Dr. Oksana Buzhdygan is a Senior Researcher in Theoretical Ecology at the Department of Biology, Faculty of Biology, Chemistry, Pharmacy at Freie Universität Berlin. She holds a PhD in Ecology from Chernivtsi National University and has held postdoctoral positions at institutions including the University of Georgia (USA) and Freie Universität Berlin. Her research focuses on biodiversity-ecosystem functioning links, multitrophic interactions, and ecological network analysis. Education: BSc, MSc, and PhD in Ecology (2000–2008), Chernivtsi National University, Ukraine Postdoctoral Fellowships: University of Georgia (2010–2012), Freie Universität Berlin (2013–2021) Research Interests: Environmental change impacts on biodiversity and ecosystem functions Ecological network analysis of multitrophic systems Food web dynamics in grasslands and freshwater ecosystems Publications reflect her expertise in biodiversity effects on energy flow, human impact on ecosystems, and plant diversity drivers. Her work bridges theoretical ecology with applied conservation challenges, particularly in grasslands and agroecosystems. Labs/Teams: Member of the Tietjen Group (Theoretical Ecology at Freie Universität Berlin), collaborating widely in international projects on biodiversity and ecosystem services.
Dr. Pedro Henrique D. Batista is a Senior Research Fellow at the Max Planck Institute for Innovation and Competition, specializing in intellectual property law with a focus on innovation, biotechnology, genetic resources, and traditional knowledge. His research addresses regulatory frameworks for biodiversity, patent law reforms, and the intersection of IP with public health and climate change. Education: PhD in Law, Ludwig-Maximilians-Universität München (2013-2024) LL.M., Ludwig-Maximilians-Universität München (2012-2013) Bachelor of Law, University of São Paulo (2006-2011) Research explores the legal challenges of emerging technologies, including CRISPR and digital genetic sequences, and policy solutions for equitable innovation. His work critically examines international agreements like the Nagoya Protocol and TRIPS flexibilities. Publications emphasize regulatory coherence in intellectual property, with recent focus on WIPO reforms, antimicrobial resistance incentives, and climate-related patent frameworks. Articles frequently engage with EU law, Latin American IP systems, and global governance. Awards: Finalist, Best Diploma Thesis Award, University of São Paulo Law School (2011) Coordinates the Smart IP for Latin America Initiative and contributes to international bodies like the CBD Working Group on Digital Sequence Information. Part-time roles included editorship of GRUR International and IIC journals.
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.
Stefan Wildermann is a Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Reconfigurable Computing Group within the Chair of Computer Science 12 (Hardware-Software Co-Design) in the Department of Computer Science. He has maintained continuous research activity at FAU since 2006, progressing from researcher to his current leadership position. Dr. Wildermann earned his Diploma degree in Computer Science from FAU in 2006 and completed his doctorate (Dr.-Ing.) in Computer Science at the same institution in July 2012. His academic career has been entirely rooted at FAU, demonstrating a strong institutional commitment and progression through the ranks. His research spans multiple cutting-edge areas in computer science and engineering, with particular emphasis on reconfigurable systems and hardware-software co-design. Wildermann's work in edge computing explores efficient processing at the network periphery, while his research in organic computing investigates self-organizing systems that can adapt to changing environments. His expertise extends to optimization techniques for embedded systems, applying game theory principles and convex optimization methods to solve complex resource allocation problems. More recently, he has integrated reinforcement learning approaches to enhance system adaptability and performance. His teaching portfolio includes courses on event-driven systems, computer engineering fundamentals, embedded systems, and hardware-software co-design. Analysis of Wildermann's publication record from 2021-2025 reveals a strong focus on hardware acceleration, security, and embedded systems. His work demonstrates consistent evolution from foundational research in reconfigurable architectures toward practical applications in IoT, robotics, and secure computing. A significant portion of his recent work addresses near-data processing using FPGAs for database acceleration, while maintaining parallel research streams in side-channel security analysis and energy-efficient embedded systems design. His publications frequently appear in top-tier conferences including DATE, FPL, ASP-DAC, and HOST, reflecting strong recognition within the computer architecture and embedded systems communities. Wildermann has held significant leadership roles including Head of the Reconfigurable Computing Group since 2015 and previously served as Head of the Self-organizing Systems Group (2012-2015) and Lab Leader of the Automotive Lab within the Embedded Systems Initiative (2016-2020). His research has been consistently funded through multiple projects investigating invasive computing, reconfigurable architectures, and embedded systems design methodologies. Currently based in Room 02.116 at Cauerstr. 11, 91058 Erlangen, Wildermann continues to lead active research in the Hardware-Software Co-Design group, supervising projects that bridge theoretical computer science with practical hardware implementation challenges.
Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.
Prof. Dr. Uwe Walz is a full Professor of Economics at Goethe University Frankfurt , holding the Chair of Industrial Organization since 2002. He serves as Dean of Studies for the Faculty of Economics and Business Administration and Deputy Scientific Director of the Leibniz Institute SAFE (Sustainable Architecture of Finance in Europe). His academic career includes professorships at the University of Bochum (1995-1997) and University of Tübingen (1997-2002), with visiting appointments at LSE and UC Berkeley. Doctorate (1991) and Habilitation (1995) in Economics Current roles: Professor, Dean of Studies, Deputy Director of SAFE His research focuses on private equity , startup finance , and innovation economics , with recent publications analyzing buyer group dynamics (2025), PE spillovers (2024), and venture capital networks (2023). Articles span corporate governance, financial regulation, and technology transfer. The 15 most recent articles (2020-2025) demonstrate consistent focus on private equity (5 papers), venture capital (4), corporate governance (3), and innovation economics (3). Key methodological themes include empirical analysis of financial data and institutional economics. Current advisees include doctoral candidate Ella-Maria Schirra . His team at Goethe University Frankfurt comprises research assistants Leo Leitzinger (since 2020) and Olaf Kehne . The Chair of Industrial Organization collaborates with the Leibniz Institute SAFE on sustainable financial architecture research.
Eva Glasmachers is a Lecturer and Managing Director of the Faculty of Mathematics at Ruhr University Bochum. She holds a PhD in Differential Geometry (2010) and has been actively involved in academic leadership since 2008. Her roles include student advising, curriculum development, and managing faculty operations. Her research focuses on university-level mathematics education, emphasizing interactive teaching methods, digital learning tools (e.g., STACK), and student motivation strategies. She leads projects like VORsprung for digital study preparation and trains tutorial group instructors. She is a founding member of HDM@RUB (Center for Higher Mathematics Education) and serves on the Ruhr University Senate and the Excellence Network for Teaching. Notable publications include works on gamification in education, adaptive learning systems, and dropout prevention strategies. She collaborates extensively with educational technology centers and contributes to national didactics conferences.
Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.
Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.
Surajit Chaudhuri is a Researcher at Microsoft , with a career spanning decades in database systems and data management . He has received the prestigious SIGMOD Edgar F. Codd Innovations Award (2011) for his contributions to query optimization , index tuning , and data lakes . Research Interests : His work focuses on database tuning , approximate query processing , fuzzy similarity joins , automated data transformations , and machine learning integration for scalable data systems. Recent Publications : In 2025, his research includes Auto-Test for unsupervised error detection in tables, Esc for budget-aware index tuning, and MMTU for multi-task table understanding benchmarks. Earlier works in 2024–2023 address spreadsheet formula recommendation , low-overhead index filtering , and time-series pattern recognition . Scientific Impact : He has co-authored influential papers in SIGMOD , VLDB , and IEEE Transactions , shaping practices in cloud databases , query optimization , and self-service BI . His collaborations span institutions like Microsoft, MIT, and ETH Zurich.
Michael J. Franklin is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. He has a prolific publication record spanning over three decades with more than 300 publications in top-tier database and systems conferences and journals, demonstrating his continued active research and leadership in the field. Franklin's research spans multiple areas within data management, with a recent focus on time-series analysis, AI-integrated database systems, cloud-native databases, and data quality. His work has evolved from traditional database systems to address modern challenges in big data, machine learning integration, and distributed systems. He has made significant contributions to data cleaning, crowdsourced data management, and stream processing systems. Analysis of his recent publications (2022-2025) reveals a strong trend toward integrating AI/ML capabilities with database systems, particularly in time-series anomaly detection, LLM applications for data management, and resource-adaptive query processing for cloud environments. His work increasingly focuses on practical systems that address real-world data challenges, often involving collaborations with industry partners and other leading academic researchers. Throughout his career, Franklin has mentored numerous PhD students who have become prominent researchers in their own right, including Sanjay Krishnan, Aaron Elmore, and Jiannan Wang. His collaborative research has frequently involved significant funding from NSF and industry partnerships, enabling large-scale systems research with real-world impact. Franklin leads research efforts that bridge theoretical database principles with practical system implementations. His work on projects like Data Station demonstrates his commitment to building trustworthy infrastructure for data sharing and analysis, addressing critical challenges in data privacy, security, and usability in collaborative environments.
Dr. Pascal Reuss is a Researcher at the Intelligent Information Systems (IIS) Division within the Institute of Computer Science , University of Hildesheim . His work focuses on Case-Based Reasoning (CBR) systems, Multi-Agent Systems , and Knowledge Management applications. Active in CBR framework development and game-based AI research Teaching Computer Science III (Databases) for winter 2025/26 Participating in university sustainability initiatives like Stadtradeln 2024/25 Reuss contributes to AI education through practical implementations in gaming environments and has developed visualization tools for CBR agent behavior. His research spans multi-agent collaboration , dynamic case bases , and domain-specific language implementations for knowledge maintenance. Notable contributions include: Co-developing the FEATURE-TAK framework for knowledge extraction Designing case factories for distributed CBR systems Implementing finite state machines for tactical game agents Creating CBR-based fitness planning systems His work appears in various CBR and Game Development publications from 2011-2024. The research demonstrates practical applications of CBR in aircraft maintenance diagnostics , training plan generation , and educational technology contexts.
Dina Dechmann is Group Leader at the Max Planck Institute of Animal Behavior, Department of Migration in Radolfzell, Germany. She leads the Ephemeral Resource Adaptations Research Group, focusing on how animals adapt to fluctuating resource availability through behavioral, morphological, and physiological strategies. Her educational background includes: Ph.D. in Animal Behavior from University of Zürich (2005) MS in Systematics & Ecology from ETH Zürich (1999) Habilitation at University of Konstanz (2018) Dr. Dechmann identifies as a classical behavioral ecologist with a passion for evolution, increasingly focusing on how resource distribution in time and space influences animal adaptations. Her research examines movement patterns (particularly in flying foxes and bats), energetics, information transfer during foraging, and morphological adaptations like wing shape. A significant focus involves seasonal phenological changes, especially in brain structure, as seen in her work on Dehnel's Phenomenon in shrews. She is actively involved in the ICARUS satellite tracking initiative to monitor bat migration. Her recent publications reveal consistent themes across animal behavior, neuroecology, and conservation biology. The work demonstrates sophisticated integration of field studies with molecular and physiological approaches, particularly in studying how animals navigate resource ephemerality. Key trends include bat migration patterns, brain plasticity in response to seasonal changes, and methodological innovations in wildlife tracking. Her research bridges fundamental behavioral ecology with practical conservation applications, especially regarding common bat species. Dr. Dechmann mentors a diverse international team including postdocs, doctoral students, and technical staff. Her group maintains strong collaborations across European institutions and with international partners, particularly in Panama where some field studies occur. While specific grant details aren't provided, her work on the ICARUS initiative and extensive publications suggest substantial research funding. The Ephemeral Resource Adaptations Group operates as a small, international team focused on resource distribution challenges for animals. Current projects examine migration as an adaptation to seasonal change, hibernation energetics in climate change contexts, social information sharing for ephemeral resources, alternative wintering strategies in small mammals, and impacts of research methodologies on animal behavior.
Yu Jiang is an Associate Professor at the School of Software, Tsinghua University, China. His research focuses on software security with emphasis on fuzz testing, embedded systems, and database security. He leads the Software System Security Assurance Group which has discovered over 1,000 bugs in major system software with 300+ CVEs registered. Dr. Jiang's research interests include Software Engineering , Embedded Systems Security , and Cross-Layer Fuzzing . His work addresses vulnerabilities in operating systems, databases, communication protocols, and IoT firmware through innovative fuzzing frameworks. Key contributions include semantic-aware fuzzing for heterogeneous software stacks and learning-based vulnerability detection for embedded systems. His recent publications demonstrate strong trends in database security (Hulk, PUPPY, THANOS), ransomware defense (Fawkes, Preventing Disruption), and web security (JANUS). Research spans both theoretical advances in fuzzing techniques and practical industrial applications, with significant impact evidenced by numerous distinguished paper awards. Career Award, NSFC: 2026 Distinguished Paper Award, ISSTA: 2025 First Prize for Technical Invention, CCF: 2024 Distinguished Paper Award, USENIX Security: 2024 SIGSOFT Distinguished Paper Award, FSE: 2022 Dr. Jiang has advised over 50 graduate students including 20 PhD candidates. His research is supported by major grants including NSFC projects ($600,000 for Software Trustworthiness Construction and $350,000 for Distributed Database Reliability), Huawei ($80,000 for LLM-Powered Fuzzing), and Tencent ($120,000 for LLM-Powered Unit Testing). The Software System Security Assurance Group maintains strong industry partnerships with Huawei, Alibaba, Tencent, and Webank. The group operates cutting-edge infrastructure for fuzz testing across multiple domains including database systems, operating kernels, blockchain platforms, and industrial control systems. Current projects integrate LLM technologies with traditional fuzzing techniques to enhance vulnerability detection in complex software stacks.
Val Tannen is a Professor at the University of Pennsylvania, specializing in database systems, provenance analysis, and programming languages. His research focuses on data management, query languages, and systems like DBSP and ORCHESTRA. Collaborations include work with co-authors such as Zachary Ives, Susan Davidson, and Todd Green. Key research interests include provenance for databases, incremental view maintenance, and data integration. His work bridges theoretical foundations and practical applications in systems like DBSP for stream processing and ORCHESTRA for collaborative data sharing. Publications span provenance frameworks, query optimization, and distributed systems. While no awards are explicitly listed, his contributions to database theory and systems are widely recognized.
