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 third-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.

Minfan Zhang, third-year Ph.D. student working with Prof. Marzyeh Ghassemi with a research focus on machine learning for healthcare and human-computer interaction. He completed his undergraduate at the Univerisity of Toronto and got the Dean’s List award in 2016 - 2018. And he is currently affiliated as a research associate at the Hospital for Sick Children in Toronto.

Aparna Balagopalan is a second year PhD student at the University of Toronto and Vector Institute, co-supervised by Prof. Marzyeh Ghassemi and Prof. Frank Rudzicz. Her research focuses on developing interpretable and justifiable machine learning models for healthcare. Prior to this, she received a Master’s degree from the University of Toronto, supported by a MITACS Accelerate scholarship, and a BTech degree from the IIT Guwahati. She currently holds a DeepMind PhD Fellowship for 2020-2021.

Natalie Dullerud is a second-year M.S. student at University of Toronto, co-supervised by Dr. Marzyeh Ghassemi and Dr. Nicolas Papernot. Her research broadly focuses on various facets of fair and equitable machine learning; intersections between conceptualizations of privacy and machine learning; and applications in medical and biological contexts. Previously, she completed her B.S. in Mathematics at University of Southern California.

Kimia is a Ph.D. student in Computer Science at U of T and Vector Institute. Her research interests involve devising strategies for learning fair representations with respect to certain protected attributes from imbalanced and long-tailed distribution data. Currently, she is conducting research focusing on fairness in self-supervised representation learning. Previously, she graduated from Sharif University of Technology with a B.Sc. in Computer Engineering and interned at IST Austria.

Hammaad is a second 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 first 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 first-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 first 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 first-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.

Shrey is an Engineering Science student at the University of Toronto. His research interests are primarily centered on domain generalization research. Shrey has raised over $200K CAD for COVID-19 rapid response efforts and received various academic scholarships at the University of Toronto for academic and leadership performance. Shrey also competes for Team Canada’s age group triathlon team.