Matthew Sotoudeh

PhD candidate at Stanford University

Bachelor's degree from the University of California, Davis with majors in Mathematics and Computer Science

I am fortunate to have been advised at various levels of formality by too many people to name, however the list of recent formal advisors is short enough to fit on a webpage:
Prof. Dawson Engler (PhD advisor),
Prof. Aditya Thakur (BS advisor),
Prof. Clark Barrett (PhD rotation advisor),
Dr. Jason Knight (Intel AIPG),
Dr. Sara Baghsorkhi (Intel Labs).

questions? email me

Conference & Journal Papers

questions? thoughts? complaints? ideas? email me

Sidekick: In-Network Assistance for Secure End-to-End Transport Protocols
Gina Yuan, Matthew Sotoudeh, David K. Zhang, Michael Welzl, David Mazières, and Keith Winstein, Stanford University
21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 2024)
Won NSDI 2024 Outstanding Paper Award
Won NSDI 2024 Community Award
[Proceedings]
SyReNN: A Tool for Analyzing Deep Neural Networks
Sotoudeh, M. (equal contribution); Tao, Z. (equal contribution); Thakur, A. V.
International Journal on Software Tools for Technology Transfer (STTT 2023)
[Paper]
Provable Repair of Deep Neural Networks
Sotoudeh, M. and Thakur, A. V.
42nd ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2021)
[Proceedings] [Conference] [Code] [arXiv]
SyReNN: A Tool for Analyzing Deep Neural Networks
Sotoudeh, M. and Thakur, A. V.
27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2021)
[Proceedings] [Code] [arXiv]
Analogy-Making as a Core Primitive in the Software Engineering Toolbox
Sotoudeh, M. and Thakur, A. V.
Proceedings of the 2020 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (SPLASH "Onward!" 2020)
[Proceedings] [Talk] [Poster] [Code] [BibTeX] [arXiv]
Abstract Neural Networks
Sotoudeh, M. and Thakur, A. V.
Proceedings of the 27th Static Analysis Symposium (SAS 2020)
[Proceedings] [Talk] [Code] [Conference] [arXiv]
Computing Linear Restrictions of Neural Networks
Sotoudeh, M. and Thakur, A. V.
Advances in Neural Information Processing Systems (NeurIPS 2019)
[Proceedings] [Poster] [Slides] [Code] [BibTeX] [arXiv (old)]
ISA Mapper: A Compute and Hardware Agnostic Deep Learning Compiler
Sotoudeh, M.; Venkat, A.; Anderson, M.; Georganas, E.; Heinecke, A.; Knight, J.
Proceedings of the 16th ACM International Conference on Computing Frontiers (CF 2019)
[Proceedings] [Slides] [BibTeX] [arXiv (old)]
C3-Flow: Compute Compression Co-Design Flow for Deep Neural Networks
Sotoudeh, M. and Baghsorkhi, S. S.
Proceedings of the 56th Annual Design Automation Conference (DAC 2019)
[Proceedings] [Slides] [BibTeX] [arXiv (old)]

Other Writings

Incremental Bounded Model Checking as Program Source Changes
Andrews, S. and Sotoudeh, M.
Formal Methods in Computer-Aided Design (FMCAD) 2023 Student Forum
System-Specific Interpreters Make Megasystems Friendlier
Sotoudeh, M.
Eighth Workshop on Live Programming (LIVE 2022)
[arXiv] [Code] [Talk] [Workshop]
A Short Survey of Weak Reasoning Assistants
Sotoudeh, M.
Third Workshop on Human Aspects of Types and Reasoning Assistants (HATRA 2022, extended abstracts)
[Extended Abstract] [Talk] [Workshop]
Sidecar: In-Network Performance Enhancements in the Age of Paranoid Transport Protocols
Yuan, G.; Zhang, D. K.; Sotoudeh, M.; Welzl, M.; Winstein, K.
Twenty-first ACM Workshop on Hot Topics in Networks (HotNets 2022)
[Proceedings] [Workshop]
Bounded Model Checking of Deep Neural Network Controllers
Sotoudeh, M. (Advised by Thakur, A. V.)
Won second place at the International Symposium on Principles Of Programming Languages (POPL) 2020 Student Research Competition
[Competition] [Code]
Correcting Deep Neural Networks with Small, Generalizing Patches
Sotoudeh, M. and Thakur, A. V.
NeurIPS 2019 Workshop on Safety and Robustness in Decision Making
[PDF] [Code] [BibTeX]

Presentations

Abstract Neural Networks, COVID Papers from SAS at SPLASH 2022, Auckland, New Zealand
System-Specific Interpreters Make Megasystems Friendlier, LIVE 2022 short talks, Auckland, New Zealand
A Survey of Weak Reasoning Assistants, HATRA 2022, Auckland, New Zealand
Provable Repair of Deep Neural Networks, PLDI 2021, Virtual
SyReNN: A Tool for Analyzing Deep Neural Networks, TACAS 2021, Virtual
Understanding and Repairing Deep Neural Networks, Waterloo ML + Logic Colloquium 2020, Virtual (with Aditya Thakur)
Abstract Neural Networks, SAS 2020, Virtual
Analogy-Making as a Core Primitive in the Software Engineering Toolbox, Onward! 2020, Virtual
Bounded Model Checking of Deep Neural Network Controllers, POPL SRC 2020, New Orleans, LA
Computing Linear Restrictions of Neural Networks, NeurIPS 2019, Vancouver, BC, Canada
ISA Mapper: A Compute and Hardware Agnostic Deep Learning Compiler, CF 2019, Alghero, Italy
C3-Flow: Compute Compression Co-Design Flow for Deep Neural Networks, DAC 2019, Las Vegas, NV
Correcting Deep Neural Networks with Small, Generalizing Patches, SRDM 2019, Vancouver, BC, Canada

Honors & Awards

Highest Honors, awarded with my B.S. in Computer Science
Citation for Outstanding Performance, UC Davis Mathematics Department
Honorable Mention, UC Davis Chancellor's Award for Excellence in Undergraduate Research/Dean Keith Simonton Prize
National Science Foundation Graduate Research Fellowship
Honorable Mention, CRA Outstanding Undergraduate Researcher Award
2nd Place, POPL 2020 Student Research Competition
Regents Scholarship, UC Davis
Rep. Ro Khanna Congressional Award
Travel Awards: NeurIPS Foundation (2019), UC Davis CS Department (2020), UC Davis URC (2019)

Teaching & Service

For some of the courses I have written day-long lab activites that introduce students to program analysis and automated reasoning in a hands-on activity. You can find most of these in this git repo.

Teaching Fellow (primary instructor) for CS 161 (Design and Analysis of Algorithms), Stanford University, Summer 2024
[Lecture Notes and Homeworks]
Teaching Fellow for CS 343S (Domain-Specific Language Design Studio), Stanford University, Spring 2024
Course Assistant for CS 103 (Mathematical Foundations of Computing), Stanford University, Winter 2024
Course Assistant for CS 240LX (Advanced Operating Systems Lab), Stanford University, Spring 2023
Course Assistant for CS 197 ("team-based introduction to Computer Science research" for undergraduates), Stanford University, Fall 2022
Course Assistant for CS 240 (Advanced Topics in Operating Systems), Stanford University, Spring 2022
TACAS 2022 Artifact Evaluation Committee member
Co-chair of the UC Davis CS Tutoring Committee

Contact

My permanent email address is matthew@masot.net. If you are considering sending me an email, please do so!

However, I use pretty crude filtering to remove the vast majority of spam I receive. If you are a real human and I have not replied to your email, please do rewrite it to be less spammy and resend it and hopefully I'll get it the next time! If you commonly send spam to mailing lists I subscribe to, your email address itself might be blocked. Please try sending from a different address.

None of those accounts are entirely managed by me, so it would be prudent to assume any communications sent to those addresses may be viewed surreptitiously by others. You are encouraged to use my PGP Key if this concerns you.

really really, email me !

Other

You can find a part of my calendar here, and a small wiki here.

You can find a variety of projects on my git lair and GitHub profile, ranging from add-ons for Google Docs to transpilers.

I used to collect a list of interesting proofs.

In 2017 I taught a one-week course on programming for middle schoolers in the Bay Area. Most of the curriculum for that course, including presentations, lesson outlines, projects, and retrospective comments are available here.

This page was last updated on 6/29/2024.