Satvik Chekuri

PhD Student, Department of Computer Science, Virginia Tech


I am a second-year PhD Student in Computer Science at Virginia Tech, Blacksburg.  I am part of the DLRL group where I am advised by Dr. Edward A. Fox.  I'm also affiliated with the Center for Human-Computer Interaction and Sanghani Center for Artificial Intelligence & Data Analytics at Virginia Tech. 

I am broadly interested in building Deep Learning & NLP models in a low-resource setting. My recent research focuses on developing knowledge graphs for heterogeneous legal documents and imbibing the learnings into the DL/NLP models for better predictions. 

On the application side, I am primarily focused on building intelligent systems for understanding & processing textual data originating from clusters of heterogeneous documents (such as pleadings, claims, depositions, medical reports, expert reports, etc.) in the legal domain and provide guidance (summarization) with explainability to increase trust & reliability. 

Recent News

  • June 2021: Filed a US patent application (Co-inventor)
  • February 2021: Passed PhD Qualifiers
  • November 2020: Presented (virtually) our paper on Aspect Classification for Legal Depositions at JURISIN'20  


Aspect Classification for Legal Depositions

Saurabh Chakravarty, Satvik Chekuri, Maanav Mehrotra, Edward A. Fox

International Symposium on Artificial Intelligence, 2020 Oct 14, pp. 179-195

Get the Job! An Immersive Simulation of Sensory Overload

Leonardo Pavanatto, Feiyu Lu, Shakiba Davari, Emily Harris, Anthony Folino, Samat Imamov, Satvik Chekuri, Leslie Blustein, Wallace S. Lages, Doug A. Bowman

2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), 2020 Feb 21, pp. 509-510

Elasticsearch (ELS) CS5604 Fall 2019

Yuan Li, Satvik Chekuri, Tianrui Hu, Soumya Arvind Kumar, Nicholas Gill

2019 Nov 11


Interactive Text Summarization using Explorable MMR

Maximal Marginal Relevance (MMR) is a rank-based technique for producing summaries. This project aims to explore the MMR technique by incorporating the human-in-the-loop and providing an interactive text summarization system.

Diffusion Graph Robustness

Understanding the Impact of Graph Diffusion on Robust Graph Learning. We propose graph diffusion convolution as a defense mechanism against adversarial perturbations in graph learning tasks. Our experiments show up to 8% improvements in accuracy.

Visualization of the impact of COVID-19 coupled with census demographics

Understanding how COVID-19 has impacted different Point of Interests (POIs) by analyzing the user behaviour (such as visits, time spent etc) and mapping it with the demographic information from US Census data.


Satvik Chekuri

Department of Computer Science

Virginia Tech

2030 Torgerson Hall,
620 Drillfield Dr,
Blacksburg, VA, USA 24060