Prof. Dr. Johannes Kinder is a Professor and Chair of Programming Languages and Artificial Intelligence at the Institute of Informatics , Ludwig Maximilian University of Munich. His research focuses on software security through program analysis and machine learning, particularly targeting malware detection , vulnerability analysis , and reverse engineering . He has held faculty positions at Royal Holloway, University of London, and Bundeswehr University Munich. Research Interests include: Securing software systems via program and machine learning techniques Detection of software vulnerabilities and malware Preventing exploitation through binary analysis Applications of formal methods in systems security Recent Publications highlight advancements in binary function embedding , malware detection in npm , and speculative execution attack modeling . His work appears in top venues like USENIX Security and IEEE S&P . Education : Diplom from TU Munich (2005), Doctorate from TU Darmstadt (2010). Professional Roles : General Chair, ACM CCS 2019 Doctoral Symposium Chair, ESSoS 2016 Program Committee member for NDSS 2026, IEEE S&P 2022-2025
Prof. Ilia Polian serves as Head of the Institute of Computer Engineering and Chair of the Hardware-Oriented Computer Science (HOCOS) department at the University of Stuttgart. His leadership spans research, teaching, and institutional coordination across multiple high-impact projects. Prof. Polian's research focuses on developing circuit and system architectures based on both traditional and novel principles, including neuromorphic, stochastic, and approximate architectures. His second major research focus is systematic design methodology and design automation, with particular emphasis on safety and reliability properties of developed systems. Current research directions include quantum computing engineering, secure mixed-signal neural networks, and resource-efficient stochastic circuits for near-sensor computing applications. His recent publications demonstrate strong trends in quantum computing (particularly circuit partitioning and compilation for multi-QPU architectures), hardware security (including memristive cryptographic implementations), and AI-driven approaches to hardware testing and reliability. These works bridge fundamental computer architecture research with practical industrial applications. University of Stuttgart's Publication Prize for Paper on Partitioning of Quantum Circuits Prof. Polian actively supervises doctoral students including Devanshi Upadhyaya, and leads significant research grants such as the DFG Priority Program Nano Security which he coordinates. His department offers numerous thesis and research opportunities for students interested in cutting-edge hardware research. The Hardware-Oriented Computer Science department maintains strong collaborations with industry partners including IBM, Infineon Technologies, and Advantest, as well as academic institutions through the IQST Graduate School and QuantumBW initiatives.
Lionel C. Briand is a Professor of Software Engineering with shared appointments at the University of Luxembourg's SnT Centre for Security, Reliability, and Trust and the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Canada Research Chair (Tier 1) in Intelligent Software Dependability and Compliance and serves as Director of Lero, Ireland's national software research center. His academic leadership spans over 25 years of collaborative research with industry partners across automotive, aerospace, energy, financial, and legal domains. Professor Briand's research focuses on software verification and validation, trustworthy AI systems, model-driven engineering, and empirical software engineering methodologies. His work bridges theoretical foundations with industrial applications, particularly in cyber-physical systems where machine learning components interact with safety-critical control systems. He has pioneered techniques for testing AI-enabled systems, GDPR compliance automation, and mutation analysis for space systems. His publication portfolio demonstrates consistent innovation in software testing, with recent emphasis on large language models for test generation, automated compliance checking, and safety analysis of deep neural networks. Key trends include black-box testing methodologies, metamorphic testing for security, and search-based approaches for DNN validation. IEEE Fellow and ACM Fellow IEEE Computer Society Harlan Mills Award (2012) ACM SIGSOFT Outstanding Research Award (2022) IEEE Reliability Society Engineer-of-the-Year Award (2013) ERC Advanced Grant recipient (2016) Fellow of the Academy of Science, Royal Society of Canada (2023) ICSE 2011 Most Influential Paper Award As Director of Lero and holder of a Canada Research Chair, Professor Briand leads major research initiatives including an ERC Advanced Grant on cyber-physical system modeling and testing. His industrial collaborations generate substantial grant funding, particularly in automotive safety validation and regulatory compliance automation. He mentors numerous researchers through his dual appointments and serves on program committees for top software engineering conferences. Professor Briand directs research activities at the SnT Centre's SVV department, focusing on software verification and validation. His team develops practical tools like MASS for space system mutation analysis and COREQQA for compliance requirements understanding, with strong industry adoption in automotive and aerospace sectors.
Tej Chajed is an Assistant Professor in the Department of Computer Science at the University of Wisconsin-Madison, where he conducts research in formal verification of systems software. His work focuses on building and proving the correctness of critical systems, particularly file systems and concurrent software. Dr. Chajed earned his PhD from MIT in the PDOS group, followed by a one-year postdoc at VMware Research before joining UW-Madison. His academic journey reflects a strong commitment to bridging theoretical formal methods with practical systems implementation. Chajed's research centers on formal verification techniques for systems software, with particular emphasis on concurrent and crash-safe systems . His work aims to eliminate bugs in critical software through mathematical proofs of correctness. Key contributions include DaisyNFS (a verified concurrent file system), the Perennial framework for reasoning about crash safety, and Goose for connecting proofs to Go code. His research spans the intersection of programming languages, operating systems, and formal methods, developing practical tools that bring verification to real-world systems. His recent publications demonstrate a consistent trajectory toward more practical and scalable verification techniques for increasingly complex systems. The research shows progression from foundational verification frameworks to applied work on specific systems like file systems, journaling, and distributed protocols. A notable trend is the focus on making verification more accessible and practical for systems developers, bridging the gap between theoretical formal methods and real-world software engineering. Dr. Chajed serves on numerous program committees including OSDI 2025 PC, PLDI 2024 PC, SySDW 2023 PC, ECOOP 2023 ERC, CPP 2023 PC, POPL 2023 PC, PLDI 2022 PC, POPL 2022 AEC, EuroDW 2021 PC, POPL 2021 AEC, PLDI 2020 AEC, POPL 2020 AEC, and SOSP 2019 AEC, reflecting his standing in the systems and programming languages research community. In teaching, Chajed has developed and instructed courses on systems verification, operating systems, and protocol verification. He previously helped create MIT's 6.826 (Principles of Computer Systems) during his PhD. His passion for technical communication was cultivated during his time as a Communication Fellow in the EECS Communication Lab at MIT, where he continues to offer guidance to students on writing and presentation skills. His research group at UW-Madison focuses on advancing the state of the art in systems verification, with current projects centered around practical verification frameworks for concurrent and crash-safe systems.
Bihuan Chen is an Associate Professor at the College of Computer Science and Artificial Intelligence, Fudan University, specializing in software engineering with focus on software supply chain security and trustworthy AI systems. His research spans multiple programming languages including JavaScript, Python, Java, and C/C++ across application and AI domains. Dr. Chen earned his B.Sc. and Ph.D. in Computer Science from Fudan University in 2009 and 2014 respectively, followed by postdoctoral research at Nanyang Technological University (2014-2017). His research interests include software supply chain risk assessment, trustworthy AI systems, and program analysis. His recent publications demonstrate strong focus on malicious package detection in NPM/PyPI ecosystems, vulnerability patch porting using LLMs, and safety verification for autonomous driving systems. The work shows increasing integration of machine learning techniques with traditional program analysis approaches, particularly evident in the 2024-2025 publications that leverage LLMs for vulnerability detection and code refinement. ACM SIGSOFT Distinguished Paper Award (FSE 2016, ASE 2018, ASE 2022, FSE 2025) IEEE TCSE Distinguished Paper Award (ICSME 2020, SANER 2023) CCF Prototype Competition Awards (2nd and 3rd Prizes) Dr. Chen has advised over 50 students including current PhD candidates and notable alumni now at Huawei, ByteDance, and other leading tech firms. His fuxi platform assesses security, legal, and maintenance risks across the software engineering lifecycle. He serves on program committees for major conferences including ICSE, FSE, ASE, and ISSTA, and as Associate Editor for the Journal of Software: Evolution and Process.
Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.
Allison Sullivan is an Assistant Professor of Computer Science at the University of Texas at Arlington (UTA), where she also serves as the Undergraduate Software Engineering Program Director. She is a member of the Software Engineering Research Center (SERC) at UTA and serves as faculty advisor for UTA's Society of Women Engineers (SWE) club. Dr. Sullivan received her PhD in Software Verification, Validation and Testing (SVVAT) from the University of Texas at Austin in 2017 under Sarfraz Khurshid. Her educational background includes: PhD in Software Verification, Validation and Testing, University of Texas at Austin (2017) M.S. in Software Engineering, University of Texas at Austin (2014) B.S. in Software Engineering, University of Texas at Dallas (2012) Dr. Sullivan's research focuses on two primary areas: Automated Software Engineering : Test/Oracle Generation, Automated Bug Localization and Repair, Mutation Testing, and Regression Testing Formal Methods and Programming Languages : Abstractions, Finite Model Finders, Program Synthesis, and SAT/SMT Solvers She leads the SCOPE lab which focuses on 'showing the correctness of all program executions' and has published extensively on Alloy modeling language applications. Her recent publications demonstrate a strong focus on applying formal methods to software engineering problems, with a growing emphasis on the intersection of large language models and software development practices. Her work spans theoretical foundations, tool development, and empirical studies of how developers use modeling languages. Her scientific achievements have been recognized with: NSF CAREER Award (2024) UTA CSE department Rising Star Research Award (2024) UTA College of Engineering Outstanding Early Career Faculty Award (2025) NSF grant for building an educational tool for software modeling ($400k) Dr. Sullivan has successfully advised two PhD students to completion: Dr. Ana Jovanovic (defended November 2024) and Dr. Anahita Samadi (defended February 2025). She actively mentors undergraduate researchers and has secured significant research funding including the NSF CAREER grant. Her service includes committee roles for major conferences including ASE, ISSRE, and FormaliSE. She leads the SCOPE lab at UTA, which brings together graduate and undergraduate researchers to develop techniques for improving software verification and validation, with particular emphasis on making formal methods more accessible to practitioners.
Juan Zhai is an Assistant Professor in the Manning College of Information & Computer Sciences (CICS) at University of Massachusetts Amherst, where she co-directs the Laboratory for Advanced Software Engineering Research (LASER) and participates in the UMass NLP group. Her academic career spans over 7 years of active service including program committee roles at top-tier conferences like ICSE, FSE, and ASE. Her research focuses on Software-AI Synergy with core areas including: Formal Specification Synthesis for precise software behavior definition Comment Generation and Maintenance using LLMs Trustworthy AI through bias detection and framework testing Deep Learning Infrastructure Reliability Recent work demonstrates strong emphasis on practical tools for AI safety and software dependability. Her publication trends show consistent output in top software engineering venues (ASE, ICSE, FSE) with increasing focus on AI/ML conferences (ACL, CVPR, ICLR). Key themes include metamorphic testing for deep learning frameworks, bias analysis in LLMs, and formal methods for specification synthesis. She actively serves the community through: Program committees for 13 major conferences Reviewing for 5 top journals including TOSEM and TSE 40+ total reviews across SE and AI venues Juan mentors PhD students including Gehao Zhang (research focus: Software Engineering, AI Safety) and teaches graduate courses like CS520 (Theory and Practice of Software Engineering) and CS692P (Hot Topics in SE Research). She leads the LASER lab which develops tools like C2S, CPC, and DevMuT for software reasoning and AI infrastructure testing.
Professor Tobias Nipkow is a leading researcher in formal methods and interactive theorem proving at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Computer Science. He is a core developer of the Isabelle proof assistant and leads the Theorem Proving Group. His work has profoundly influenced program verification, semantics, and formalized mathematics. University: Technical University of Munich School: School of Computation, Information and Technology Department: Department of Computer Science Research Group: Theorem Proving Group Key Projects: Isabelle, Archive of Formal Proofs, Concrete Semantics His research focuses on formal verification, higher-order logic, semantics of programming languages, and verified algorithms. He has pioneered the formalization of textbook algorithms, data structures like B+-trees and quadtrees, and logical systems. His work bridges theoretical foundations with practical tools for software correctness. The most recent publications show a strong trend in verifying classical algorithms (e.g., Gale-Shapley, Earley parser), data structures (B+-trees, deques), and decision procedures, primarily using Isabelle/HOL. His contributions span foundational logic, program analysis, and educational approaches to formal methods. Best Paper Award at CADE 28 (2021) Tobias Nipkow has made extensive contributions to advising and collaborative research, co-authoring with numerous researchers and students. He has secured support for large-scale formalization efforts and contributed to major projects like the Flyspeck proof of the Kepler conjecture. His work is supported by ongoing development of the Isabelle framework and the Archive of Formal Proofs. He leads the Theorem Proving Group at TUM, which is central to the development and application of Isabelle. The group fosters international collaboration, contributes to the Archive of Formal Proofs, and advances research in automated reasoning, semantics, and verified systems.
Robert Bruce Findler is a Professor of Computer Science at Northwestern University, specializing in programming languages and software engineering. He serves as a core developer of the Racket programming language and has contributed extensively to language design, macro systems, and gradual typing. Affiliation: Department of Electrical Engineering and Computer Science, McCormick School of Engineering Research Interests: Programming Languages (PL), Domain-Specific Languages (DSLs), Macro Systems, Gradual Typing, Contracts His work spans both theoretical and practical domains, including the development of Racket's Redex framework for semantics engineering and innovative approaches to contract systems in gradual typing. He has been actively involved in the PL community through committee memberships and program organization. Recent research focuses on macro systems (Rhombus), contract optimization (Collapsible Contracts), and language interoperability (The Functional, the Imperative, and the Sudoku). His GitHub contributions reflect ongoing development in Racket and related tools. Key Collaborations: Racket development ecosystem, PLDI/POPL/ICFP/SPLASH conferences Committee Roles: ICFP Programme Committee, REBLS Program Committee, POPLmark Retrospective Panelist
Philip Wadler is Professor of Theoretical Computer Science at the University of Edinburgh and Senior Research Fellow at IOHK. He is an ACM Fellow, Fellow of the Royal Society, and Fellow of the Royal Society of Edinburgh. His work spans programming language design, type systems, and formal verification, with significant contributions to Haskell, Java, and XQuery. He has held leadership roles in ACM SIGPLAN and served on editorial boards for major journals. Research Interests: Wadler's research focuses on the foundations of programming languages , including Gradual and session typing Language-integrated query Functional and logic programming XML data models Parametricity and free theorems Verification of smart contracts Publication Trends: Recent articles emphasize type safety, formal verification, and blockchain applications. Key themes include gradual typing (blame calculus), session types for concurrency, and logical foundations of programming. His 2015–2025 papers show sustained focus on type theory and language design . Awards & Recognition: POPL Most Influential Paper (2003 for 1993 work) SIGPLAN Distinguished Service Award Best Paper SBMF 2018 Royal Society-Wolfson Fellowship (2004–2009) ACM Fellow (2007) Fellow of Royal Society of Edinburgh (2005) Advising & Grants: He has supervised numerous PhD students in programs like the Centre for Doctoral Training in Pervasive Parallelism. His EPSRC Programme Grant "From Data Types to Session Types" (2013–2020) funded major advances in concurrency theory. Current work with IOHK explores blockchain verification using Haskell-based Plutus.
Kihong Heo is an Associate Professor in the School of Computing and Graduate School of Information Security at KAIST (Korea Advanced Institute of Science and Technology) in South Korea. His academic career includes serving as an Assistant Professor at KAIST from 2017-2019 before being promoted to Associate Professor in 2020, following his postdoctoral research at the University of Pennsylvania. He earned both his Ph.D. and B.S. in Computer Science & Engineering from Seoul National University. Dr. Heo's research focuses on developing program reasoning systems for safe and reliable software, with specific interests in AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges the gap between programming languages, program analysis, and machine learning techniques to create next-generation programming systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional program analysis methods, with significant contributions in compiler validation, software security, fault localization, and program debloating. His research has practical impact, with some of his work incorporated into Facebook's Infer static analyzer. ACM SIGSOFT Distinguished Paper Award, FSE 2025 Amazon Research Award, 2024 The Soo-Young Lee Teaching Innovation Award, KAIST, 2024 Prize for Excellence in Teaching, KAIST, 2024 Best Artifact Award, ICSE 2022 ACM SIGPLAN Distinguished Paper Award, PLDI 2019 ACM SIGSOFT Distinguished Paper Award, ICSE 2019 Dr. Heo actively mentors graduate students, currently advising several Ph.D. candidates including Yeonhee Ryou, Taeeun Kim, and Sujin Jang, as well as master's students. He has served on program committees for major software engineering and programming language conferences including PLDI, ICSE, POPL, and SPLASH, demonstrating his active role in the academic community. His laboratory, the Programming Systems Laboratory at KAIST, focuses on creating innovative programming systems that leverage both semantic-based program analysis and AI techniques.
Craig Knoblock serves as Keston Executive Director of the Information Sciences Institute (ISI) at the University of Southern California (USC), Vice Dean of the USC Viterbi School of Engineering, and Research Professor of Computer Science and Spatial Sciences. He also directs the Data Science Program and the Center on Knowledge Graphs at USC. His educational background includes a Ph.D. and M.S. in Computer Science from Carnegie Mellon University (1991, 1988) and a B.S. with honors in Computer Science from Syracuse University (1984). Knoblock's research focuses on data semantics , specializing in source modeling, schema and ontology alignment, entity and record linkage, data cleaning, Web data extraction, and knowledge graph construction. His work bridges computer science, geospatial analysis, and artificial intelligence to solve complex data integration challenges. Recent projects emphasize historical map digitization, geospatial knowledge graphs, and smart city applications. His 300+ publications demonstrate consistent contributions to knowledge graphs and geospatial data integration, with a growing emphasis on historical map analysis and urban applications. The research trajectory shows increasing interdisciplinary collaboration across computer vision, geoinformatics, and domain-specific applications. IEEE Fellow (2020) ACM Fellow (2017) AAAI Fellow (2004) Robert S. Engelmore Memorial Lecture Award (2014) Donald E. Walker Distinguished Service Award (IJCAI, 2018) Use-Inspired Research Award (USC Viterbi, 2018) As Executive Director of ISI, Knoblock oversees one of USC's premier research centers with significant federal funding. His leadership extends to directing the Center on Knowledge Graphs and the Data Science Program. While specific grant details aren't provided, his extensive publication record and leadership roles indicate substantial research funding across data integration, knowledge representation, and geospatial applications. His work bridges theoretical computer science with practical applications in historical preservation, urban planning, and resource management through collaborative projects with government agencies and industry partners. Knoblock leads the Center on Knowledge Graphs at USC, focusing on developing techniques for building and utilizing knowledge graphs across diverse domains. His team combines expertise in artificial intelligence, geospatial analysis, and data integration to tackle challenges in historical map digitization, urban applications, and resource discovery. The research group maintains strong connections with both academic and government partners through the Information Sciences Institute's extensive network.
Matthias Breuer serves as Heisenberg-Professor for Corporate Reporting & Regulation at Goethe University Frankfurt's Faculty of Economics and Business, where he investigates corporate transparency mechanisms and regulatory solutions for sustainability reporting challenges. His interdisciplinary work bridges historical accounting contexts with modern ESG assurance frameworks. His academic foundation includes: PhD from the University of Chicago Master’s degree from the London School of Economics and Political Science Bachelor’s degree from WHU Breuer's research program centers on information verification dynamics in corporate reporting, with particular emphasis on how financial regulation shapes resource allocation, innovation incentives, and disclosure practices. He examines historical precedents (e.g., 1890s streetcar industry) to inform contemporary sustainability assurance systems, employing advanced econometric techniques to analyze regulatory impacts across public and private firms. His scholarship consistently addresses the tension between voluntary and mandatory disclosure regimes in capital markets. Analysis of his 15 most recent publications reveals escalating focus on sustainability regulation, with 60% directly addressing ESG-related disclosure challenges. Methodologically, he integrates Bayesian statistics, fixed effects modeling, and historical analysis to dissect reporting anomalies, while recent work increasingly explores spillover effects in peer disclosure networks and minority representation dynamics within corporate environments. Key recognitions include: DFG Heisenberg Fellowship TRR 266 Research Fellowship (Accounting for Transparency) Fellowship at the Centre for Advanced Studies on Law and Finance Affiliate Fellowship at Chicago Booth's Stigler Center As principal investigator of the DFG-funded Heisenberg project, Breuer directs substantial research resources toward transparency regulation while maintaining active collaboration with Columbia University and Chicago Booth through prior appointments. His grant portfolio reflects sustained interest in regulatory economics, with current projects examining auditing mandates and corporate innovation under disclosure requirements. Breuer contributes to Goethe University's Accounting Research Seminar series and is embedded in the TRR 266 collaborative research center, where he coordinates interdisciplinary teams analyzing transparency standards. His methodological laboratory combines archival financial data with historical industry records to model disclosure dynamics under varying regulatory regimes.
Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.
