suchisaria.jhu.eduSuchi Saria – Machine Learning, Computational Health Informatics

Skip to content Home Home Suchi Saria John C. Malone Assistant Professor Johns Hopkins University Department of Computer Science Department of Applied Math & Statistics Department of Health Policy & Management Contact: prefix@suffix where prefix=ssaria and suffix=cs.jhu.edu Twitter: Follow @suchisaria Other Affiliations: Mathematical Institute for Data Science (MINDS) , Institute for Computational Medicine , Laboratory for Computational Sensing and Robotics , Armstrong Institute for Patient Safey and Quality , Center for Population Health Information Technology , and Center for Language and Speech Processing Research Interests: My interests span Bayesian and probabilistic modeling approaches for addressing challenges associated with modeling and prediction in complex, real-world temporal systems. My recent work has focused on large scale modeling with Bayesian methods, methods for counterfactual reasoning, Bayesian nonparametrics, and Gaussian Processes. I am also excited about addressing challenges related to the use of data-driven tools for decision-making. I direct the Machine Learning and Healthcare Lab at Johns Hopkins University. We are interested in enabling new classes of diagnostic and treatment planning tools for healthcare—tools that use statistical machine learning techniques to tease out subtle information from “messy” observational datasets, and provide reliable inferences for individualizing care decisions. In order to accomplish these goals, our lab (1) identifies domains/disease areas where such approaches can make an impact, (2) identifies gaps where current technologies fail, (3) designs new statistical machine learning techniques that solve associated fundamental computational challenges, and (4) develops and deploys solutions to measure impact. See my recent article on why I think this topic is so exciting. Also, this (undeservingly) generous article by the ACM’s XRDS Crossroads (the ACM Magazine for Students) highlights some of the work in our lab. Prior to joining Johns Hopkins, I did my PhD at Stanford with Dr. Daphne Koller. I also spent a year at Harvard University collaborating with Dr. Ken Mandl and Dr. Zak Kohane as an NSF Computing Innovation Fellow. While in the valley, I also spent time as an early employee at Aster Data Systems , a big data startup acquired by Teradata. I am an advisor to Patient Ping . I’m also an advisor on data quality and analysis to CancerLinQ , a learning health system by the American Society of Clinical Oncology. I’m originally from Darjeeling, India. I can be bribed with good tea. Example press on our lab’s work: NSF Science Nation , Baltimore Sun , IEEE Spectrum , Hopkins Magazine , Science , Hopkins Engineering Magazine , Healhcare IT News , Popular Science , NSF Bits and Bytes , Stanford Medicine , Pittsburgh Post-Gazette on the Frontiers meeting , Talking Machines podcast , Popular Science , and TEDxBoston . PhD applicants: You will likely find my FAQ below useful. Please read before you send a note. Regarding specific areas of study, we’re looking to accept students interested in probabilistic modeling, scalable inference, causal inference and sequential decision making. If you’re interested in a program that allows you to get training in both computer science and statistics, our PhD students have the flexibility to do so. Apply here . POS: PhD, Postdoctoral and Research Scientist Openings: Email me a copy of your CV. We are especially interested in candidates with experience or strong interest in (1) large scale modeling with Bayesian methods, approximate inference, non-parametric methods, and causal inference, or (2) human-in-the-loop decision-making. We welcome candidates from all backgrounds. POS: Interdisciplinary PhD program in Computational Biology . Interested students apply here . POS: In 2013, I founded an interdisciplinary summer program in Computational Sciences, Systems and Engineering. Predoctoral students interested in summer internships, apply here . Selected Honors, Awards and Notable Events: 2018 Honored to be named a Sloan Research Fellow. To read more about this highly competitive award, see here , here , and here . 2018 Selected as one of World Economic Forum’s Young Global Leader. To learn more about this recognition, see here . 2017 In National Science Foundation (NSF) Director Dr. France Cordova’s testimony to the Commerce, Justice and Science Appropriations Committee, our lab’s work was one of four pieces of research presented across all areas of NSF (two from CISE) on discussing the NSF budget. It’s a privilege be able to help make the case for increased funding for scientific innovation and research. 2017 Invited tutorial at Uncertainty in Artificial Intelligence (UAI) on machine learning and counterfactual reasoning for “Personalized” Decision-Making in Healthcare. More here . Slides . Video . 2017 Excited to speak on “Machines that Learn to Spot Diseases” at the National Academy of Engineering Frontier’s of Engineering Meeting. More here . 2017 Honored to be included in MIT Technology Review’s 35 Innovators Under 35 ( TR35 ). More here . 2017 Excited to speak on Machine Learning and its Impact at the upcoming National Academy of Sciences Annual Meeting. More here . 2016 Invited Tutorial at NIPS on “ML Methods for Personalization with Application to Medicine.” More here . 2016 DARPA Young Faculty Award . More here and here . 2016 Excited to speak on AI and Healthcare at the White House Frontiers Meeting in the National Track. More here . 2016 Selected to Popular Science’s “Brilliant 10” . More here and here . 2016 Excited to speak at the CCC, AAAI and White House’s Office of Science and Technology Policy (OSTP) workshops on the Future of Artificial Intelligence . I gave a talk at the AI for Social Good meeting held in DC on making “meaningful use” of healthcare data using machine learning. More here . 2015 AI’s 10 to Watch . Selected by the IEEE Intelligent Systems once every two years to celebrate “young stars” in the field of artificial intelligence (AI). Selected for research on “Reasoning Engine for Individualizing Healthcare” here . 2016 IJCAI’s Early Career Spotlight . Invited by IJCAI to the “early career spotlight”. Here are the other spotlight presenters. 2015 Science Transtional Medicine Cover article for work on early detection of patients at high risk for septic shock using routinely collected EHR data. 2015 Discovery Award Our work received two (!) of the Hopkins Discovery awards, the first on a new computational framework for large-scale discovery of autoimmune regulators in rheumatic diseases and the second for translating our models for sepsis. These are highly competitive awards and ours were 2 of the 23 that were selected from a pool of 230 submissions. 2014 National Science Foundation Smart and Connected Health Research Grant award for developing computational models for prediction in complex, chronic conditions. More here . 2014 Google Research Award for developing machine learning tools for extracting information from electronic health records. More here . 2014 Annual Scientific Award given to the top submission by the Society of Critical Care for our work on early detection of sepsis (selected from 1000+ submissions). 2013 Betty and Gordon Moore Foundation Research award on building safer ICUs. More here . 2011 National Science Foundation Computing Innovation Fellowship; 17 awarded nationally. 2010 Science Transtional Medicine Cover article . More here and here . 2010 American Medical Informatics Association Best Paper Finalist for work on automated annotation of outcomes from electronic health record data. 2007 Uncertainty in Artificial Intelligence Best Student Paper for work on inference for continuous time discrete space models. 2004 Rambus Fellowship awarded for 3 years. 2002 Microsoft Full Scholarship . More here . Selected Publications: (ML=Machine Learning, HI=Health Informatics) [ML] A. Subbaswamy, P. Schulam, S. Saria. ...