I am a research scientist in the Laboratory for Computational Physiology (LCP) at the MIT Institute for Medical Engineering & Science (IMES). My research focuses on the use of machine learning techniques for physiological and patient monitoring. My interests include multivariate physiological and clinical time series analysis; application of machine learning techniques to track disease progression in patients; Bayesian non-parametric learning of patient phenotypes; and more generally, structure discovery and predictive modeling using large-scale physiological and clinical databases for improved patient monitoring.
Within IMES at MIT, I work 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. I also work on the MIMIC-II project in “Integrating Data, Models, and Reasoning in Critical Care” , which is an NIH-sponsored Bioengineering Research Partnership (BRP) to develop and evaluate advanced intensive care unit (ICU) patient monitoring and decision support systems. I received my Master’s degree in Computer Science from Georgia Institute of Technology, and my Ph.D. from Massachusetts Institute of Technology in June 2005.
I am currently looking for a postdoc. Please see here for more details.
“A Model-Based Machine Learning Approach to Probing Autonomic Regulation from Nonstationary Vital-Sign Time Series,” Li-wei H. Lehman, Roger G. Mark, Shamim Nemati, IEEE Journal of Biomedical and Health Informatics, Vol. 22, No. 1, January 2018. doi:10.1109/JBHI.2016.2636808. Preprint available at IEEE Explorer.
“A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction,” Li-wei H. Lehman, Ryan P. Adams, Louis Mayaud, George B. Moody, Atul Malhotra, Roger G. Mark, Shamim Nemati, IEEE Journal of Biomedical and Health Informatics, 19(3):1068-1076, May 2015. doi:10.1109/JBHI.2014.2330827. Preprint .
“Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring,” Li-wei H. Lehman, Matthew J. Johnson, Shamim Nemati, Ryan P. Adams, Roger G. Mark, Chapter 11 in Advanced State Space Methods for Neural and Clinical Data, Cambridge University Press, 2015. Publisher's Version.
Local organizing committee of the Computing in Cardiology Conference held in Boston, September 2014.
Member of the IEEE and the IEEE Engineering in Medicine and Biology Society.
Closing plenary presentation in the 2014 Computing in Cardiology Conference on “Uncovering Clinical Significance of Vital Sign Dynamics in Critical Care,” Boston, September 2014.
Closing plenary presentation in the 2010 Computers in Cardiology Conference on “Hypotension as a risk factor for acute kidney injury in ICU patients,” Belfast, September 2010.
Best poster presentation award, Computers in Cardiology Conference, September 2007.
Recipient of Schoettler Fellowship, awarded to outstanding incoming graduate students, MIT.
Appointed as Head TA for 1.00, Introduction to Computers and Engineering Problem Solving, an MIT course comprising 200 students and 18 student teaching staff, 2001.
MIT class awards: (1) algorithm & implementation in C++ won 2nd place in final project contest in MIT course 1.124; (2) voted one of the top 3 term papers in MIT course 6.853, Computer Systems.