute coronary syndrome using a machine learning approach. Before coming to MIT, He earned his Bachelor’s at Peking University in 2017.

Mihir Khambete

Mihir Khambete is an M.Eng. student in the Department of Electrical Engineering and Computer Science. He is interested the application of generative models to noninvasively predict central hemodynamics in an interpretable manner. Mihir previously received his SB in Computer Science and Engineering from MIT in 2021.

Alicia Rodriguez

Alicia is a graduate student towards a Master of Science in Engineering and Management at the Integrated Design Management program at the Massachusetts Institute of Technology (MIT). She worked for 6 years in the semiconductor industry at Hewlett Packard Networking and Teradyne Inc. Currently, her work is gender-focused, oriented towards uncovering gaps with data to identify and improved care of cardiovascular diseases patients at high risk.

Katherine W Young

Katherine Young is a graduate student in the Department of Electrical Engineering and Computer Science at MIT. Currently she is working with physician-scientists to develop optimal treatment regimens for patients with congestive heart failure using reinforcement learning. She received her B.S. in Computer Science and Music from MIT in 2018, and in her free time enjoys playing the violin and running long distances.

Kristy A Carpenter

Kristy Carpenter is an undergraduate majoring in Computer Science and Molecular Biology. She is developing machine learning methods to identify non-hemolytic peptides that have antimicrobial properties.  Her research interests include machine learning, molecular dynamics simulations, and drug discovery.

Cara Berg

Cara Berg is an aspiring medical student working as a Clinical Research Coordinator in her gap years. She is currently coordinating multiple trials investigating Hypertrophic Cardiomyopathy. These trials are principally investigated by Dr. Stultz of MIT, Dr. Fifer of MGH, and Dr. Tower-Rader of MGH.

Paul Myers

Paul Myers is a graduate student in the Department of Electrical Engineering and Computer Science. He is currently developing new computational biomarkers for use in statistical models to identify patients at a high risk of cardiovascular death.

Erik Reinertsen

Erik is a postdoctoral research fellow at MIT and the Massachusetts General Hospital. He builds machine learning algorithms to read ECGs, predict cardiovascular emergencies from physiological time series data, and phenotype shock. Previously Erik interned on the investment team at at Takeda Ventures and co-founded a non-profit to help startups launch pilots at academic medical centers. He holds an MD from Emory University, a PhD in biomedical engineering from Georgia Tech, and a BS in bioengineering from UCLA.

http://erikreinertsen.com

Angela C Zhang

Angela Zhang is an undergraduate majoring in Computer Science and Mathematics.