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.
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.
Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )
Zhenchang Xing is a Research Professor at CSIRO's Data61 and holds a Hans Fischer Senior Fellowship at TUM-IAS. With a Ph.D. in Computer Science from the University of Alberta (2008), he previously served as Associate Professor at Australian National University and Assistant Professor at Nanyang Technological University. His research focuses on software engineering for AI systems and human-centered computing. Current projects include: Automated Software-Hardware Co-Design for AI Systems Software Supply Chain Security frameworks Data Bill of Materials (DataBOM) for verifiable data ecosystems Software Testing Knowledge Graph development Professor Xing has received 10 Distinguished Paper Awards from ACM and IEEE, including the 2005 Most Influential Paper Award for UMLDiff. His work combines software engineering with responsible AI development.
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.
Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.
Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.
Shin Yoo is a tenured Full Professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), where he leads the Computational Intelligence for Software Engineering (COINSE) research group. He received his PhD from King's College London in 2009 under the supervision of Prof. Mark Harman. Currently, he serves as the General Chair for ASE 2025, which will be held in Seoul, Korea. Professor Yoo earned his PhD in Computer Science from King's College London (2009), following an MSc in Software Engineering with Distinction from the same institution (2006). His academic journey includes positions as Tenured Associate Professor (2021-2025), Associate Professor (2018-2021), and Assistant Professor (2015-2018) at KAIST, as well as Lecturer and Research Associate positions at University College London and King's College London. His research focuses on the intersection of software engineering and artificial intelligence, particularly in search-based software engineering, software testing, automated debugging, SE4AI (Software Engineering for AI), and AI4SE (AI for Software Engineering). Professor Yoo's work bridges theoretical foundations with practical applications, developing innovative techniques for fault localization, test case generation, and debugging using machine learning and genetic programming approaches. His research has significant implications for improving software reliability and development efficiency in both traditional software systems and AI-powered applications. Professor Yoo's recent publications demonstrate a clear trend toward leveraging large language models and deep learning techniques for software engineering tasks. His work spans fault localization, automated debugging, GUI testing, and program analysis, with increasing focus on the challenges and opportunities presented by AI systems. His research shows a consistent evolution from traditional search-based software engineering to AI/ML-enhanced approaches, reflecting the broader trends in the field. ACM SIGEVO HUMIES Silver Medal (2017) for human competitive application of genetic programming to fault localization research IEEE TCSE Most Influential Paper Award (ICST 2024) for work on mutation-based fault localization Professor Yoo has supervised five PhD students to completion, with his former students now holding positions as assistant professors, post-doctoral researchers, and software engineers at institutions including Kyoungpook National University, Max-Planck Institute Security & Privacy, Università della Svizzera Italiana, Roku Korea, and NUS. He currently serves as an associate editor for the Journal of Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology, and has held significant leadership roles in major software engineering conferences including Program Co-chair for SSBSE (2014), ICST (2018), and ICSE NIER track (2020), General Chair for SSBSE (2022), and Testing & Analysis Area Chair for ICSE (2024). As leader of the Computational Intelligence for Software Engineering (COINSE) group at KAIST, Professor Yoo directs research that combines computational intelligence techniques with software engineering challenges. The group focuses on developing novel approaches to software testing, debugging, and analysis using search-based and AI-driven methods. Their work spans both theoretical foundations and practical implementations, with strong connections to industry challenges and applications.
Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He serves as Director of the SPEAR lab (Software Performance, Analysis, and Reliability lab), which focuses on improving the quality of large-scale software systems through research in log analysis and AIOps, software performance analysis, software testing, and mining software repositories. His research group maintains extensive collaborations with industry partners including ERA Environmental, Ericsson, Microsoft, and BlackBerry. Dr. Chen received his PhD and MSc in Computer Science from Queen's University and his BSc in Computer Science from the University of British Columbia. Dr. Chen's research addresses critical challenges in modern software engineering, including leveraging Large Language Models to assist developers with development, debugging, and maintenance; helping developers debug production systems by utilizing rich software data; providing optimization suggestions by analyzing user usage data; improving software quality assurances in DevOps environments; and mining software development history for useful developer suggestions. His work spans Software Engineering, Performance Engineering, DevOps & AIOps, Software Testing, and Mining Software Repositories, with a strong emphasis on practical applications that bridge academic research and industrial practice. His recent publications (2024-2025) demonstrate a pronounced shift toward integrating Large Language Models into various aspects of the software engineering lifecycle, particularly in log analysis, fault localization, code generation, and performance testing. This trend reflects the growing importance of AI in software engineering research and practice. Gina Cody Research award (2022) Ranked as one of the most active software engineering researchers worldwide by an independent study published in JSS Dr. Chen has successfully advised numerous PhD and Master's students, many of whom have secured prestigious academic positions. Several of his graduated PhD students now hold tenure-track assistant professor positions at institutions including York University, University of Alberta, DePaul University, and IIT Gandhinagar. His SPEAR lab has developed research tools that have been integrated into industrial practice for ensuring the quality of large-scale enterprise systems. The SPEAR lab, under Dr. Chen's leadership, has established itself as a leading research group in software engineering, with particular expertise in software performance analysis, log analysis, and AI applications for software engineering. The lab maintains strong industry connections and has produced numerous high-impact publications in top-tier software engineering venues including ICSE, FSE, ASE, and TSE.
Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.
Roles & Affiliations: Dagmar Gromann is an Associate Professor for Terminology Science and Translation Technology at the University of Vienna's Centre for Translation Studies. Previously, she served as a Research Associate at TU Dresden's International Center for Computational Logic (ICCL) and held postdoctoral roles in Barcelona within the ESSENCE Marie Curie Training Network. She completed her PhD at the University of Vienna under Prof. Gerhard Budin, focusing on ontology-terminology integration. Education: PhD in Computer Science and Linguistics, University of Vienna (2015) Postdoctoral Research, Artificial Intelligence Research Institute (IIIA), Barcelona (2015–2017) Research Assistant, Vienna University of Economics and Business (until 2015) Research Interests: Her work bridges computational linguistics, cognitive science, and terminology science. Key areas include: Ontology learning and neurosymbolic AI Image schemas in natural language processing Machine translation ethics and genderfair language Multilingual knowledge extraction and linked data Terminology modeling and semantic web applications Publications & Awards: Over 60 peer-reviewed papers in journals such as Future Generation Computer Systems and Journal of Lexicography . Notable awards include the Best Paper Award at MuC 2021 (GenderFairMT team) and the ISWC 2019 Best PC Award. Her research has pioneered methods for extracting embodied cognition concepts like image schemas from text. Grants & Leadership: PI of the European Language Grid (ELG) pilot project Text2TCS, member of the COST Action NexusLinguarum, and organizer of conferences like LDK 2023. Editorial board roles include the Semantic Web Journal and Applied Ontology . Labs & Teams: Former member of TU Dresden's ICCL and currently part of the University of Vienna’s translation technology initiatives. Active in interdisciplinary collaborations spanning computational linguistics, AI ethics, and multilingual systems.
Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.
Timothy Baldwin is a Professor at the University of Melbourne, School of Computing and Information Systems, with additional affiliation at Mohamed bin Zayed University of Artificial Intelligence in UAE. His research spans natural language processing, large language models, and multilingual AI systems. His research interests focus on the safety, reliability, and ethical aspects of large language models. He investigates bias evaluation and debiasing techniques, uncertainty quantification methods, fact-checking systems, and multilingual model safety. His work addresses critical challenges in making AI systems more transparent, reliable, and culturally aware, with particular attention to low-resource languages and cross-cultural differences. Baldwin's recent publications demonstrate a strong focus on evaluating and improving the safety of language models across diverse linguistic contexts, developing tools for fact verification, and understanding the internal mechanisms of large language models. His research shows increasing emphasis on practical applications with real-world impact, particularly in multilingual settings and safety-critical domains. His scientific contributions include foundational work on multilingual NLP, bias mitigation techniques, and frameworks for evaluating LLM safety across different cultural contexts. His research has been published in top-tier venues including ACL, NAACL, EMNLP, and ICLR. Baldwin actively mentors students and junior researchers, with frequent collaborations with Haonan Li, Xudong Han, and Fajri Koto, among others. His research group appears to focus on practical applications of NLP with strong ethical considerations, particularly regarding model safety and cultural sensitivity.
Prof. Dr. Moritz Helias is a University Professor and leads the Theory of Multi-Scale Neuronal Networks group at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience, Forschungszentrum Jülich. His research bridges biological and artificial neural networks, focusing on dynamics, information processing, and the physics of AI. The group is part of a larger interdisciplinary institute that integrates theory, simulation, and data analysis to understand the brain. Institution: Forschungszentrum Jülich School: Institute for Advanced Simulation Department: IAS-6, Computational and Systems Neuroscience Position: Professor and Group Leader Email: m.helias@fz-juelich.de His research interests lie at the intersection of statistical physics and neuroscience. He investigates how structure shapes dynamics in both biological and artificial networks, aiming to uncover general principles of information processing. Using methods from statistical physics, his work enables a unified framework for understanding collective phenomena, learning, and generalization. Key areas include spiking neural networks, renormalized field theory, and the theoretical foundations of AI. The recent publications reflect a strong trend toward multi-scale modeling of neural systems, integrating statistical physics with neuroscience. Topics include spiking network dynamics, mean-field theory, renormalization, and applications of machine learning in physics. The work spans biological realism and artificial intelligence, with implications for neuromorphic computing and brain-inspired AI architectures. While no scientific awards are listed in the provided texts, his group actively contributes to open science through tools like NEST and theoretical frameworks that influence both neuroscience and AI. Prof. Helias supervises a research group focused on theoretical and computational approaches, contributing to collaborative projects involving large-scale simulations and data analysis. His team works closely with experimentalists and theorists to validate models and advance understanding of brain function. The group is also involved in developing simulation technologies and theoretical tools that support reproducible neuroscience. The Theory of Multi-Scale Neuronal Networks group is embedded within a vibrant research environment at IAS-6, collaborating with teams in statistical neuroscience, computational neurophysics, and future simulation architectures. This fosters a loop between data, theory, and simulation, enabling cutting-edge research on brain function and artificial intelligence.
