Dr. Ajitesh Srivastava

Research Assistant Professor (starting Spring 2021)

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.

I am a member of DS Lab and Parallel Computing Group led by Prof. Viktor K. Prasanna. Please visit these pages to explore our research groups.

Here is a list of my publication.

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


My research interests include Graph Algorithms, Machine Learning, and Parallel Computing applied to social good, crime, social networks, architecture, and smart grids.

If you are interested in working with me, please follow the instructions at https://sites.usc.edu/dslab/prosepective/. My current work includes the following:

1. COVID-19 – How to accurately forecast epidemics when dynamics change due to changing policies? How to manage resources based on the forecasts? How to come out of “stay-home” order?

Skills involved: Machine learning, epidemiological models, 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Selected Publications: KDD 2020 (Health Day), Pre-print (not peer-reviewed), GitHub code, Web-interface; Contributing to the CDC, US forecast Hub, Germany+Poland forecast Hub.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 learning-based prefetcher – How to design highly compact deep learning models to predict future memory accesses? How to utilize the predictions to design a prefetcher? How to implement the prefetcher on hardware (FPGA/eFPGA)?

Skills involved: Deep learning, meta-learning, reinforcement learning, cache, FPGA

Selected Publications: MEMSYS 2019

3. Safer connected communities – Using the full potential of machine learning, social network analysis, and algorithms to improve society. How to predict and evaluate crime? How to reduce violence among the homeless?

Skills involved: Machine Learning, algorithms, social network diffusion

Selected Publications: SNAM 2019, SIGSPATIAL 2019

4. ML-based Compiler for DSL to 1000 node systems – For a given scientific computing program, what is the best way to decompose it into tasks that potentially leads to least execution time? How to map tasks to available resources (CPUs and GPUs in 1000s of nodes) to minimize execution time? How to train such ML-based mappers when we do not have access to actual resources during training? How to decide during runtime if the task mapping needs to be changed? How to predict execution time of programs?

Selected Skills involved: Deep learning, reinforcement learning, meta-learning, integer linear programming approximations

5. Scaling Neural Networks – How to compress neural networks that translate well in terms of increasing throughput in hardware implementation? How to distribute training of neural networks? How to design algorithms for faster training of neural networks?

Skills involved:  Convolutional neural networks, model compression, algorithms, FPGA, graph convolutional neural networks, parallel computing

Selected Publications: HiPC 2019, FPGA 2020, ICLR 2020, IPDPS 2019

My resume that has been updated at least once in the last 153 years. My LinkedIn is updated within the last 28 years.


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