Janek Thomas is a researcher actively contributing to the fields of Machine Learning and Automated Machine Learning (AutoML). His work focuses on hyperparameter optimization, multi-objective algorithms, and improving the interpretability of machine learning models. Collaborating with institutions like TU Munich and LMU Munich, he has authored over 35 publications since 2016. Key contributions include the AMLB benchmark suite for AutoML systems and foundational research on multi-objective hyperparameter tuning. His research bridges theoretical advancements with industrial applications, emphasizing robust model verification and scalable optimization techniques.
Philipp Wendler is an academic lecturer in the Department of Computer Science at Ludwig-Maximilians-Universität München (LMU Munich). He is affiliated with the Software and Computational Systems Lab and actively involved in research, teaching, and open-source tool development. As an employee representative in the steering committee of the Institute of Informatics, he contributes to institutional governance. His research focuses on software verification, formal methods, and program analysis. Key projects include CPAchecker (a configurable verification framework) and BenchExec (a benchmarking tool). His work emphasizes practical applications in automated testing, energy-efficient algorithms, and reproducible benchmarking. Publications span topics like interpolation-based model checking, energy measurement tools, and strategies for software verification competitions. He has contributed to advancing predicate analysis, k-induction, and refinement selection techniques. Notable achievements include leading the development of CPAchecker and BenchExec, which are widely used in academic and industrial verification efforts. His research addresses challenges in scalable verification, flaky test analysis, and energy-aware computing.
Prof. Dr. Heike Trautmann is a leading researcher in statistics and optimization at the University of Twente (2021-2026) and former Professor at WWU Münster (2013-2016). Her work bridges computational statistics, evolutionary optimization, and social media analytics. She has held visiting positions at TU Dortmund, Leiden University, and RWTH Aachen. Current affiliation: University of Twente (Data Science: Statistics and Optimization) Previous roles: WWU Münster (Professor for Information Systems and Statistics), TU Dortmund (Postdoctoral researcher) Research Focus: Multi-criteria optimization, automated algorithm selection, data stream mining, and disinformation detection in social media. Her methodological innovations in exploratory landscape analysis and evolutionary computation have transformed algorithm configuration practices. Developed COSEAL consortium for algorithm selection Co-founder of Benchmarking Network (2019) Principal investigator in projects like PropStop and MODERAT! Academic Contributions: Over 150 publications in top venues like GECCO, PPSN, and Evolutionary Computation journal. Pioneered feature-based landscape analysis tools (flacco, pflacco) and stream clustering frameworks.
Prof. Dr. Sven Völker is a faculty member at Technische Hochschule Ulm , affiliated with the Faculty of Production Engineering and Production Management and the Institute for Business Organization and Logistics (IBL) . He specializes in technologies for Industry 4.0 , with a focus on material flow simulation, digital factory design, and optimization methods in production planning and control. Teaches Enterprise Information Systems and Digital Factory Planning in the Systems Engineering and Management program. Active in simulation-based optimization and digitalization scenarios for logistics and manufacturing systems. Research Interests: His work bridges industrial engineering and computer science , emphasizing simulation tools, ERP systems, and lifecycle management of digital models. Recent publications explore Machine Learning applications in production planning, OPC UA communication for logistics systems, and augmented reality in manufacturing demonstrations. Publications: His research spans 2010–2023, covering simulation-based optimization of supply networks, discrete-event modeling, and Industry 4.0 integration. Keywords include Operations Research , Robotics , and Digital Twins , with sub-fields like Logistics Automation and Flexible Manufacturing . Memberships: Institute for Higher Education Didactics (IHD) Institute for Applied Research (IAF)
Xingang Shi is a Professor in the Department of Computer Science and Technology at Tsinghua University's School of Information Science and Technology. With over 126 publications spanning from 2006 to 2025, he maintains an active and influential research program in computer networking, with recent expansion into quantum network optimization. His work consistently appears in top-tier venues including INFOCOM, IEEE/ACM Transactions on Networking, and SIGCOMM. Dr. Shi's research focuses on network security, software defined networking, traffic engineering, and network anomaly detection. His recent work shows increasing emphasis on quantum network optimization and advanced security verification techniques. He has developed innovative approaches to routing protocols, congestion control, and inter-domain routing analysis, with practical applications in real-world network infrastructure. His publication trends reveal a steady evolution from traditional networking topics toward more sophisticated security and optimization frameworks. The 15 most recent publications demonstrate strong integration of machine learning techniques with networking fundamentals, particularly in anomaly detection and traffic engineering. His work increasingly addresses quantum networking challenges while maintaining strong contributions to conventional network security and performance optimization. As a leading researcher in his field, Dr. Shi has made significant contributions to network verification methodologies and security protocol testing frameworks. His recent papers on model checking-based security testing and proactive network policy verification represent important advances in ensuring network reliability and security. Through his extensive collaboration network (particularly with Zhiliang Wang, Xia Yin, and Han Zhang), Dr. Shi has built a productive research ecosystem that bridges theoretical networking concepts with practical implementation challenges. His work on quantum network optimization represents an emerging frontier in his research portfolio.
Cornelius Born , M.Sc., is a researcher affiliated with KrcmarLab at the Technical University of Munich (TUM) , specifically within the Department of Information Systems and Business Process Management (Informatik 17). His work sits at the intersection of information systems, healthcare, and human-AI collaboration. Education: M.Sc. in Information Systems – Technical University of Munich Visiting Graduate Student – ETH Zurich, Switzerland Exchange Semester – Tsinghua University, Beijing, China B.Sc. in Information Systems – Duale Hochschule Baden-Württemberg, Mannheim Research Interests: Cornelius focuses on Information Systems in Healthcare , investigating how digital technologies transform clinical workflows. He explores Digital Transformation in Hospitals , from electronic health records to advanced analytics, and studies Human-AI Collaboration , ensuring that AI-driven decision support tools integrate seamlessly and ethically into healthcare settings. Project Involvement: He is actively contributing to the ZNAFlow project, which targets data-driven process optimization in healthcare environments. Industry Experience: Prior to his academic role, Cornelius gained practical experience at Roland Berger and EY , providing strategic IT and process consulting to healthcare and public-sector clients. Contact: cornelius.born@tum.de
Ziawasch Abedjan is a full professor of computer science and chair of the Data Integration and Data Preparation (D2IP Lab) Group at Technische Universität Berlin, part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a PhD from the Hasso Plattner Institute (2014) and was previously a junior professor at TU Berlin (2016–2020), with postdoctoral work at MIT (2014–2016). His research focuses on scalable methods for processing large heterogeneous datasets, emphasizing automated data preparation, extraction, and cleaning for data science workflows. Education: PhD in Computer Science, Hasso Plattner Institute (2014) MSc in Computer Science, Hasso Plattner Institute (2010) BSc in Computer Science, Hasso Plattner Institute (2008) Research Interests: Dr. Abedjan’s work bridges database systems and machine learning, addressing challenges in data integration, automated data cleaning, and scalable data science tools. His team develops systems like Blend for unified data discovery and MATE for multi-attribute table extraction. Recent projects include exploring the environmental impact of AutoML and advancing catalog enrichment techniques. Awards & Recognition: First Prize, GI Data Science Challenge (BTW 2023) SIGMOD Reproducibility Award (2019) Best Dissertation Award (2013/2014) Teaching & Service: Teaches foundational courses in data structures and databases. Serves on committees for major conferences (SIGMOD, VLDB) and chairs roles such as Reproducibility Chair for BTW (2023/2025). Active in editorial roles for journals including ACM JDIQ and IEEE Data Engineering Bulletin. Labs & Teams: Leads the D2IP Lab, collaborating with universities and industry. Current projects include TDC2 NFDI4DS. Offers thesis topics in data science lifecycle optimization and ML system design.
Andreas Ziegler is a Professor at the Department of Computer Science (INF) of Friedrich-Alexander University Erlangen-Nürnberg. His research focuses on system software optimization , particularly in Linux kernel configurability and binary tailoring . He leads the Chair of Computer Science 4 (System Software) and develops open-source tools for minimizing software stacks while preserving functionality. University: Friedrich-Alexander University Erlangen-Nürnberg School: College of Engineering Department: Department of Computer Science Rank: Professor Research Interests: Ziegler investigates methods to automate software stack tailoring. His work spans: Configuration Interface Utilization (e.g., Linux kernel modules) Binary-Level Code Removal (ELF file manipulation without source access) Maintenance Impact Quantification (AST hashing for change detection) Publication Trends: Recent works emphasize attack surface reduction and scalable configuration testing , while earlier studies focus on feature modeling and compilation redundancy . Tools developed include GitHub-hosted open-source solutions . Advising: Supervised multiple Master’s theses on topics like header analysis for dead code detection and dynamic variability management in Linux systems.
Victor Zappek is a Researcher and PhD candidate at the Institute for Rotorcraft and Vertical Flight, Technical University of Munich (TUM), supervised by Prof. Dr. phil. Ilkay Yavrucuk. His office is located at Boltzmannstr. 15, 85748 Garching, Germany, with contact number +49 (0)89 / 289-16366. His research focuses on VTOL preliminary design, aircraft performance analysis, hydrogen powertrains for UAVs/eVTOLs, and extraterrestrial unmanned aircraft. He actively contributes to projects like ENGEL (Energy Efficient Flight Guidance), VARI-SPEED II, ARCTIS, and LaBouR, advancing sustainable aviation and rotorcraft innovation. His publication trends reveal a strong shift toward sustainable propulsion systems (hydrogen fuel cells) and extraterrestrial applications, bridging traditional helicopter engineering with eVTOL and space exploration technologies. Recent works analyze landing gear performance, VTOL concept selection, and Mars mission vehicle configurations. Scientific Awards: No awards or fellowships were documented in the source material. Advising and Grants: While no formal advisees are listed, his PhD candidacy suggests mentoring involvement. Project participation (e.g., VaMEx, HyDDEn) implies grant-funded research, though specific awards remain unreported. Labs and Teams: He utilizes TUM's rotorcraft facilities including the Whirl Tower, Flight Simulator Facilities, Unmanned Rotorcraft Testbed (AREA), and GPU/CPU clusters for computational analysis within the Institute for Rotorcraft and Vertical Flight.
Professor Heike Trautmann is a distinguished academic at Paderborn University, where she serves as Professor of Machine Learning and Optimisation in the Department of Computer Science within the Faculty of Computer Science, Electrical Engineering and Mathematics. Since April 1, 2025, she also holds the position of Vice President for International Relations at the university. Her academic career spans prestigious institutions including the University of Münster, where she was Professor of Data Science: Statistics and Optimization from 2013 to 2023, and the University of Twente, where she serves as Guest Professor of Data Science until February 2026. Dr. Trautmann's educational background includes: University Studies in Statistics (Diploma), TU Dortmund, Germany (1997-2000) University Studies in Economic Mathematics (First Diploma), TU Dortmund, Germany (1996-1998) PhD student at Graduate School of Production Engineering and Logistics, TU Dortmund University (2002-2004) Habilitation in Statistics, TU Dortmund University, Germany (April 15, 2013) Professor Trautmann's research program centers on cutting-edge topics in artificial intelligence and optimization. Her primary research interests include (Trustworthy) Artificial Intelligence, Machine Learning, Data Science, Automated Algorithm Selection and Configuration, Exploratory Landscape Analysis, (Multiobjective) Evolutionary Optimisation, and Data Stream Mining. She leads the Machine Learning and Optimisation research group at Paderborn University, which develops innovative approaches for understanding and improving optimization algorithms through landscape analysis and automated configuration techniques. Her work bridges theoretical foundations with practical applications, particularly in the domains of trustworthy AI and algorithm selection. Her extensive publication record reveals a clear trajectory toward increasingly sophisticated integration of deep learning with traditional optimization techniques. Recent work demonstrates a strong focus on multi-objective optimization problems, exploratory landscape analysis using deep learning methods, and the development of automated algorithm configuration systems. A notable trend is the application of transformer architectures to landscape analysis, as seen in her Deep-ELA work, which represents a significant innovation in the field. Her research consistently addresses the challenge of characterizing complex optimization problems to enable better algorithm selection and configuration. Professor Trautmann has received notable recognition for her scholarly contributions, including: GECCO Best Paper Award for "Deep reinforcement learning for instance-specific algorithm configuration" As an academic leader, Professor Trautmann has secured significant research funding for projects including "Towards Robustness of Disinformation Campaign Detection Algorithms in Open Online Media in the Context of Trustworthy AI" and "Automated rail transport as a backbone for sustainable, networked mobility in rural areas." She actively mentors students through her teaching of advanced courses in machine learning, optimization, and data science. Her industry connections, stemming from her previous work as an Analytics Consultant at Roland Berger Strategy Consulting, enable her to bridge academic research with practical applications. Professor Trautmann leads the Machine Learning and Optimisation research group at Paderborn University, which collaborates extensively with international partners. She is a key supporter of the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) and a member of the European Research Center for Information Systems (ERCIS). Her group maintains strong connections with research centers across Europe, particularly through her involvement with the Transregional Collaborative Research Centre 318.
Marius Lindauer is a Professor of Machine Learning at the Department of Artificial Intelligence , Leibniz University Hannover , and Deputy Head of the Institute since 2025. Previously, he served as Spokesperson of Computer Science Professors (2023-2025) and Head of the Institute (2022-2024). PhD (Dr. rer. nat, 2010-2015), Master (2008-2010), and Bachelor (2005-2008) in Computer Science from University of Potsdam His research focuses on democratizing AI through AutoML innovations, including: Green AutoML for sustainable deep learning Human-Centered AutoML for user-centric optimization Dynamic Algorithm Configuration in reinforcement learning Generalization techniques for production and health applications Recent publications show strong multi-objective optimization trends across medical imaging , protein design , and time series forecasting , with 15+ papers in 2024-2025 at venues like NeurIPS, AAAI, and IEEE TPAMI. Key scientific awards : ERC Starting Grant (2022), NeurIPS BBO-Challenge winner (2020), multiple AutoML/ML competition victories Advisory role in 140+ publications and leadership of LUHAI Institute
Matthias Dangl is a researcher at the Department of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), within the Software and Computational Systems Lab. Previously, he was affiliated with the University of Passau, where he contributed to software engineering education and research. His research focuses on software verification, model checking, and formal methods, with a particular emphasis on witness-based validation and SMT-based techniques. He has developed tools like CPAchecker, which are widely recognized in academic and industrial verification communities. Dangl has won multiple awards, including categories in SV-COMP'15 and medals for his contributions to software verification. His work bridges theoretical advancements with practical implementations, enhancing the reliability of software systems through automated reasoning and verification. Education: PhD in Computer Science (2022), LMU Munich Master's in Computer Science (2013), University of Passau Research Interests: Software Verification Model Checking Witness-Based Validation SMT Solvers Formal Methods in Software Engineering Grants & Collaborations: His research is supported by collaborations with academic and industrial partners, focusing on advancing verification frameworks and tools. Labs/Teams: He is a core member of the Software Systems Lab at LMU Munich, contributing to projects like the VerifierCloud and CPAchecker.
Pascal Kerschke is a Professor at the Chair of Big Data Analytics in Transportation at TU Dresden, Germany. Previously, he held positions at the University of Münster, including Head of the Research Group for Machine Learning and Data Science. His research focuses on Exploratory Landscape Analysis, Black-Box Optimization, Algorithm Selection, and Multi-Objective Optimization. He earned his PhD in Information Systems from the University of Münster (2013–2017), and Master's and Bachelor's degrees in Data Science and Management from TU Dortmund. Education: PhD in Information Systems, University of Münster (2013–2017) MSc in Data Science, TU Dortmund (2010–2013) BSc in Data Analysis and Management, TU Dortmund (2007–2010) His research interests span algorithm selection, multi-objective optimization, and the application of machine learning in optimization problems. He has contributed to the development of the R package flacco for landscape analysis and co-organized conferences like EMO 2017. Awards include the Dissertation Prize (2018) and PPSN XIV Best Paper Award (2016). He has supervised over 15 students and actively participates in initiatives like the Benchmarking Network and COSEAL. Key projects include work on automated algorithm selection, multimodal optimization, and benchmarking frameworks for iterative heuristics.
Pascal Kerschke is a Professor at the Chair of Big Data Analytics in Transportation at Technische Universität Dresden (TU Dresden). His research focuses on exploratory landscape analysis, multi-objective optimization, and automated algorithm selection for complex computational problems. Research Areas: Exploratory Landscape Analysis, Multi-Objective Optimization, Traveling Salesperson Problem (TSP), Machine Learning, Continuous Optimization Affiliation: Chair of Big Data Analytics in Transportation, TU Dresden Kerschke has pioneered methodologies for characterizing optimization problem landscapes using machine learning and statistical features, bridging gaps between algorithm design and practical applications in transportation analytics. His work includes developing frameworks like FLACCO for fitness landscape analysis and advancing visualizations for multi-objective optimization. Recent publications highlight his contributions to deep learning integration for landscape analysis (e.g., Deep-ELA), multiobjectivization techniques, and benchmarking challenges. He has also explored adversarial robustness in neural networks and parameter tuning methodologies. Scientific Awards: PPSN 2016 Best Paper Award Kerschke's collaborations with optimization heuristic developers and his role in creating benchmarking libraries (e.g., ASlib, OpenML) underscore his interdisciplinary impact in computational optimization and data-driven decision-making.
Dr. Daniel Horn is affiliated with the Department of Statistics at the Technical University of Dortmund, specifically within the Faculty of Statistics. He is part of the working group led by Prof. Dr. Andreas Groll and serves as the Study Coordinator for the B.Sc. and M.Sc. Data Science programs. His research focuses on machine learning algorithms, statistical methods for big data, and optimization techniques, with a particular emphasis on hyperparameter tuning, ensemble methods, and robust outlier detection. Key research areas include the development of efficient algorithms for high-dimensional data analysis, such as tree ensembles for ordinal prediction, kernelized support vector machines, and model-based optimization frameworks like mlrMBO. His work also addresses industrial applications of machine learning, emphasizing practical qualification concepts for data-driven production processes. Dr. Horn's publications span topics such as multi-objective optimization, robust outlier detection (RODD), and the Contextual Shift Method (CSM). His contributions highlight advancements in both theoretical methodologies and applied computational tools, supporting data science education and industrial innovation. Contact: dhorn@statistik.tu-dortmund.de
