Matthieu Sozeau is a prominent researcher at Inria in the Gallinette team in Nantes, France, and a key contributor and coordinator of the Coq/Rocq proof assistant project. His work bridges theoretical computer science and practical software development, focusing on creating reliable formal verification tools. His research interests span Type Theory, Proof Assistants, Functional Programming, and Unification. He has made significant contributions to the development of Coq (recently renamed to Rocq Prover), particularly through the MetaCoq project which aims to verify Coq's kernel within Coq itself, the Equations plugin for dependent pattern matching, and CertiCoq, a verified compiler from Coq to assembly. His work enables stronger guarantees about formalized mathematics and verified software. Sozeau's publications reveal a consistent focus on foundational aspects of proof assistants. His recent work includes verified type checking ('Coq Coq Correct!'), verified extraction from Coq to OCaml, and sort polymorphism for proof assistants. These contributions advance both theoretical understanding and practical implementation of dependently-typed programming languages. Distinguished paper award for Verified Compilation from Coq to OCaml at PLDI'24 As an academic mentor, Sozeau has supervised PhD students including Théo Winterhalter and Antoine Allioux. He regularly teaches courses on proof assistants, notably at MPRI (Master Parisien de Recherche en Informatique), and actively participates in the academic community through program committees, invited talks, and workshops. His work has significantly influenced both the theoretical foundations and practical applications of interactive theorem proving.
Yuyu Zhou is a Professor in the Department of Geography at The University of Hong Kong. With an extensive publication record of 301 papers and over 18,000 citations, Dr. Zhou is a leading researcher in urban environmental studies, climate change, and sustainability science. Dr. Zhou received their PhD in Environmental Science from the University of Rhode Island (2004-2008) and previously worked as a Research Scientist at Pacific Northwest National Laboratory's Joint Global Change Research Institute (2010-2015). They currently serve as Chief Editor for Earth System Science Data (Copernicus Publications), Associate Editor for Ecological Processes, and Section Editor for All Earth. Dr. Zhou's research focuses on the intersection of urbanization, climate change, and environmental sustainability. Their work spans several key areas including urban heat island effects, vegetation phenology in urban environments, energy modeling, and sustainable urban development. Through innovative remote sensing approaches and spatial analysis, Dr. Zhou investigates how urban environments respond to and influence global environmental change. Analysis of Dr. Zhou's recent publications reveals a strong emphasis on urban environmental challenges, with particular attention to urban heat islands, vegetation dynamics, and climate change impacts in cities. Their work combines remote sensing data with ground observations to develop high-resolution models of urban environmental processes. Recent research has focused on urban greening effects, building energy use under climate change, and environmental justice issues related to urban heat exposure. Dr. Zhou has received significant recognition for their work, as evidenced by the high citation count of their publications. Their research has important implications for urban planning, climate adaptation strategies, and sustainable development policies worldwide. As an educator and mentor, Dr. Zhou advises numerous graduate students and collaborates with researchers globally. Their work with international teams has resulted in significant contributions to understanding urban environmental systems across different geographical contexts.
Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
Dr. Anke Kirchner is a Researcher at the Leibniz Institute for Solid State and Materials Research Dresden (IFW Dresden) in the Department of Functional Oxide Layers and Superconductors. Her work focuses on superconducting materials, magnetic systems, and advanced thin-film deposition techniques for applications in levitation and energy-efficient transportation. Her research spans high-temperature superconductivity, nanocrystalline magnetic materials, and REBCO coated conductor development. Key contributions include optimizing artificial pinning centers in superconducting films, analyzing grain boundary structures in permanent magnets, and pioneering microacoustic sol atomization (MASA) for thin-film deposition. Her interdisciplinary approach bridges fundamental materials science with practical engineering applications in transportation and energy. Analysis of her 15 most recent publications (2000-2024) reveals consistent focus on superconducting levitation technologies and REBCO conductor performance enhancement. Her work demonstrates evolution from foundational studies of NdFeB magnet microstructures to cutting-edge innovations in coated conductor joints and tape-stack levitation systems, with strong emphasis on nanoscale characterization and process optimization. No scientific awards are mentioned in the provided text. Information regarding student advising, doctoral supervision, or research grants is not specified in the source material. The department specializes in oxide layer engineering and superconductor development, with Dr. Kirchner contributing to IFW Dresden's internationally recognized research on quantum levitation and magnet-superconductor interactions, frequently collaborating with Prof. L. Schultz on applied superconductivity projects.
Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.
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.
Thomas Blank is a Professor of Ancient Cultural History at Johannes Gutenberg University Mainz (JGU) since August 2016, affiliated with the Department of History. Previously, he held positions as Junior Professor of Greek History at Saarland University (2013-2016) and served as Acting Chair of Ancient History at the University of Münster (2019-2020). His academic trajectory includes research assistantships in Tübingen and Mainz. Education: Dr. phil. in Ancient History (summa cum laude) from Eberhard Karls Universität Tübingen (2012) State Examination in History, Latin, and Greek from Eberhard Karls Universität Tübingen (2007) Undergraduate studies in History, Latin, and Greek at Eberhard Karls Universität Tübingen (2001-2007) Research Focus: Blank's work centers on ancient rhetoric's intersection with political philosophy (especially in Classical Athens), Roman religious practices, and cultural communication mechanisms. He investigates how social 'third spaces' form through religious secrecy, neighborly relations, and theatrical performance. Current projects include urban foreignness studies and the cultural role of Graeco-Roman rhetoric. Publication Trends: His 15 most recent articles demonstrate consistent focus on Isocratean rhetoric, political communication in Athenian/Roman contexts, and reception of antiquity in modern media. Methodologically, they blend historical analysis with literary criticism and cultural theory, frequently examining power dynamics through rhetorical frameworks. Awards & Fellowships: Research Scholarship from Friedrich-Ebert-Stiftung (2008-2010) Study Grant from Friedrich-Ebert-Stiftung (2002-2007) Academic Leadership: Blank directs doctoral candidates in projects ranging from Athenian legal systems to Ptolemaic athletic competitions. He leads the DFG-funded graduate program 'Urban Difference' and co-leads subprojects in the LOEWE Center 'DynaRel' and MSCA-DN 'TheSPIS'. As liaison lecturer for the Friedrich-Ebert-Stiftung since 2015, he mentors scholarship recipients. Professional Engagement: Secretary General of the International Society for the History of Rhetoric (ISHR) and founding president of Societas Isocratica. He has held administrative roles including Managing Director of JGU's History Department (2022-2023) and serves on multiple academic committees.
Prof. Dr. Hannah Schildberg-Hörisch is a Professor of Economics at the Faculty of Business and Economics, Heinrich Heine University Düsseldorf, specializing in Behavioral Economics and Empirical Economic Research. She serves as Senior Research Fellow at the Max Planck Institute for Research on Collective Goods and leads the Düsseldorf Institute for Competition Economics (DICE). Her research spans self-control, socio-emotional skills, economic preferences, affirmative action, and behavioral interventions, often using experimental methods and longitudinal data. Education: PhD in Economics (University of Munich, 2008), MSc in Economics (University of Mannheim, 2004), Studies in Economics, Political Sciences, and Environmental Science (University of Heidelberg, 2001). Positions: Professor (DICE, HHU Düsseldorf, 2016–present), Senior Research Fellow (Max Planck Institute, 2021–present), Postdoctoral Researcher (University of Bonn, 2009–2016), Federal Ministry of Economics and Technology (Berlin, 2008–2009), Research Assistant (University of Munich, 2004–2008). Her research combines behavioral economics, psychology, and public policy, focusing on self-control stability, socio-economic disparities in child development, and the effectiveness of affirmative action. Recent work examines spatial patterns in preference formation, parenting impacts on skills, and behavioral interventions during public health crises like the COVID-19 pandemic. Key contributions include: Identifying sensitive periods for socio-emotional skill development. Analyzing self-control’s predictive power for life outcomes. Evaluating the interplay between surveillance and behavioral compliance. Investigating fairness perceptions of affirmative action policies.
Ori Lahav is a faculty member in the School of Computer Science at Tel Aviv University. His research is generously supported by an ERC Starting Grant and an ISF Grant. He actively supervises PhD and MSc students, and seeks highly motivated candidates for postdoc, PhD, and MSc positions in programming language theory, concurrency, and formal methods. Dr. Lahav completed his PhD at Tel Aviv University under the supervision of Arnon Avron. In 2014, he was a postdoctoral researcher at Tel Aviv University hosted by Mooly Sagiv. From 2014 to September 2017, he was a postdoctoral researcher at MPI-SWS in Germany hosted by Viktor Vafeiadis and Derek Dreyer. His primary research areas focus on programming languages and verification, with specialization in concurrency and relaxed memory models. He also has significant interests in proof-theory, semantics of non-classical logics, and automated reasoning. His work bridges theoretical foundations with practical applications in programming language design and implementation. Dr. Lahav's publication record shows a consistent trajectory of high-impact research in top-tier conferences including PLDI, POPL, OOPSLA, and ESOP. His recent work (2023-2025) demonstrates continued leadership in memory models, concurrency semantics, and verification techniques. His research spans both theoretical contributions in denotational semantics and practical tools for verification. Best Paper Award DISC 2024 Best Student Paper Award DISC 2024 Distinguished Artifact Award ESOP 2022 Distinguished Paper Award OOPSLA 2021 Kleene Award for Best Student Paper LICS 2013 Dr. Lahav actively advises students including Yoav Ben Shimon, Yotam Dvir, Amir Karniel, and Roy Margalit (PhD students), Yuval Katsman Ezra (MSc student), and has alumni including Ori Saporta (MSc) and Abhishek Kr Singh (postdoc, now Assistant Professor at IIIT Hyderabad). He has organized significant events including VMCAI 2024 and Dagstuhl Seminars on persistent programming. His teaching portfolio includes courses on Shared Memory Concurrency Semantics, Programming Language Foundations, and Software Foundations in Coq.
Insa Feinkohl is a Professor at the Chair of Medical Biometry and Epidemiology within the Faculty of Health at the University of Witten/Herdecke . Her research focuses on risk factors for cognitive dysfunction and mental health in older adults, particularly post-surgery, with emphasis on metabolic and cognitive risk factors. Bachelor of Science (BSc) in Psychology (1 st class honors) – University of Dundee (2006-2009) Master of Science (MSc) in Psychology of Individual Differences (with distinction) – University of Edinburgh (2009-2010) PhD in Community Health Sciences – University of Edinburgh (2010-2014) Post Doc in Knowledge Construction Group – Leibniz Institute for Knowledge Media, Tübingen (2014-2015) Postdoc in Molecular Epidemiology Group – Max Delbrück Center, Berlin (2015-2022) Habilitation in Molecular Epidemiology – Charité Universitätsmedizin Berlin (2021) Her research integrates medical biometry and epidemiology to study postoperative cognitive dysfunction (POCD), delirium, and aging-related cognitive decline. Key areas include biomarker validation (e.g., leptin, interleukins), brain connectivity (dopaminergic networks, thalamus), and metabolic risk factors (diabetes, obesity). She contributed to the BioCog project , an EU-funded initiative for personalized risk prediction of postoperative cognitive impairment. Her recent publications highlight trends in perioperative neuroscience, including brain mineralization, cytokine associations with neurocognitive disorders, and structural/functional imaging in delirium. Articles also explore metabolic syndrome, cognitive reserve, and delirium prediction models using machine learning. Insa Feinkohl is affiliated with major academic societies, including the German Society for Epidemiology , German Society for Medical Informatics, Biometry and Epidemiology , and the German University Association .
Prof. Dr. Steffi Pohl holds the Chair of Methods and Evaluation/Quality Assurance at the Faculty of Education and Psychology, Freie Universität Berlin since 2019. Previously, she was a Junior Professor (2013-2019) and researcher at institutions including Friedrich-Schiller University Jena and University of Bamberg. She earned her PhD in Psychometrics from Friedrich-Schiller University Jena (2010) and holds a Diplom in Psychology (2004) from Freie Universität Berlin. Her research focuses on advanced statistical methods in educational and psychological testing, including response time modeling, missing data mechanisms, and causal inference in assessment. She has pioneered work on test engagement detection via response patterns and log数据分析. Awards include the 2020 Psychometric Society Early Career Award and 2011 Gustav A. Lienert Dissertation Prize. Pohl serves on editorial boards of Psychometrika , Journal of Educational and Behavioral Statistics , and Zeitschrift für Psychologie . She chairs the Berlin School of Mind and Brain faculty and holds governance roles in academic senates. Her research projects include the National Educational Panel Study (NEPS) and collaborations on test design innovations. Current teaching includes advanced courses in empirical research methods, multivariate statistics, and educational measurement. She actively develops methodologies for analyzing log数据 from digital testing platforms and improving assessment reliability in large-scale studies.
Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.
Pramita Bagchi is an Assistant Professor in the Department of Biostatistics & Bioinformatics at The George Washington University (GWU), affiliated with the Milken School of Public Health. She holds a Ph.D. in Statistics from the University of Michigan and completed a postdoctoral fellowship at Ruhr Universitat Bochum in Germany. Her research focuses on developing statistical methodologies for analyzing dependent data, particularly in high-dimensional and functional contexts such as time series, spatial data, and functional observations. Education: Ph.D. in Statistics, University of Michigan, Ann Arbor Postdoctoral Research, Department of Mathematics, Ruhr Universitat Bochum Research Interests: Functional Data Analysis Spatiotemporal Modeling High-Dimensional Data Non-Parametric Inference Healthcare Applications Methodological Development for Biomedical Data Publications span statistical theory (e.g., functional time series analysis) and applied health research (e.g., heart transplant biomarkers, acculturation effects in immigrant health). Recent work emphasizes methodological innovations for complex data structures, blending theoretical rigor with real-world applications in cardiology and epidemiology. Grants & Collaborations: NSF Grant: "Empirical Frequency Band Analysis for Functional Time Series" (2022–2025) INOVA Hospital Grant: "Clinical Data Analytics in Cardiac Transplantation" (2020–2023) Teaching includes advanced courses like Mathematical Statistics I (STAT 872), reflecting her expertise in statistical theory and methodology.
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.
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.
