Find Academic Supervisors

Amey KarkareView profile

Professor

• Compilers
• Functional Programming
• Program Analysis
• +3 more

Amey Karkare is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). He holds a PhD from IIT Bombay and has been affiliated with IIT Kanpur since 2010, progressing through academic ranks to his current role since 2021. Education: PhD, IIT Bombay (2003-2008); B.Tech., IIT Kanpur (1994-1998) Research Interests: His work spans compilers , functional programming , program analysis , code optimization , and programming education . He focuses on improving compiler error messages, automated error repair, and educational tools like Prutor for programming courses. Recent Publications highlight advancements in pedagogical error repair , intelligent problem indicators , error localization , and GPU energy optimization . Notably, his 2023 paper on automated error repair received a Best Paper Award. Awards & Recognitions: Poonam and Prabhu Goel Chair Fellowship (2022) IIT Kanpur 1989 Batch Faculty Award for innovative teaching (2019) Best Faculty of the Year by Computer Society of India (2018) P. K. Kelkar Young Faculty Fellowship (2013-2016) Teaching & Advising : He supervises PhD, M.Tech, and B.Tech students, offering courses like Advanced Compiler Optimizations and Principles of Programming Languages . His projects integrate AI and functional programming in education.

• Automating Design and Diagnosis of Information Systems
• Computer Graphics
• Web Technologies

Konstantin Nikolaevich Kasyan serves as Associate Professor at the Department of Computer Systems and Networks within the Faculty of Computer Science and Technologies at Zaporizhzhia National Technical University, where he has maintained academic activity since 1998. He graduated with honors from Zaporizhzhia Machine-Building Institute's Faculty of Electronic Engineering in 1993, specializing in Radio Engineering with qualification as Radio Engineer. His Candidate of Sciences degree (defended 1998/1999) established his expertise in diagnostic methodologies for electronic systems. His research spans three core domains: automating design and diagnosis of information systems, computer graphics methodologies, and web technologies. This interdisciplinary focus manifests in practical applications ranging from hardware diagnostics to smart home systems. His work demonstrates consistent evolution from foundational electronics reliability research in the 1990s toward contemporary IoT and computer vision applications. His publication trajectory reveals distinct chronological phases: 1990s-2000s concentrated on electro-radio diagnostics and reliability engineering; 2000s-2010s expanded into computer graphics and text recognition; while 2010s-2021 shifted toward IoT integration, smart home technologies, and machine learning applications. This progression reflects both technological advancements and his adaptability across computing subfields. Kasyan teaches modern Internet technologies, computer graphics, computer systems design, and computational methods in scientific research, directly translating his research into pedagogical practice. His academic presence is maintained through Scopus, Web of Science, Google Scholar, and ORCID profiles, indicating active scholarly engagement despite the absence of formal awards or documented grants in available records.

• High-Performance Computing
• Matrix Factorization
• Multicore Processing
• +7 more

Dr. hab. Beata Bylina is a Professor at the Faculty of Mathematics, Physics and Computer Science , Maria Curie-Skłodowska University (UMCS) , affiliated with the Department of Information Systems Software . She specializes in high-performance computing, numerical methods, and energy-efficient parallel programming. ORCID ID : 0000-0002-1327-9747 Contact : beata.bylina@umcs.pl / beata.bylina@mail.umcs.pl Office : Room D-522 (Institute of Computer Science) or Room B-2, Akademicka Street 9, Lublin Consultations : Wednesdays 10:00–12:00 (in-person or remote via Microsoft Teams) Her research focuses on: Parallelization and vectorization techniques for multicore architectures Energy consumption optimization in numerical algorithms Matrix factorization methods (WZ, LU, QR) for CPU/GPU hybrid systems Markov chain modeling for network performance analysis Compiler optimization impact on performance and energy metrics Recent publication trends show emphasis on: Time-energy correlations in multithreaded algorithms OpenMP/OpenACC for hybrid CPU-GPU implementations Efficient sparse matrix storage schemes for GPUs Impact of hardware-specific optimizations (Xeon Phi, frequency scaling) Comparative studies of parallelization strategies

• Embedded Systems
• Compartmentalization
• Firmware
• +5 more

Arslan Khan is an Assistant Professor specializing in Computer Science and Engineering. His research focuses on enhancing security for embedded systems and industrial control systems through innovative techniques like symbolic differentiation and privilege separation. Research Interests: Embedded Systems, Compartmentalization, Firmware Security, Software Guard Extensions, Fuzzing, and Industrial Control Systems Security. Recent publications (2023-2024) highlight his work on kernel isolation, enclave fuzzing, and state-aware attack detection. These studies emphasize reducing attack surfaces and improving security for resource-constrained devices. While no specific awards or students are mentioned, his contributions to security frameworks and binary analysis are evident through multiple conference proceedings in high-impact venues like USENIX and IEEE Security & Privacy.

• Computer Science
• Cybersecurity
• Internet of Things
• +5 more

Gang Tan is an Associate Professor at the Pennsylvania State University's College of Engineering, Department of Computer Science and Engineering. He also holds the James F. Will Career Development Professorship and is affiliated with the Institute for Computational and Data Sciences (ICDS). His research focuses on binary reverse engineering , cybersecurity , Internet of Things (IoT) security , machine learning fairness , and information flow security . He has led numerous NSF-funded projects, including work on precise binary analysis, IoT policy enforcement, and automated fairness repair in AI systems. Recent work trends include memory safety validation , pseudocode extraction , and control-flow integrity mechanisms. His 127+ research outputs reflect deep engagement with static program analysis , cache side-channel detection , and secure kernel-driver interfaces . Scientific Awards: James F. Will Career Development Professorship Gang Tan has secured multiple grants from the National Science Foundation (NSF) and U.S. Navy for projects like Sliver (information flow verification) and Semantics-Directed Binary Reverse Engineering . His work involves advising teams on IoT safety, and he has 19 active or completed grants since 2008.

Swarnendu BiswasView profile

Assistant Professor

• Programming Languages
• Compilers
• Runtime Systems
• +7 more

Swarnendu Biswas is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He teaches courses including Programming for Performance (CS 610), Analysis of Concurrent Programs (CS 636), and Compiler Design (CS 335), demonstrating his expertise across multiple areas of computer systems. His research interests center on Programming Languages, Compilers, Runtime Systems, and Parallel Software Systems. He leads the PROSPAR (Programming Languages and PARallel Systems) research group, which focuses on developing techniques to build efficient and correct parallel software through program analysis, compiler optimizations, and runtime systems. His recent publications reveal a strong trend in addressing fundamental challenges in parallel computing, with work spanning cache coherence, false sharing detection, data race analysis for GPUs, verification of neural networks, and thermal-aware management of heterogeneous systems. His research bridges theory and practice with significant contributions to both hardware and software aspects of parallel systems. His scientific achievements have been recognized through multiple prestigious awards: Google India Research Award 2021 Google Explore CSR 2022 Research Grant from Intel Corporation SERB Start-up Research Grant 2019 Google Cloud Platform Research Credits (2019, 2020) IITK Initiation Grant 2019 As an advisor, he mentors several PhD and MTech students working on cutting-edge research in parallel systems. His PROSPAR group has secured significant funding from industry and government sources, supporting innovative research in programming languages and parallel systems. The group actively collaborates with industry partners including Google and Intel, addressing real-world challenges in parallel computing. He leads the PROSPAR research group at IIT Kanpur, which brings together faculty, PhD students, and MTech researchers to tackle challenging problems at the intersection of programming languages, compilers, and parallel systems. The group maintains strong industry connections and focuses on practical solutions that can be deployed in real systems.

Jiyuan WangView profile

Assistant Professor

• Software Engineering
• Quantum Computing
• Heterogeneous Computing
• +4 more

Dr. Jiyuan Wang serves as an Assistant Professor in the Department of Computer Science within Tulane University's School of Science & Engineering, commencing his appointment in Fall 2025. Prior to Tulane, he worked as an Applied Scientist at Amazon Web Services, focusing on program analysis and formal verification for cloud systems. His academic background includes a Ph.D. in Computer Science from UCLA (2025) and a B.S. in Physics from Tsinghua University (2019). His educational journey: Doctor of Philosophy (Ph.D.) in Computer Science, University of California, Los Angeles, 2025 Bachelor of Science (B.S.) in Physics, Tsinghua University, 2019 Dr. Wang's research centers on Software Engineering for next-generation computing platforms, with particular emphasis on Quantum Computing and Heterogeneous Computing (including FPGAs and GPUs). He develops innovative techniques for testing and debugging, such as multi-layer compiler debugging, full-stack compiler testing, and fuzz testing tailored for heterogeneous applications. His work bridges the gap between software reliability and the complexities of modern hardware accelerators, addressing critical challenges in compiler correctness and application robustness. An analysis of his publication record from 2020 to 2025 reveals a consistent focus on advancing software testing methodologies for heterogeneous systems. The research evolved from big data analytics (BigFuzz) to compiler testing (HeteroFuzz, DuoReduce) and quantum software stacks (QDiff), demonstrating increasing sophistication in tackling platform-specific challenges. His recent 2025 papers highlight breakthroughs in MLIR compiler debugging and fuzzing. His notable recognitions include: SIGSOFT Research Highlight for QDiff (ASE 2021) Best Poster Award at ICST 2021 Dr. Wang is currently building his research group at Tulane and actively seeks motivated graduate students interested in software engineering for quantum and heterogeneous computing. His industry experience at AWS and strong publication record in top venues (ASE, ICSE, FSE, ASPLOS) indicate active research funding and collaborative opportunities. He teaches Quantum Computing (CMPS-4660/6660) and mentors students in cutting-edge research projects. While a dedicated lab name isn't specified, Dr. Wang's research group focuses on developing practical tools for compiler and application testing in heterogeneous environments, with growing emphasis on quantum software stacks.

Soham ChakrabortyView profile

Assistant Professor

• Programming Languages
• Compilers
• Computer Architecture
• +3 more

Soham Chakraborty is an Assistant Professor at the Programming Languages Group, TU Delft since 2021. He previously served as an Assistant Professor at the CSE Department, IIT Delhi before joining TU Delft. His academic journey includes a PhD from Max Planck Institute for Software Systems (MPI-SWS) in 2019 and a Master's degree from CSE Department, IIT Kharagpur in 2008. Affiliation: TU Delft, IIT Delhi, MPI-SWS Research Interests: Programming Languages, Compilers, Architecture, Verification, Concurrency His research focuses on formal semantics, compiler correctness, architecture-to-architecture translation, and verification of weak memory models. He has contributed to major conferences including POPL, PLDI, CGO, and ASPLOS. His work addresses challenges in heterogeneous concurrency, relaxed memory concurrency, and binary translation for weak memory architectures. Recent works include How Hard is Weak-Memory Testing? (POPL'24) and Toast: A Heterogeneous Memory Management System (PACT'24). He also received the Distinguished Paper Award at DATE'25 and Distinguished Artifact Award at PODC'22 . Teaching: Courses like Concepts of Programming Languages (CS2120), Parallel and Concurrent Programming (CS4555), Compiler Construction (CS4555), Analysis of Concurrent and Distributed Programs (CS4405), Software Fundamentals (Rust), and Seminar Programming Languages (COL 380) Students: PhD student Dennis Sprokholt, MSc students Michal Raczkiewicz, Alexandru Dumitriu, Huixuan Wu, and alumni including Pieter van den Ham, Mingyu Gao, Jeroen Kloppenburg, and others. Service: Served on program committees for POPL, PLDI, CGO, VMCAI, APLAS, and Eurosys workshops.

Yufei DingView profile

Associate Professor

• Compiler Technology
• Machine Learning
• Quantum Computing
• +3 more

Yufei Ding is an Associate Professor in the Computer Science & Engineering Department at the University of California, San Diego (UCSD), where she leads the PICASSO Lab. Her research spans domain-specific language design, architecture and compiler optimization, and hardware acceleration, with current focus on developing high-performance, energy-efficient, and high-fidelity programming frameworks for quantum computing and machine learning. Dr. Ding received her Ph.D. in Computer Science from North Carolina State University and a B.S. in Physics from the University of Science and Technology of China. Her interdisciplinary background bridges physics and computer science, enabling her to tackle challenges in emerging computing paradigms. Her research interests focus on Compiler Technology, Machine Learning, and Quantum Computing , with specific expertise in domain-specific language design, architecture and compiler optimization, and hardware acceleration. Dr. Ding's work addresses critical challenges in programming frameworks for emerging technologies, particularly in making quantum computing more accessible and efficient through innovative compiler techniques and runtime systems. Dr. Ding's scientific contributions have been recognized with prestigious awards including the NSF CAREER Award (2020) and the IEEE Computer Society TCHPC Early Career Researchers Award for Excellence in High-Performance Computing (2019) . As an active researcher and educator, Dr. Ding serves on program committees for major conferences including PLDI, PPoPP, and SPLASH. She currently has Ph.D. openings in quantum computing and machine learning systems research, as well as a postdoc position in quantum computing for physics Ph.D. candidates with relevant background. Dr. Ding founded and leads the PICASSO Lab at UCSD, which focuses on developing innovative solutions for programming emerging computing technologies. The lab's work bridges theoretical foundations with practical implementations to address real-world challenges in high-performance computing.

Qirun ZhangView profile

Associate Professor

• Program Analysis
• Programming Languages
• Software Engineering
• +4 more

Qirun Zhang is the Catherine M. and James E. Allchin Early Career Associate Professor in the School of Computer Science at Georgia Institute of Technology. His research focuses on program analysis, compiler optimization, and formal language theory, with numerous publications in top-tier programming language and software engineering conferences including PLDI, POPL, OOPSLA, and FSE. He teaches courses on compilers, program analysis, and software testing. Dr. Zhang's research interests center on improving software reliability and security through advanced program analysis techniques. He approaches problems from perspectives including computational complexity, analytic combinatorics, graph theory, and formal languages. His work often bridges theoretical foundations with practical applications in compiler design and program verification. His recent publications show a strong focus on context-free language reachability, Dyck-language based analyses, and SMT solving techniques. His research demonstrates consistent innovation in making program analysis more precise while maintaining scalability, with applications ranging from debug information validation to software debloating and type inference. PLDI Distinguished Paper Award (2020) SIGSOFT Distinguished Paper Award (2023) OOPSLA Distinguished Artifact Award (2022) Dr. Zhang actively mentors PhD and MS students, with current advisees including Camille Bossut and Benjamin Mikek. His service to the academic community includes Artifact Evaluation Co-Chair roles for PLDI 2025 and 2026, and program committee membership for numerous top conferences including PLDI, POPL, and OOPSLA. He leads research projects including SLOT, Context-Free Language Reachability with Transitive Redundancy Elimination, and Debug Information Validation.

Mohamed Faouzi AtigView profile

Professor

• Formal Verification
• Concurrency
• Model Checking
• +4 more

Mohamed Faouzi Atig is a Professor in Computer Systems at Uppsala University's Department of Information Technology since July 2021, following a progression from Assistant Professor (2014-2018) to Associate Professor (2018-2021). His academic career began with a post-doctoral position at Uppsala University (2010-2012) after earning his PhD from University of Paris Diderot-Paris 7 in 2010, followed by a docent degree (habilitation equivalent) from Uppsala University in 2017. His research focuses on formal verification of concurrent and infinite-state systems, with particular expertise in model checking , weak memory models (including x86-TSO, Release-Acquire), and automata theory applied to string constraints. His work bridges theoretical foundations with practical verification techniques for modern hardware and programming language semantics. Analysis of his publication record reveals a sustained focus on verification challenges in concurrent systems, evolving from foundational work on memory models (2015) to sophisticated techniques for string constraints (2017) and persistent memory (2024-2025). His research demonstrates consistent contributions to top venues like PLDI and POPL, with increasing complexity in handling real-world memory models while maintaining theoretical rigor. At Uppsala University, he has served on program committees for major conferences including POPL, VMCAI, and SPLASH, demonstrating active engagement with the programming languages research community.

Mayur NaikView profile

Professor

• Programming Languages
• Artificial Intelligence
• Neurosymbolic Programming
• +5 more

Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He holds office in Room 642B, Amy Gutmann Hall and maintains an active research program focused on the intersection of programming languages and artificial intelligence. Before joining UPenn, he was faculty at Georgia Institute of Technology and a researcher at Intel Labs, Berkeley. Naik received his PhD in Computer Science from Stanford University in 2008 under Alex Aiken, a Masters from Purdue University in 2003 under Jens Palsberg, and a Bachelors from BITS Pilani in 1999. He grew up in Goa, India. His primary research interests center around neurosymbolic programming, which combines symbolic reasoning with machine learning to create more accurate, interpretable, and domain-aware AI systems. His group develops language design, learning algorithms, and compiler optimizations in this space, with their most mature effort being the Scallop neurosymbolic programming language and compiler toolchain. He also conducts research in trustworthy AI for healthcare applications and AI-enabled programming tools that improve programmer productivity. Analysis of his recent publications shows a strong trend toward neurosymbolic programming frameworks (Scallop, TorchQL), LLM-assisted program analysis (IRIS), and applications of these techniques to security, healthcare, and computer vision. His work consistently bridges theoretical foundations with practical implementations, often releasing open-source systems. Misra Family Professor (endowed chair, effective July 2024) Multiple distinguished paper awards (PLDI 2019, FSE 2015, PLDI 2014) Test-of-Time Paper Awards (FSE 2013, FSE 2012, EuroSys 2011) His student Elizabeth Dinella won the 2025 ACM SIGSOFT Outstanding Dissertation award Naik has advised numerous PhD students who have gone on to faculty positions at top institutions including Peking University, University of Toronto, Ashoka University, Bryn Mawr College, and Johns Hopkins University. His research is supported by grants from NSF, Google, Amazon, and other industry partners. His lab maintains active collaborations with clinicians and bioinformatics researchers to apply neurosymbolic programming to healthcare problems. His research group, which includes current PhD students and postdocs, develops practical open-source systems and applies them to diverse domains including computer vision, cybersecurity, medicine, and bioinformatics. The group maintains strong industry connections with Google, Microsoft, Amazon, and other tech companies.

Fernando Magno Quintão PereiraView profile

Associate Professor

• Compilers
• Code Generation
• Compiler Optimizations
• +3 more

Fernando Magno Quintão Pereira is an Associate Professor at the Federal University of Minas Gerais (UFMG), Brazil, specializing in compiler design and program analysis. His academic journey began with a Ph.D. from UCLA in 2008 under Jens Palsberg's supervision, establishing his foundation in compiler research. His research focuses on compilers , with core expertise in code generation , compiler optimizations , and static program analyses . Recent work explores quantum compilation, binary analysis, and security-aware compilation techniques. His publications reveal consistent contributions to major conferences including PLDI, CGO, and SPLASH, with emphasis on practical optimization frameworks and theoretical compiler advancements. Analysis of his 15 most recent publications (2020-2026) shows dominant themes in binary optimization (e.g., AnghaBench), security-aware compilation (e.g., Memory-Safe Elimination of Side Channels), and emerging architecture support (e.g., Quantum Computing Compilation). His work bridges theoretical compiler principles with real-world systems challenges. He actively contributes to the academic community through: Program committees for PLDI (2020-2025), CGO (2021-2026), and SPLASH conferences Leadership roles including CGO Finance Chair (2026) and PLDI Diversity & Inclusion Co-Chair (2023-2024) Organizing JENSFEST 2024 and serving on multiple conference steering committees Pereira maintains an active research group evidenced by continuous publication output and conference leadership, with his personal website ( homepages.dcc.ufmg.br/~fernando/ ) serving as a hub for his academic activities.

Vincent WeaverView profile

Associate Professor

• Hardware Performance Counters
• Computer Architecture
• High Performance Computing
• +8 more

Vincent Weaver is an Associate Professor in the Electrical and Computer Engineering Department at the University of Maine's College of Engineering. He leads the VMW Research Group, focusing on low-level systems research including hardware performance counters, computer architecture, and operating systems. Weaver received his BS in Electrical Engineering from the University of Maryland College Park in December 2000, followed by MS (January 2009) and PhD (May 2010) degrees in Electrical and Computer Engineering from Cornell University. He joined the University of Maine faculty in July 2012 as an Assistant Professor and earned tenure and promotion to Associate Professor in September 2018. His research centers on hardware performance analysis, architectural simulation, and systems programming with emphasis on Linux kernel development and embedded systems. Weaver's work bridges theoretical computer architecture with practical systems implementation, often resulting in open-source tools that advance the field. His publications reveal a consistent focus on performance analysis techniques, code optimization, and security through low-level system understanding. Weaver maintains an active teaching schedule including courses in embedded systems, operating systems, and network engineering. He values students with strong programming skills and encourages open source contributions as part of the learning process. His research group provides hands-on experience with cutting-edge processor architectures and performance analysis tools.

Limin JiaView profile

Research Professor

• Security
• Programming Languages
• Formal Methods

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 .