The MLxMIT organizing committee is made up of current PhD students across MIT. 

Marie-Laure Charpignon, IDSS

I am a second-year PhD student in the Interdisciplinary Doctoral Program in Statistics. My main research is on causal inference methods for drug repurposing for Alzheimer's Disease, using observational data from Electronic Health Records in the US and UK. I am currently working with Professor Roy Welsch from the Operations Research Center, and the MGH Biomedical Informatics lab. I am also collaborating with Dr. Leo Celi on data science questions in critical care medicine, with a focus on time series models for the prediction of adverse respiratory event occurrence in the Intensive Care Unit. More generally, I am interested in leveraging non-traditional data for public health. An ongoing side project is on the use of news media data to model disease outbreaks, and assess the differences in health-related language between US states.

Geeticka Chauhan, EECS
WebsiteTwitter, LinkedIn

I am a 3rd year PhD student in the Clinical Decision-Making Group at CSAIL in MIT, supervised by Prof. Peter Szolovits. My research interests lie in Natural Language Processing for healthcare. I have worked on projects related to relation extraction, and clinical machine learning (learning from unstructured data present in Electronic Health Records). Currently, I am looking at joint-modeling of medical imaging and text to improve disease prediction. Previously, I completed a Master’s at MIT and bachelor’s degree in computer science at Florida International University. I have also worked on projects related to logic-based rule generation and character identification from folktales.

Rumen Dangovski, EECS

Hello! I am a second semester PhD student at EECS, supervised by Professor Marin Soljačić. I work on learning systems that generalize well across a variety of domains. In particular, I tackle real world problems, such as text summarization of scientific articles, by developing methods inspired by fundamental science, such as novel recurrent neural networks that use rotations to remember and recall information better or efficient convolutional layers based on optimal connectivity patterns. The primary applications of my algorithms are in physics and natural language processing. Previously, I have earned my BSc from MIT in Mathematics and Physics. I have also worked in Lightelligence on optical chips for AI acceleration.

Abhi Dubey, Media Lab
WebsiteTwitter, LinkedIn

Hi! I'm a third-year graduate student in the Human Dynamics group at MIT, supervised by Professor Alex Pentland. My research interests are in robust machine learning and social cognition, and I also work occasionally in their applications in computer vision. Prior to this, I received a master's degree in Computer Science and bachelor's degree in Electrical Engineering at IIT Delhi. I've also spent time as a research intern at Facebook AI, and was a post-baccalaureate fellow at the Department of Economics at Harvard, under Professor Ed Glaeser. My research has been supported by a Snap Research Scholarship (2019) and an Emerging Worlds Fellowship (2017).


Katie Lewis, EECS

I am a third-year graduate student in the Clinical and Applied Machine Learning (CAML) group in CSAIL. I recently completed my Master's thesis on deep learning for registering sparse clinical MRI scans. I am now investigating multimodal (audio+visual) applications in healthcare. Outside of healthcare, I am also working on a project involving machine learning + art.

Aniruddh Raghu, EECS

I am a second year PhD student in the Computer Science and Artificial Intelligence Laboratory at MIT, working with John Guttag and Collin Stultz. I graduated in 2018 with a BA and MEng from the University of Cambridge, where I studied Information and Computer Engineering. My research interests are in machine learning and its applications to healthcare.

Sohil Shah, ORC

I am a second-year graduate student in the Online Optimization and Learning Research Group (OOL) in LIDS supported by the NSF Graduate Research Fellowship (2018). I am currently working on designing algorithms that can leverage data-driven learning in order to improve average performance while maintaining guaranteed theoretical benchmarks. I am currently studying how online learning and information gathering mechanisms can be used in ride-sharing networks to assuage demand shocks and improve match quality across users. I have bachelor’s degrees in mathematics, computer science, economics and chemistry from the University of Wisconsin. Outside of ride-sharing, I am very interested in healthcare, sports analytics, chemical synthesis, and supply chain.

Will Stephenson, EECS

I'm a fourth year PhD student in CSAIL advised by Tamara Broderick. My research focuses on issues of approximate computation and robustness in machine learning. Before coming to MIT, I received a bachelor's degree from Brown University and worked at Vision Systems, Inc. as a research engeineer.

Anny Zheng, EECS

I am a 6th year PhD candidate advised by Prof. Vincent Chan in the Claude E. Shannon Communication and Network Group in RLE. My research focuses on the architectural design for cognitive all-optical networks in dynamic environments. I’m exploring the use of cognitive techniques for the fast reconfiguration of optical networks to quickly and accurately adapt to the operating conditions. Previously, I completed an electrical engineer’s degree and a master’s degree in EECS at MIT, and a bachelor’s degree in Information Engineering at The University of Hong Kong. I’m also working on projects related to machine learning for healthcare.