Xuhai “Orson” Xu is a postdoctoral associate in the MIT CSAIL. Orson received his Ph.D. in the Information School from the University of Washington. His research straddles human-computer interaction, ubiquitous computing, machine learning, and health. Intrigued by the potential of harnessing everyday device data, Orson seeks to better understand, model, and influence human behavior, particularly for long-term health and well-being. Orson’s work has been recognized by several awards, including several Best Paper, Best Paper Honorable Mention, and Best Artifact awards. His research has been covered by media outlets such as The Washington Post, ACM News, and UW News. He earned the Gaetano Borriello Outstanding Student Award Winner at ACM UbiComp 2022.

Jiacheng Zhu is a postdoctoral associate at MIT CSAIL. He earned his Ph.D. from Carnegie Mellon University and holds an M.S. in Machine Learning. Jiacheng’s research is rooted in statistical inference, trustworthy machine learning, and the application of AI/ML in heterogeneous environments. His primary goal is to design ML systems that are generalizable, robust, and can function as foundational models that can be seamlessly transported to out-of-distribution domains. His work addresses challenges in areas such as physiological and cardiovascular health, as well as robotics and autonomy, and earned him the 2022 Qualcomm Innovation Fellowship.

Walter Gerych is a postdoctoral associate at MIT CSAIL. He earned his Ph.D. and M.S. in Data Science from Worcester polytechnic Institute . Walter’s research focuses primarily on designing ML and deep learning systems that are robust to labeling and sampling errors and biases. His primary application areas revolve around mobile sensor time series for human activity recognition and mobile healthcare.

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 fifth-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.

Aparna Balagopalan is a fourth 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.

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.

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.

Hammaad is a fourth 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.

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.

Vinith is a third 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 third 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 third 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 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.

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.

Eileen Pan is a 2nd-year EECS PhD student at MIT co-advised by Marzyeh Ghassemi and Ashia Wilson. Her research focuses on developing scalable ways to audit and mitigate bias in deep learning models, with applications to healthcare. She also completed her BS in Computation and Cognition at MIT as a Questbridge Scholar. Her work is supported by the Jameel Clinic Fellowship and NSF Graduate Research Fellowship.

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.

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.

Yan is concurrently a first-year Master of Engineering student at MIT EECS Department and 4th-year undergraduate student at MIT studying Computer Science and Mathematics. She is interested in research topics around general AI alignment areas such as model fairness, robustness and interpretability.