Lena Stempfle is a postdoctoral associated with MIT CSAIL starting in the fall of 2025 with WASP International Postdoctoral Scholarship. She completed her PhD in Computer Science at Chalmers University of Technology in Sweden, where she was affilicated with the Healthy AI lab. Her research focuses on the intersection of machine learning and healthcare, with a particular interest in predictions with missing values at test time, time series, and causality. Lena’s aims to develop interpretable and accurate models to support clinical decision-making. Prior to her PhD, she completed a Master’s degree in Information Systems and Management at Karlsruhe Institute of Technology (KIT) in Germany.

Sydney Bailes is a postdoctoral researcher at MIT co-advised by Laura Lewis. Her research interests lie at the intersection of neuroimaging and machine learning with a particular focus on understanding brain aging. She completed her PhD in Biomedical Engineering where she used simultaneous EEG and fast fMRI to study both the causes and conseqeunces of impaired sleep in the aging human brain. She is currently working on a project to develop a multimodal generative AI framework for predicting individual brain aging trajectories funded by the MIT Generative AI Impact Consortium (MGAIC).

Yuexing Hao is an MIT EECS PostDoc in the Healthy ML Group. She holds Computer Science degrees from Rutgers University (B.A.), Tufts University (M.S.), and Cornell University (Ph.D.). Her research focuses on AI for Healthcare and Human-Computer Interaction. She interned at Google Research, Mayo Clinic, and Scale AI. Her work has been published at Nature Digital Medicine, CHI, ICLR, AAAI, and Bioinformatics. She actively serves the research community as Registration Co-Chair for ACM FAccT and Associate Chair for CSCW and CHI.

Vinith is a fifth 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 fifth 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.

Hyewon Jeong is a Ph.D. student in EECS at 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, signal data to multi-modal data. She is also interested in solving robustness, fairness, and causal inference applied to clinical and biomedical problems. Hyewon received B.S. in biological sciences and M.S. in Computer Science from Korea Advanced Institute of Science and Technology, M.D. in Yonsei University.

Qixuan (Alice) Jin is a fifth 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.

Yuxin Xiao is a Ph.D. candidate at MIT IDSS. His research focuses on building safe, robust, and trustworthy LLMs and advancing their reasoning and decision-making capabilities for healthcare and other high-stakes applications. 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.

Kumail is pursuing his PhD in EECS at MIT. He is supported by the Jameel Clinic Fellowship. His primary focus lies in developing trustworthy and adaptable machine learning models. He is interested in designing ways to evaluate models under distribution shifts arising in real-world healthcare applications. Before MIT, he completed his BS in Electrical and Computer Engineering at Cornell University, and his MS in Computer Science at King Abdullah University of Science and Technology (KAUST), where he conducted computer vision research with Professor Bernard Ghanem. You will catch him riding his road bike around Massachusetts in his free time.

Isha is a PhD student at MIT EECS and CSAIL where she is co-advised by Professor Marzyeh Ghassemi and Professor Yoon Kim. Her research focuses on building language models that can learn to reason like humans, as well as deployable, robust, and ethical AI. She graduated with her B.A. in Applied Mathematics and Computer Science from Harvard University in 2023, where she was an HBS Technology Innovation Fellow. She currently holds the MIT Great Educators Fellowship and the National Science Foundation’s Graduate Research Fellowship.

Cassandra is a third year Ph.D. student in the Medical Engineering and Medical Physics (MEMP) program at Harvard and MIT. Cassandra’s research focuses on equitable machine learning models that target underserved health conditions. She is particularly interested in utilizing regularly captured healthcare data to create models that can be applied both in and out of hospital settings. She previously completed her B.S. in Computer Science and Biomedical Engineering at Johns Hopkins.

Abinitha is a second year EECS Ph.D. student at MIT. Her research focuses on mitigating bias and ensuring trustworthiness in language models. She is particularly interested in algorithmic fairness and robustness in sensitive domains like healthcare. She completed her B.S.E. in Operations Research and Financial Engineering at Princeton University.

Awa is a PhD student in EECS at MIT. Her research focuses on building reliable machine learning systems that can be deployed safely, with a particular interest in identifying and understanding sources of bias to inform effective mitigation strategies. She previously worked as a machine learning researcher at Google DeepMind and Google Brain. Her work is supported by the MIT Presidential Fellowship.

Aaron Serianni is a first year PhD student in EECS at MIT, coadvised by Professor Marzyeh Ghassemi and Professor Ashia Wilson. His research broadly focuses on fairness for machine learning, understanding and mitigating biases in ML models, and studying the societal impacts of AI. Aaron previously received an A.B. in Mathematics at Princeton University. He is supported by the NSF Graduate Research Fellowship and the Schwarzman College of Computing Fellowship.

Lucia is a second-year M.S. student in the Technology Policy Program at the Institute for Data, Systems and Society (IDSS) at MIT. Her interests include using machine learning to understand health outcomes and shaping policies for AI and health. She earned her B.A.S. in Computer Science and Philosophy from the University of Pennsylvania.