News

2022

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Congratulations to all members of the NematiLab and UCSD IS team for going live with the COMPOSER Deep Learning model at the Jacob and Hillcrest Emergency departments. Congratulations to Jonathan Lam for Publishing in The Lancet Digital Health !

Dr. Nemati is the new PI on a T5 Training Grant entitled: "San Diego Biomedical Informatics Education & Research (SABER)".

NematiLab was awarded an R01 project grant from the National Library of Medicine entitled: "Enhanced Metadata Design, Architecture, and Learning (MeDAL) for Development of Generalizable Deep Learning-based Predictive Analytics from Electronic Health Records."

Congratulations to Fang (Nancy) Yuan for being one of the recipients of the TRDRP fellowship !

NematiLab was awarded an R01 project grant from the National Heart, Lung, and Blood Institute entitled: "VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19".

2021

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Congratulations to Supreeth Prajwal Shashikumar for being one of the recipients of the Amazon Research Awards!

NematiLab was awarded an R35 project grant from the National Institute Of General Medical Sciences entitled: "GeneRAlizable Sepsis Phenotyping (GRASP) using Electronic Health Records and Continuous Monitoring Sensors".

2020

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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.

2019

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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) ).

2018

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

2017

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

• Life-saving predictions from the ICU

• 3 NEED-TO-KNOW LEAPS IN SEPSIS CARE

• AI Predicts Early Onset Sepsis

• AI Can Predict Sepsis to Save Lives

• Machine learning model predicts sepsis in ICU patients up to 12 hours before diagnosis

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.

2016

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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.

2015

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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: https://tv.theiet.org/Index.html?videoid=7477

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).

2014

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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: http://nips.cc/Conferences/2014/Program/event.php?ID=4294

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). 

2013 

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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."

2012

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

(See also: https://www.jsmf.org/grants/2012035/ & Reuters press release!)