Sundeep Prabhakar Chepuri

 

Sundeep Prabhakar Chepuri
Assistant Professor
Department of Electrical and Communication Engineering
Indian Institute of Science

Biography

I received the M.Sc. degree (cum laude) in Electrical Engineering and the Ph.D. degree (cum laude) from TU Delft, in July 2011 and January 2016, respectively. Currently, I am an Assistant Professor at the Electrical Communication Engineering department at IISc. I was an Associate Editor of the EURASIP Journal on Advances in Signal Processing (2016 - 2020). I am an elected member of the IEEE SPS Society's Sensor Array and Multichannel Technical Committee (2021 - ) and EURASIP Signal Processing for Multisensor Systems’ Special Area Team (2019 - ).

My research interests include mathematical signal processing, statistical inference and learning, applied to communication systems, network sciences, and computational imaging. The main themes of my research are computational sensing, sparse sampling, signal processing and machine learning for communications, graph signal processing, and machine learning over graphs.

Some of the topics that I am currently pursuing are:

  • Graph neural networks, graph representation learning, and graph signal processing

  • Federated learning

  • Algorithm unfolding

  • Signal processing and machine learning for wireless communications

  • Applications in sensing, wireless communications, radars, networks, biomedical imaging, computational biology, among others.

IEEE biography       CV

Covid-19 Drug Repurposing

Picture from DrCOVID preprint 

The availability of large volumes of biological data has enhanced the understanding of various biological systems. Data-driven computational techniques in fields like biology, medicine, genomics, and neuroscience are gaining attention due to advances in artificial intelligence and machine learning tools. We apply recent tools from artificial intelligence to repurpose already approved drugs for the 2019 novel coronavirus diseases (COVID-19). Specifically, we propose a graph neural network (GNN) model to capture local and structural information in a complex interaction network comprising drugs, diseases, genes, and anatomies as entities. The proposed graph neural network model learns from known treatments of many diseases and complex interactions between the four entities and successfully predicts unknown links between approved drugs and novel diseases, such as COVID-19.

Here is our preprint and software on Drug Repurposing for SARS-CoV-2 using Graph Neural Networks.

Contact

ECE MP building 128
Department of Electrical Communication Engineering (ECE)
Indian Institute of Science
Bangalore - 560 012
INDIA
spchepuri@iisc.ac.in
+91 80 2293 3173