Michael Menth is a Professor at the Department of Computer Science, University of Tübingen, specializing in communication networks. His work bridges theoretical and applied research in network resilience, software-defined networking (SDN), and time-sensitive networking (TSN). University: University of Tübingen Department: Computer Science Research Interests: Network security, P4 programming, multicast protocols, and congestion control. Recent publications focus on advancements in SDN, TSN, and network security. His team has developed tools like P4TG for high-speed traffic generation and SENSOR for flow monitoring. Key projects include BIER-TE for multicast optimization and OIDC² for secure authentication. His research trends emphasize stateless network architectures, machine learning integration for network management, and automotive/E/E system softwarization. Collaborative work spans eHealth platforms like SSTeP-KiZ and security frameworks for industrial networks. Publications from 2022-2025 demonstrate technical depth in P4-based implementations, TSN scheduling, and resilient multicast protocols. While no explicit awards are listed, his extensive RFC contributions (e.g., RFC 5696, RFC 9262) highlight industry-standard impact.
Mario Krenn is a Full Professor (W3) of "Machine Learning in Science" at the University of Tübingen since June 2025, leading the Artificial Scientist Lab. His research bridges artificial intelligence , quantum physics , and experimental design , focusing on developing AI systems that act as "artificial muses" to inspire novel scientific discoveries. ERC Starting Grant recipient (2024) for ArtDisQ project Developed PyTheus, a framework for AI-driven quantum experiment design Created XLuminA, a JAX-based simulator for microscopy and photonics Co-inventor of SELFIES, a robust molecular string representation His work has led to experimental implementations of AI-designed quantum protocols (e.g., entanglement without pre-existing resources) and gravitational wave detector concepts. He explores scientific understanding in human-AI collaboration, nonlocal interference phenomena, and the philosophical implications of AI-generated discoveries. Recent projects include predicting research trends via knowledge graphs and developing virtual reality tools to visualize AI-conceived quantum experiments. Scientific awards include the ERC Starting Grant 2024 and International Quantum Technology Emerging Researcher Award (Highly Commended) 2020 . He serves on the editorial board of Machine Learning: Science and Technology and actively promotes open science through GitHub repositories and community-driven initiatives.
Dr. Alain D. Starke is an Assistant Professor in the Department of Informatics at the University of Bergen's Faculty of Mathematics and Natural Sciences. With a prolific publication record spanning from 2015 to 2025 (including forthcoming works), his research focuses on the intersection of human-computer interaction and recommender systems, particularly in health and sustainability domains. His primary research interests include recommender systems , behavioral nudging for healthy food choices , conversational user interfaces , and news recommendation systems . Starke's work uniquely combines technical algorithm development with human-centered evaluation, often examining how recommendation interfaces can influence user behavior toward healthier eating habits and more sustainable consumption patterns. His research has significant implications for both industry applications and public health initiatives. Analysis of his recent publications reveals a clear trajectory toward integrating AI ethics and normative design principles into recommender systems. His 2023-2025 work shows increasing focus on explainable AI for food recommendation, emotional reframing of news content using LLMs, and the ethical implications of personalized recommendation systems. The Norwegian context features prominently in his work, particularly in studies examining news consumption patterns and political selective exposure. Starke has been instrumental in organizing the NORMalize workshop series on normative design and evaluation of recommender systems, demonstrating his leadership in addressing ethical challenges in the field. His collaborative approach is evident through extensive partnerships with researchers across Europe, particularly with Christoph Trattner, with whom he has co-authored numerous papers. While specific grant information isn't detailed in the provided materials, his research program appears well-funded given the scope of user studies involving representative samples and multi-year publication output. His work bridges academic research with practical applications in food technology, news media, and sustainability initiatives.
Jinhan Kim is a Postdoctoral Researcher at the Software Institute of USI University of Lugano, Switzerland, working in the TAU lab under the guidance of Prof. Paolo Tonella. He completed his Ph.D. in Software Engineering at KAIST, South Korea, under the supervision of Prof. Shin Yoo, where his research focused on mutation testing and the intersection of artificial intelligence and software engineering. His educational background includes: Ph.D. in Software Engineering, KAIST, South Korea (completed February 2023) Kim's research spans software engineering and artificial intelligence, with a focus on mutation testing, testing of deep learning systems, and security of AI models. He investigates techniques for improving the reliability and robustness of AI systems, particularly in safety-critical domains like autonomous driving. His work bridges traditional software engineering practices with modern AI systems, leading to novel approaches in fault localization, program repair, and adversarial testing. His recent publications reveal a strong trend toward testing and securing deep learning models in autonomous systems. He has developed taxonomies for attacks, frameworks for testing autonomous agents, and empirical studies on fault localization for neural networks. His work increasingly addresses securing AI systems against adversarial attacks and improving robustness of security detectors generated by large language models. Kim has received notable recognition including: Best Paper Award at the 18th International Workshop on Mutation Analysis (Mutation 2023) As an advisor, Kim supervises two PhD students: Masoud Jamshidiyan Tehrani and Samuele Pasini, working on security of deep learning models and robustness of security attack detectors. He actively serves the research community through program committees for ASE, ICSE, ISSTA, and ICST, and as organizer of DeepTest and SBFT workshops. His service includes being a Distinguished Reviewer for TOSEM. Kim is a core member of the TAU (Testing: Analysis and Understanding) lab at USI, which pioneers innovative approaches to software testing and analysis for modern AI-based systems.
Junjie Chen is a Professor at the College of Intelligence and Computing, Tianjin University, where he leads the Software Engineering Team. He has been a Professor since January 2024, after serving as an Associate Professor from July 2019 to January 2024. Prior to his position at Tianjin University, he completed his PhD in Computer Science at Peking University under the supervision of Prof. Bing Xie, Prof. Lu Zhang, Prof. Dan Hao, and Prof. Yingfei Xiong. During his PhD studies, he was also a visiting PhD student at The University of Texas at Dallas under Prof. Lingming Zhang. His educational background includes a Bachelor's degree in Software Engineering from Beihang University (2010-2014). Professor Chen's research focuses on four main areas: Software Fuzzing : Focusing on fundamental software testing for compilers, operating systems, chip design systems, and AI infrastructure. Intelligent Software Engineering : Applying LLMs and deep learning to solve software engineering challenges like code generation, test generation, and code review. Trusted AI : Improving AI security, fairness, robustness, and performance through adversarial attacks, data optimization, and software engineering methodologies. Software Maintenance : Researching bug localization and fixing, as well as AIOps for anomaly detection and diagnosis. His recent publications (2024-2025) demonstrate a strong focus on the intersection of software testing, compiler infrastructure, and large language models. His work spans from practical compiler testing techniques to advanced applications of AI in software engineering. His research shows a clear trajectory toward addressing the challenges of modern software systems, particularly those involving AI and deep learning components. Professor Chen has received numerous prestigious awards including: National Key R&D Program Young Scientist (2024) Huawei Spark Award (2024) National Excellent Young Scientists Fund recipient (2023) China Institute of Electronics Natural Science First Prize (2023) CAST Young Elite Scientists Sponsorship Program (2022) Multiple ACM SIGSOFT Distinguished Paper Awards He is actively involved in academic service, serving on editorial boards for JCST and ASEJ, and as a program committee member for major conferences including ICSE, ASE, FSE, and ISSTA. He has also co-organized workshops and seminars, including the Compiler Technology Seminar under the CCF System Software Committee. Professor Chen leads a research team at Tianjin University that is actively recruiting PhD and Master's students with strong programming skills and interests in Software Engineering, LLMs, Security, and Program Analysis.
Lingming Zhang is an Associate Professor in the Department of Computer Science at the Grainger College of Engineering, University of Illinois Urbana-Champaign. He maintains an active research program focused on the intersection of Software Engineering, Programming Languages, and Machine Learning, with particular emphasis on LLM-based software testing, repair, and synthesis. His research has resulted in over 100 publications with an h-index exceeding 50. His work has practical impact, having helped detect over 1,000 bugs and vulnerabilities in open-source projects from Apache and GitHub, as well as software systems from major technology companies including eBay, Google, Meta/Facebook, Microsoft, NVIDIA, OctoML, Oracle, and Yahoo!. Zhang's research interests span several key areas in software engineering and programming languages: LLM-based software testing, debugging, and repair Code generation and understanding with large language models Automated program repair Software testing and fuzzing techniques Deep learning for software engineering His recent publications demonstrate a strong focus on leveraging large language models for software engineering tasks, with notable contributions including Agentless, TitanFuzz, AlphaRepair, and ChatRepair. His group has also released influential open-source code language models including StarCoder2 and Magicoder, which have garnered over 1 million downloads worldwide. Zhang has received numerous prestigious awards and recognitions for his work: ACM Distinguished Member ACM SIGSOFT Early Career Researcher Award NSF CAREER Award UIUC Dean's Award for Excellence in Research Multiple ACM SIGSOFT Distinguished Paper Awards He has successfully mentored numerous PhD and MS students, many of whom have gone on to positions at top technology companies and academic institutions. His research is supported by grants from major technology companies including Alibaba, Amazon, Google, Kwai Inc, Meta/Facebook, NVIDIA, and Samsung. Currently, he serves as program co-chair for ASE 2025 and LLM4Code 2025, demonstrating his leadership in the software engineering research community.
Dr. Chetan Arora is a Senior Lecturer in Software Engineering and Director of Education for Software Systems and Cybersecurity at Monash University's Faculty of Information Technology in Australia. With a PhD in Computer Science from the University of Luxembourg and a Master's degree from Technische Universität Kaiserslautern in Germany, he brings both academic rigor and extensive industry experience to his research and teaching. Current Position: Senior Lecturer in Software Engineering at Monash University Leadership Role: Director of Education for Software Systems and Cybersecurity Previous Positions: Academic Director at Deakin University, Innovation Programs at SES Satellites Education: PhD (University of Luxembourg), MSc (TU Kaiserslautern), B.Tech (Thapar University) Dr. Arora's research focuses on the intersection of Software Engineering, Requirements Engineering, and Applied Artificial Intelligence. His work investigates how Natural Language Processing and Machine Learning can improve software reliability and trustworthiness. He has published extensively in top-tier venues including ICSE, FSE, RE, and ASE, with recent publications examining the application of large language models in software engineering practices. His research also extends to satellite communications (particularly dynamic resource allocation) and Internet of Things applications. His work demonstrates clear trends toward integrating AI technologies with traditional software engineering practices, particularly in requirements analysis, quality assurance, and testing. He has secured significant research funding from Australian Defence organizations for projects related to satellite communications and AI-based decision making. His recent publications show growing emphasis on ethical considerations of AI in software development and human-centered aspects of software engineering. 2022 Deakin School of Information Technology International and Partnership Award for Excellence in Advancing International Teaching Partnership 2022 Best Paper Award for Automated Question Answering for Improved Understanding of Compliance Multiple publications in top-tier software engineering conferences and journals As an educator and academic leader, Dr. Arora supervises numerous PhD students working on cutting-edge topics at the AI-software engineering intersection. His industry experience across multiple countries (Australia, Luxembourg, Germany, and India) provides valuable practical perspective to his academic work, particularly in satellite communications, IoT, and AI applications in critical systems. He serves on program committees for major conferences including ICSE, FSE, and RE, contributing to the broader software engineering research community.
Peng Di serves as an Adjunct Associate Professor at the University of New South Wales, Sydney while working as a Senior Staff Engineer and leading the Intelligent Platform Engineering team at Ant Group in Hangzhou, China. They also serve as an Industrial PhD Mentor at Zhejiang University. PhD from UNSW under supervision of Scientia Professor Jingling Xue Former Research Associate at Compiler Research Group Former Technical Expert of AI compiler at Huawei Peng Di's research spans the intersection of programming languages, artificial intelligence, and software engineering. Their work focuses on leveraging large language models for software development, program analysis techniques, compiler technologies, and security applications. They have pioneered approaches to integrate code large language models with traditional program analysis, creating hybrid systems that enhance software development efficiency and security. Their research in parallel computing and optimizations for heterogeneous systems has resulted in practical industrial applications, particularly in the domain of AI compilers and microservices architecture. Recent publications demonstrate a clear trend toward integrating graph-based representations with large language models for code understanding, advancing static analysis techniques using datalog and neural networks, and applying these innovations to practical industrial settings. Their work bridges theoretical program analysis with real-world software engineering challenges, particularly in microservices architectures and security-critical applications. T-Star Award (Ant Group's highest technological award, 2024) ACM SIGSOFT Distinguished Paper Award (2025) Distinguished Dissertation Award by the CCF Systems Software (2024) distinguished paper award of ASE'24 (2024) Peng Di leads the Intelligent Platform Engineering team at Ant Group, which has developed several significant open-source projects including CodeFuse (a pretrained code large language model), Ling-Coder-Lite (a MoE code LLM), and OpenDeRisk (an AI-native risk intelligence system). Their team collaborates with academic institutions including UNSW, East China Normal University, ETH Zurich, Chinese Academy of Sciences, and Nanjing University on cutting-edge research. Peng Di actively mentors PhD students as an Industrial PhD Mentor at Zhejiang University and recruits technical experts and interns for research in machine learning, program analysis, and AI software engineering. The Ant Intelligent Platform Engineering team, led by Peng Di, is committed to building a safe and trusted framework for intelligent, automated software engineering. They manage the CodeFuse AI Coding and DeRisk AIOps communities, which encompass pre-trained Code LLMs, agents, frameworks, toolchains, and databases. Their team has also contributed to agent development platforms Tbox and WeaveFox, with the latter being an AI frontend development platform launched by Ant Group.
Dr. Yuan Tian is an Assistant Professor in the School of Computing at Queen's University, Canada. Her research focuses on applying artificial intelligence and machine learning techniques to solve software engineering challenges, particularly in the areas of code analysis, technical debt management, and developer productivity enhancement. Dr. Tian received her Ph.D. in Information Systems from Singapore Management University in May 2017 under the supervision of Prof. David Lo (IEEE/ACM fellow). Prior to joining Queen's University, she worked as a data scientist at the Living Analytics Research Centre (LARC) in Singapore. She has also conducted research visits at Carnegie Mellon University in 2015 and Inria Paris in 2013. Dr. Tian's research spans several key areas in software engineering with AI: Automatic technical debt, bug, and code change management LLM applications for code transformation and generation Human-AI collaboration in software development Analysis of developer interactions with AI tools like ChatGPT Mining software repositories for insights into development practices Her recent work has increasingly focused on leveraging Large Language Models to address software engineering challenges, with publications examining code translation, technical debt identification, and the dynamics of developer-AI interactions. Her research demonstrates a strong empirical approach, often analyzing large datasets from GitHub and other software development platforms. Dr. Tian has received recognition for her work, including the Best Research Paper Award at AI Foundation Models and Software Engineering (Forge), 2024 for her paper "Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation." Dr. Tian leads the RISE research lab at Queen's University, which currently includes 5 PhD students, 2 MSc students, and 2 undergraduate research assistants. She has successfully supervised several graduate students to completion, with alumni now working at institutions including Duke University and Veeva Systems. Her research is supported by funding including an NSERC Alliance-Mitacs project on "Pragmatic Automated Code Transformation Leveraging Large Language Models" in collaboration with industry partner Ross Video. The RISE lab (Goodwin 621) is dedicated to developing reliable and intelligent support for software engineering. The lab's current research focuses on three main thrusts: automatic technical debt/bug/code change management, LLM for code transformation, and human-AI collaboration in software development.
Maliheh Izadi is a tenure-track assistant professor in the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), Netherlands. She leads the AISE (AI-enabled Software Engineering) research lab and serves as the scientific manager for the TU Delft/JetBrains Collaboration (AI4SE). She is also a member of the Software Engineering Research Group (SERG) at TU Delft and actively supervises PhD, MSc, and BSc students. Dr. Izadi's research focuses on enhancing software development tools through building smarter software and tailoring machine learning and NLP techniques to source code. Her primary interests include building and tailoring large language models (LLMs) and autonomous agents to source code, with specific focus areas including evaluation, benchmarking, model memorization, IDE integration, in-IDE Human-AI interaction, and extending models' capabilities to low-resource programming languages. Her work bridges the gap between deep learning and source code analysis, with applications in code understanding, generation, documentation, and developer productivity enhancement. Her recent publications reveal a strong trend toward evaluating and improving LLMs for code, with emphasis on safety, usability, and efficiency. She has made significant contributions to benchmark development, harmfulness assessment of LLMs in programming contexts, and improving the integration of AI tools within development environments. Her research consistently addresses real-world industrial challenges while advancing theoretical understanding of model behavior. Google Research Scholar Award (2025) for proposal on Tackling LLM Hallucinations Amazon Research Award (2024) for proposal on Addressing Memorization in Code LLMs ACM SIGSOFT Distinguished Paper Award (2025) for How Much Do Code Language Models Remember? ACM SIGSOFT Distinguished Paper Award (2024) for A Transformer-Based Approach for Smart Invocation of Automatic Code Completion Best Tool Award at SaTML'22 competition for STACC Best Tool Award at NLBSE'22 competition for Catiss Dr. Izadi actively collaborates with industry partners, particularly JetBrains Research, where she leads the AI4SE ICAI lab. She has supervised multiple PhD and master's students and has served on program committees for major software engineering conferences including ASE, ICSE, FSE, and MSR. Her research has been published in top venues such as IEEE/ACM ICSE, FSE, ASE, TOSEM, EMSE, MSR, ICSME, SANER, and JSS. She is also organizing the First International workshop on Autonomous Agents in Software Engineering (AgenticSE) co-located with ASE'25.
Tianyi Zhang is a Tenure-Track Assistant Professor of Computer Science and Societal Impact Fellow at Purdue University's College of Science, where he leads the Human-Centered Software Systems Lab. His research focuses on building interactive intelligent systems that synergize human expertise with machine intelligence to improve programming productivity and software robustness. Dr. Zhang's research interests span Software Engineering, Human-Computer Interaction, and Artificial Intelligence. His work centers on developing systems that augment human intelligence with data-driven insights and augment machine intelligence with human guidance, primarily for programming domains including software developers, novice programmers, and computer end-users. His research on code mining and visualization helps programmers make more informed decisions through GitHub and Stack Overflow analysis, while his work on program synthesis assists novices with enriched feedback loops and interpretability. His recent publications (2024-2025) demonstrate a strong focus on leveraging large language models for code generation, program repair, and data wrangling, with particular emphasis on interactive systems that incorporate human feedback. This research direction shows consistent growth in understanding the intersection between human cognition and AI capabilities in programming contexts. Awards and Recognition: NSF Career Award for research on safe and reliable LLM-based code generation Amazon Research Award for human-in-the-loop deep learning optimization Best Paper Honorable Mention Award from SIGCHI for visualizing examples of deep neural networks Best Paper Honorable Mention Award from VAHC for interactive cohort analysis Dr. Zhang actively serves the research community as Program Committee member for major conferences including ICSE, ASE, FSE, CHI, and UIST. His service includes chairing workshops and student research competitions, demonstrating his commitment to mentoring the next generation of researchers. His lab develops systems that address real-world challenges in programming productivity and software safety, with applications spanning from autonomous driving systems testing to data science workflows.
Qi Xin is an Associate Professor at the School of Computer Science, Wuhan University. He is a member of the Centre of Software Testing, Analysis and Reliability (CSTAR) and serves on program committees for major software engineering conferences including ASE, ISSTA, and ICSE. Dr. Xin received his Ph.D. from Brown University under the supervision of Dr. Steven Reiss and was previously a Postdoctoral Researcher at Georgia Institute of Technology working with Dr. Alex Orso. His research focuses on software engineering, particularly on developing automated and semi-automated approaches to improve software development processes and enhance software quality, security, and reliability. His recent work centers on automated program repair, software debugging, and program debloating. Dr. Xin has published extensively in top-tier software engineering venues, with research that frequently addresses practical challenges in real-world software systems. Dr. Xin's publication record shows a consistent focus on improving software reliability through automated techniques. His work spans from foundational program analysis to practical tool development, with several publications focusing on Android applications and addressing challenges specific to mobile software development. His recent work has begun exploring the application of large language models to software engineering problems, as evidenced by his 2025 paper on conversational LLM-based repair. ACM SIGSOFT Distinguished Paper Award for fault localization research Guest editor for Automated Software Engineering (AUSE) Special Issue Reviewer for ACM TOSEM, IEEE TSE, and EMSE journals Dr. Xin actively mentors students and serves the academic community through conference organization and journal reviewing. He teaches courses including Compiler Design and Software Testing and Practice at Wuhan University, bridging his research expertise with classroom instruction to train the next generation of software engineers.
Fiorella Zampetti is a researcher at the University of Sannio, Italy, specializing in empirical software engineering with a focus on continuous integration and delivery, static analysis tools, mining software repositories, and software maintenance and evolution. Her work bridges theoretical research with practical applications in real-world software development environments. Her research interests span multiple critical areas of modern software engineering, with particular emphasis on Experimental methodologies for evaluating software engineering practices Technical debt identification and management, especially in emerging domains like deep learning systems Continuous integration and delivery pipelines, including common anti-patterns and solutions Mining software repositories to extract valuable insights about development processes Software quality assessment through static analysis and empirical studies Analysis of her recent publications reveals an evolving research trajectory that has increasingly incorporated artificial intelligence and large language models into traditional software engineering concerns. Her 2023-2025 work shows a significant focus on how AI-generated code impacts software development practices, licensing concerns, and educational contexts. She has also maintained a strong thread of research on technical debt, particularly examining self-admitted technical debt across different software domains. Dr. Zampetti has been actively involved in the software engineering research community as a program committee member for major conferences including ASE, ICSE, ESEC/FSE, and ICSME. Her service on these committees demonstrates recognition by her peers as a subject matter expert in her research domains. Her research has practical implications for software development teams looking to improve their CI/CD practices, manage technical debt effectively, and understand the emerging challenges posed by AI-assisted development tools. She has conducted numerous empirical studies that provide evidence-based insights into software engineering practices across both open-source and industrial contexts.
Guowei Yang is a Senior Lecturer (equivalent to U.S. Associate Professor) at the School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland. His research bridges software engineering, machine learning, and programming languages, focusing on enhancing software and ML system reliability/security. Dr. Yang leads projects in symbolic execution, fuzzing techniques, Android app testing, and LLM-based tool development. Research Interests: Core interests include software testing automation, Android ecosystem analysis, ML system robustness, and symbolic execution optimization. His work emphasizes practical tools for API compatibility detection, security patch generation, and fuzzing efficiency. Recent projects leverage large language models for document-guided testing and dynamic vulnerability detection. Professional Service: Dr. Yang actively contributes to top-tier conferences as a program committee member (ICSE, FSE, ASE, ISSTA) and organizer (DSN 2024 Local Chair, TAIC PART Co-Chair). He mentors through Google Summer of Code and NSF Research Experiences for Undergraduates programs, and reviews for journals including IEEE TSE and ACM TOSEM.
Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI). His work spans computational linguistics, artificial intelligence, and biomedical informatics, with a focus on information extraction, question answering systems, and knowledge graph reasoning. Education: PhD in Computer Science (1994) and Computational Linguistics (2004), both from Saarland University. Neumann's research interests include multilingual natural language processing, low-resource language modeling, and neural architectures for information retrieval and biomedical relation extraction. He pioneered techniques in dense passage retrieval for Urdu, feature textualization for BERT interpretability, and low-rank temporal knowledge graph embeddings. Recent publications focus on cross-lingual transfer learning, code-mixed clinical text de-identification, and robust biomedical benchmarks. He has led EU/national projects in language technology and served on program committees for ACL, EMNLP, and LREC. His work with the EXCITEMENT Open Platform and DOMLIN system demonstrates expertise in textual entailment and fact verification, achieving top rankings in competitions like CLEF and TAC.
