MIT Critical Data

MIT Critical Data is an affiliation of research labs at Massachusetts Institute of Technology who are engaged in research in and around “critical data”, more specifically data that has a critical impact on human health. If you’d like to reach out to us, please see the bottom of this page.

Leo Anthony Celi

Leo has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP), and as an attending physician at the Beth Israel Deaconess Medical Center (BIDMC), he brings together clinicians and data scientists to support research using data routinely collected in the process of care. He is one of the course directors for HST.936 – global health informatics to improve quality of care, and HST.953 – collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. The textbook “Secondary Analysis of Electronic Health Records” came out in October 2016 and was downloaded more the 100,000 times in the first year of publication. The massive open online course HST.936x “Global Health Informatics to Improve Quality of Care” was launched under edX in February 2017. Finally, Leo has spoken in 25 countries about the value of data in improving health outcomes.

Alistair Johnson

Alistair is a research scientist at the Massachusetts Institute of Technology. Alistair received his B.Eng in Biomedical and Electrical Engineering at McMaster University, Canada, and subsequently read for a DPhil in Engineering Science at the University of Oxford. Alistair’s research interests focus around the reuse of routinely collected clinical data to improve healthcare, including topics such as: predicting the risk of mortality for intensive care unit patients, detecting physiologic deterioration for hospitalized patients, and identifying and characterizing septic patients. In addition, Alistair routinely co-organizes “datathons”, weekend long events which aim to spark research on focused questions, form international collaborations, and generate new clinical knowledge.

Tom Pollard

Tom is a Research Scientist at the MIT Laboratory for Computational Physiology. Most recently he has been working with colleagues to release MIMIC-III, an openly-accessible critical care database. Prior to joining MIT in 2015, Tom completed his PhD at University College London, UK, where he explored models of health in critical care patients in an interdisciplinary project between the Mullard Space Science Laboratory and University College Hospital. Tom has a broad interest in how we can improve the way that critical care data is managed, shared, and analysed for the benefit of patients. He is a Fellow of the Software Sustainability Institute.

Tristan Naumann

Tristan is a Ph.D. candidate the Clinical Decision Making Group (MEDG) at MIT CSAIL, advised by Professor Peter Szolovits. His research focuses on exploring relationships in complex, unstructured healthcare data using natural language processing and unsupervised learning techniques. His work has been published in KDD, AAAI, AMIA, JMIR, Science Translational Medicine, and Nature Translational Psychiatry.

While at MIT, Tristan has been an Instructor for HST.953 (Collaborative Data Science for Medicine) and co-authored its textbook, “Secondary Analysis of Electronic Health Records.” He co-organized the NIPS 2017 Machine Learning for Health (ML4H) workshop and several “datathon” events, which bring together participants to address problems of clinical interest. He served as a mentor for the MIT Summer Research Program (MSRP), and has spent time as a Software Engineering Intern at Intel Corporation. Prior to MIT, Tristan was a Program Manager at Microsoft Corporation, an Associate Product Manager Intern at Google, and received B.S. and M.S degrees in computer science from Columbia University, where he was a MS-TA fellow and recipient of the Andrew P. Kosoresow Memorial Award for Outstanding Performance in TA-ing and Service.

Christina Chen

Christina is a physician scientist who is an Instructor at Harvard Medical School, staff nephrologist at Beth Israel Deaconess Medical, and a research scientist at the MIT Laboratory for Computational Physiology. She currently attends on the nephrology consult service at BIDMC and has an outpatient renal clinic where she works with medical students, residents, and fellows. With her background in engineering and medicine, she hopes to help bridge the gap between data scientists and clinicians to answer innovative questions. Her current research interests include studying acute kidney injury as well as using echocardiography to determine effects of cardiac dysfunction on outcomes.

Aaron Kaufman

Aaron is a PhD candidate in Political Methodology and American Politics at Harvard University. His research interests leverage cutting-edge methods in computer science and causal inference to answer substantive questions about public opinion, voting patterns, and elite behavior. Additionally, he produces open-source tools to help survey researchers conduct more efficient and unbiased research, and is committed to research transparency and open science.

Ned McCague

Ned is a lecturer at MIT, teaching courses on global health informatics and data science in medicine. He also works as a data scientist at Kyruus, where he focuses on using data to drive innovation and insights. He has an MPH from BU in Biostatistics & Epidemiology and previously worked as a statistician for Blue Cross Blue Shield of Massachusetts.

David Sasson

David is a graduate student at the Harvard T.H. Chan School of Public Health studying Health Data Science. Prior to joining MIT Critical Data he completed his undergraduate degree in Biochemistry and held research appointments in private, government, and academic sectors. In addition to working in research, David has volunteered in medically underserved areas spanning the greater New York City area, rural Appalachia, and central Bolivia. His current interests include the microbiome, democratized AI, and the diminishing separations between biology and technology.

Anna Schlimm

Anna is a graduate student on the service design program at the Royal College of Art in London. Her design work always has a strong focus on sustainability and ethics at its core. She has worked with clients as diverse as CERN and the Royal Opera House in London and is currently employing her design and systems thinking skills to find solutions to the complex and interrelated issues that face scientific research.


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Massachusetts Institute of Technology (MIT)
77 Massachusetts Avenue
MA 02139, USA

About us

Critical data is a loose affiliation of research labs at Massachusetts Institute of Technology who are engaged in research in and around "Critical Data", more specifically data that has could be harnessed to better the provision of healthcare.


Massachusetts Institute of Technology (MIT)
77 Massachusetts Avenue
MA 02139, USA

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