Satvik Chekuri


PhD Student, Department of Computer Science, Virginia Tech

About


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  

Publications


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

Projects




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.


Contact


Satvik Chekuri



Department of Computer Science


Virginia Tech


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


Share