Vasudev Gohil
Thanks for visiting! Explore my site to learn more about me, my background, and what I have to offer. If you have questions or would like to discuss an opportunity to work together, feel free to get in touch.
About
Background
I am a Software Engineer at Siemens Industry Software Inc. I work at the intersection of machine learning and chip design. On my own time, I research the security of AI-based systems, especially LLMs. I hold a Ph.D. from the Department of Electrical and Computer Engineering at Texas A&M University, where I worked at the intersection of machine learning, hardware chip design, and security under the guidance of Dr. JV Rajendran. Before that, I did my undergraduate studies in the Department of Electrical Engineering at IIT Gandhinagar.
Research Interests
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Machine Learning: Reinforcement Learning and Large Language Models
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Security of AI
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Red-Teaming LLMs
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Adversarial Robustness
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ML for Chip Design and Hardware Security
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Embedded Systems Security
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Security of Integrated Circuits Supply Chain
Awards
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Travel Awards: ACM SIGDA Travel Grant to attend Student Research Competition at ICCAD, 2023; Design Automation Conference Young Fellows program, 2023; IEEE HOST Student Travel Grant, 2023; Texas A\&M University ECEN Graduate Student Travel Grant, 2022; USENIX Security Grant for attending conference, 2021; IEEE HOST Student Travel Grant for attending conference, 2020
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One Time ECEN Departmental Scholarship, 2018
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Academic Excellence Scholarship for securing top position at IIT Gandhinagar, 2014-2015
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Dean's List at IIT Gandhinagar, 2014-2015
News
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3/25: I released the findings of my independent research project "JBFuzz: Jailbreaking LLMs Efficiently and Effectively Using Fuzzing" on arXiv
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2/25: I presented our paper "LLMPirate: LLMs for Black-box Hardware IP Piracy" at Network and Distributed System Security (NDSS) Symposium, 2025
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11/24: Our paper "LLMPirate: LLMs for Black-box Hardware IP Piracy" has been accepted at the Network and Distributed System Security (NDSS) Symposium, 2025
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8/24: I presented our paper "AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning" at the USENIX Security Symposium, 2024
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8/24: I graduated with a Ph.D. degree from Texas A&M University
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6/24: Our paper "CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation" was presented at the LLM-Aided Design (LAD) Workshop, 2024
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6/24: I successfully defended my doctoral dissertation on reinforcement learning for hardware security
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5/24: Our paper "CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation" has been accepted at the LLM-Aided Design (LAD) Workshop, 2024
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3/24: I presented our paper "MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors" at IEEE Design, Automation and Test in Europe Conference, 2024
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2/24: Our paper "AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning" has been accepted at the USENIX Security Symposium, 2024
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1/24: IEEE Spectrum, IEEE's flagship publication, published a news article on our work on using Multi-Armed Bandit Algorithms for Fuzzing Processors, MABFuzz
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12/23: I successfully defended my Ph.D. research proposal and have been admitted to Candidacy
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12/23: Our paper "MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors" was picked up by Semiconductor Engineering (link)
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11/23: Our paper "MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors" has been accepted at Design, Automation and Test in Europe Conference, 2024
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11/23: Our paper "PSOFuzz: Fuzzing Processors with Particle Swarm Optimization" was presented at International Conference on Computer-Aided Design (ICCAD), 2023
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10/23: My research abstract "Reinforcement Learning Frameworks for Protecting Integrated Circuits" has been accepted at SRC@ICCAD 2023. I also received a grant from ACM SIGDA to attend the SRC event and present a poster on my research.
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8/23: Our paper "DETERRENT: Detecting Trojans Using Reinforcement Learning" has been accepted at IEEE Transcations on Computer-Aided Design of Integrated Circuits and Systems (TCAD)
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7/23: Our paper "PSOFuzz: Fuzzing Processors with Particle Swarm Optimization" has been accepted at International Conference on Computer-Aided Design (ICCAD), 2023
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7/23: Our paper "ExploreFault: Identifying Exploitable Fault Models in Block Ciphers with Reinforcement Learning" was presented at Design Automation Conference (DAC), 2023
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2/23: Our paper "ExploreFault: Identifying Exploitable Fault Models in Block Ciphers with Reinforcement Learning" has been accepted at Design Automation Conference (DAC), 2023
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11/22: The winners of the "AI vs. Humans" competition have been announced. Thank you to all the teams for participating!
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11/22: Our paper "ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning" was presented at ACM SIGSAC Computers and Communications Security (CCS), 2022
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10/22: Finalists for the "AI vs. Humans" competition have been announced (link)
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09/22: "AI vs. Humans" competition is now live
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09/22: Our paper "ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning" has been accepted for publication at ACM SIGSAC Computers and Communications Security (CCS), 2022
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08/22: Co-organizing a first-of-its-kind competition, "AI vs. Humans" at Cyber Security Awareness Week (CSAW), 2022
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07/22: Our paper "DETERRENT: Detecting Trojans Using Reinforcement Learning" was presented at Design Automation Conference (DAC), 2022
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02/22: Our paper "DETERRENT: Detecting Trojans Using Reinforcement Learning" has been accepted at Design Automation Conference (DAC), 2022
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10/21: Our paper "Game Dollars Splits: A Game Theoretic Analysis of Split Manufacturing" has been accepted for publication at IEEE Transactions on Information Forensics and Security (TIFS), 2021
Publications
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V. Gohil, "JBFuzz: Jailbreaking LLMs Efficiently and Effectively Using Fuzzing," on arXiv, 2025
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V. Gohil, M. DeLorenzo, V. Nallam, J. See, and J. Rajendran, "LLMPirate: LLMs for Black-Box Hardware IP Piracy," in Network and Distributed System Security (NDSS) Symposium, 2025
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M. DeLorenzo, V. Gohil, and J. Rajendran, "CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation," in IEEE LLM-Aided Design (LAD) Workshop, 2024
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R. Kande, V. Gohil, M. DeLorenzo, C. Chen, and J. Rajendran, "LLMs for Hardware Security: Boon or Bane?," in IEEE VLSI Test Symposium (VTS), 2024
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V. Gohil, S. Patnaik, D. Kalathil, and J. Rajendran, "AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning," in USENIX Security Symposium, 2024
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V. Gohil*, R. Kande*, C. Chen, A. Sadeghi, and J. Rajendran, "MABFuzz: Multi-Armed Bandit Algorithms for Fuzzing Processors," in IEEE Design, Automation and Test in Europe Conference (DATE), 2024
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V. Gohil, S. Patnaik, H. Guo, D. Kalathil, and J. Rajendran, "DETERRENT: Detecting Trojans Using Reinforcement Learning," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2024
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C. Chen*, V. Gohil*, R. Kande, A. Sadeghi, and J. Rajendran, "PSOFuzz: Fuzzing Processors with Particle Swarm Optimization," in IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2023
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H. Guo, S. Saha, V. Gohil, S. Patnaik, D. Mukhopadhyay, and J. Rajendran, "ExploreFault: Identifying Exploitable Fault Models in Block Ciphers with Reinforcement Learning," in ACM/IEEE Design Automation Conference (DAC), 2023
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S. Patnaik, V. Gohil, H. Guo, and J. Rajendran, "Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges," in 19th International SoC Conference (ISOCC), South Korea, 2022 (Invited)
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V. Gohil, H. Guo, S. Patnaik, and J. Rajendran, "ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning," in ACM SIGSAC Conference on Computer and Communications Security (CCS), 2022
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V. Gohil, S. Patnaik, H. Guo, D. Kalathil, and J. Rajendran, "DETERRENT: Detecting Trojans using Reinforcement Learning," in ACM/IEEE Design Automation Conference (DAC), 2022
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V. Gohil, M. Tressler, K. Sipple, S. Patnaik, and J. Rajendran, "Games, Dollars, Splits: A Game-Theoretic Analysis of Split Manufacturing,” in IEEE Transactions on Information Forensics and Security (TIFS), 2021
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C. Jin, V. Gohil, R. Karri, and J. Rajendran, “Security of Cloud FPGAs: A Survey,” in arXiv, 2020
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* Equal Contributions
Experience
Software Engineer
Siemens Industry Software Inc.
September 2024 - Present
I work in the research and development team of the Catapult business unit of Siemens EDA. I research novel algorithms and techniques to improve the quality of results of the Catapult HLS product.
Graduate Research Assistant
ECEN Department
Texas A&M University
August 2018 - August 2024
I work as a research assistant at the intersections of machine learning, specifically RL and LLMs, chip design, and hardware security. My work is primarily focused on applying machine learning to improve the chip design process as well as increase the security of the IC supply chain by designing solutions to thwart reverse engineering, intellectual property piracy, and hardware Trojan insertion.
Technical Intern
Silicon Realization Group
Synopsys
May 2022 - August 2022
I worked as a technical intern in the DSO.ai team in the Silicon Realization Group of Synopsys. I worked on the DSO.ai tool, the EDA industry's first autonomous artificial intelligence application for chip design. I assisted team members in their tasks and performed experiments to improve DSO.ai.
Research Intern
ECEN Department
Texas A&M University
May 2017 - July 2017
I contributed to an ongoing project in Dr. Shakkottai's research group. I helped modify and develop appropriate decision trees for QoS to QoE mapping with the help of machine learning methods.
Summer Research Intern
IIT Gandhinagar
May 2016 - July 2016
I contributed to an ongoing project in Dr. Chakraborty's research group. Initially, I performed a thorough literature review, and eventually, I worked with a Fiber Bragg grating as a pressure sensor.
Education
Ph.D. in Computer Engineering
Texas A&M University, College Station
August 2018 - August 2024
B. Tech in Electrical Engineering
IIT Gandhinagar
July 2014 - August 2018
Let’s Connect
Email: vasudev.gohil@siemens.com
LinkedIn: vasudevgohil