• About Me
• Research
• Teaching
• Publications
• Service
• Students
• Awards
• Talks
   

I am an assistant professor at the Dept. of Computer Science and Engineering, IIT Hyderabad. My research interests are compilers, program analysis, high-performance computing and software engineering. Prior to joining IIT Hyderabad, I was a post-doctoral researcher at Microsoft Research Lab, Bangalore. I received my PhD (+M.Tech) from IIT Madras, supported by the TCS PhD fellowship. Before that, I worked as a Software Engineer at Hewlett Packard, Chennai.

  Research

I am broadly interested in compilers, program analysis, high-performance computing and software engineering. Specifically, I work on designing new (or enhancing existing) program-analysis techniques for practical applications, with the main focus on the challenges related to scalability and interoperability. I am also interested in devising techniques to achieve scalable performance, correctness and programmability in real-world systems that appear in varied environments: single-core/multicore, heterogeneous and distributed systems.

  Here are some of the projects I worked/am working on. (Give you a flavour of my research!)
• Compiler Optimizations. Heterogeneous systems combining CPUs, GPUs, and FPGAs offer immense performance potential but pose a trade-off between ease of programming and optimization. While abstractions like Unified Memory simplify development, they often degrade performance, whereas streams and asynchronous APIs improve efficiency but increase complexity. To address this, we propose compiler optimizations that enable cooperative CPU-GPU execution. Our compiler framework, GSOHC, introduces hetero-sync motion optimization to improve CPU utilization by relocating global barriers, while our frameworks UVMemcpy and StreamAlloc provide efficient yet user-friendly memory management. Together, these solutions enhance both programmability and performance in heterogeneous systems.


• ML for PL. While traditional program analysis tools are very powerful, they become challenging to use in real settings due to their whole-program requirements and scalability issues. As AI systems learn from examples, they overcome some of these challenges easily; however, current Code Language Models (CLMs) such as CodeBERT, GraphCodeBERT, UniXcoder, and Codex still struggle to capture the deeper program semantics required for building advanced developer productivity tools. At IITH, we are developing lightweight CLMs that can accurately perform tasks such as vulnerability detection, bug classification, and code summarization. By combining targeted input representations, pattern-aware encodings, and efficient classifiers, our models capture deep semantic information and overcome token length constraints, making advanced code intelligence more practical and sustainable. As part of this project, we collaborate with Microsoft Research, IIT Kharagpur, and Deakin University.


• Parallelization. The goal of this project is to address the problem of many applications not taking full advantage of the parallelism provided by massively parallel GPUs due to limitations in: efficiently utilizing GPU memory, understanding and leveraging domain-specific data patterns, etc. We provide efficient parallel algorithms and application-level optimizations for achieving high performance on GPUs. At IITH, we are currently working on two sub-problems: (1) Efficient Approximate Nearest Neighbour search on high-dimensional billion-point data (coming from deep learning-based embeddings) using a single GPU, (2) Multi-Query optimization for subgraph isomorphism search on GPUs. Along the same lines, in the past, I worked on developing an efficient tensor transposition library (TTLG) for GPUs.


• Concurrency Testing. Testing the correctness of a concurrent system is challenging. During my post-doc at Microsoft Research, I worked on two problems which deal with automated testing of concurrent systems: (1) A Language-agnostic Concurrency Testing Library: Designed for developers to build their own custom systematic testing solutions. The library detects concurrency bugs by taking over the scheduling of concurrent operations (tasks/threads/actors etc.) and systematically exploring the various interleavings of operations. (2) Testing Storage-backed Applications Against Weak Isolation Levels: Develops a mock storage system that exercises weak behaviors possible under multiple isolation levels to detect assertion violations in a database/KV-store application. The operational semantics of this storage system is based on axiomatic definitions of various weak isolation levels.


• Program Analysis for Code Refactoring. My dissertation research involves proposing efficient static analyses (mainly points-to analyses) for automatic identification of challenging refactoring opportunities in object-oriented software, with particular emphasis on the issues that occur when the refactoring tools are coupled into resource-bound environments like IDEs (Integrated Development Environments).


• GRANTS
• PI. Global Compiler Optimizations for CPU-GPU Heterogeneous Computing, SERB SRG, approx. 20 Lakhs. Start Date: 19th Dec 2023. Duration: 2 years.
• PI. Development of Coding Convention Compliance Checker, WESEE (Indian Navy), approx. 38.5 Lakhs. Start Date: 16th October 2023. Duration: 2 years.
• PI. WiseWarps: Smart Warp Mapping Technique for GPU, AMD, approx. 6.5 Lakhs. Start Date: 3rd October 2023. Duration: 3 years. Funding an Mtech RA student.
• PI. Towards High Programmer Productivity Using AI and Program Analysis, IIT Hyderabad, 25 Lakhs, seed grant. Start Date: 12th November 2021. Duration: 2 years.
• PI. Automatic Inference Of Semantic Properties From Big Code, Microsoft Research, 7 Lakhs. Start Date: 6th July 2021. Duration: 3 years. Funding an MTech RA student.
Drop me an email if you are a motivated student with similar research interests and good programming skills, and want to work with me.

  Teaching

• CS3423 Compilers II (Jul 2021, Jul 2023)


• CS5863 Introduction to Program Analysis and Optimization (Jan 2022, Jan 2024, Jan 2025)


• CS6300 Topics in Compiler Optimization (Jul 2022, Jan 2024, Jan 2025)


• CS5013 GPU Programming (Jul 2025)


• CS4443 Software Engineering (Jan 2023)


• ID1063 Introduction to Programming (Jul 2024)

  Publications

• BANG: Billion-Scale Approximate Nearest Neighbour Search using a Single GPU, Karthik V., Saim Khan, Somesh Singh, Harsha Vardhan Simhadri and Jyothi Vedurada, Transactions on Big Data, 2025. (paper)


• [Distinguished Artifact Award] GSOHC: Global Synchronization Optimization in Heterogeneous Computing, Soumik Kumar Basu, Jyothi Vedurada, Proceedings of the 39th European Conference on Object-Oriented Programming, ECOOP'25. (paper)


• Cell2Doc: ML Pipeline for Generating Documentation in Computational Notebooks, Tamal Mondal, Scott Barnett, Akash Lal, Jyothi Vedurada, Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering, ASE'23. (paper)

• SYNC: A Structurally guided Hard Negative Curricula for Efficient Neural Code Search, Atharva Naik, Soumitra Das, Jyothi Vedurada and Somak Aditya, Proceedings of the 13th International Joint Conference on Natural Language Processing, IJCNLP-AACL 2023. (paper)

• Nekara: Generalized Concurrency Testing, Udit Agarwal, Pantazis Deligiannis, Cheng Huang, Kumseok Jung, Akash Lal, Immad Naseer, Matthew Parkinson, Arun Thangamani, Jyothi Vedurada, Yunpeng Xiao, Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering, ASE'21. (paper)

• [Distinguished Paper Award] MonkeyDB: Effectively Testing Correctness against Weak Isolation Levels, Ranadeep Biswas, Diptanshu Kakwani, Jyothi Vedurada, Constantin Enea, Akash Lal (to appear), Proceedings of the ACM on Programming Languages (PACMPL); ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications; OOPSLA'21 (paper)

• Batch Alias Analysis, Jyothi Vedurada and V. Krishna Nandivada, Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering, ASE'19. (paper)

• Identifying Refactoring Opportunities for Replacing Type Code with Subclass and State, Jyothi Vedurada and V. Krishna Nandivada, Proceedings of the ACM on Programming Languages (PACMPL); ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications; OOPSLA'18. (paper)

• TTLG - An Efficient Tensor Transposition Library for GPUs, Jyothi Vedurada, Arjun Suresh, Aravind Sukumaran Rajam, Jinsung Kim, Changwan Hong, Ajay Panyala, Sriram Krishnamoorthy, V. Krishna Nandivada, Rohit Kumar Srivastava, P. Sadayappan, Proceedings of the 32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS'18. (paper)

• Refactoring Opportunities for Replacing Type Code with State and Subclass, Jyothi Vedurada and V. Krishna Nandivada, Proceedings of the 39th IEEE/ACM International Conference on Software Engineering Companion, ICSE-C'17. (paper)

PATENTS

• Method and System for Automatic CUDA Stream Allocation using Static Analysis, Soumik Kumar Basu, Jyothi Vedurada, Patent(IN) 2025. Patent No: 558197. Filed on: 07/05/2024. Granted on: 18/01/2025.

  Service

• Program Committee Member: SCI (IEEE Supercomputing India) 2025, SPLASH 2025 Student Research Competition, ISEC 2024 (research papers + docotoral symposium), PPoPP 2023, VSTTE'22, ISEC 2022, HiPC 2021, PPEE'21


• Program Vice-Chair: HiPC 2022


• Reviewer: TOPLAS 2021, TACO 2025


• Organizing Committee Member: ACM India Women Grad Cohort 2024 at IITH (July 2024).


• Organizing Committee Member and Speaker: ACM Summer School on Programming Language Analysis and Optimizations (July 2021).


• Artifact Evaluation Chair: SAS 2020


• Artifact Evaluation Committee Member: OOPSLA 2019, ASPLOS 2020.


• Reviewer for INAE Letters 2018.


• Session Chair: ISEC 2024.