Find Academic Supervisors

Limin Jia is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with a courtesy appointment in the Computer Science Department. She is affiliated with CyLab, CMU's security and privacy research institute. She received her PhD in Computer Science from Princeton University and a BE from the University of Science and Technology in China. Her research applies formal techniques to enhance software security, focusing on programming languages and distributed systems. Key interests include: Language-based security mechanisms Formal verification of distributed systems Secure compilation techniques Intermittent computing foundations Her publications demonstrate strong emphasis on security guarantees in programming languages (Rust/WebAssembly), formal methods for intermittent systems, and software supply chain security. Recent works frequently address type systems, compiler verification, and energy-constrained computing. Dr. Jia maintains an extensive advising portfolio with current and former students spanning PhD and Master's programs. She teaches foundational security courses including Browser Security and Introduction to Information Security .

• Security
• Programming Languages
• Formal Methods

Andrew K. Hirsch is an Assistant Professor at the University at Buffalo, SUNY , Department of Computer Science and Engineering. He leads the Databases and Programming Languages group and focuses on programming languages for decentralized systems, particularly choreographic programming and information-flow security. Education: Ph.D. in Computer Science (2019) from Cornell University, supervised by Ross Tate on computational effects. B.S. in Computer Science and Pure Mathematics from The George Washington University. Research Interests: His work centers on choreographic programming, a paradigm ensuring deadlock-free concurrent systems, and information-flow security for decentralized applications. He also explores computational effects and type systems in programming language theory. Publications: Recent work includes advancements in process polymorphism (OOPSLA 2025), type-level polymorphism (PLACES 2025), and security definitions for higher-order declassification (OOPSLA 2023). Students: Doctoral: Michael Piskozub, Keith Allen Mason Masters: Alexander Bohosian, Gianna Bossoreale Undergraduate: Alex Doyoon Kim, Julia Montouri Recent Alumni: Ethan Canton, Tiffany Cai, Vamsi Krishna Bellam, Vincent Chan, Frank (Feng-Mao) Tsai Projects: Leads initiatives such as Choret (open choreographies) and The Pirouette Language and Compiler , which translate choreographic programs into concurrent system implementations.

• Choreographic Programming
• Information-Flow Security
• Computational Effects
• +3 more

Michael Norrish is an Associate Professor at the School of Computing, Australian National University (ANU) , specializing in formal methods, programming language semantics, and interactive theorem proving. His career spans roles at NICTA, Data61, and ANU, with a focus on mechanised mathematics and verified systems. PhD in Computer Science (University of Cambridge, 1999) Undergraduate degree from Victoria University of Wellington His research bridges interactive theorem-proving (ITP) systems like HOL4 with real-world systems verification, particularly in programming languages and compilers. He leads the CakeML project, developing a verified compiler for functional languages. His work intersects formal verification with practical system design, including projects on reproducibility debt in scientific software and verified processors. Recent publications highlight verified compilation techniques, reproducibility challenges, and Kolmogorov complexity formalization. He actively participates in conference program committees (e.g., CPP, PLDI) and promotes trustworthy systems development through tools like HOL4. Current affiliations: ANU, CakeML Project, Trustworthy Systems Research Group (UNSW) Collaborations: Chalmers University (postdoc opportunities), seL4 microkernel ecosystem

• Formal Methods
• Interactive Theorem Proving
• Programming Languages
• +4 more

Aymeric Fromherz is a researcher at Inria Paris, focusing on formal methods for secure systems. He leads projects in Rust verification, high-assurance cryptography, and formalization of computational legal texts. Education includes a PhD from Carnegie Mellon University (co-advised by Bryan Parno and Corina Păsăreanu) and degrees from École Normale Supérieure. His research spans Rust verification (via Aeneas toolchain), verified cryptographic primitives , and computational law (through the Catala language). Recent publications address memory allocators, borrow-checking, and legal ambiguity detection. Major Scientific Awards : Distinguished Artifact Award (CAV 2025) Best Tool Paper Award (ESOP 2024) ACM SIGSAC Dissertation Award (2021) A.G. Milnes Dissertation Award (2021) He contributes to conferences like POPL, ICFP, and CPP, and participates in the Everest Project. The Prosecco Team at Inria Paris supports his research on formal methods and security.

• Formal Methods
• Secure Systems
• Rust Verification
• +6 more

Matthew Fluet is an Associate Professor and Graduate Program Director in the Department of Computer Science at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. He received his PhD in Computer Science from Cornell University and his BS in Mathematics from Harvey Mudd College. Prior to joining RIT, he was a research assistant professor at the Toyota Technological Institute at Chicago. Dr. Fluet's research focuses on programming languages, with particular emphasis on: Functional programming Compiler construction Program analysis Type systems Parallelism and concurrency His research has resulted in several significant projects including Manticore (a heterogeneous-parallel functional programming language), MaPLe/MPL (a functional language for provably efficient and safe multicore parallelism), and contributions to MLton (a whole-program optimizing Standard ML compiler). His work is supported by multiple National Science Foundation grants. Dr. Fluet has published extensively in top programming languages conferences including ICFP, POPL, PLDI, and PPoPP. His recent work focuses on automatic parallelism management, type-and control-flow analysis, and memory management for parallel systems, demonstrating a consistent research trajectory in making parallel programming safer and more accessible through language design. His notable research grants include: National Science Foundation (CISE Research Infrastructure): $224,329 (2014-2017) National Science Foundation (Software and Hardware Foundations): $236,744 (2014-2018) National Science Foundation: $412,261 (2011-2014) National Science Foundation: $91,867 (2008-2012) Dr. Fluet actively mentors graduate students, currently advising several MS project and thesis students. He teaches courses including Programming Skills (with focus on Rust), Compiler Construction, and Programming Language Concepts. He also serves in leadership roles including as Graduate Program Director for the Computer Science MS program and participates in departmental governance through the CS Curriculum Committee and GCCIS Curriculum Committee. He is an active member of the programming languages community, having served on program committees for major conferences and as Information Director for ACM SIGPLAN (2015-2018), demonstrating his commitment to advancing the field through research, education, and community service.

• Functional programming
• Compiler construction
• Program analysis
• +2 more

Shing-Chi Cheung is a Professor of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), School of Engineering. He founded the CASTLE research group and co-founded the International Workshop on Automation of Software Testing (AST) in 2006. His leadership includes serving as General Chair of FSE 2014 and chairing multiple APSEC conferences. His research focuses on software quality enhancement through program analysis, testing, debugging, and AI techniques, targeting Android apps, open-source software, deep learning systems, smart contracts, and spreadsheets. Current projects include metamorphic testing frameworks, binary analysis tools, and vulnerability detection systems for emerging technologies. His publication portfolio demonstrates consistent contributions to software engineering since 2016, with recent work emphasizing AI-integrated testing methodologies, smart contract security, and deep learning system reliability. Key trends show increasing focus on cross-language analysis, data visualization quality, and compiler-level verification for modern software stacks. Distinguished Member of the ACM Fellow of the British Computer Society Editorial board member: Science of Computer Programming (SCP), Journal of Computer Science and Technology (JCST) Former editorial board member: IEEE Transactions on Software Engineering (2006-2009), Information and Software Technology (2012-2015) Four patents in China and the United States Cheung actively mentors through the CASTLE research group and serves on program committees for major conferences including ICSE, ESEC/FSE, and ISSTA. His work bridges academic research with practical applications through industry collaborations and tool development. He has contributed to numerous workshops and symposia as steering committee member and program chair.

• Software Engineering
• Program Analysis
• Software Testing
• +7 more

Chao Zhang is a Tenured Associate Professor at Tsinghua University, specializing in software security, system security, data security, and AI security. He leads the VUL337 research group and serves as the coach of the Blue-Lotus CTF team. His educational background includes a Ph.D. in Computer Science from Peking University (2008-2013), a B.S. in Mathematical Science from Peking University (2004-2008), and a postdoctoral position at UC Berkeley (2013-2016). Dr. Zhang's research focuses on Software Security , System Security , Data and AI Security , Program Analysis , and Vulnerability Discovery . His work spans binary code analysis, fuzzing techniques, blockchain security, and AI security. His recent publications demonstrate a strong emphasis on developing novel frameworks for vulnerability detection, binary code analysis, and securing AI systems against adversarial attacks. His publication trends show a consistent focus on practical security solutions with increasing attention to AI security challenges. Over the past decade, he has published extensively in top security conferences including IEEE S&P, USENIX Security, CCS, NDSS, and ISSTA, with a significant acceleration in publications since 2020. Tencent CSS TSec Professional Prize (2nd place, 2019) Tencent CSS TSec Breakthrough Prize (1st place, 2018) DARPA Cyber Grand Challenge CFE, 2nd in exploiting (2016) DARPA Cyber Grand Challenge CQE, 1st in defense (2015) Microsoft BlueHat Prize Contest's Special Recognition Award (2012) 5th place in Defcon CTF 2017 2nd place in Defcon CTF 2016 5th place in Defcon CTF 2015 Dr. Zhang leads the VUL337 research group at Tsinghua University, which focuses on vulnerability discovery and security analysis. He also serves as the coach of the Blue-Lotus CTF team and is a member of the V group of LiST. His research has received significant attention in the security community, with numerous publications in top-tier security venues and practical contributions to vulnerability discovery and mitigation techniques.

• Software Security
• System Security
• Data Security
• +6 more

Zhou Yang is an Assistant Professor at the University of Alberta and Fellow at the Alberta Machine Intelligence Institute (Amii), with research focusing on the intersection of software engineering and artificial intelligence. His academic journey includes a Ph.D. from Singapore Management University, an M.Sc. in Software System Engineering from University College London, and undergraduate studies at Yangzhou University. His research interests span Software Engineering, Artificial Intelligence, AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), Large Language Models, and Cybersecurity. Yang's work explores how human and AI collaboration can improve code writing, how AI impacts open-source communities, and how developers build software in emerging environments like VR/AR. His recent publications demonstrate strong trends in code language models, with significant contributions to ASE, ICSE, ISSTA, and top journals like TOSEM and TSE. His research addresses critical challenges including token efficiency in code generation, user perception of AI coding assistants, privacy preservation in code models, and fairness in AI systems. 2024 IEEE Computer Society Best Paper Award (1 out of 183 submissions) ACM Distinguished Paper Award from ISSTA 2024 Distinguished Reviewer Award from Internetware 2024 SMU Research Staff Excellence Award (1 of 4 university-wide) SMU Presidential Fellowship Award SMU Dean's List Award 1st Place in ACM Student Research Competition at ICSE 2024 Yang actively mentors graduate students with regular one-on-one meetings, constructive feedback, and support for top-venue publications. He provides full funding through teaching assistantships and research grants, including travel support for conferences. His lab focuses on responsible research conduct and societal implications of AI work. Though early-career, he actively builds professional networks for students through collaborations and supports diverse career paths in academia and industry. He leads research in the Alberta Machine Intelligence Institute, focusing on practical applications where software engineering principles enhance AI systems and where AI techniques solve real-world software engineering challenges.

• Software Engineering
• Artificial Intelligence
• AI for Software Engineering
• +3 more

Gang (Gary) Tan is a Professor in the Computer Science and Engineering Department at Pennsylvania State University, co-directing the Institute for Networking and Security Research (INSR). His research bridges computer security, formal methods, and programming languages to develop practical solutions for software vulnerabilities and AI fairness. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University Dr. Tan specializes in applying compiler techniques and formal verification to security challenges, with seminal work on cache side-channel attacks and fairness in machine learning. His Security of Software (SOS) Group develops frameworks that integrate theoretical guarantees into real-world systems, emphasizing measurable security outcomes and ethical AI. Recent projects focus on quantifying bias in neural networks and mitigating speculative execution vulnerabilities. Analysis of his 2021-2025 publications reveals a strategic pivot toward AI security, where he pioneers methods for fairness testing (e.g., information-theoretic debugging) and repair (e.g., NeuFair). Concurrently, his security work evolves from foundational side-channel research (SpecSafe, 2021) toward hardware-software co-design solutions, demonstrating consistent innovation across theoretical and applied domains. Scientific Awards: James F. Will Career Development Professorship NSF CAREER Award Google Research Award (two instances) Distinguished Reviewer Award at 2018 IEEE Symposium on Security and Privacy Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Best Paper Award at PLDI 2024 Dr. Tan leads the SOS Group with funding from NSF (including CAREER), DARPA (ISAT study group membership), and industry partners like Google. His grants support interdisciplinary projects spanning secure compilation, fairness engineering, and hardware security, while his teaching excellence award reflects commitment to pedagogy in core systems courses. He co-directs Penn State's Institute for Networking and Security Research (INSR), fostering collaboration between systems, security, and AI researchers. The SOS Group maintains active partnerships with industry security teams and contributes to open-source tools for vulnerability detection, with recent work expanding into fairness certification for machine learning pipelines.

• computer security
• formal methods
• programming languages
• +3 more

Đorđe Žikelić is an Assistant Professor of Computer Science at the School of Computing and Information Systems at Singapore Management University (SMU) in Singapore. He completed his PhD in 2023 at the Institute of Science and Technology Austria (ISTA) under Krishnendu Chatterjee and Petr Novotný, receiving both Outstanding PhD Thesis and Outstanding Scientific Achievement awards. Prior to his doctorate, he earned bachelor's and master's degrees in mathematics from the University of Cambridge. His educational background includes: PhD in Computer Science, Institute of Science and Technology Austria (ISTA), 2023 Bachelor's and Master's in Mathematics, University of Cambridge Dr. Žikelić's research focuses on advancing formal methods to ensure software and AI systems are correct, safe, and trustworthy. His work bridges theoretical aspects of formal reasoning about probabilistic systems with practical automated verification methods. His primary research interests span three interconnected areas: Program Analysis and Verification: He develops techniques for analyzing probabilistic programs, numerical programs, and efficient quantifier elimination methods, addressing fundamental challenges in verifying complex software systems. Trustworthy AI and Safe Autonomy: He creates formal verification frameworks for learning-enabled control systems and neural networks, ensuring AI operates safely in uncertain environments through methods like runtime monitoring and certificate repair. Probabilistic System Verification: He explores broader applications including bidding games on graphs and blockchain protocol analysis, extending formal methods to novel domains beyond traditional finite-state verification. His publication trajectory shows a consistent progression from theoretical foundations to practical applications, with recent work increasingly focused on integrating formal verification with machine learning. His 2024-2025 publications demonstrate growing expertise in verifying learning-based systems and developing practical tools like PolyQEnt for quantified entailment solving. His scientific achievements have been recognized with: Outstanding PhD Thesis Award at ISTA Outstanding Scientific Achievement Award at ISTA Distinguished Paper Award at FM 2024 Dr. Žikelić serves on program committees for major conferences including TACAS, PLDI, AAAI, and CAV. He actively mentors through the Programming Languages Mentoring Workshop (PLMW) at PLDI 2025. His research group at SMU focuses on developing novel algorithms for verifying correctness of programs and AI systems, with current projects spanning formal methods, artificial intelligence, and programming languages.

• Formal Methods
• Program Analysis and Verification
• Trustworthy AI
• +5 more

He Ye serves as an Assistant Professor at University College London (UCL), specializing in AI-driven software engineering solutions. His work bridges academic research and industry applications through EuniAI, a startup transforming research into developer tools. Research focuses on code agents for automating software tasks, with three core thrusts: Codebase context retrieval to enhance LLM capabilities Automated issue resolution systems Code agent memory construction His publications (2021-2025) demonstrate consistent innovation in fault localization and program repair. Current advising includes PhD students Zhaoyang Chu and Xiang Li (starting Fall 2025), alongside research assistants Yue Pan, Jiayi Xu, and Han Li. He co-founded EuniAI to commercialize research solutions for practical developer challenges. He actively shapes the field through workshop organization ( LMPL@SPLASH 2025 , APR@ICSE 2025 ) and program committee roles across major conferences including ASE, ICSE, and ESEC/FSE.

• AI for Software Engineering
• Fault Localization
• Automated Program Repair
• +2 more

Anitha Gollamudi is an Assistant Professor in the Department of Computer Science at the Miner School of Computer and Information Sciences, University of Massachusetts Lowell. She teaches core courses including Systems Security (COMP 5300), Compiler Construction (COMP 4060/5340), and Organization of Programming Languages (COMP 3010). Her research focuses on language-based security through hardware-assisted mechanisms, cryptography, and formal methods. Current projects include: Automatic compartmentalization of Trusted Execution Environment (TEE) programs Formal foundations of Fully Homomorphic Encryption (FHE) compilers Privacy-preserving machine learning with encrypted learning Analysis of her 8 recent publications (2016-2025) reveals dominant themes in TEE security and secure compilation. Key contributions address memory safety in WebAssembly, authorization logic for computation principals, and formal verification of cryptographic systems. Her work consistently bridges theoretical foundations with practical implementations for real-world security challenges. No scientific awards were mentioned in the available information. Professor Gollamudi actively mentors students across levels, currently advising PhD candidates Wesley B. Nuzzo and Samuel Dodson alongside undergraduate Benjamin Houle. Her former students include Nam Bui, James Chen, Yuka Akiyama (honors thesis), and Andrew Eggleston. She emphasizes collaborative research and encourages prospective students to contact her with research interests and academic background. She leads a research group focused on applying programming language techniques to security problems, with ongoing projects targeting enclave placement optimization, FHE correctness verification, and encrypted machine learning frameworks.

• Language-based Security
• Trusted Execution Environments
• Formal Methods
• +4 more