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.


I have a Masters in Intelligent Robotics and a PhD in Computer Science from University of Southern California. 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:

Ajitesh Srivastava




Paul Erdős


Albert Einstein








MR0166139 (29 #3417)




MR0012947 (7,87j)


Tyll Krüger


Michael D. Boshernitzan


Lee A. Rubel


Ernst Gabor Straus


Recent News

Open Position: 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.


[Nov 2021] My work with Scenario Modeling Hub was used by ACIP as part of the evidence to recommend vaccines for 5-11 years old children.


[Aug 2021] I have been awarded an NSF RAPID grant of $186,835 titled: "Fast COVID-19 Scenario Projections in Presence of Vaccines and Competing Variants". This work will support my contribution to the US/CDC Scenario Modeling Hub in projecting the long-term impact of variant dynamics, vaccines, waning immunity, etc.




Here is a word cloud of the titles of my paper. For the full list of my publications, please see my Google Scholar page.

My research interests include Machine Learning and Graph Algorithms applied to epidemics, social good, and social networks. 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.

My current work includes the following:

1. Epidemic Forecasting: Forecasting the trajectory of an epidemic in the presence of changing policies, under-reporting, competing variants, waning immunity, and vaccinations. Year-long effort of many teams around the world with the CDC has shown that there is high disagreement between different forecasting methodologies. Identifying the peak and dynamics during multiple competing variants is difficult. ML methods in the application often overfit.  Research challenges: (i) Learning model parameters without overfitting! (ii) Developing sophisticated and explainable ensemble models to utilize multiple forecasts (iii) Developing models of temporal dynamics of competing variants and parameter estimation from genomic data.

Skills involved: Machine learning, epidemiological models, probability, GUI design

Selected Publications: KDD 2020 (Health Day), Pre-print (not peer-reviewed), GitHub code, Web-interface; Contributing to the CDC, US forecast Hub, Germany+Poland forecast Hub.

2. Deep GNNs on Large Graphs: Problem: Training and inference on deep GNNs on large graphs are difficult due to computational complexity and lack of accuracy improvements with deeper layers. Subgraph-based methods to address training on large graphs exist, but they do not apply during inference, making inference the bottleneck. Such methods also do not address poor accuracy for deep networks due to “oversmoothing”. Research challenges: (i) Develop subgraph-based schemes that apply to training and inference. (ii) Identify sampling probabilities that eliminates oversmoothing (converging embedded nodes to the same point in space) and maximizes separation. (iii) Pruning weights to reduce computations during inference.

Selected Publications: NeurIPS 2021, VLDB 2021, ICLR 2020, IPDPS 2019,

Wishlist: Areas I wish to pursue in the future

1. Addressing homelessness SNAM 2019

2. Improving fairness in machine learning and algorithms.

3. In general, I am happy to collaborate on machine learning, algorithms, and network science applied to real-world problems.


In Spring 2022, I am co-teaching “EE 638: Applications of Machine Learning for Medical Data” with Prof. Cauligi Raghavendra.


In Fall 2021, I taught “EE155L: Introduction to Computer Programming for Electrical Engineers” with Prof. Sandeep Gupta.


My LinkedIn updated within the last 28 years.


For my standup comedy page, please visit: https://sites.google.com/view/ajcomedy/home