Arjun Guha
Associate Professor
Research interests
- Programming languages
Education
- PhD in Computer Science, Brown University
- BA in Computer Science, Grinnell College
Biography
Arjun Guha is an associate professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.
Using the tools and techniques of programming languages, Guha researches security and reliability problems in web programming, systems, and robotics. For example, one recent project aims to make serverless computing more cost-effective, reliable, and applicable. Guha is a member of the Programming Research Laboratory.
Prior to joining Northeastern, Guha was an associate professor at the University of Massachusetts Amherst and a postdoctoral research associate at Cornell University. His work has received several awards, including an OOPSLA Most Influential Paper Award, a PLDI Distinguished Paper Award, and a PACT Best Paper Award.
In his free time, Guha enjoys running, cooking, and reading.
Projects
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Game Theoretic Updates for Network and Cloud Functions
Lead PI: Arjun Guha -
A Language-based Approach to Faster and Safer Serverless Computing
Lead PI: Arjun Guha -
Interactive Synthesis and Repair For Robot Programs
Lead PI: Arjun Guha
Recent publications
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SelfCodeAlign: Self-Alignment for Code Generation
Citation: Yuxiang Wei , Federico Cassano, Jiawei Liu , Yifeng Ding, Naman Jain, Zachary Mueller, Harm de Vries, Leandro von Werra, Arjun Guha, Lingming Zhang . (2024). SelfCodeAlign: Self-Alignment for Code Generation NeurIPS. http://papers.nips.cc/paper_files/paper/2024/hash/72da102da91a8042a0b2aa968429a9f9-Abstract-Conference.html -
Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs
Citation: Federico Cassano, John Gouwar, Francesca Lucchetti, Claire Schlesinger, Anders Freeman, Carolyn Jane Anderson, Molly Q. Feldman, Michael Greenberg , Abhinav Jangda, Arjun Guha. (2024). Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs Proc. ACM Program. Lang., 8, 677-708. https://doi.org/10.1145/3689735 -
Flexible and Optimal Dependency Management via Max-SMT
Citation: Donald Pinckney, Federico Cassano, Arjun Guha, Jonathan Bell , Massimiliano Culpo, Todd Gamblin. (2023). Flexible and Optimal Dependency Management via Max-SMT ICSE, 1418-1429. https://doi.org/10.1109/ICSE48619.2023.00124 -
Solver-based gradual type migration
Citation: Luna Phipps-Costin, Carolyn Jane Anderson, Michael Greenberg, and Arjun Guha. 2021. “Solver-based gradual type migration”. Proc. ACM Program. Lang. 5, OOPSLA, Article 111 (October 2021), 27 pages. DOI: 10.1145/3485488 -
Iterative Program Synthesis for Adaptable Social Navigation
Citation: J. Holtz, S. Andrews, A. Guha and J. Biswas, "Iterative Program Synthesis for Adaptable Social Navigation," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, pp. 6256-6261. DOI: 10.1109/IROS51168.2021.9636540 -
Accelerating graph sampling for graph machine learning using GPUs
Citation: Abhinav Jangda, Sandeep Polisetty, Arjun Guha, and Marco Serafini. 2021. Accelerating graph sampling for graph machine learning using GPUs. In Proceedings of the Sixteenth European Conference on Computer Systems (EuroSys ’21). Association for Computing Machinery, New York, NY, USA, 311–326. DOI: 10.1145/3447786.3456244 -
Wasm/k: delimited continuations for WebAssembly
Citation: Donald Pinckney, Arjun Guha, and Yuriy Brun. 2020. Wasm/k: delimited continuations for WebAssembly. In Proceedings of the 16th ACM SIGPLAN International Symposium on Dynamic Languages(DLS 2020). Association for Computing Machinery, New York, NY, USA, 16–28. DOI: 10.1145/3426422.3426978 -
TacTok: semantics-aware proof synthesis
Citation: Emily First, Yuriy Brun, and Arjun Guha. "TacTok: semantics-aware proof synthesis." Proceedings of the ACM on Programming Languages, v.4 , 2020. DOI: 10.1145/3428299 -
Robot Action Selection Learning via Layered Dimension Informed Program Synthesis
Citation: J. Holtz, S. Andrews, A. Guha and J. Biswas, "Robot Action Selection Learning via Layered Dimension Informed Program Synthesis," Conference on Robot Learning (CoRL), 2020. DOI: 10.48550/arXiv.2008.04133 -
Making High-Performance Robots Safe and Easy to Use For an Introduction to Computing
Citation: Joseph Spitzer, Joydeep Biswas, Arjun Guha. (2020). Making High-Performance Robots Safe and Easy to Use For an Introduction to Computing AAAI, 13412-13419. https://ojs.aaai.org/index.php/AAAI/article/view/7065 -
Formal Foundations of Serverless Computing
Citation: Abhinav Jangda, Donald Pinckney, Yuriy Brun, and Arjun Guha. Formal Foundations of Serverless Computing. ACM SIGPLAN Conference on Object Oriented Programming, Systems, Languages and Applications (OOPSLA), 2019. Distinguished Paper Award -
Interactive Robot Transition Repair With SMT
Citation: Jarrett Holtz, Arjun Guha, and Joydeep Biswas. Interactive Robot Transition Repair with SMT. International Joint Conference on Artificial Intelligence and the European Conference on Artificial Intelligence (IJCAI-ECAI), 2018 -
Rehearsal: A Configuration Verification Tool for Puppet
Citation: Rian Shambaugh, Aaron Weiss, and Arjun Guha. Rehearsal: A Configuration Verification Tool for Puppet. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2016
