Dr. Ajitesh Srivastava
Research Assistant Professor
Ming Hsieh Department of Computer and Electrical Engineering
University of Southern California,
EEB 226, 3740 McClintock Ave, Los Angeles, CA 90089-2562.
ajiteshs [AT] usc [DOT] edu
I have a Ph.D. in Computer Science from the University of Southern California. My research interests include Machine Learning, Modeling, and Graph Algorithms applied to epidemics, social good, and social networks. Please check my Research Highlights for my recent work.
I am looking for motivated Ph.D. students in these areas. If you are interested in working with me, please get in touch with me with your resume and a short paragraph on your research interests.
My resume that has been updated at least once in the last 153 years. My Erdős Number is 4. My Einstein Number is 5. Here are the paths according to AMS:
[Feb 2022] Our paper on Autism detection using Graph Neural Networks has been accepted at ICASSP 2023.
[Oct 2022] My student Satwant Singh won the first prize for Graduate Student Best Poster award at the 11th Annual Research Festival of the Ming Hsieh Department of Electrical and Computer Engineering.
[Oct 2022] Our paper on Shape-based Representation and Evaluation has been accepted at IEEE BigData 2022 and selected for Student Travel Award.
[Sep 2022] I have been awarded a grant from CSTE of $125,000 to lead the development of products and tools for influenza forecasting and submission of hospitalization forecasts every week during the coming Flu season.
[Aug 2022] Dr. Sina Jahandari joined my group as a Postdoctoral Research Associate.
[May 2022] I have been awarded the Scenario Modeling Consortium Fellowship of $150,000 to support my contributions in the projections of COVID-19, influenza, and future pathogens.
[May 2022] I have been awarded an NSF RAPID grant of $199,167 titled: "Data-driven Understanding of Imperfect Protection for Long-term COVID-19 Projections". This project will use models and machine learning on vaccine breakthrough data and reinfection data to understand the dynamics driving the reduction in COVID-19 immunity.
Here is a word cloud of the titles of my paper. For the full list of my publications, please see my Google Scholar page. To explore them interactively, check my Publication page.
My research interests include Machine Learning, Modeling, and Graph Algorithms applied to epidemics, social good, and social networks. Please check my Research Highlights for my recent work.
In the past, I have worked on information diffusion, parallel computing, FPGA acceleration, and smartgrids. If you are a student interested in working with me, please send me your resume and a half-page research proposal on what you would like to pursue.
[Spring 2023] EE 638: Applications of Machine Learning for Medical Data and Smart Systems [syllabus]
[Spring 2022] EE638: Applications of Machine Learning for Medical Data (co-instructor Prof. Cauligi Raghavendra)
[Fall 2021] EE155L: Introduction to Computer Programming for Electrical Engineers (co-instructor Prof. Sandeep Gupta)