Aneesh Shetty

I am a Master's Student at UT Austin. I intered at Amazon in the Annapurna Labs Software Team, where I worked on automatic orchestration and scheduling of several benchmarking tasks for the AWS Graviton processor and other architectures at scale.

Previously, I worked at Adobe in the Document Cloud Team as a Software Engineer, where I worked on their Core C++ Library for PDF, optimizing it for integration with Microsoft Edge.

I completed my Bachelors in Computer Science from IIT Bombay, where I worked with Prof. Krishna S. on research in Multi-pushdown systems and Graph Monoids, and with Prof. Abir De on Active Data Subset selection for Regression.


  Updates
May 2023 I will be joining Amazon as a Software Development Engineering Intern this Summer
Aug 2022 I will be working with Prof. Isil Dillig and Prof. Joydeep Biswas as a GRA at UT Austin
Aug 2022 I will be starting my MS in Computer Science at UT Austin
Aug 2021 Presented our work on Scope-Bounded Reachability in Valence Systems at CONCUR 2021 (Virtual)
Aug 2021 Graduate from IIT Bombay with a B.Tech in Computer Science and Minor in Statistics
Jul 2021 I will be joining Adobe as a Software Engineer, working on their Core C++ PDF Library
Jun 2021 Our work on Scope-Bounded Reachability in Valence Systems was accepted at CONCUR 2021
Aug 2020 I started working as a Teaching Assistant for Automata Theory with Prof. Akshay S.

  Publications
sym

Scope-bounded Reachability in Valence Systems
Aneesh Shetty, Krishna S., Georg Zetzsche
CONCUR 2021

[webpage] [abstract] [bibtex] [arXiv]

  @misc{shetty2021scopebounded,
    title={Scope-Bounded Reachability in Valence Systems},
    author={Aneesh K. Shetty and S. Krishna and Georg Zetzsche},
    year={2021},
    eprint={2108.00963},
    archivePrefix={arXiv},
    primaryClass={cs.FL}
  }
    

  Projects
  • Automatic Visual Question Generation and Answering for Image Descriptions: We used LLM as question generator and VQA model as answerer to produce substantially descriptive paragraphs for images, and ground the generated questions using Image Segmentation.
    [report]
  • GNN: A Survey on Architectures and Optimization: We wrote a term paper on different GNN architectures and various optimzations to speed them up.
    [report]
  • Optimizing cp -r: We used io_uring released in Linux 5.1 to wrote a faster implementation of cp -r.
    [report]

Template credits: Deepak Pathak