Affiliations & Roles Michael W. Godfrey is a Professor in the David R. Cheriton School of Computer Science at the University of Waterloo . He holds the David R. Cheriton Faculty Fellowship and has served as an associate director of Cornell's M.Eng. program. His roles include: General Chair for ICPC 2025 (IEEE Program Comprehension) Member of steering committees for ICSME, MSR, SCAM, and SWAN Course coordinator for CS138/CS246 and instructor for advanced topics courses Research Focuses on software evolution , program comprehension , and mining software repositories . His work addresses challenges in code clone analysis, developer productivity, and empirical software engineering. Notable contributions include: Advocating for intentional cloning as valid design practice Pioneering studies on code review quality and anomaly detection Developing tools like JavaDUCK (educational project) and mel (model extraction) Awards & Recognition Recipient of: Best Paper Awards at WCRE 2006, 2011, 2013 Most Influential Paper Award at SANER 2016 Outstanding Reviewer Awards (ICSME 2019/2020) Service & Outreach Active in: Program committee roles for ICSE, ICSM, MSR, and 30+ conferences University service: Undergraduate Recruitment Committee (2016–present) Industry collaborations with CWI (Amsterdam), Sun Microsystems, and automotive software teams
Hani Ragab Hassen is an Associate Professor leading the Cybersecurity with AI Research Group at the School of Mathematical & Computer Sciences, Heriot-Watt University. He specializes in cybersecurity, particularly in applying machine learning and NLP to address challenges such as malware analysis, intrusion detection, and access control systems. His roles include Associate Director of PGR Studies, Academic Integrity Officer, and former leadership in Learning & Teaching and Research programs. Research interests focus on cybersecurity fundamentals, including deep learning for malware detection, NLP-driven vulnerability assessment, and P2P systems. He has supervised numerous PhD students, currently advising three candidates on airport cybersecurity, NLP-based vulnerability detection, and network intrusion systems. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure security. Publications span malware detection frameworks (e.g., ActDroid, Droiddissector), network security tools (NTFA), and machine learning optimization (D2TS). He has developed datasets like SI22 for DDoS analysis and contributed to open-source tools. His research emphasizes practical applications of AI in securing networks and cloud environments. Notable contributions include advancing Android malware detection via control flow graphs and text analysis, and pioneering cloud security protocols like Stick for social platforms. His interdisciplinary work bridges cybersecurity with machine learning and data science, addressing real-world threats through innovative solutions.
Christopher Eibel is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander University Erlangen-Nuremberg (FAU). He specializes in energy-aware systems, distributed computing, and middleware design. His work focuses on optimizing energy efficiency in dynamic applications, heterogeneous computing environments, and real-time systems. His research includes projects such as BATS (tracking wildlife via sensor networks), E³ (energy-aware execution environments), and SEEP (symbolic execution for energy optimization). He has contributed to frameworks like Empya (energy-efficient middleware) and Albatross (profit-driven HPC runtime systems). Eibel has published extensively on topics like energy-aware programming, cloud computing, and heterogeneous cluster management. Notably, his work on Strome (energy-aware data-stream processing) won the Best Paper Award at DAIS 2018. He has supervised multiple theses on energy-efficient systems and middleware. He has taught courses on distributed systems and cloud computing since 2013, focusing on practical exercises and master’s seminars. His lab collaborates on projects involving high-performance computing, sensor networks, and operating system-level optimizations.
Andrea Lattuada leads the Principled Systems Group at the Max Planck Institute for Software Systems. He co-founded the Verus project for verifying Rust programs and holds a PhD from ETH Zürich. His research develops tools for verifying systems software correctness, focusing on Rust-based verification frameworks and distributed systems reliability. He explores the intersection of formal methods and practical system design. Publications demonstrate advancements in automated verification techniques for concurrent systems and language design. Recent work establishes foundations for scalable system verification. Awards include distinguished paper awards at PLDI, SOSP, and OSDI conferences. He mentors PhD students and collaborates with VMware Research and Microsoft Research. He teaches Formal Verification of Systems Software at Saarland University and leads the Rust Verification Workshop.
Mikhail Barash is an Associate Professor at the Department of Informatics, University of Bergen. His research focuses on software language engineering, domain-specific languages (DSLs), and graphical user interface (GUI) frameworks. He explores innovative approaches to DSL design, including spreadsheet-based workbenches and reusable GUI structures. His work emphasizes user involvement in language standardization, such as in ECMAScript/JavaScript evolution. He also investigates formal methods for GUI abstraction and constraint-based systems. Barash collaborates with industry and academia, notably through the Magnolia programming language ecosystem and teaching experiences with JetBrains' MPS tooling. His contributions span over 20 peer-reviewed publications in top conferences like ACM SIGPLAN and IEEE. Key research areas include declarative GUI manipulation, language workbench democratization, and legacy grammar modernization. He has presented at venues such as the International Symposium on Formal Methods and contributes to frameworks like Event-B IDE development. His work bridges theoretical advances with practical tooling, aiming to make language engineering accessible to broader audiences.
Raj Sunderraman is a Professor of Computer Science at Georgia State University, specializing in databases, data mining, and logic programming. His research focuses on deductive databases, semantic web technologies, bioinformatics, and graph data modeling. He holds a B.E. (Honors) in Electronics Engineering from Birla Institute of Technology and Science, an M.Tech. in Computer Technology from Indian Institute of Technology Delhi, and a Ph.D. in Computer Science from Iowa State University. His research projects include NeuronBank—a tool for cataloging neuronal circuitry—and work on scalable graph storage systems for big data. He has developed a programming environment for protein structure data and contributed to the paraconsistent relational data model. His teaching and research emphasize practical applications in bioinformatics, geoinformatics, and software systems. Key areas of exploration include reasoning with incomplete/inconsistent data, deductive database semantics, and graph query languages. Recent work involves lambda calculus visualization tools and 3D perception benchmarks for UAVs. He has authored over 150 publications and a textbook on Oracle 10g programming.
Mae Milano is an Assistant Professor in the Department of Computer Science at Princeton University. She joined in 2024 after earning her Ph.D. from Cornell University in 2020. Her research focuses on designing programming languages to address challenges in distributed systems, with particular emphasis on concurrency safety, language interoperability, and formal verification. Education: Ph.D. in Programming Languages and Systems, Cornell University, 2020 Research Interests: Milano's work bridges programming languages and distributed systems. She develops type systems for safe concurrency (e.g., fearless concurrency ), designs languages for distributed state management (e.g., Gallifrey, Hydro), and explores compiler techniques for interoperability between systems. Her projects include: Fearless Concurrency – Safe shared-memory programming Gallifrey – Language for geographically distributed applications Hydro – Cloud-native programming models Grants & Collaborations: Milano collaborates with researchers like Alvin Cheung (University of Washington) and Joseph M. Hellerstein (UC Berkeley). Her work has been supported by projects such as the Hydro Framework and the Gallifrey language initiative. Labs & Teams: She leads the Princeton Systems Group and collaborates with the Programming Languages Group , fostering interdisciplinary research between PL and distributed systems.
Welf Löwe is a资深 researcher and faculty member at Linnaeus University's Faculty of Technology, Department of Computer Science and Media Technology, and also teaches at Linköping University's Department of Computer and Information Science. His research focuses on data-intensive technologies, software metrics, design pattern detection, and context-aware systems. He leads the Data Intensive Software Technologies and Applications (DISTA) group and contributes to the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA). He actively collaborates on projects like the Data Intensive Applications (DIA) graduate school and the High-Performance Computing Center (HPCC). His work spans machine learning applications in healthcare, forestry, and industrial automation. Recent research includes feature engineering in medical data, skeleton avatar technology for aging studies, and AI-driven diagnostics. He has authored over 150 peer-reviewed publications and participates in interdisciplinary initiatives such as the iSchool project.
Georgios Manis is an Associate Professor in the Department of Computer Science and Engineering at the School of Engineering, University of Ioannina, Greece. He holds a PhD from the National Technical University of Athens and has been a faculty member at the University of Ioannina since 2002, progressing from Lecturer to Associate Professor in 2018. He has also served as temporary teaching staff at the University of Patras, University of Crete, and University of Ioannina in the late 1990s and early 2000s. Education: B.Sc. in Computer Engineering (Diploma), National Technical University of Athens (NTUA), 1987–1992 MSc in Advanced Methods in Computer Science (Distributed and Parallel Systems), Queen Mary, University of London, 1992–1993 PhD in Computer Engineering, NTUA, School of Electrical and Computer Engineering, 1993–1997 His research interests lie at the intersection of Biomedical Engineering and Computing Systems , with a strong emphasis on Biomedical Signal Processing , Entropy Analysis , and Machine Learning . He has pioneered work in Bubble Entropy —a parameter-free entropy measure—and developed fast algorithms for entropy computation. His work also extends to compiler design and parallel computing, particularly in the automatic parallelization of recursive functions and loops. The trends in his recent publications reflect a dual focus: (1) biomedical applications involving entropy, heart rate analysis, and disease diagnosis using machine learning (especially Random Forests and SVMs), and (2) high-performance computing, including parallelization techniques and compiler optimizations for multi-core and SVP architectures. His research is highly interdisciplinary, combining signal processing, algorithm design, and clinical applications. Scientific Leadership and Recognition: Guest Editor, Special Issue on “Entropy in Biomedical Engineering”, Entropy (MDPI) Member of the IPAN Laboratory, University of Ioannina Active contributor to IEEE, Elsevier, and MDPI journals He has supervised several graduate students and is involved in funded research projects such as Palimpsest and Homore , focusing on smart systems for cultural interaction and elderly monitoring. His advising contributions are evident in co-authored papers with students like Evanthia Tripoliti and Aristeidis Mastoras. He teaches both undergraduate and postgraduate courses, including Compilers I/II and Biomedical Data Analysis . Laboratories and Teams: He is a member of the IPAN lab at the University of Ioannina, which supports interdisciplinary research in informatics and biomedical applications. His collaborative network includes researchers from Greece and abroad, particularly in the fields of biomedical signal analysis and entropy-based methods.
Andrei Popescu is a Senior Lecturer (Associate Professor level) in the Department of Computer Science at the University of Sheffield, where he conducts research in formal methods, proof assistants, and information flow security. He previously held academic positions at Middlesex University and TU Munich. University: University of Sheffield Department: Department of Computer Science Previous Affiliations: Middlesex University, TU Munich His research focuses on the logical foundations and practical applications of proof assistants, particularly Isabelle/HOL. He has made foundational contributions to inductive and coinductive datatypes, syntax with bindings, higher-order logic, and the formal verification of secure systems. His work bridges theoretical logic with real-world systems such as conference management (CoCon) and social media platforms (CoSMeDis). The recent publications highlight a strong trend in formalizing deep logical results (e.g., Gödel’s incompleteness theorems), advancing datatype theory, verifying complex security properties, and applying formal methods to practical systems. His work consistently appears in top-tier venues such as POPL, CAV, ITP, and CSF. Distinguished Paper Award at POPL 2025 Distinguished Paper Award at POPL 2024 Distinguished Paper Award at POPL 2023 RS 3 Best Paper Award for 2012–2013 He has advised PhD students including Lorenzo Gheri and has been actively involved in organizing major academic events such as the Midlands Graduate School, CPP, ITP, and TABLEAUX conferences. He has served on numerous program committees including POPL, ITP, CSF, and CAV, and has led research projects funded by VeTSS and industrial partners. He is a key contributor to the Isabelle proof assistant ecosystem, particularly in the development of the (co)datatype package and foundational consistency results. His work combines deep theoretical insight with practical implementation, making significant impacts in both academia and applied security.
Ludovic Apvrille is a Professor at Telecom Paris (Institut Polytechnique de Paris), where he leads research in the Communications and Electronics (Comelec) department and previously headed the LabSoC (Laboratory on System on Chip). His work focuses on embedded systems design , with particular emphasis on safety, security, and formal verification of complex systems including automotive applications, drones, and cyber-physical systems. Research Areas: Embedded Systems, Cybersecurity, Model-Driven Engineering, Formal Verification, AI-assisted Design Key Tools: TTool, SysML-Sec, AVATAR, SMASHUP, DIPLODOCUS His recent publications analyze security vulnerabilities in RISC-V architectures using the gem5 simulator, while his ongoing work explores AI integration in system modeling and unified verification techniques for hardware/software co-designs. He actively supervises research projects in safety-security-performance trade-offs and microarchitectural security , with applications to autonomous vehicles and critical infrastructure systems. Grants & Projects: EVITA project, PEPR-5G HISEC, MoVe4SPS, PEPR-Security ARSENE
Iftach Haitner is a Professor at Tel Aviv University's School of Computer Science, currently on leave while serving as Principal Researcher at the Stellar Development Foundation. His primary academic affiliation remains with Tel Aviv University where he maintains an active research group and supervises multiple PhD students. His educational background includes a PhD (2008) and Master's degree from the Weizmann Institute of Science under Omer Reingold and Oded Goldreich respectively, and undergraduate studies in Mathematics and Computer Science at Tel Aviv University. His research focuses on Cryptography and Computational Complexity , with significant contributions to coin-flipping protocols, one-way functions, differential privacy, and secure computation. Analysis of his 15 most recent publications reveals consistent focus on foundational cryptographic problems. His work demonstrates strong emphasis on computational entropy concepts (inaccessible entropy, next-block pseudoentropy), protocol security against adaptive adversaries, and tight complexity bounds for cryptographic primitives. The publications span top venues including STOC, FOCS, Crypto, and Eurocrypt, showing sustained high-impact research output. The Kadar Family Award for Outstanding Research (2018) Tel Aviv University Rector's Awards for Excellence in Teaching (2017, 2018) SIAM Outstanding Paper Prize (2011) Intel Israel award for outstanding PhD students (2008) Multiple best paper awards (CRYPTO 2006, ICALP 2006) Haitner has advised numerous PhD students including Noam Mazor, Jad Silbak, and Eliad Tsfadia. His research has been supported by multiple Israel Science Foundation grants (2011-2023), an ERC Starting Grant (2015-2020), and Blavatnik ICRC grants. He also maintains industry connections through roles at Coinbase (2022-2024) and previous consulting positions at Unbound Security and Team8. His professional service includes editorial work for SIAM Journal on Computing and program committee memberships for major cryptography conferences including Crypto, Eurocrypt, and TCC. He co-organizes the Greater Tel Aviv Area Cryptography Seminar and other specialized workshops.
David Walker is a Professor and Director of Undergraduate Studies in the Department of Computer Science at Princeton University, where he joined in 2002, earned tenure in 2008, and was promoted to full professor in 2013. His educational background includes: Ph.D. in Computer Science from Cornell University (2001) Master's degree in Computer Science from Cornell University Bachelor's degree from Queen's University in Kingston, Ontario Professor Walker's research centers on programming language theory, design, and implementation with specialized focus on domain-specific languages. His work bridges formal theoretical frameworks with practical compiler development, advancing both foundational knowledge and real-world language engineering applications in systems programming and software reliability. His scientific contributions have been honored with prestigious recognitions: NSF Career Award Sloan Fellowship 2015 ACM SIGPLAN Robin Milner Young Researcher Award 10-year retrospective award for ACM POPL 1998's most influential paper Best paper award at ACM PLDI 2007 Community Award at USENIX NSDI 2013 Professor Walker has secured significant research funding through his NSF Career Award and held influential academic service roles including Associate Editor for ACM TOPLAS (2007-2015) and Program Chair for ACM POPL 2015. His sabbatical appointments included visiting researcher positions at Microsoft Research (Redmond 2008, Cambridge 2009) and Associate Visiting Faculty at the University of Pennsylvania (2015-2016).
James R. Goodman is a Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison, affiliated with the College of Engineering. His research focuses on transactional memory, speculative execution, cache coherence, and parallel computing. University: University of Wisconsin-Madison School: College of Engineering Department: Electrical and Computer Engineering Email: goodman@cs.wisc.edu Research Interests include transactional memory for concurrent programming, speculative execution techniques, and cache coherence protocols. He has contributed to advancements in parallel computing architectures and distributed systems. Scientific Awards ACM Fellow (2011) Eckert-Mauchly Award Recipient (2013) Publications span topics from computer architecture to interdisciplinary studies in biotechnology and history. His recent work emphasizes concurrency, memory systems, and high-performance computing. Advising and Contributions include mentoring students and leading research initiatives. He has received grants for projects in computer architecture and speculative execution technologies.
Min Yun is a Professor of Astronomy at the University of Massachusetts Amherst, where she serves as Honors Program Director, Graduate Program Director, and Project Scientist for the Large Millimeter Telescope (LMT)—a major international collaboration between UMass and Mexico's National Institute of Astrophysics, Optics and Electronics (INAOE). Her role bridges observational astronomy, instrumentation development, and academic leadership within the Department of Astronomy. Dr. Yun earned her PhD from Harvard University, establishing a foundation for her expertise in extragalactic astrophysics. Her research focuses on galaxy evolution through gas content and star formation across cosmic time, specializing in infrared and radio wavelength tracers to penetrate obscuring dust. Technical mastery in radio interferometry (VLA, ALMA) underpins her observational work, while her LMT leadership drives advancements in millimeter-wave capabilities including the TolTEC camera and FINER receiver. Analysis of her 2023-2025 publications reveals consistent emphasis on gravitational lensing, high-redshift galaxy studies, and multi-wavelength synergy (JWST, ALMA, VLA). Key themes include neutral hydrogen evolution via the CHILES survey, protocluster characterization in PASSAGES, and dusty starburst physics using Planck-selected lensed systems. Her work prioritizes gas dynamics, star formation efficiency, and environmental impacts in galaxy evolution, often leveraging cosmic web contexts. As LMT Project Scientist, Dr. Yun oversees scientific operations and instrumentation development for this 50-meter telescope, coordinating international teams for projects like the CHILES continuum survey and MeerKAT studies of Hickson Compact Groups. Her leadership extends to COSMOS field spectroscopy and JWST time-domain initiatives, demonstrating integration of large-scale data with cutting-edge facilities to probe cosmic evolution.
