Tom is a postdoctoral associate at MIT CSAIL. His research spans data mining and machine learning, particularly for time series and text, and applications that improve healthcare. He received his PhD from Worcester Polytechnic Institute in 2021.

My objective is to develop automated agents that mimic human intelligence not only in their ability to perceive known aspects of their environment but also embody the traits of uncertainty and causality. I obtained my Ph.D. at University of Maryland, College Park and worked in a promising healthcare AI startup, where I helped launch a real world ML healthcare product, before heading to MIT for my postdoc.

Elizabeth Bondi-Kelly is an MIT CSAIL METEOR Postdoctoral Fellow, with a Ph.D. in Computer Science from Harvard University. Her research interests include multi-agent systems and machine learning, especially applied to conservation and public health. She has been recognized as an MIT EECS Rising Star and awarded Best Paper Runner Up at AAAI 2021, Best Application Demo at AAMAS 2019, and Best Paper at SPIE DCS 2016. She has also founded Try AI, a nonprofit aiming to broaden participation in AI.

I am studying the generalisability of machine learning models applied to healthcare. The dynamic and adaptive nature of healthcare is reflected in the data that is collected in electronic health records. Sometimes we can anticipate these changes, and other times we need the model to be robust to these changes so that their decisions are reliable. In order to integrate machine learning into clinical models, we must understand when it fails, where it fails, and whom it fails to serve.

Nathan is a PhD student at the University of Toronto visiting at MIT. He is interested generally in natural language processing and semi-supervised learning, specifically in improving the generalization properties of models using unlabelled data.

Taylor is a fourth-year PhD student in the CS Department at the University of Toronto, affiliated with Vector Institute, MIT’s CSAIL and IMES. His research interests combine Reinforcement Learning, Causal Inference and Representation Learning in pursuit of developing clinical decision support tools that generalize beyond the environment they were trained in, robust to sources of uncertainty such as distribution shift or covariate mismatch.

I’m a graduate student at the University of Toronto advised by Prof. Marzyeh Ghassemi. I am broadly interested in causal inference and machine learning, specifically in their application to healthcare. I graduated from NIT Rourkela, India in 2017 with an integrated B.Tech-M.Tech degree in Electronics and Communication Eng. I am currently working as a part-time intern at Microsoft Research Montreal. I’d visited the University of Toronto in the summer of 2016 as a MITACS Globalink research intern.

Aparna Balagopalan is a third year PhD student in EECS at MIT. Her research broadly focuses on developing fair and robust models by re-evaluating and surfacing assumptions in machine learning-based measurements in socially-relevant contexts like healthcare. Prior to this, she received a Master’s degree from the University of Toronto and a BTech degree from IIT Guwahati. She currently holds an Amazon Doctoral Fellowship from MIT’s Science Hub.

Kimia is a PhD student at University of Toronto and Vector Institute visiting MIT. Her research focuses on understanding how self-supervised pre-training strategies represent data to build models that generalize well out-of-distribution, and developing methods that enable efficient and reliable adaptation. She is also interested in leveraging properties of large models for reasoning and robustness to distribution shifts.

Intae is a PhD student in EECS at MIT. He is also affiliated with the Dana-Farber Cancer Institute, and his PhD research focuses on developing robust machine learning models at the intersection of longitudinal Electronic Health Records (EHR) and genomics data to better manage patients with cancer. He received B.S. in electrical and computer engineering from the University of Illinois, Urbana-Champaign.

Hammaad is a third year PhD student at the Institute for Data Systems and Society (IDSS) at MIT. His work focuses on questions at the intersection of AI and healthcare equity, and aims to understand how the increased use of machine learning in healthcare can impact existing disparities. He is especially passionate about investigating ways in which we can use AI to create more equitable systems.

Vinith is a second year PhD student at MIT EECS, IMES, CSAIL, and LIDS. His research focuses on the theory and practice of differential privacy, algorithmic fairness, distributive justice, and optimization in machine learning. He completed his Masters in Computer Science from the University of Toronto and his Bachelors in Computing from Queen’s University. He is currently a Wellcome Trust Fellow at MIT and previously was an Ethics of AI Fellow at the University of Toronto.

Haoran is a second year PhD student in EECS at MIT. He is generally interested in building robust machine learning models that maintain their performance and fairness across out-of-distribution environments, as well as applying such models to the healthcare setting. Haoran previously received his M.Sc. at the University of Toronto under the co-supervision of Dr. Marzyeh Ghassemi and Dr. Quaid Morris, and his B.Eng. from McMaster University.

Qixuan (Alice) Jin is a second year EECS PhD student doing research in Machine Learning + Healthcare. She is broadly interested in how to incorporate expert domain knowledge in data-driven models within the context of medical and biological datasets. Alice completed her B.S. in Computer Science in 2021 at Caltech. During her time at Caltech, she did research related to COVID-19 time series prediction with Professor Yaser Abu-Mostafa.

Hyewon Jeong is a second-year Ph.D. student at EECS, MIT. Her primary research focus has been on applying machine learning models to solve real-world clinical problems, specifically tasks from time-series EHR data and signal data. She is also interested in causal inference applied to clinical and biomedical problems.

Yuxin Xiao is a Ph.D. student at MIT IDSS. His research focuses on developing fair and robust machine learning models that are aware of the uncertainty in structured data and generalize well out-of-distribution, with applications to the domain of healthcare. Yuxin obtained his M.S. in Machine Learning at Carnegie Mellon University and his B.S. in Computer Science and B.S. in Statistics and Mathematics at the University of Illinois at Urbana-Champaign.

Neha Hulkund is a MEng student, having completed her undergraduate degree in computer science and mathematics at MIT. She studies model interpretability and robustness to distribution shift, specifically in healthcare settings.

Ming is a EECS Master’s student at MIT, working under Prof. Marzyeh Ghassemi and Prof. Leo Anthony Celi. Her work involveS investigating ways machine learning can be used to improve quality of care, focusing on healthcare equity and social determinants of health. Before this, she was an undergraduate researcher in the group through the SuperUROP program at MIT, where she completed her B.S. in Chemical Engineering and Computer Science in 2022.