Find Academic Supervisors

Zeyu SunView profile

Researcher

• Software Engineering
• Program Repair
• Machine Learning for Code
• +4 more

Zeyu Sun is a researcher at the Institute of Software, Chinese Academy of Sciences, with an extensive publication record in premier software engineering conferences including ASE, ICSE, and ESEC/FSE from 2020 through 2025. Their academic service includes program committee membership for multiple conferences, demonstrating recognition as a domain expert in software engineering and programming languages. Dr. Sun's research program bridges traditional software engineering with artificial intelligence, focusing on neural program repair, software testing, and compiler optimization. Their work develops innovative techniques for code understanding and generation, with particular emphasis on type-aware repair systems, syntax-guided editing approaches, and context-enriched representations of code changes. Recent publications show increasing sophistication in integrating machine learning with software engineering tasks, moving from basic program analysis to specialized architectures that maintain structural integrity of code. Analysis of publication trends reveals a consistent focus on improving software quality through AI-enhanced methods, with expanding applications across compiler design, smart contract security, and fairness testing. The research demonstrates both technical depth in core software engineering challenges and breadth across multiple application domains, reflecting an evolving research trajectory that continually identifies new intersections between AI and software development practices. Dr. Sun actively contributes to the academic community through program committee service at major conferences including ASE, ICSE, and ESEC/FSE across multiple years. This sustained service commitment indicates peer recognition as a subject matter expert. While specific grant details aren't provided in available materials, the consistent publication output across multiple funding cycles suggests successful acquisition of research support for their work in software engineering and AI for code.

Liqian ChenView profile

Professor

• Program analysis
• Program verification
• Abstract interpretation
• +3 more

Liqian Chen is a Full Professor and PhD Supervisor in the College of Computer Science and Technology at the National University of Defense Technology (NUDT) in Changsha, China. With a prolific research career spanning over a decade, Professor Chen has established himself as a leading expert in program analysis, verification, and automated program repair. His work bridges theoretical foundations with practical applications, particularly in the areas of abstract interpretation and numerical program analysis. Professor Chen's primary research interests include: Program analysis and verification Abstract interpretation Automated program repair Floating-point analysis Neural network verification His research focuses on developing rigorous theoretical frameworks for program analysis while ensuring practical applicability to real-world software systems. A significant portion of his work addresses the challenges of numerical accuracy in software, particularly in floating-point computations, and has extended these techniques to the emerging domain of neural network verification. Analysis of Professor Chen's recent publications reveals several key trends in his research trajectory. His work has evolved from foundational abstract interpretation techniques to address increasingly complex software systems, including neural networks. There's a clear progression from theoretical developments in abstract domains to practical applications in software verification and repair. His recent work demonstrates a growing emphasis on the intersection of traditional program analysis with machine learning systems, particularly in verifying neural network behavior and addressing numerical instability in AI systems. Professor Chen has received notable recognition for his contributions to the field: ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) Best Paper Award (APSEC 2017) Best Paper Award Nomination (EMSOFT 2015) As an academic advisor, Professor Chen supervises numerous PhD and Master's students working on cutting-edge research in program analysis and verification. His team has developed several influential tools including AutoRNP (for automatically detecting and repairing floating-point errors), Software Apron (a library for numerical abstract domains), and F-IKOS (a static analyzer for Fortran programs). Professor Chen has secured substantial research funding to support his work, though specific grant details are not provided in the available information. He actively serves the research community through program committee roles for major conferences including ASE, SAS, and VMCAI, and as a guest editor for journals such as Automated Software Engineering Journal and Journal of Systems Architecture. Professor Chen leads a vibrant research group focused on high-confidence software technologies. His team, comprising PhD students, Master's students, and research engineers, collaborates closely on projects spanning from theoretical foundations of program analysis to practical tool development. The group maintains strong connections with both academic institutions and industry partners, facilitating the transfer of research innovations to real-world applications. Their work is characterized by rigorous theoretical underpinnings combined with practical validation on real software systems.

Chao NiView profile

Associate Professor

• Software Quality Assurance
• Mining Software Repositories
• Software Security
• +3 more

Chao Ni is an active Associate Professor at the School of Software Technology, Zhejiang University, specializing in software engineering with a focus on software quality assurance and mining software repositories. His research spans multiple key areas including vulnerability detection, defect prediction, automated program repair, and software security. He has consistently published in top-tier software engineering conferences such as ASE, ICSE, ESEC/FSE, and ISSTA, demonstrating significant contributions to the field. Ni also serves on program committees for major conferences, indicating recognition by his peers in the software engineering community. Dr. Ni's research interests center around Software Quality Assurance and Mining Software Repositories , with particular emphasis on vulnerability detection, defect prediction, and automated program repair. His work often combines traditional software engineering approaches with machine learning techniques to address challenging problems in software development and maintenance. Recent publications show a strong focus on security aspects of software engineering, including smart contract security, JavaScript breaking changes, and vulnerability detection in C/C++ code. Analysis of Dr. Ni's recent publications reveals a consistent research trajectory focused on improving software quality through advanced analysis techniques. His work spans multiple subdomains including vulnerability detection (particularly in smart contracts and C/C++ code), defect prediction (especially just-in-time approaches), and automated program repair. A notable trend is his increasing integration of machine learning techniques with traditional software engineering methods, creating hybrid approaches that leverage both semantic features and expert knowledge. His research has practical applications in software security, maintenance, and testing. Dr. Ni has served on program committees for numerous prestigious conferences including APSEC, ASE, ESEC/FSE, and Mining Software Repositories. His service as a committee member across multiple tracks (Technical Track, Research Papers, Data and Tool Showcase) demonstrates his broad expertise and recognition in the software engineering community. His GitHub profile (jacknichao) shows active engagement with open source projects related to his research, particularly in vulnerability detection tools.

Marsha ChechikView profile

Professor

• Software Engineering
• Formal Methods
• Software Specification and Verification
• +7 more

Marsha Chechik is a Professor in the Department of Computer Science at the University of Toronto within the Faculty of Arts and Science. She previously served as Chair of the Department of Computer Science (2019-2022) and as Acting Dean in the Faculty of Information (July-December 2022). Professor Chechik received her Ph.D. from the University of Maryland in 1996 and has established herself as a leading researcher in software engineering with a focus on formal methods. Her research interests include software safety and security, automated verification, software product lines, and model management. Professor Chechik has authored numerous papers in formal methods, software specification and verification, computer safety and security, and requirements engineering. Her work has evolved to incorporate newer areas such as machine vision reliability analysis, normative requirements with large language models, and machine learning development lifecycle analysis. Her research consistently bridges theoretical foundations with practical applications in software engineering. Professor Chechik has served in numerous leadership roles including Program Committee Co-Chair for major conferences such as ICSE 2018, TACAS 2016, VSTTE 2016, ASE 2014, CONCUR 2008, CASCON 2008, and FASE 2009. She is currently the Area Chair for ASE 2025 in Security and Other Non-Functional Properties. She has also served as Associate Editor in Chief of Journal on Software and Systems Modeling and as associate editor of IEEE Transactions on Software Engineering (2003-2007, 2010-2013). Best paper award at RE'12 (2012) SIGSOFT Distinguished Paper at ICSE'12 (2012) Winner of Best Student Paper Award at CASCON'07 (2007) Winner of Distinguished Paper Award at ICSE'07 (2007) Professor Chechik regularly mentors PhD students, with notable advisees including Michalis Famelis, Shiva Nejati, Jocelyn Simmonds, Ou Wei, and Aws Albarghouthi. Her advising philosophy emphasizes rigorous formal methods while addressing practical software engineering challenges. She has been involved in numerous research grants focusing on software verification, safety-critical systems, and requirements engineering. Currently, her work explores the intersection of formal methods with emerging technologies like large language models and machine vision systems. Professor Chechik maintains an active research lab focused on model-based software engineering, with projects spanning software product lines, model management, and verification techniques. Her team collaborates with industry partners to address real-world software engineering challenges while advancing theoretical foundations. Current work includes developing techniques for normative requirements operationalization, machine vision reliability analysis, and automated code reconciliation using AI techniques.

Tao ZhangView profile

Professor

• AI for Software Engineering
• Software Security
• Mining Software Repositories

Dr. Tao Zhang is a Full Professor at the School of Computer Science and Engineering, Macau University of Science and Technology (MUST), Macau SAR. He serves as an Associate Editor for IEEE Transactions on Software Engineering (TSE), IEEE Transactions on Reliability (TRel), and the Journal of Systems and Software (JSS), and is an Editorial Board Member for Empirical Software Engineering (EMSE) and Science of Computer Programming (SCP). His educational background includes: Ph.D. in Computer Science from the University of Seoul B.S. in Automation and M.Eng in Software Engineering from Northeastern University, China Postdoctoral Research Fellow at Hong Kong Polytechnic University Dr. Zhang's research primarily focuses on three interconnected areas that represent the cutting edge of modern software engineering: AI for Software Engineering : Utilizing neural language models and large language models to create automated software engineering tools that help developers produce high-quality software. His work includes evaluating whether pretrained language models truly understand software engineering tasks and developing universal representations for bug reports. Software Security : Employing static analysis, AI technologies, and formal methods to detect malware, vulnerabilities, and privacy leaks in mobile apps and smart contracts. His research spans Android malware detection, smart contract vulnerability analysis, and state manipulation attacks in blockchain systems. Mining Software Repositories : Applying information retrieval and machine learning to extract meaningful insights from software artifacts to improve development efficiency. This includes work on app review analysis, change request localization, and code similarity metrics. His publications demonstrate significant impact across the software engineering community, with over 100 high-quality papers in top venues including ICSE, ESEC/FSE, ASE, TSE, TOSEM, EMSE, JSS, TIFS, and TDSC. Dr. Zhang has received numerous honors and recognitions: Distinguished Member, China Computer Federation (CCF), September 2025 Top Reviewer Award 2023, Journal of Systems and Software (JSS), April 2024 Distinguished Reviewer in 2023, ACM Transactions on Software Engineering and Methodology (TOSEM), February 2024 Senior Member of ACM (October 2020) and IEEE (February 2020) Best Paper Award, 16th Korea Conference on Software Engineering (KCSE), February 2014 As an academic leader, Dr. Zhang serves/served as General or Program Chair for numerous conferences including APSEC 2025, Internetware 2024, SANER 2023, and DSA 2021. He mentors a vibrant research group with multiple postdocs, PhD students, and master's students working on innovative projects in intelligent software engineering and security. His lab actively recruits highly motivated students interested in Data Mining, Artificial Intelligence, Software Security, and Software Engineering. Dr. Zhang leads the "Intelligent Software Data Analysis and Software Security" research team at MUST, which focuses on leveraging AI technologies to solve critical challenges in software development and security. The team maintains strong collaborations with international researchers and regularly publishes in top-tier venues.

DongGyun HanView profile

Assistant Professor

• Software Engineering
• Code Review
• Empirical Study
• +2 more

DongGyun Han is a Lecturer (equivalent to Assistant Professor) in the Department of Computer Science at Royal Holloway, University of London. His research specializes in Software Engineering, with emphases on Empirical Study, AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), and Code Review. He holds a PhD from University College London (UCL), an MPhil from Hong Kong University of Science and Technology (HKUST), and a B.Eng from Jeju National University. Dr. Han's research integrates empirical methods with AI techniques to address challenges in software maintenance, code quality, and developer productivity. His work spans automated code review, defect prediction, vulnerability repair, and AI model applications in software artifacts. He actively collaborates with industry partners including Amazon Web Services. His recent publications focus on leveraging large language models for software tasks, analyzing dataset biases, and improving automated developer tools. Common themes include empirical validation of AI techniques, security enhancement, and optimizing developer workflows. Awards: No scientific awards mentioned in source materials. Academic Service: Dr. Han contributes extensively to program committees for top-tier conferences including ICSE, FSE, ASE, and MSR. He has chaired tracks at ICTSS 2024 and reviewed for journals including TOSEM, EMSE, and JSS. Affiliations: Previously affiliated with Singapore Management University's SOftware Analytics Research (SOAR) group and Secure Mobile Centre. Maintains industry connections through past roles at Amazon Web Services.

Yingfei XiongView profile

Associate Professor

• Software Engineering
• Programming Languages
• Program Synthesis
• +4 more

Yingfei Xiong is an Associate Professor at Peking University specializing in Software Engineering and Programming Languages. His research focuses on program synthesis, automated program repair, and software analysis techniques with extensive contributions to top-tier conferences including ASE, ICSE, PLDI, and SPLASH/OOPSLA. Dr. Xiong's primary research interests include: Program Synthesis and Inductive Programming Automated Program Repair and Bug Fixing Static and Dynamic Program Analysis Machine Learning Applications in Software Engineering Compiler and Language Implementation Techniques Software Testing and Verification His publication trends show a strong shift toward neural approaches in program repair, with significant integration of large language models in synthesis tasks since 2023. His work bridges theoretical foundations with practical applications, evidenced by industrial collaborations such as the Alibaba case study on static taint analysis. Recent contributions include GrammarT5 for code generation, Tare for type-aware neural repair, and Equality Saturation techniques guided by LLMs. At Peking University, Dr. Xiong is affiliated with the School of Electronics Engineering and Computer Science (SEI) as indicated by his institutional website domain. He actively contributes to the academic community through program committee service for major software engineering conferences and mentoring student researchers.

• Developmental Neurotoxicity
• Adult Neurotoxicity
• Alternative Methods to Animal Testing
• +7 more

Katharina Koch serves as Junior Research Group Leader at the Leibniz Institute for Environmental Medical Research (IUF) in Düsseldorf, Germany, where she heads the Environmental Toxicants and the Brain Research group. Her work focuses on developing and validating alternative methods to animal testing for assessing neurotoxicity, with particular emphasis on developmental and adult neurotoxicity. She collaborates extensively with national and international partners including researchers from Ruhr University Bochum, University of Konstanz, and institutions in the United States and Switzerland. Dr. Koch's research interests center on creating cross-species 3D neurosphere test systems for characterizing developmental neurotoxicity (DNT), which form an integral component of the DNT in vitro battery supported by EFSA and OECD. Her work extends to endocrine-mediated DNT, embryotoxicity, and developmental immunotoxicity. She has established several neurotoxicity Adverse Outcome Pathways (AOPs) and contributes to novel toxicology concepts for cognitive dysfunction with regulatory applications. Her group's work on BrainSphere models is advancing next-generation risk assessment within the EU-PARK project and investigating Cockayne syndrome B disease mechanisms. Her research group operates within a robust funding framework, with projects supported by the German Research Foundation (DFG), European Union Horizon programs, the US National Toxicology Program, and industry partnerships through Leibniz Alternatives. The group has developed AI-based cell identification from high-content imaging analyses and established biostatistical pipelines for comparative data processing. Dr. Koch actively mentors a team of doctoral students, master's students, and postdoctoral researchers, fostering the next generation of toxicology scientists. Her collaborative network spans multiple disciplines, connecting environmental science, neuroscience, and regulatory toxicology to advance human-relevant testing approaches that reduce reliance on animal models.

Carl CoreaView profile

Researcher

• Business Informatics
• Theoretical Informatics
• Artificial Intelligence
• +3 more

Dr. Carl Corea is a postdoctoral researcher at the University of Koblenz-Landau, Germany, affiliated with the Process Science Group within the Department of Computer Science. He holds a PhD in Computer Science (with distinction, 2020) and has served in roles such as Acting Professor of Business Information Systems (2023/24) at Justus Liebig University Giessen and Visiting Researcher at SAP Signavio. His research bridges business informatics and theoretical computer science, focusing on process mining, declarative process specifications, and decision modeling (DMN). He actively contributes to academic leadership roles, including memberships on doctoral committees, examination boards, and interdisciplinary research centers at the University of Koblenz. Corea's research interests include business process management, artificial intelligence applications in processes, and inconsistency measurement in business rules and process models. He has delivered courses on AI in Accounting, Project Management, and Business Process Management at multiple institutions. His work has been recognized with awards such as the Best Paper Award at WI 2019 and the Debeka Innovationspreis for research projects like 'Predictive Process Monitoring'. He serves on the program committees of major conferences in AI and business process management, including KR, AAAI, BPM, and ECAI. His publications span topics like declarative process modeling, DMN verification, and carbon-aware process execution. Corea also chairs Minitracks at events like HICSS and oversees conference organization for tracks such as Business Process Technology. Education: PhD in Computer Science (2020, with distinction) Awards: Multiple research and teaching awards, including nominations for early-career recognition Administration: Member of doctoral committees, academic boards, and interdisciplinary research centers Grants: DAAD Fellowship for international conference participation (2020)

Zhenyang XuView profile

Researcher

• Software Testing
• Debugging
• Program Reduction
• +2 more

Zhenyang Xu is a researcher affiliated with the University of Waterloo, specializing in Software Testing and Debugging . He has contributed to advancements in program reduction techniques, including projects like PPR (Pairwise Program Reduction), WDD (Weighted Delta Debugging), and LPR (Large Language Models-Aided Program Reduction). His work focuses on improving the efficiency and effectiveness of debugging through empirical studies and novel algorithms. Research Interests : Software Testing, Debugging, Program Reduction, Software Engineering Key Publications : Kitten (2025, ISSTA) for LLM-based compiler testing T-Rec (2025, ICSE) on lexical syntax-guided reduction Pushing the Limit of 1-Minimality (2023, SPLASH) Conferences : Active in ISSTA, ICSE, SPLASH, ESEC/FSE

Viktor VafeiadisView profile

Researcher

• Weak Memory Models
• Concurrency
• Program Verification
• +4 more

Viktor Vafeiadis is a tenured researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. He received his BA and PhD from the University of Cambridge (2004, 2008) and worked as a postdoctoral researcher at Microsoft Research Cambridge and the University of Cambridge before joining MPI-SWS in 2010. His research focuses on concurrency , verification of concurrent programs, weak memory consistency , weak persistency , and formal semantics of programming languages. Research Areas: Weak Memory Models, Program Verification, Persistent Programming, Automated Model Checking Key Tools: Cave (automated verification tool), GenMC (model checker), Kater (consistency checker) His recent work explores linearizability under relaxed memory , mixed-size memory accesses , and formalizing ARMv8 persistency . Awards include the ACM SIGPLAN John C. Reynolds Award (2008), ERC Consolidator Grant (2020), and Robin Milner Young Researcher Award (2022). Selected Awards: ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award (2008) ERC Consolidator Grant (2020) ACM SIGPLAN Robin Milner Young Researcher Award (2022) Distinguished Paper Awards: PLDI'17, OOPSLA'21, ECOOP'17 He has mentored numerous PhD students including Ellen Arlt , Pavel Golovin , and Aristotelis Koutsouridis , while alumni advisees include Soham Chakraborty and Michalis Kokologiannakis . His collaborative work spans institutions like University of Cambridge , Microsoft Research , and ETH Zürich .

Artur AndrzejakView profile

Professor

• Artificial Intelligence
• Programming Systems
• Distributed Systems
• +6 more

Prof. Dr. Artur Andrzejak is a full professor (W3) at Heidelberg University since 2010, serving as the Head of the research group Artificial Intelligence for Programming (AIP), and currently as Executive Director of the Institute of Computer Science. He has held significant academic roles including Deputy Dean of the Faculty of Mathematics and Computer Science (2019-2024), Dean of Studies for Computer Science (2015-2017), and Liaison Professor of the German National Academic Foundation (2014-2023). Habilitation in Computer Science (2009), Freie Universität Berlin Ph.D. in Computer Science (2000), ETH Zurich Diploma in Mathematics (1995), Freie Universität Berlin His research focuses on artificial intelligence applications in software engineering, particularly code generation and error correction techniques. He has also made significant contributions to software aging analysis, distributed systems, and cloud computing, with publications spanning federated learning, secure computation, and software rejuvenation. Recent publications demonstrate a strong focus on code generation and error correction methods using large language models, while earlier work includes foundational studies in distributed systems, grid computing, and software rejuvenation techniques. His research combines theoretical computer science with practical applications in enterprise software systems. As an academic leader, he has organized Dagstuhl Seminars on Parallel Data Analysis (2013) and Self-Healing Systems (2009), and served on editorial boards for the journals Parallel Processing Letters and Multiagent and Grid Systems. He has held various leadership roles in academic institutions and research networks.

• Stem Cell Biology
• Genome Editing
• Developmental Biology
• +5 more

Mo Li serves as an Associate Professor in the Bioscience department within the Biological, Environmental Sciences and Engineering (BESE) division at King Abdullah University of Science and Technology (KAUST), a position held since July 2022 after serving as Assistant Professor from December 2016. Their research program bridges stem cell engineering, genome editing, and developmental biology with significant contributions to embryo modeling and genomic analysis technologies. Education: PhD in Cellular Biology, University of Georgia (2001-2007) Research focuses on leveraging pluripotent stem cells to model human development and disease, with particular emphasis on CRISPR-Cas9 optimization, DNA repair mechanisms, and mitochondrial genomics. Their lab pioneers blastoid models for studying early embryogenesis while developing computational tools like deepBlastoid for high-throughput embryo model analysis. Recent work explores microhomology-mediated end joining pathways to improve gene editing precision and employs long-read sequencing for single-cell genomic disorder detection. Analysis of 2023-2025 publications reveals three dominant research thrusts: (1) Advancement of stem cell-based embryo models with focus on trophoblast differentiation and primate aging interventions; (2) CRISPR-Cas9 refinement through DNA repair pathway modulation; (3) Nanopore sequencing applications for mitochondrial heteroplasmy analysis and genomic disorder diagnostics. Key interdisciplinary connections span computational biology, reproductive medicine, and regenerative therapeutics. Scientific awards: None documented in source materials. As principal investigator, Mo Li mentors graduate students and postdoctoral researchers in genome editing and stem cell differentiation projects, with collaborative grants supporting KAUST's core sequencing facilities. Their team maintains active partnerships with international institutions including Salk Institute (evidenced by salk.edu email domain) and clinical genomics centers for translational applications. The research group operates within KAUST's state-of-the-art Bioscience facilities, utilizing single-cell sequencing platforms, stem cell culture suites, and computational infrastructure for AI-driven image analysis. Current initiatives include developing senescence-resistant progenitor cell therapies and establishing standardized protocols for blastoid model validation through the BESE division's developmental biology consortium.

Norbert SchreierView profile

Professor

• Electromobility
• Automotive Diagnostics
• After Sales Service Strategies
• +3 more

Prof. Norbert Schreier serves as Dean of the Faculty of Mobility and Technology at Esslingen University of Applied Sciences, where he also holds a professorship. His roles include membership in the Senate and Building Committee of the faculty. His research focuses on electromobility, automotive diagnostics, and future service technologies, with a particular emphasis on high-voltage systems, battery technology, and after-sales strategies. He has been actively involved in presenting research at international conferences such as EVS 33 and EVS 34. His work addresses challenges in electric vehicle maintenance, workshop adaptation for emerging technologies, and global automotive industry trends. Education: Diplom-Ingenieur (Dipl.-Wirt.-Ing.) in Automotive Engineering Research interests span the technical, economic, and social impacts of electromobility, including battery diagnostics, service infrastructure evolution, and cross-cultural technology adaptation. His publications highlight advancements in electric powertrain research, safety protocols for EVs, and future automotive diagnostics integration with connected and autonomous systems. Prof. Schreier’s contributions extend to curriculum development, notably the 10-year anniversary of the Vehicle Technology program with a focus on service engineering. He emphasizes the importance of technician training for emerging technologies and sustainable mobility solutions. Grants and collaborations include international research partnerships, though specific funding details are not disclosed. His work influences both academic and industrial sectors, shaping the future of automotive service and electromobility standards.

Volker CoorsView profile

Professor

• Urban Digital Twins
• 3D Geoinformatics
• Spatial Data Visualization
• +8 more

Prof. Volker Coors is a Professor of Computer Science and Geoinformatics at the University of Applied Sciences Stuttgart (HFT Stuttgart). He currently serves as Vice Rector for Research and Digitalization (since March 2023) and previously held roles such as Scientific Director of the Institute for Applied Research (2017–2023), Dean of the Information Logistics Program (2002–2017), and Head of the Steinbeis Transfer Center for Technical Consulting (2006–2017). Education: He obtained a Diplom in Computer Science with a minor in Civil Engineering from TU Darmstadt (1997, summa cum laude), followed by a Dr.-Ing. in Computer Graphics (2001). He held a DAAD scholarship for research at Georgia Institute of Technology (1997–2002). His research focuses on urban digital twins, 3D geodata infrastructures, and spatial data visualization, with contributions to projects like CityGML standardization and the Virtual Environmental Planning System (VEPs). Research Areas: Urban digital twins, smart city technologies, geospatial data management, energy simulations, and 3D GIS. Projects: Includes 3ProzentPlus (energy-efficient urban renovation), CircularGreenSimCity (resource-efficient urban districts), CityDoctor 2 (automated city model repair), and SIM TOWN (urban energy simulation). Awards: Received the Best Paper Award (2012) and Digital Earth Award (2007), and contributed to BMBF’s VR/AR competition (2000). Leadership: Active in standardization via the Open Geospatial Consortium (CityGML SWG), founder of the German 3D City Models Commission, and Vice President of the Urban Data Management Society.