LCP personnel and collaborators
Research in the Laboratory for Computational Physiology takes place both in the lab itself and through collaborations with other researchers elsewhere on campus. LCP researchers and principal collaborators are listed below.
- Roger Mark
- Leo Celi
- Alistair Johnson
- Li-wei Lehman
- Tom Pollard
- Mohammad Ghassemi
- Jesse Raffa
- Felipe Torres Fábregas
- Benjamin Moody
- Chen Xie
- Kenneth Paik
- Alon Dagan
- Rodrigo Deliberato
- Christina Chen
- Miguel Armengol
Dr. Mark is Distinguished Professor of Health Sciences and Technology in the Institute of Medical Engineering and Science at MIT. He received the SB and PhD degrees in EE from MIT, and the MD degree from Harvard Medical School. He trained in internal medicine at the Harvard Medical Unit at Boston City Hospital, and then spent two years in the Medical Corps of the USAF studying the biological effects of laser radiation. He joined the faculty of the EE Department at MIT in 1969, and also the faculty of the Department of Medicine at Harvard Medical School. He has been active in teaching cardiovascular pathophysiology to HST students, and quantitative physiology to undergraduate biomedical engineering students at MIT. Dr. Mark is a fellow of the IEEE, a fellow of the American College of Cardiology, and a founding fellow of the American Institute of Medical and Biological Engineering.
He conducts research aimed at improving health care through the generation of new knowledge, monitoring technology and clinical decision support by applying physiological signal processing, data science and machine learning to large collections of critical care data.
Dr. Celi directs all the interdisciplinary clinical research of the lab and mentors many young clinicians who collaborate with the core engineering staff on research projects. He is a physician with board certifications in internal medicine, infectious disease and critical care medicine. He also holds master’s degrees in Biomedical Informatics (MIT) and Public Health (Harvard). He is Associate Professor of Medicine at Harvard Medical School and Principal Research Scientist at MIT. He actively attends and teaches in the Medical Intensive Care Units at BIDMC during 6 weeks each year. He teaches two MIT courses—HST.936 Global Health Informatics to Improve Quality of Care, and HST.953 Secondary Analysis of Health Data and oversees efforts to transition the course into a massive open online course under edX. His research interest is in data mining and the application of machine learning on large databases. He works with MIMIC, the publicly-available de-identified ICU database from BIDMC. He is working on a data-driven decision support system known as Collective Experience that (1) allows a clinician to draw on the experience of other clinicians who have taken care of similar patients as recorded in a clinical database, and (2) uses models performed on relatively homogeneous patient subsets.
Alistair joined the Laboratory for Computational Physiology in 2015 and is currently a full time Research Scientist. He received his B.Eng in Biomedical and Electrical Engineering at McMaster University, Canada, and subsequently read for a D.Phil in Healthcare Innovation at the University of Oxford. His thesis was titled "Mortality and acuity assessment in critical care", and its focus included using machine learning techniques to predict mortality and develop new severity of illness scores for patients admitted to intensive care units. Before joining the LCP, Alistair spent a year as a research assistant at the John Radcliffe hospital in Oxford, where he worked on building early alerting models for patients post-ICU discharge. He has extensive experience and expertise in working with ICU data, and in building decision support tools for critically ill patients. He is actively engaged in research investigations with clinical colleagues. He has worked extensively on creating and releasing the Medical Information Mart for Intensive Care (MIMIC)-III and eICU databases, and in actively supporting its user community.
Dr. Li-wei Lehman is a full-time Research Scientist in the Laboratory for Computational Physiology (LCP) in IMES. She received her Master's degree in Computer Science from Georgia Institute of Technology, and her Ph.D. in Information Systems and Technology from MIT in June 2005. Her work focuses on the NIH-funded project Research Resource for Complex Physiologic Signals (PhysioNet), which is aimed to stimulate current research and new investigations in the study of complex biomedical and physiologic signals. Her research interests include searching, mining, and detection of physiologically significant events in biomedical databases. She is particularly interested in probabilistic modeling and inferencing algorithms on physiological and clinical data to identify patients with similar pathophysiologies, and to discover "hidden" information that may be predictive of disease progressions. Prior to joining the PhysioNet team, she worked on (and continues to be involved in) several projects in the research program, including an annotation system, a pattern-matching de-identification system, and a temporal search engine for multi-parameter biomedical databases.
Tom earned his PhD in biomedical engineering at University College London and University College London Hospitals (UCLH) where he obtained focused experience in the intensive care environment and computational modelling of patient physiology. He is currently a full time Research Scientist at MIT. He is expert in the development, support, and analysis of critical care databases, including MIMIC and the recently released eICU Collaborative Research Database. He conducts retrospective studies in collaboration with clinical specialists, develops and supports critical care databases that are widely used around the world in research and education, and he has created software used by hundreds of researchers and students internationally. Tom has a broad interest in how we can improve the way that critical care data is managed, shared, and analyzed for the benefit of patients. He is a Fellow of the Software Sustainability Institute.
Dr. Jesse Raffa, PhD, is a biostatistician Research Scientist at MIT and provides statistical and epidemiological expertise to the group. His primary area of interest in methodological research has been the analysis of complex longitudinal data, in particular using different types of latent variable structures. This is highly relevant in the critical care setting, where most data is both longitudinal and complex in nature. He provides study design and analytical support for LCP’s clinical collaborations both as a consultant and data analyst.
Felipe Torres Fábregas
Felipe grew up in the island of Puerto Rico, where he completed the Computer Science degree from UPR-RP. While completing his undergraduate studies, he worked as a System Administrator for the Computer Science Department in the University of Puerto Rico. After graduation, he was offered the position of System and Network Administrator in the University of Puerto Rico, where he created a 10Gbps intra-network under the sponsorship of Dr. José Ortiz. In 2016, he moved to MA to start working at MIT as a Software Research Engineer. Currently he administers the servers of the Laboratory of Computational Physiology, and develops new applications.
Mr. Benjamin Moody is a scientific programmer with extensive experience in physiologic signal processing. He manages the acquisition, reformatting, de-identification and archiving of the physiologic waveform data that accumulates continuously at a rate of 400 GB (3,000 waveform records) per month! The data is collected from all patients in the ICUs and NICUs at BIDMC by the Philips monitors. The waveforms are archived by the Philips Data Warehouse Connect system, and are transmitted to MIT via a VPN network connection. Mr. Moody extracts the data, reformats it into wfdb flat files, de-identifies and time-shifts it, and ultimately matches it to associated clinical data.
Chen has been a research programmer at the LCP since receiving his MEng in Biomedical Engineering from Imperial College London in 2015. He works to develop and expand the tools and content of Physionet, including the WFDB software packages. His interests include signal processing of high resolution physiological waveforms, and the open distribution of healthcare data.
Kenneth E. Paik, MD MBA MMSc 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 MIT his research encompasses the secondary analysis of health data and the application of digital health in resource limited settings.
Christina Chen, MD 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.
Dr. Mohammad Ghassemi
Alon Dagan is an emergency medicine physician with a background in biomedical engineering. He is interested in developing pragmatic and innovative healthcare tools by bridging the divide between technical and clinical fields. He is currently working clinically as an attending emergency medicine physician at both Lahey Medical Center and Beth Israel Deaconess Medical Center, and serves an Instructor of Emergency Medicine at Harvard Medical School. In addition, Dr. Dagan is a Research Affiliate at the MIT Laboratory of Computational Physiology. His work is focused on the power of bringing together practicing clinicians with technical experts in order to create meaningful and practical healthcare solutions both locally and in the global health setting. To this end he has acted as clinical faculty for both “HST 953: Collaborative Data Science in Medicine” and “HST 936: Leveraging Big Data in Global Health” hosted at the Harvard-MIT Program in Health Sciences and Technology. Additionally, he is one of the founding course instructors of “Global Health Informatics to Improve Quality of Care” a massive open online course, which has been freely accessed by more than 6000 learners in 155 countries. Dr. Dagan has also co-organized collaborative health hackathons at MIT as well as in Mexico, Colombia, Taiwan and Thailand and he has been invited to speak on the topic of fostering healthcare innovation both locally and internationally. Research interests include secondary analysis of electronic health records, machine learning and artificial intelligence in healthcare, mobile health implementation in low resource areas, global health informatics and low-cost wireless monitoring systems.
Rodrigo Deliberato is an Internal Medicine and Critical Care Physician from Sao Paulo, Brazil. After his residency, he spent one year completing a clinical fellowship in Critical Care Medicine at the University of Toronto. Following that he got his MSc and his PhD at Universidade Federal do Estado de São Paulo (UNIFESP) in Brazil studying infectious disease — biomarkers — medical devices and their interactions with critical care patients. His research moves him towards the digital healthcare field including the creation of a mobile application to manage the multidisciplinary working shift exchange at the Albert Einstein Hospital. This application continues to be used comercially at hospitals across Brazil. Nowadays he is a post-doctoral fellow at the Laboratory of Computational Physiology - MIT studying secondary analysis of electronic health record data in critical care patients.
Miguel Ángel Armengol de la Hoz, MS is a Senior Research Associate at Harvard-MIT Division of Health Sciences. Moreover he is Chair of the study group 'Big Data and Machine Learning: Shaping the Future of Healthcare' at Harvard. He is a Telecommunication Engineer with a M.S. in Biomedical Engineering from Universidad Politécnica de Madrid and is currently a PhD Candidate in Biomedical Engineering at the same university.
Working at the Department of Anesthesia, Critical Care and Pain Medicine - Center for Anesthesia Research Excellence he is helping to expand the current analytic capabilities and design new strategies and applications within this dynamic and innovative organization. He has being applying state-of-the-art advanced analytic and quantitative tools and modelling techniques to derive insights, solve complex problems and improve decisions about both patients and providers from the department.
As an affiliate at the LCP, Laboratory of Computational Physiology and MIT Critical Data group, he has experience working with large and complex data sets related to critically ill patients (Intensive Care Unity and Emergency Room).