Philipp Neumann is a Professor and Chair of High-Performance Computing at Helmut Schmidt University since 2019. Previously, he held roles including Senior Researcher at the German Climate Computing Center (2016–2019), Postdoc at the University of Hamburg (2017–2019), and completed his Habilitation in Scientific Computing at TU Munich (2019). He transitioned to DESY/Universität Hamburg in May 2024. Education : PhD (Dr. rer. nat.) in Scientific Computing at TU Munich (2008–2013) Habilitation in Scientific Computing at TU Munich (2019) Studies in Technomathematics at Friedrich-Alexander University Erlangen-Nuremberg (2003–2008) Research Interests focus on high-performance computing, parallel and distributed systems, computational science, and applications in climate modeling. His work bridges computational methods with medical challenges, including surgical simulation training, wound healing mechanisms, and gastrointestinal pathology. Professional Activities include leadership roles in major conferences such as steering committee member for ISPDC (since 2021) and program committee roles in IPDPS, ICCS, and IEEE Cluster. He also managed the DFG priority program SPPEXA (2013–2016). Labs/Teams include the Chair for High-Performance Computing at Helmut Schmidt University and collaborations with the German Climate Computing Center. His current work at DESY/Universität Hamburg likely expands these computational efforts into interdisciplinary research.
Michael Boutros is a Full Professor at Heidelberg University and Head of Division at the German Cancer Research Center (DKFZ). He currently serves as Dean of the Medical Faculty at Heidelberg University (since 2023) and Director of the Marsilius Kolleg (since 2020). He has held leadership roles including Coordinator of the Functional and Structural Genomics Program at DKFZ (2014–2023) and Acting Scientific Director (2015–2016). His academic base is within the Medical Faculty, focusing on molecular oncology and functional genomics. PhD, Witten/Herdecke University (1993–1996) Postdoctoral Research, Harvard Medical School (1999–2003) MPA, John F. Kennedy School of Government, Harvard University (1999–2001) Additional training: Cold Spring Harbor Laboratory, SUNY Stony Brook His research centers on Wnt signaling, functional genomics, and cancer pathways. He leads major research initiatives such as CRC 1324 on Wnt signaling and the ERC Synergy Grant DECODE. His work integrates high-throughput screening, CRISPR, and systems biology to dissect signaling networks in cancer and development. He has pioneered genome-wide RNAi and CRISPR screens to identify novel regulators of Wnt signaling across models. The 15 most recent articles reflect a strong focus on Wnt pathway regulation using functional genomics in both Drosophila and mammalian systems. Themes include high-throughput screening, CRISPR-based validation, cross-species conservation, and therapeutic targeting. Keywords span Cancer Biology, Systems Biology, and Signal Transduction, with subfields like RNAi, ubiquitination, stem cell regulation, and machine learning in image analysis. Michael Boutros has received numerous scientific honors: Elected member, Leopoldina National Academy of Sciences (2022) Elected member, Heidelberg Academy of Sciences (2022) EMBO Member (2013) ERC Advanced Grant (2012) Johann-Georg Zimmermann Research Award (2007) EMBO Young Investigator (2005) Member, 'Die Junge Akademie' (2003) He has been a recipient of the Emmy-Noether Program, McCloy Fellowship, Boehringer Ingelheim PhD Fellowship, Studienstiftung Fellowship, and Fulbright Fellowship. As a mentor and research leader, he has supervised numerous early-career scientists and coordinated large collaborative grants including the FP7 'CancerPathways' project. He currently serves as Speaker of the Research and Strategy Commission at Heidelberg University and Managing Director of the Health and Life Science Alliance Heidelberg Mannheim. He leads the CRC 1324 on Wnt signaling and is Coordinating PI of the ERC Synergy Grant DECODE. He is also Spokesperson of DFG Research Group 1036 and Coordinator of the former FP7 Coordinated Project 'CancerPathways'. His lab employs cutting-edge functional genomics tools to decode signaling networks in cancer and development.
Michael Pradel is a full professor at the University of Stuttgart, specializing in software engineering, programming languages, and machine learning. He will join CISPA as a faculty member from September 2025 while retaining his Stuttgart position. His research focuses on: Neuro-symbolic software analysis Web application analysis Dynamic analysis and test generation Quantum software testing Machine learning for code Recent publications address: LLM-based program repair (RepairAgent, Treefix) WebAssembly analysis (Wasm-R3, LintQ) Python security and analysis (DyLin, DyPyBench) Quantum program analysis (LintQ) Scientific awards: Ernst-Denert Software Engineering Award Emmy Noether grant (1.3M Euro) ERC Starting Grant (1.5M Euro) 3x ACM SIGSOFT Distinguished Paper Award at FSE ACM Distinguished Member Best Paper/Distinguished Paper Awards at ISSTA, ASE, ASPLOS, MSR Key contributions include: DeepBugs for name-based bug detection Getafix for automated bug fixing LintQ for quantum program analysis DyLin for Python dynamic analysis Neuro-symbolic developer tools
Yannic Noller is a Professor at the Faculty of Computer Science at Ruhr University Bochum (RUB), leading the Software Quality group. Previously, he held positions as Assistant Professor at Singapore University of Technology and Design (SUTD) and Research Assistant Professor at National University of Singapore (NUS). His research focuses on automated software engineering, including program repair, machine learning analysis, and software testing. He earned his Ph.D. from Humboldt-Universität zu Berlin under Prof. Lars Grunske, with a thesis on hybrid differential software testing. Education: Ph.D. in Computer Science (2016-2020, Humboldt-Universität), M.Sc. (2013-2016, University of Stuttgart), B.Sc. (2010-2013, University of Stuttgart). Research interests include automated program repair techniques, machine learning model analysis, and intelligent tutoring systems for programming education. Notable contributions include HyDiff (hybrid differential analysis tool) and CPR (concolic program repair). Awards include the Distinguished Artifact Reviewer at ISSTA'2021 and multiple scholarships for academic excellence. Teaching includes courses on software engineering, requirements engineering, and automated software engineering.
Dr. Yu Huang is an Assistant Professor in the Department of Computer Science at Vanderbilt University's School of Engineering, with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her academic journey began with a BS in Aerospace Engineering from Harbin Institute of Technology in China (2011), followed by an MS in Computer Engineering from the University of Virginia (2015), and culminated with a PhD in Computer Science and Engineering from the University of Michigan in 2021 under Professor Westley Weimer. Dr. Huang's research bridges human cognition and machine intelligence to enhance software development. Her work spans software, hardware, AI, medical imaging (fMRI/fNIRS), eye tracking, and mobile sensing through collaborations with Security, Education, Psychology, and Neuroscience researchers. She leads the MIND Lab (Mixed INtelligence Development for programming lab), investigating programming expertise formation, code comprehension processes, cognitive error patterns, and diversity in programming communities. Her innovative approach combines empirical human studies with AI model development to create more effective programming tools. Her recent publications reveal a growing emphasis on leveraging human attention data to improve code language models, analyzing cognitive biases in security contexts, and examining social factors in technical communication. The research shows strong interdisciplinary connections between neuroscience, psychology, and software engineering, with increasing applications of LLMs in developer tooling. Dr. Huang's work consistently demonstrates how understanding human cognition can inform better AI systems for programming tasks. Dr. Huang has received numerous prestigious recognitions including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards (ICSE 2019, FSE 2023, ICSE 2024). Her lab has earned the Best Presentation Award at GI2024, while her students have received the Richard Bennett/Dorothy Danforth Compton Prize scholarship and the C. F. Chen Best Paper award. She actively mentors a diverse team of graduate students (Yifan Zhang, Zach Karas, Zihan Fang, Yueke Zhang, Jiahao Zhang) and undergraduate researchers, with many former students advancing to top institutions (Stanford, Harvard, Duke, UC Berkeley) and organizations (NASA JPL). Her research is supported by a 4-year NSF grant, GitHub Tech for Social Good funding, and the Provost's Faculty Immersion Vanderbilt Grant, enabling comprehensive studies of human-AI collaboration in software engineering. The MIND Lab maintains a strong collaborative culture, frequently working with Professor Kevin Leach's research group and organizing retreats to locations like Radnor State Park and the Great Smoky Mountains. This environment fosters innovation at the intersection of human cognition and software engineering while supporting the professional development of emerging researchers in the field.
Chang Xu is a Professor and Ph.D. supervisor at Nanjing University, affiliated with the State Key Laboratory for Novel Software Technology, School of Computer Science, and Institute of Computer Software (ICS). He has been a full-time faculty member since 2010, when he joined as an associate professor and was later promoted to full professor in 2015. Education: Ph.D. from The Hong Kong University of Science and Technology (HKUST) in 2008 (advisor: Prof. S.C. Cheung) M.Eng. from Institute of Software, Chinese Academy of Sciences (ISCAS) in 2003 B.Eng. from University of Science and Technology of China (USTC) in 2000 Research Interests: Professor Xu's research focuses on big data software engineering, intelligent software testing and analysis, and adaptive and autonomous software systems. His recent work centers on constructing and providing runtime support for intelligent software in open environments, with emphasis on inconsistency detection and resolution for environments, and quality assurance for adaptive, concurrent, learning-based, smartphone-based, and spreadsheet-based applications. His work bridges theoretical foundations with practical applications in software engineering, particularly in program analysis, software testing, and self-adaptive systems. Scientific Awards: ACM SIGSOFT Distinguished Paper Award from ICSE 2025 Best Student Paper Award from EUROSYS 2025 ACM Distinguished Member in 2024 Best Paper Award from SOSP 2023 Best Paper Candidate from ISSRE 2022 Yangtze River Scholar by the Ministry of Education in 2021 Multiple ACM SIGSOFT Distinguished Paper Awards from conferences including ASE, ICSE National Science and Technology Progress Award (Second Class) in 2011 Academic Service and Advising: Professor Xu has served on numerous program committees for top software engineering conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He is an editorial board member for several journals including Journal of Computer Science and Technology and Frontiers of Computer Science. He has supervised numerous Ph.D. and MSc students, with research topics spanning program analysis, software testing, self-adaptive systems, and more. His students have gone on to successful careers in both academia and industry. Research Groups: Professor Xu is associated with the SPAR research group at Nanjing University and the CASTLE research group at HKUST, focusing on software analysis, reliability, and testing.
James Urquhart Allingham is a Research Scientist at Google DeepMind , working on the Gemini project. He completed his PhD in the Machine Learning Group at the University of Cambridge under the supervision of José Miguel Hernández-Lobato, with funding from EPSRC, the Michael E. Fisher Studentship in Machine Learning, and the Qualcomm Innovation Fellowship. He was also part of the ELLIS PhD program, advised by Eric Nalisnick at AMLab UvA. Current affiliation: Google DeepMind (Research Scientist) PhD: University of Cambridge (Machine Learning Group) Academic networks: ELLIS PhD program, Darwin College His research focuses on the intersection of Bayesian deep learning and probabilistic methods in deep learning. Key areas include deep generative models , zero-shot classification , prompt engineering , Monte Carlo gradient estimation , and applications to sustainability and climate change . His work has explored energy-based models , neural architecture search , and equivariance in convolutional networks . Selected scientific awards and grants include the Michael E. Fisher Studentship , Qualcomm Innovation Fellowship , and MPhil in Advanced Computer Science with Distinction . He has collaborated with institutions such as the Amsterdam Machine Learning Group (AMLAB) and University of the Witwatersrand .
Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.
Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.
Ralf Zimmer is a Full Professor for Practical Informatics and Bioinformatics at Ludwig Maximilian University of Munich (LMU) since 2001, affiliated with the Department of Informatics in the Faculty of Mathematica, Informatics and Statistics. He concurrently serves as Head of Section III and Head of the Research Group Network Regulation and Modeling / Machine Learning at the Leibniz Institute for Food Systems Biology at TUM (Leibniz-LSB@TUM) in Freising, Germany. His academic foundation includes a Diploma with distinction in Computer Science, Applied Mathematics and Operations Research from the University of Bonn (1981-1986), followed by a summa cum laude Doctorate in Computer Science and Applied Mathematics from CAU Kiel in 1990, where he received dual honors: the CAU Dissertation Award and Best Dissertation Award. Zimmer's research pioneers the integration of bioinformatics, systems biology, and machine learning to decode molecular food-consumer interactions. His group develops causal system models for biological networks, validated through in silico simulations and multi-omics perturbation experiments (transcriptomics/proteomics). Core methodologies include network regulation modeling, algorithmic bioinformatics, and database construction linking food compounds to biochemical networks and cellular phenotypes, with translational goals for food and biotech innovation. His 14 most recent publications (2012-2024) reveal dominant trends in multi-omics immunology and cardiovascular research, featuring computational innovations for high-throughput data analysis. Key themes include host-pathogen dynamics (viral infections), inflammatory disease mechanisms (atherosclerosis), and methodological advances in proteomics/transcriptomics, consistently bridging fundamental bioinformatics with clinical applications. Major scientific recognitions include: CAU Dissertation Award and Best Dissertation Award (1990) Director of LMU's Informatics Department (2010-2012) DFG Review Board membership for biomedical foundations (2008-2016) Leadership of the DFG Bioinformatics Munich Center (2001-2008) Academic Senate election at LMU München (2011) Zimmer directs LMU/TUM's joint B.Sc./M.Sc. bioinformatics programs since 2001 as founding architect of the DFG-funded Bioinformatics Munich initiative. His educational leadership spans spokesperson roles for international training groups (IRTG RECESS), collaborative research centers (SFB1123 Atherosclerosis), and elite programs (Data Science, Munich Center for Machine Learning). Grant stewardship includes directing the DFG Bioinformatics Munich Center and shaping national funding policy via the DFG review board. At Leibniz-LSB@TUM, his research group pioneers databases connecting food compounds to cellular phenotypes through molecular networks, collaborating with Munich universities, clinics, and biotech partners to develop high-throughput sequencing/proteomics applications for future food and health innovations.
Steve Zdancewic is the Schlein Family President's Distinguished Professor and Associate Chair in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He is a leading researcher in programming languages, formal methods, and computer security with over two decades of impactful contributions to the field. His research interests span programming languages, type theory, logic, computer security, quantum programming, and formal verification. Zdancewic has made significant contributions to information-flow security, memory safety, program synthesis, and the verification of low-level systems. His work often bridges theoretical foundations with practical applications, particularly through the development of verified systems using Coq and other proof assistants. Analysis of his recent publications reveals a strong focus on formal verification techniques, particularly using Interaction Trees and the Coq proof assistant. His research trajectory shows consistent evolution from foundational work on information-flow security toward increasingly sophisticated verification of complex systems including LLVM, quantum computing, and distributed systems. His work demonstrates a commitment to building practically useful verification tools while maintaining rigorous theoretical foundations. Distinguished Paper Award for Semantics for Noninterference with Interaction Trees (ECOOP 2023) Schlein Family President's Distinguished Professor (2021) Distinguished Paper Award for Interaction Trees (POPL 2020) Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching (2018) IEEE MICRO top picks (2013) Alfred P. Sloan Fellow (2009-2010) NSF CAREER award (2004) Zdancewic has advised numerous PhD students who have gone on to successful careers in academia and industry. His research has been supported by significant grants from NSF, including the NSF Expedition on the Science of Deep Specification. He is actively involved in multiple major research projects including Vellvm (verified LLVM), DeepSpec, and quantum programming verification. Zdancewic also co-organizes Penn's PL Club programming languages research group with Benjamin Pierce and Stephanie Weirich.
Professor Sabine Eming is a Principal Investigator at the Department of Dermatology & FMNS at the University of Cologne, affiliated with the CMMC and collaborating with CECAD. Her research focuses on the molecular basis of age-related skin pathologies and regenerative responses. Investigates tissue regeneration, immunometabolism, and TOR signaling Develops therapeutic strategies for injured/aging tissues Uses cross-species models (mice, zebrafish, Drosophila, humans) Her work bridges basic science and clinical expertise to translate findings into therapeutic approaches, particularly examining: Metabolic reprogramming in wound healing Immune system's role in regeneration vs. scarring Lipid synthesis and filaggrin processing in skin barrier formation Scarless repair mechanisms in zebrafish vs. mammals She leads research into molecular control systems including: Cell death regulation (FADD-RIPK3 pathways) Nutrient-sensing TOR pathway in skin aging Glutamine metabolism in stem cell maintenance Her group develops genetically modified mouse models and collaborates on clinical trials for impaired healing conditions.
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.
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.
