Marco Vassena is an Assistant Professor at the Department of Information and Computing Sciences, Utrecht University, within the Faculty of Science. He focuses on developing principled methods for building secure systems, bridging Security and Programming Languages research communities. His work applies type systems, compilers, program analysis, and verification to ensure reliable security guarantees in software systems. PhD in Computer Science from Chalmers University of Technology Visiting Assistant Professor at Stanford Postdoctoral Researcher at CISPA Helmholtz Center for Information Security Member of the CISPA-Stanford Center for Cybersecurity His research spans language-based security (constant-time programming, memory safety, information flow control), software defenses against Spectre and side-channel attacks, and WebAssembly sandboxing. He leads projects like MSWasm and Blade, targeting microarchitectural attack mitigation through formal verification and compiler design. Scientific recognition includes the Veni Grant (2023). Current projects explore speculative execution security, dynamic information flow control, and secure WebAssembly implementations.
R. Hai is a researcher active in the fields of Machine Learning , Relational Databases , and Quantum Computing . Their work bridges the integration of large language models (LLMs) with database systems, focusing on optimizing query processing and data management through linear algebraic methods. Hai's research emphasizes seamless data-ML workflows and innovative applications of relational databases in emerging domains. Key Research Themes : LLM compilation to SQL, quantum circuit simulation via RDBMS, and convergence of data integration with ML. Collaborations : Active in international academic networks, with contributions to conferences like SIGMOD and IEEE journals. Scientific Awards: Veni grant AES2022 (2023) Publications demonstrate expertise in overcoming data barriers, enhancing database performance for ML tasks, and simulating quantum computations using relational database management systems.
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.
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.
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.
Jingbo Wang is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, where he conducts research at the intersection of software engineering and formal methods. His work emphasizes developing rigorous program analysis and verification techniques to improve the security, robustness, and fairness of software systems. Prior to joining Purdue in August 2024, he was a Postdoctoral Researcher in the Department of Computer Science at University of Texas, Austin, working with Professor Isil Dillig. He obtained his PhD in Computer Science from the University of Southern California in 2023 under the supervision of Professor Chao Wang. Dr. Wang's educational background includes: PhD in Computer Science, University of Southern California, 2023 Postdoctoral Researcher, University of Texas, Austin, 2023-2024 Dr. Wang's research focuses on bridging software engineering and formal methods to create more secure, robust, and fair software systems. His work spans several key areas: Program Analysis and Verification : Developing techniques for static and dynamic analysis of software systems Security and Privacy : Creating methods to detect and prevent security vulnerabilities and privacy leaks Fairness in Machine Learning : Certifying and quantifying fairness properties of AI systems Formal Methods for Neural Networks : Verification techniques for deep learning models His recent publications demonstrate a strong trend toward applying formal methods to machine learning systems, particularly in ensuring fairness and robustness. He has published extensively in top-tier venues including PLDI, POPL, ICSE, and CAV, with multiple papers on verifying properties of neural networks and decision trees. His work often combines program analysis techniques with constraint solving and optimization approaches. Dr. Wang has received numerous awards and recognitions for his research: ACM SIGPLAN Distinguished Paper Award, PLDI, 2023 MIT EECS Rising Star, MIT, 2021 WiSE Merit Award, USC, 2021 Selected to participate in the 7th Heidelberg Laureate Forum, 2019 Selected for CRA-W Grad Cohort for Women Workshop, 2019 Multiple conference scholarships including VMW Scholarship (CAV'19) and PLMW Scholarship (PLDI'19) Dr. Wang is actively mentoring students and planning to recruit PhD students for Fall 2025. He currently advises: Siyu Chen (PhD student, 2024 Fall -- present) Xuyang Li (PhD student, 2024 Fall -- present) Multiple undergraduate researchers including Weiyi Chen, Yaoyang Ye, Paul Jiang, and Sarthak Tandon He has also served as a mentor for the PLMW @ PLDI'21 and USC Viterbi Graduate Mentorship Program. Dr. Wang is deeply involved in the programming languages and formal methods research community, serving on multiple program committees including OOPSLA, PLDI, CAV, ICSE, and ISSTA. His GitHub repository shows active work on fair decision trees and formal verification techniques, indicating an active research lab focused on the intersection of formal methods and machine learning.
Xiaokang Qiu is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University , with a Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2013). His research focuses on Programming Languages and Software Engineering , particularly theories, algorithms, and tools for program synthesis, verification, and logic-based analysis. Research Interests His work addresses: Formal methods for heap-manipulating programs using separation logic Automated deduction and decision procedures for data structures Syntax-guided synthesis of concurrent and bit-vector programs Integration of machine learning with formal verification Scalable verification of hardware memory consistency Network protocol optimization through program synthesis Recent Publications Recent work includes PLDI 2025 on concurrent string synthesis, POPL 2024 on bit-vector synthesis, and POPL 2023 on comparative network design. His tools like STRAND and VCDryad enable automated verification of complex data-structure manipulations. Grants & Awards Recipient of: NSF SHF Small Award (2024, co-PI, $593K) NSF FMitF Award (2023, PI, $750K) Tenure at Purdue (2023) Professional Service Active in program committees for PLDI , POPL , CAV , and ATVA conferences. Developed tools like DryadSynth (PLDI 2020), ImpSynt (OOPSLA 2017), and JSketch (ESEC/FSE 2015).
Stefan K. Muller is an Assistant Professor in the Computer Science Department at Illinois Institute of Technology. His research focuses on applying programming language techniques to improve correctness and efficiency in parallel computing, with applications spanning AI, computer science education, and systems design. He earned his PhD at Carnegie Mellon University (2018) under Umut A. Acar, following a postdoc there (2018–20), and holds an AB in Computer Science from Harvard University (2012). PhD, Carnegie Mellon University (2018) Postdoc, Carnegie Mellon University (2018–20) AB, Harvard University (2012) Stefan’s work integrates type systems, static resource analysis, and concurrency to address challenges in parallelism. His recent projects include Graph Types for language-agnostic parallel computation analysis, Responsive Parallelism models for interactive systems, and Resource-aware GPU Programming tools. He has contributed to conferences like POPL, PLDI, ICFP, and SPLASH, often focusing on deadlock detection, futures-based parallelism, and efficient compiler optimizations. His publications (2012–2024) emphasize formal methods for parallel systems. Key trends include type-driven concurrency, static analysis of GPU programs, and responsive scheduling for interactive applications. He mentors students in programming language theory and parallel computing, with former advisees now at institutions like Apple, Amazon, and UPenn. Stefan’s teaching includes courses on Types and Programming Languages (CS534), Science of Programming (CS536), and Compiler Construction (CS443). Outside academia, he is a homebrewer, runner, and singer, with a Bacon number of 2 and Erdős number of 4 .
William J. Bowman is an Assistant Professor in the Department of Computer Science at the University of British Columbia. He focuses on secure and verified compilation, dependently typed programming, and meta-programming systems. His work bridges high-level language design with low-level code generation to maintain correctness and security invariants throughout compilation. Education: PhD in Computer Science from Northeastern University Research spans type-preserving compilation, including: Typed closure conversion for dependently typed languages Allocation-aware type universes Hybrid embedding techniques Secure interoperability via multi-language semantics Recent publications examine: Heap allocation modeling through type universes Flat closure representations WebAssembly extensions with indexed types Control-effect semantics
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.
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.
José Fragoso Santos is an Assistant Professor in the Department of Computer Science and Engineering at Instituto Superior Técnico, University of Lisbon, and a member of INESC-ID where he conducts research as part of the SAT group. His research focuses on embedding formal methods into software development processes, with particular emphasis on JavaScript program analysis and verification. His educational background includes: PhD in Computer Science from University of Nice Sophia Antipolis (2014) Master's degree in Information Systems and Computer Engineering from Instituto Superior Técnico, Universidade de Lisboa (2008) Dr. Santos' research centers on JavaScript verification, symbolic execution, and secure information flow. He led the development of JaVerT, the first separation-logic-based tool for JavaScript analysis and testing, which has gained significant interest from both industry and academia. His work bridges theoretical formal methods with practical applications, particularly in web security and program analysis. He has made substantial contributions to understanding JavaScript semantics, symbolic execution techniques, and secure information flow in web applications, with publications in premier venues like PLDI, POPL, and ECOOP. His recent publications demonstrate a strong focus on symbolic execution for JavaScript and related languages, with applications in web security and program verification. The research shows progression from foundational work on information flow security to advanced techniques like compositional symbolic execution and multi-language analysis platforms. His work on JaVerT and Gillian has established significant research directions in program analysis for dynamic languages. His notable scientific achievements include: Facebook research award for the JaVerT project Dr. Santos has supervised numerous graduate students on projects related to JavaScript verification, symbolic execution, web security, and formal methods. His research has practical applications, as evidenced by the collaboration with Amazon R&D engineers to verify critical components of the AWS Encryption SDK using JaVerT. He has served on program committees for major conferences including PLDI, OOPSLA, and IJCAI, demonstrating his standing in the programming languages community. As a member of the SAT group at INESC-ID, Dr. Santos collaborates with researchers working on formal methods, program verification, and software security. His current projects include extending JavaScript symbolic execution to Web Workers, developing formal semantics for JavaScript regular expressions, and creating first-order solvers for program analysis. He continues to push the boundaries of what's possible in JavaScript program analysis and verification.
Rajiv Gupta is a Distinguished Professor and the Amrik Singh Poonian Professor of Computer Science at the University of California, Riverside (UCR), where he serves as Associate Dean for Academic Personnel in the Bourns College of Engineering (BCOE). He is a member of the RIPLE research group and has co-authored 327 papers with an h-index of 69 and over 16,600 citations. His extensive service includes chairing major conferences such as FCRC 2015, PPoPP 2020, ASPLOS 2011, and PLDI 2008. Professor Gupta's research focuses on Programming, Compiler, Runtime & Architectural Support for Parallel & Distributed Heterogeneous Systems and Software Tools for Monitoring and Managing Runtime Behavior . His work spans graph analytics with scalability and performance, understanding and managing the dynamic behavior of parallel programs, software speculation for irregular parallelism, dynamic program analysis for secure and reliable computing, and compiler optimizations with architectural support. His research has significant applications in high-performance computing, GPU programming, and distributed systems. Analysis of his recent publications reveals a strong focus on graph processing systems, with particular emphasis on evolving and streaming graph analytics. His work addresses critical challenges in memory management for large-scale graph processing, hardware acceleration for graph algorithms, and optimization techniques for concurrent and distributed graph computations. The research demonstrates a progression from foundational compiler and architecture work to increasingly sophisticated systems for handling modern data-intensive computing challenges. Fellow of the ACM (2009) Fellow of the IEEE (2008) Fellow of the AAAS (2011) NSF Presidential Young Investigator Award (1991) UCR Doctoral Dissertation Advisor/Mentor Award (2012) Multiple best paper awards across major conferences Two students won ACM SIGPLAN Outstanding Doctoral Dissertation Award Five advisees received NSF CAREER Award Professor Gupta has supervised 42 PhD students to completion and currently advises several doctoral candidates. His advising success is reflected in his students' achievements, including multiple award-winning dissertations and significant career accomplishments in academia and industry. His research has been supported by numerous grants from NSF, DARPA, and industry partners, enabling sustained investigation into parallel computing systems. The RIPLE research group under his leadership has produced influential work that bridges theoretical foundations with practical system implementations. As the leader of the RIPLE research group at UC Riverside, Professor Gupta oversees a vibrant team focused on innovative approaches to parallel and distributed computing. The group maintains strong collaborations with industry partners and other academic institutions, contributing to the development of next-generation computing systems. Current projects include GRASP (Graph Analytics with Scalability & Performance) and research on understanding and managing the dynamic behavior of parallel programs, reflecting the group's continued focus on cutting-edge computing challenges.
Dr. Sankha Narayan Guria is a Professor at the University of Kansas specializing in programming languages research. He completed his PhD at the University of Maryland, College Park and has industry experience at Meta (Facebook), BrowserStack, and Firefox. His primary research focuses on programming language theory, type systems, and automated program synthesis techniques. His research explores cutting-edge techniques in program verification and synthesis, including abstract interpretation-guided synthesis, refinement types for security applications, and effect-guided program synthesis. His work consistently appears at top-tier conferences including PLDI, ECOOP, and SPLASH. Dr. Guria actively contributes to the programming languages community through committee service, including roles as Artifact Evaluation Co-Chair for SPLASH/OOPSLA (2023-2025) and committee member for PLDI Research Artifacts (2020-2026). He maintains an active research profile with publications spanning programming language design, type systems, and formal methods.
Sang-Hoon Kim is an Associate Professor in the Department of Software and Computer Engineering and Department of Artificial Intelligence at Ajou University, South Korea. He leads the Systems Software Lab (Paldal Hall 1004-2) and maintains active collaborations with Virginia Tech as a Visiting Scholar since August 2024. His academic journey includes a Ph.D. in Computer Science from KAIST (2016) under advisors Seungryoul Maeng and Jin-Soo Kim, and a B.S. in Computer Science from KAIST (2002). His research spans operating systems, memory management, and storage systems with focus on mobile platforms, heterogeneous architectures, and SSD technologies. Key interests include memory fragmentation control , distributed thread execution , key-value storage optimization , and resource disaggregation . His work bridges theoretical innovation with practical system implementations, particularly for mobile and datacenter environments. Kim's publication portfolio shows consistent output in top-tier venues including USENIX FAST, VLDB, ICDCS, and ASPLOS. His research demonstrates evolution from mobile memory management (2015-2017) toward distributed systems and hardware-aware software (2019-present), with recent emphasis on resource-disaggregated environments and heterogeneous-ISA computing. The 2024 Best Paper Award at USENIX FAST highlights his impact in storage systems research. Best Paper Award at USENIX FAST'24 Multiple patents including US-9588912B2 for memory control He directs significant research projects funded by ETRI, NRF, and US ONR, including current work on memory-centric computing systems (2020-2023) and disaggregated non-volatile memory systems using RDMA (2018-2020). His Systems Software Lab maintains strong industry partnerships with Samsung Electronics and NHN, with prior projects improving Android memory management and developing SSD-based storage systems for large-scale internet services.
