Suchi Saria

Assistant Professor
Johns Hopkins University

Department of Computer Science

Department of Health Policy & Management


Other Affiliations: 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

Contact: prefix@suffix where prefix=ssaria and suffix=cs.jhu.edu

Brief Bio: My interests span machine learning, its applications to domains such as natural language and time series data, and health informatics. I am particularly motivated by difficult and important problems that involve drawing inferences from large scale heterogeneous data sources such as electronic health records and sensing platforms (e.g., smart phones, kinects, body sensors).

I joined Johns Hopkins University in 2012 after my PhD and a visiting fellowship at Stanford and Harvard respectively. I'm originally from Darjeeling, India. I can be bribed with good tea.

Selected Publications: (ML=Machine Learning, HI=Health Informatics)
[ML] S. Saria, D. Koller, A. Penn. Discovering shared and individual latent structure in multiple time series arXiv:1008.2028, August 2010. short, long

[ML] S. Saria, U. Nodelman, D. Koller. Reasoning at the Right Time Granularity. Uncerainty in Artificial Intelligence (UAI), July 2007. pdf (Best student paper award)

[ML] S. Saria, A. Duchi, D. Koller. Learning Deformable Motifs in Continuous Time Series data. International Joint Conference on Artificial Intelligence (IJCAI), 2011. short, long

[HI] S. Saria, A. Rajani, J. Gould, D. Koller, A. Penn. Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants. Science Translational Medicine, September 2010. Vol. 2, Issue 48. Link (Cover article)

[HI] C. Paxton, A. Niculescu-Mizil, S. Saria. Challenges in Developing Predictive Algorithms Using Electronic Medical Records. American Medical Informatics Association, 2013. pdf

Notable Recent Events:
- I was invited to the expert's panel at the Moore Predictive Analytics Symposium (Sept. 2013) to speak on predictive models from EMR and sensing data
- I recently gave an invited talk at the Data Science for Social Good program in Chicago (August 2013)
- I gave an invited panel talk the National Science Foundation and National Institutes of Health joint meeting on Computing and Health; I spoke with three other invited panelists on the 'Exploiting Data in Abundance' panel. (Oct. 2012)
- I gave an invited presentation at the DARPA Defense Science Office workshop on opportunities in healthcare computing (Nov. 2012)
- I gave an invited talk at INFORMS Healthcare on the big data in healthcare session (July 2013). INFORMS is the largest meeting in Operations Research. Informs Healthcare is a new meeting focused entirely on healthcare applications. There were ~600 attendees to the meeting in its 2nd year.
- I co-chaired ICML workshop on Role of Machine Learning in Transforming Healthcare (July 2013)
- I co-chaired Meaningful Use of Complex Medical Data (MUCMD) Symposium at the Children's Hospital LA (August 2012)
- Other selected invited talks: Google (Oct. 2013), Carnegie Mellon University (Oct. 2013), Institute for Computational and Experimental Research in Mathematics at Brown University (Nov. 2012), University of Vanderbilt Grand Rounds in Informatics (2012), University of Maryland Machine Learning Seminar (2012), International Society for Bayesian Analysis (ISBA) (July 2012).

Teaching:
Current: 600.476/676 Machine Learning in Complex Domains

Previous: 600.476/676 Machine Learning in Complex Domains, 600.775 Seminar in Machine Learning and Data-Intensive Computing

FAQ:
Q00. I'm interested in machine learning but I have never worked in medicine/biology/healthcare. Do I have to have an extensive medical background?
No. I primarily worked on time series modeling at Stanford when we got drawn into the problem of drawing inferences from temporal measurements made using sensing devices attached to patients during their hospital stay. This application motivated new methods in machine learning. In the process, we also generated new and interesting clinical results which was incredibly satisfying as a researcher. You will learn everything you need to about the domain along the way by interacting closely with knowledgeable colleagues and through reading.
Our healthcare expenses are upwards of 2.5 trillion dollars and we're in desperate need of better approaches for improving outcomes and lowering cost. Our health system produces vasts amount of messy and heterogeneous data that we need smarter modelers to be looking at and gleaning insights from. If you're looking to learn more, browse through these (SPH, ICM and CPHIT) websites to get a sense of the variety of broad problem areas where your knowledge of machine learning could be immensely useful. Though my schedule is busy, if you are curious you're also welcome to drop me an email and we can setup a time to talk. You can also look through the ML@JHU to learn about the variety of resources for machine learning at Hopkins.

Q0. I'm primarily interested in machine learning. But, I'm unsure of the application area. Do I need to have determined this ahead of time?
No. There a number of faculty including myself that work on machine learning problems applicable to multiple domains. Look through ML@JHU. Also, look through application areas at Human Language Center of Excellence, and IDIES.

Q1. I'm a student at Hopkins and I'm interested in working with you. How can I get involved?
Please take a look at my papers. If you still remain interested, please send me an email. It's often also helpful to speak with the students in the research group to get a flavor of the problems you could get involved in.

Q2. I'm not at Hopkins currently. Can I apply to your lab for a PhD?
Yes, we are looking for creative and brilliant students to join us. However, you must formally apply to the PhD program for me to be able to consider you. It might be helpful to read through this site on how to put together a strong graduate school application. To gain a better understanding of the types of problems I work on, please read through my papers. Also, click through my center affiliations to see what broader classes of problems I and faculty in this area of research are working on.

Q3. I'm an undergraduate and I am looking for internship opportunities. Can I visit your lab?
Yes, we started a new internship program called the Summer Research Expeditions (SRE) in 2013. The program brings together faculty from multiple departments in engineering and is a great opportunity to gain exposure to multidisciplinary applications of computing.

Q4. I'm looking for postdoctoral or research scientist positions. Are there positions in your lab?
We recently hired a research scientist. We have room to hire 1-2 more fellows or research scientists. There is flexibility in terms of the projects you can get involved with. Please send me a copy of your CV if you'd like to learn more.