Eman Alnabati is a postdoctoral researcher at MIT in the HealthyML Lab. Her research lies at the intersection of machine learning and healthcare, with interests in multimodal models, fairness, medical imaging, and genomics. She is currently working on projects involving vision-language models, diagnostic prediction from gene expression data, and evaluating model performance across diverse populations. Eman earned her PhD in Computer Science from Purdue University, where she focused on bioinformatics and protein structure modeling. Outside of research, she enjoys discovering new coffee spots and unwinding with good conversations or a long walk.

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.

Sana is a postdoctoral fellow at the Broad Institute of MIT and Harvard. Her research focuses on developing methods that integrate multimodal biomedical data to better understand human health. She is also interested in challenges of deploying clinical ML in healthcare environments and finding solutions for effective and safe use of such tools in practice. Sana received her PhD in computer science from the University of Toronto, under supervision of Dr. Anna Goldenberg, where she was an Apple scholar in AI/ML and a CIHR health system impact fellow.

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 ethical and deployable LLMs for healthcare, with a particular interest in evaluating and enhancing LLM alignment in safety and faithfulness. 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.

Kimia is a PhD student at MIT EECS. Her research focuses on understanding how self-supervised pre-training strategies represent data to build models that generalize well out-of-distribution, as well as developing post-training strategies that ensure safety of models. She previously received her MSc at the University of Toronto and her BSc from Sharif University of Technology.

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.

Yuexing Hao is an IvyPlus Exchange Ph.D. Scholar and Ph.D. student at Cornell University. She earns two Computer Science degrees from Rutgers University (B.A.) and Tufts University (M.S). During her study, Yuexing won the NCWIT AIC Collegiate Award (Honorable Mention), second prize in IEEE Communication Society Student Competition, ACM SIGCHI 2023 Gary Marsden Travel Award, Meritorious Prize in the 2020 Interdisciplinary Contest in Modeling (ICM), and Graduate Student Research Competition Award at Tufts University. Yuexing also published several papers at CHI Conference on Human Factors in Computing Systems, Intelligent System Conference, AAAI, and Bioinformatics. She reviewed top-venue conferences serving as registration co-chair for FAccT, associate chair for CSCW and CHI. Currently, her research focus is on Health Intelligence, Human-Computer Interaction, and AI.

Olawale (Wale) Salaudeen is a visiting scientist at Schmidt Science. He completed his PhD in Computer Science at the University of Illinois at Urbana-Champaign, where he was affiliated with the Stanford Trustworthy AI Research (STAIR) Lab. Wale’s research focuses on developing machine learning methods for reliable and trustworthy real-world decision-making, specifically robust generalization, domain adaptation, and evaluation under distribution shifts. His work spans diverse applications, including neuroscience/neuroimaging, healthcare, and algorithmic fairness.

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.

Jennifer is an M.Eng. student in EECS. Her research interests primarily involve using machine learning to predict healthcare outcomes. She is also broadly interested in robust deep learning and algorithmic fairness. Jennifer completed her B.S. in computer science and engineering at MIT. In her free time, she enjoys singing and reading.

Quinn is an MEng in the EECS department at MIT. Their research focuses on understanding and mitigating bias in multimodal models, as well as leveraging multimodal data to better understand links between health outcomes and our lived environment. They previously completed their BSc in Computer Science (Artificial Intelligence and Decision Making) at MIT.