The Nemati Lab at UC San Diego




NematiLab was awarded an R56 from the National Liberary of Medicine entitled: "Enhanced Metadata Design, Architecture, and Learning (MeDAL) for Development of Generalizable Deep Learning-based Predictive Analytics from Electronic Health Records".

NematiLab was awarded a generous grant from the Moore Foundation to design a novel sepsis compliance Measure.


BARDA DRIVe Solving Sepsis program announces partnership with our team to develop an advanced predictive analytics for early prediction of sepsis in hospitalized patients ( (See Emory's press release) ).


A short documenrary on our work in collaboration with the Google Cloud team :


See recent news converage of our work on early prediction of sepsis:

Dr. Nemati delivered an invited talk at the IEEE Signal Processing Chapter of Atlanta, titled: "From Prediction of Life Threatening Events to Optimization of Treatment Strategies: An Overture to a Continuously Learning Healthcare System”

Dr. Nemati was awarded and NIH Data Commons Pilot grant for his proposal to implement a Cloud-based FHIR-enabled Deep Learning platform for early prediction of sepsis. The NIH Commons is a program dedicated to providing access to scalable storage and computation capabilities to support NIH-funded research programs. It also encourages the sharing of digital objects resulting from NIH research including data, metadata, software, workflows, and other electronic artifacts.


October: Dr. Nemati was selected among the Top 2% faculty of Emory School of Medicine @ the 2016 Researcher Appreciation Day. A great honor!

June: Congratulations to Sahar Harati for winning a travel grant to attend the KDD 2016 conference. Thanks to the generosity of ACM SIGKDD and NSF.


October 1: I am honored to be a recipient of NIH Mentored Career Development Award (K01) in biomedical big data science (FOA: HG14-007) to develop advanced analytic techniques for prediction of adverse events in the ICU.

September 21: Great meeting at the Oxford "Machine Learning in Healthcare" Symposium. Videos of the event including my talk are available here:

June 25: I gave a talk at the Georgia Tech/Emory Neuromodulation and Technology Innovation Center (ENTICe), titled "Learning Representations for Improved Control."

May 16: I delivered a lecture at the Emory BMI Academic Exchange, titled "Big Data and Deep Learning for Sequential Decision Making in Medicine."

April 01: Very delighted to join the Emory School of Medicine as an Assistant Professor of Biomedical Informatics (BMI).


December 12: I will be presenting my work on "Supervised Learning in Dynamic Bayesian Networks" at the NIPS Workshop on Deep Learning and Representation Learning:

December 2: I delivered an invited talk at the Emory University, Department of Biomedical Informatics, titled: "From Reaction to Prediction: Deep Learning and Streaming Analytics for Prediction of Adverse Events in Critical Care."

May: I presented my work on Machine Learning & Bedside Analytics at the 2014 James S. McDonnell Complex Systems Scholars & Postdoc Conference.

March: Presented at the Harvard Radcliffe Exploratory Seminar “Moving from Reaction to Prediction: Leveraging High Speed Data Recording and Advanced Computational Tools for Prediction Sedation-Related Adverse Events in Children.” Organized by Drs. B. Krauss (HMS) and G. Verghese (MIT). 


November: Had a great meeting at the NIPS Workshop on Machine Learning for Clinical Data Analysis and Healthcare at Lake Tahoe, Nevada.

July: Presented at the workshop on statistical analysis of neurophysiological and clinical data in Kyoto, Japan. Presentation title: "Outcome-Discriminative Learning in Switching Linear Dynamical Systems: Applications to Neural Decoding."


I have been selected among the lucky-10 recipients of this year's James S. McDonnell Postdoctoral Fellowship in Studying Complex Systems!

(See also: & Reuters press release!)