Find Academic Supervisors

Yu JiangView profile

Associate Professor

• Software Engineering
• Embedded Systems
• Fuzz Testing
• +3 more

Yu Jiang is an Associate Professor at the School of Software, Tsinghua University, China. His research focuses on software security with emphasis on fuzz testing, embedded systems, and database security. He leads the Software System Security Assurance Group which has discovered over 1,000 bugs in major system software with 300+ CVEs registered. Dr. Jiang's research interests include Software Engineering , Embedded Systems Security , and Cross-Layer Fuzzing . His work addresses vulnerabilities in operating systems, databases, communication protocols, and IoT firmware through innovative fuzzing frameworks. Key contributions include semantic-aware fuzzing for heterogeneous software stacks and learning-based vulnerability detection for embedded systems. His recent publications demonstrate strong trends in database security (Hulk, PUPPY, THANOS), ransomware defense (Fawkes, Preventing Disruption), and web security (JANUS). Research spans both theoretical advances in fuzzing techniques and practical industrial applications, with significant impact evidenced by numerous distinguished paper awards. Career Award, NSFC: 2026 Distinguished Paper Award, ISSTA: 2025 First Prize for Technical Invention, CCF: 2024 Distinguished Paper Award, USENIX Security: 2024 SIGSOFT Distinguished Paper Award, FSE: 2022 Dr. Jiang has advised over 50 graduate students including 20 PhD candidates. His research is supported by major grants including NSFC projects ($600,000 for Software Trustworthiness Construction and $350,000 for Distributed Database Reliability), Huawei ($80,000 for LLM-Powered Fuzzing), and Tencent ($120,000 for LLM-Powered Unit Testing). The Software System Security Assurance Group maintains strong industry partnerships with Huawei, Alibaba, Tencent, and Webank. The group operates cutting-edge infrastructure for fuzz testing across multiple domains including database systems, operating kernels, blockchain platforms, and industrial control systems. Current projects integrate LLM technologies with traditional fuzzing techniques to enhance vulnerability detection in complex software stacks.

Jinqiu YangView profile

Associate Professor

• software reliability
• quality assurance of machine learning systems
• autonomous vehicles
• +5 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.

Chao PengView profile

Researcher

• Software Testing
• Program Repair
• LLM for Code Generation
• +3 more

Chao Peng is a Principal Research Scientist at ByteDance where he leads the Software Engineering Lab, focusing on AI agents for software engineering. He holds a part-time position as a Postgraduate Student Mentor at Fudan University's School of Computer Science. His research bridges industry and academia, with significant contributions to software testing, program repair, and LLM applications in software development. Education: PhD in Informatics, 2021, University of Edinburgh, UK MSc in High Performance Computing and Data Science, 2017, University of Edinburgh, UK BEng in Computer Science and Technology, 2016, Xuzhou University of Technology, China Dr. Peng's research interests center on the intersection of artificial intelligence and software engineering. He explores how large language models can transform traditional software development practices, particularly in code generation, testing, and bug fixing. His work on LLM4Code has led to innovative frameworks like CodeVisionary for evaluating code generation capabilities and Trae Agent for software engineering tasks with test-time scaling. He investigates the synergy between machine learning techniques and compiler optimizations to enhance software reliability and developer productivity. His recent publications reveal a strong focus on practical evaluation frameworks for LLMs in real-world software engineering contexts. Rather than theoretical benchmarks, his work emphasizes real-world applicability, as seen in RepoMasterEval which evaluates code completion in actual repository settings. He examines multi-faceted challenges including code generation, bug reproduction, issue resolution, and repository-level question answering, consistently addressing the gap between laboratory evaluations and practical development environments. Scientific Awards: Distinguished Reviewer for FSE'25 Invited to program committees for FSE'26, SANER 2026, ASE 2025, and others School of Informatics Scholarship (fully-funded PhD) Multiple national scholarships during undergraduate studies Honours Spot Bonus at ByteDance Dr. Peng actively mentors postgraduate students at Fudan University while leading research initiatives at ByteDance that foster university collaborations. His laboratory work translates academic research into practical tools for software development, with several frameworks deployed in industrial settings. He serves on multiple conference program committees, contributing to the advancement of software engineering research through rigorous peer review and community building. His Software Engineering Lab at ByteDance operates at the forefront of AI-assisted development, exploring how agent-based systems can automate complex software engineering tasks. The team's work on frameworks like AEGIS for bug reproduction and DialogAgent for code question answering demonstrates their commitment to solving practical challenges faced by developers in real-world settings.

Benjamin C. PierceView profile

Professor

• Programming Languages
• Type Systems
• Language-based Security
• +5 more

Benjamin C. Pierce is the Henry Salvatori Professor of Computer and Information Science in the School of Engineering and Applied Science at the University of Pennsylvania. As a Fellow of the ACM, he has made significant contributions to programming language theory and formal methods. His academic leadership includes previous editorial roles as co-Editor in Chief of the Journal of Functional Programming and Managing Editor for Logical Methods in Computer Science. His research spans multiple interconnected domains in programming language theory, with particular emphasis on type systems and their applications to security and verification. Pierce's work bridges theoretical foundations with practical implementations, most notably through his development of the Unison file synchronization tool and contributions to the Clowdr virtual conference platform. His research interests form a cohesive trajectory from foundational type theory to applied security and verification techniques. Pierce's scholarly output shows consistent focus on property-based testing, type systems, and formal verification methods. His recent publications demonstrate evolving interests in differential privacy verification, synchronization technologies, and the practical challenges of implementing formal methods in real-world systems. The progression of his work reflects both theoretical depth and practical relevance to software development challenges. Fellow of the ACM Author of influential textbooks Types and Programming Languages and Software Foundations Lead designer of the Unison file synchronizer Co-developer of the Clowdr virtual conference platform Former editorial leadership for multiple prominent programming languages journals As an educator and mentor, Pierce has contributed to the Programming Languages Mentoring Workshop (PLMW) and has served on numerous conference program committees. His academic service extends to SIGPLAN leadership roles including SIGPLAN Vice Chair and Steering Committee membership. His textbook Software Foundations has become a standard resource for teaching formal methods and proof assistants.

Alastair F. DonaldsonView profile

Professor

• Programming Languages
• Compilers
• Verification
• +5 more

Alastair F. Donaldson is a Professor in the Department of Computing at Imperial College London, where he leads the Multicore Programming Group. His primary affiliation is with Imperial College London's Department of Computing within the broader Faculty of Engineering structure. He serves as a Program Committee Member for major conferences including ASE, PLDI, and POPL. His research spans Programming Languages , Compilers , Verification , Testing , and Multicore Programming , with significant contributions to randomized testing techniques. He pioneered GraphicsFuzz (acquired by Google in 2018) and developed innovative approaches like grammar mutation for parser testing, metamorphic fuzzing for C++ libraries, and specialized tools for GPU API validation. His work bridges theoretical foundations with industrial impact, particularly in compiler correctness and GPU computing. Analysis of his recent publications reveals a strong trend toward fuzzing infrastructure development (40%), GPU/compiler testing (30%), and formal methods integration (30%). His research increasingly focuses on large-scale automated testing for complex systems including WebGPU, Dafny, and Rust, while maintaining rigorous theoretical grounding in concurrency models and memory semantics. Donaldson has held significant leadership roles including General Chair for PLDI 2020 and Program Chair for ECOOP. His research has been supported through conference organizational roles and industrial collaborations, notably the GraphicsFuzz spinout. He actively contributes to the PL community through mentoring initiatives like PLMW and community-building efforts such as The PLDI Song. He leads the Multicore Programming Group at Imperial College London, focusing on practical tools for compiler and GPU driver validation. The group's work combines theoretical program analysis with real-world testing frameworks, maintaining strong industry connections through projects adopted by Google and other technology companies.

Anders MøllerView profile

Professor

• Static Program Analysis
• Dynamic Program Analysis
• JavaScript
• +5 more

Anders Møller is a Professor at the Department of Computer Science , Aarhus University , Denmark. His career spans roles as an author , committee member , and session chair in conferences like SPLASH, OOPSLA, ECOOP, ISSTA, ICSE, and PLDI. Affiliation: Aarhus University Co-founder: Coana Research Focus : Specializing in static and dynamic program analysis for JavaScript, TypeScript, Java, and Node.js applications, his work addresses: Pointer analysis precision in Java Race condition detection in Node.js Library evolution and semantic patching Soundness improvements in static analyzers Type safety in modern languages Concolic execution for web testing Publication Trends : Recent work (2021–2024) emphasizes security-critical static analysis (taint specifications, Node.js security), soundness optimization (approximate interpretation), and program verification (channel-based communication). Earlier work (2013–2018) includes foundational contributions to JavaScript refactoring , Dart type safety , and AJAX race detection . Scientific Recognition : ISSTA 2019 Distinguished Paper Award Leadership Roles : Active in steering committees for SPLASH, ECOOP, and SIGPLAN, with chairs in OOPSLA, ECOOP, and PLDI program committees.

Dan HaoView profile

Professor

• software testing
• debugging
• program comprehension
• +7 more

Professor Dan Hao is a distinguished faculty member at the Institute of Software, School of Computer Science, Peking University, where he has established himself as a leading researcher in software engineering. His extensive service to the academic community includes membership on the Steering Committee for The International Conference on Automated Software Engineering (ASE) since 2021, The ACM SIGSOFT International Symposium on Software Testing and Analysis since 2025, and The International Systems and Software Product Line Conference (SPLC) from 2018-2022. He has served as Program Co-Chair for multiple major conferences including ISSTA 2027, ICSME 2025, ICST 2023, SANER 2022, and ASE 2021. Professor Hao received his Bachelor's degree from Harbin Institute of Technology in 2002 and completed his Ph.D. at Peking University in 2008, followed by post-doctoral research at the same institution until 2009. His academic journey reflects a deep commitment to advancing software engineering research and education in China. Professor Hao's research primarily focuses on software testing and debugging, program comprehension, and software maintenance. His work has significantly contributed to compiler testing, fault localization, regression testing, and automated program repair. He has pioneered approaches in compiler auto-tuning, test-case prioritization, and history-guided testing techniques. His research bridges theoretical foundations with practical applications, addressing real-world challenges in large-scale software systems, particularly in online service environments. His publication record demonstrates a consistent trajectory of high-impact research in top-tier software engineering venues. Professor Hao's work shows increasing integration of machine learning techniques with traditional software engineering problems, particularly evident in his recent publications on LLM applications for code generation, neural theorem proving, and contrastive learning for vulnerability detection. His research maintains strong connections between theoretical rigor and practical applicability in industrial settings. ACM SIGSOFT Distinguished Paper Award for PDCAT: Preference-Driven Compiler Auto-Tuning at FSE 2025 Distinguished Paper Award for Formalizing, Mechanizing, and Verifying Class-Based Refinement Types at ECOOP 2024 ACM SIGSOFT Distinguished Paper Award for History-Guided Configuration Diversification for Compiler Test-Program Generation at ASE 2019 ACM SIGSOFT Distinguished Paper Award for History-driven Build Failure Fixing: How Far Are We? at ISSTA 2019 As an advisor, Professor Hao has mentored numerous graduate students, currently supervising 9 Ph.D. students and 7 Master's students. His former students have gone on to prestigious positions at institutions including King's College London, Tianjin University, Fudan University, and major technology companies like Huawei and China Construction Bank. His academic leadership extends through editorial roles as Deputy Editor-in-Chief of Software Testing, Verification and Reliability (STVR) and membership on the editorial boards of several premier journals including ACM Transactions on Software Engineering and Methodology, ACM Computing Surveys, and Empirical Software Engineering. Professor Hao leads a vibrant research group at Peking University's Institute of Software, focusing on cutting-edge problems at the intersection of traditional software engineering and artificial intelligence. His team actively collaborates with both academic institutions and industry partners to address practical challenges in software development and maintenance processes.

Zeyu SunView profile

Researcher

• Software Engineering
• Program Repair
• Machine Learning for Code
• +4 more

Zeyu Sun is a researcher at the Institute of Software, Chinese Academy of Sciences, with an extensive publication record in premier software engineering conferences including ASE, ICSE, and ESEC/FSE from 2020 through 2025. Their academic service includes program committee membership for multiple conferences, demonstrating recognition as a domain expert in software engineering and programming languages. Dr. Sun's research program bridges traditional software engineering with artificial intelligence, focusing on neural program repair, software testing, and compiler optimization. Their work develops innovative techniques for code understanding and generation, with particular emphasis on type-aware repair systems, syntax-guided editing approaches, and context-enriched representations of code changes. Recent publications show increasing sophistication in integrating machine learning with software engineering tasks, moving from basic program analysis to specialized architectures that maintain structural integrity of code. Analysis of publication trends reveals a consistent focus on improving software quality through AI-enhanced methods, with expanding applications across compiler design, smart contract security, and fairness testing. The research demonstrates both technical depth in core software engineering challenges and breadth across multiple application domains, reflecting an evolving research trajectory that continually identifies new intersections between AI and software development practices. Dr. Sun actively contributes to the academic community through program committee service at major conferences including ASE, ICSE, and ESEC/FSE across multiple years. This sustained service commitment indicates peer recognition as a subject matter expert. While specific grant details aren't provided in available materials, the consistent publication output across multiple funding cycles suggests successful acquisition of research support for their work in software engineering and AI for code.

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +5 more

Torsten Hoefler is a Professor affiliated with ETH Zurich, leading research in parallel computing, distributed systems, and high-performance computing (HPC). His work bridges theoretical foundations and practical implementations, focusing on optimizing algorithms, network topologies, and hardware-software co-design. Research Interests: His primary areas include parallel algorithms, distributed systems, machine learning infrastructure, and network architectures. He emphasizes scalable solutions for large-scale applications, particularly in data-centric computing and serverless environments. Publications: Recent work highlights include innovations in network topologies (e.g., HammingMesh), serverless benchmarking frameworks (SeBS), and optimizations for large language models (LLMs). His publications often address performance bottlenecks and energy efficiency in HPC and cloud systems. Awards & Grants: While no specific awards are listed here, his prolific publication record and leadership in HPC indicates significant recognition in the field. Active in grant-funded projects related to exascale computing and AI infrastructure. Labs & Teams: Leads the Communication Systems Lab at ETH Zurich, collaborating with industry partners like NVIDIA and IBM on hardware-accelerated computing and cloud-native systems.

Dirk PflügerView profile

Professor

• Scientific Computing
• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• +3 more

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

Kaveh Haghighi MoodView profile

Researcher

• High Performance Computing
• GPU Computing
• Quantum Monte Carlo
• +3 more

Dr. Kaveh Haghighi Mood is a computational researcher at Forschungszentrum Jülich's Jülich Supercomputing Centre (JSC), specializing in high-performance computing with emphasis on GPU acceleration and scientific application enablement. His work bridges atmospheric science, materials simulation, and exascale computing through the Helmholtz Association research infrastructure. His research focuses on optimizing computational methods for next-generation supercomputers, particularly in three domains: GPU-accelerated atmospheric modeling through the MPTRAC framework for Lagrangian transport simulations Exascale benchmarking via the JUPITER suite for evaluating future supercomputing architectures Quantum Monte Carlo methods applied to electronic structure theory and materials science Recent publications demonstrate expertise in CUDA, OpenACC, and performance portability across diverse hardware platforms. Haghighi Mood's publication trends reveal an evolving focus from foundational quantum chemistry (2010-2019) toward GPU optimization and exascale readiness (2020-2025). His work increasingly addresses atmospheric science applications while maintaining strong connections to materials simulation, reflecting JSC's strategic emphasis on climate modeling and computational materials design. The interdisciplinary nature spans computer architecture, numerical methods, and domain-specific scientific computing.

Lingming ZhangView profile

Associate Professor

• Software Engineering
• Programming Languages
• Machine Learning
• +5 more

Lingming Zhang is an Associate Professor in the Department of Computer Science at the Grainger College of Engineering, University of Illinois Urbana-Champaign. He maintains an active research program focused on the intersection of Software Engineering, Programming Languages, and Machine Learning, with particular emphasis on LLM-based software testing, repair, and synthesis. His research has resulted in over 100 publications with an h-index exceeding 50. His work has practical impact, having helped detect over 1,000 bugs and vulnerabilities in open-source projects from Apache and GitHub, as well as software systems from major technology companies including eBay, Google, Meta/Facebook, Microsoft, NVIDIA, OctoML, Oracle, and Yahoo!. Zhang's research interests span several key areas in software engineering and programming languages: LLM-based software testing, debugging, and repair Code generation and understanding with large language models Automated program repair Software testing and fuzzing techniques Deep learning for software engineering His recent publications demonstrate a strong focus on leveraging large language models for software engineering tasks, with notable contributions including Agentless, TitanFuzz, AlphaRepair, and ChatRepair. His group has also released influential open-source code language models including StarCoder2 and Magicoder, which have garnered over 1 million downloads worldwide. Zhang has received numerous prestigious awards and recognitions for his work: ACM Distinguished Member ACM SIGSOFT Early Career Researcher Award NSF CAREER Award UIUC Dean's Award for Excellence in Research Multiple ACM SIGSOFT Distinguished Paper Awards He has successfully mentored numerous PhD and MS students, many of whom have gone on to positions at top technology companies and academic institutions. His research is supported by grants from major technology companies including Alibaba, Amazon, Google, Kwai Inc, Meta/Facebook, NVIDIA, and Samsung. Currently, he serves as program co-chair for ASE 2025 and LLM4Code 2025, demonstrating his leadership in the software engineering research community.

Bo WangView profile

Assistant Professor

• Compiler Testing and Tuning
• Mutation Analysis
• Automated Program Repair
• +3 more

Bo Wang is an Assistant Professor in the School of Computer Science and Technology at Beijing Jiaotong University (BJTU), where he serves as a Master's Supervisor. He received his PhD in Computer Science from Peking University in 2021, with prior educational background from University of Science and Technology of China (Master) and Central South University (Bachelor). His academic journey includes significant international experience, having been a visiting Ph.D. student at the National University of Singapore in 2019 under the supervision of Prof. Abhik Roychoudhury, and a visiting scholar at Singapore Management University in 2024. These experiences have contributed to his global perspective on software engineering research. Dr. Wang's research spans four primary areas of software engineering: Compiler Testing and Tuning - Focusing on identifying bugs in C++ compilers through innovative fuzzing techniques and type-driven mutation Mutation Analysis - Developing efficient approaches to mutation testing that reduce computational overhead while maintaining effectiveness Automated Program Repair - Creating systems that automatically fix software bugs, with emphasis on efficiency and validation techniques AI for Software Engineering - Exploring how large language models can enhance various software engineering tasks including fault localization and test generation His publication record demonstrates a consistent focus on practical software engineering problems with significant technical depth. Recent work shows an increasing integration of AI/ML techniques with traditional software engineering approaches, particularly in evaluating how large language models can contribute to automated program repair and fault localization. His research often targets efficiency improvements, seeking to make advanced software engineering techniques more practical for real-world application. Dr. Wang has been recognized with prestigious awards including the ACM SIGSOFT Distinguished Paper Award for his ISSTA 2017 work on mutation analysis and the IEEE TCSE Distinguished Paper Award for his ICSME 2025 paper on automated program repair. As an active member of the software engineering community, Dr. Wang serves on program committees for major conferences including ASE, FSE, and ICSE. He has advised multiple Master's students at Beijing Jiaotong University, guiding research in software testing, program analysis, and automated repair techniques. His work has been supported by academic grants that enable his research group to pursue innovative approaches to challenging software engineering problems. Dr. Wang maintains an active research group focused on software testing and analysis, with projects spanning compiler testing, mutation analysis, automated program repair, and AI applications in software engineering. His lab employs both traditional program analysis techniques and cutting-edge AI approaches to address longstanding challenges in software reliability and maintenance.

Chandrakana NandiView profile

Assistant Professor

• programming languages
• compilers
• computational geometry
• +5 more

Chandrakana Nandi is the Director of US R&D at Certora and an Affiliate Assistant Professor at the University of Washington (UW), Seattle. She earned her PhD from the UW PLSE group under advisors Zachary Tatlock and Dan Grossman. Education & Affiliation PhD in Computer Science (2021) from University of Washington MS in Computer Science (2014) from Georgia Institute of Technology BS in Computer Science (2012) from Georgia Institute of Technology Research Interests Her work focuses on automated formal verification for real-world programs, particularly DeFi applications. She develops verification tools for languages like WASM and uses mutation testing to simplify specification writing. She pioneered equality saturation applications in synthesis, verification, and optimization through projects like egg and Ruler . Her computational fabrication research includes LambdaCAD and Reincarnate. Publications & Awards Distinguished Paper Award (OOPSLA 2021) Distinguished Paper Award and Sigplan Research Highlight (POPL 2021) Community Involvement She chairs sessions at PLDI and OOPSLA, co-organizes EGRAPHS workshops, and contributes to the egg open-source library. Her NSF grants (#1813166, #1644558) and Adobe Research Fellowship support her work. Contact Email: chandra@certora.com

Annie LiuView profile

Professor

• Programming Languages
• Algorithms
• Systematic Design and Optimization
• +6 more

Annie Liu serves as Professor of Computer Science at Stony Brook University, holding a PhD from Cornell University alongside prior degrees from Peking and Tsinghua Universities. Her research centers on systematic methods for language and algorithm design, with emphasis on optimization frameworks applicable across critical computing domains. Her educational credentials include: BS in Computer Science from Peking University MEng in Computer Science from Tsinghua University PhD in Computer Science from Cornell University Liu’s research program investigates systematic analysis and transformation techniques for design optimization, particularly through incrementalization—the discrete counterpart of calculus differentiation. Her work spans optimizing compilers , interactive environments , database systems , distributed computing , and security , with projects addressing modeling, verification, code generation, and testing for real-time systems, semantic Web, and big data applications. The Design and Analysis Research Laboratory she directs bridges theoretical foundations with practical implementations. Analysis of her 2012-2025 publications reveals an evolution from foundational distributed algorithm design toward unifying frameworks for solver semantics and assurance. Key trends include increasing integration of incremental computation principles across compiler optimizations, database queries, and distributed system reliability. Her scientific recognition includes: State University of New York Chancellor’s Award for Excellence in Scholarship and Creative Activities Professor Liu has delivered over 100 international talks and maintains active conference leadership, serving on program committees for POPL, PEPM, and SPLASH through 2026. While specific grant details aren’t provided, her sustained involvement in high-impact venues like PADL and LPOP demonstrates significant research influence. She directs the Design and Analysis Research Laboratory where interdisciplinary teams advance optimization methodologies for emerging computational challenges.