Dr. Daniel Schlör is a researcher at the Chair of Data Science (Informatics X) at the University of Würzburg, with additional affiliations to the CLiGS (Computational Literary Genre Stylistics) research group in Digital Humanities. His work focuses on machine learning for cybersecurity, fraud detection, and explainable AI, with recent projects exploring synthetic data generation, knowledge graph integration, and deep learning for imbalanced datasets. Research interests include Explainable AI (XAI) for anomaly detection Deep learning architectures for domain-specific relationships Multi-agent simulations for fraud scenario modeling Computational stylistics in digital humanities Article trends show expertise in Developing novel neural units (e.g., ModeConv) for structural anomaly differentiation Advancing XAI methods with generative inpainting techniques Creating open ERP datasets for occupational fraud research Applying graph neural networks to water distribution leakage detection Labs & collaborations include the Data Science Chair’s AI Institute at Hubland Nord campus and CLiGS research group for computational literary analysis.
Dr. Philipp Bach is a Researcher at the University of Hamburg Business School's Department of Statistics with Application in Business Administration. He holds a PhD in Economics from Hamburg University (2021) and a Postdoc in Statistics (since 2021). His research focuses on causal inference using machine learning methods, high-dimensional econometrics, and applications in labor, health, and financial economics. Key areas include hyperparameter tuning for causal estimation, sensitivity analysis, and difference-in-difference models. His work emphasizes practical implementations, such as the DoubleML package for R and Python, which facilitates double machine learning techniques. He has published in top journals like the Journal of the Royal Statistical Society and Journal of Statistical Software. Current projects explore multimodal data causal estimation and pandemic shielding strategies using SEIR models. No formal awards are listed, but his contributions are widely recognized in computational econometrics. Bach advises no listed students but collaborates with institutions like Booking.com on sensitivity analysis applications. His lab focuses on bridging machine learning and traditional econometric methods for real-world policy analysis.
Jovita Lukasik is a Research Fellow in the Institute for Vision and Graphics at the University of Siegen. Her research focuses on neural architecture search (NAS), model robustness, and efficient deep learning methods, with applications in computer vision and performance prediction. Key Research Areas: Developing zero-cost proxies for efficient neural architecture evaluation Improving CNN robustness via frequency-based regularization Creating benchmark datasets for architecture design and robustness analysis Exploring generative approaches for efficient NAS Her work has been published in TMLR, GCPR, ECCV, and ICLR. She contributed to the organization of the NAS workshop at ICLR 2021 and co-leads the AutoML seminar series. Recent work investigates transferable surrogates for expressive search spaces and texture/shape biases in vision-language models.
Martin Kies is a Research Fellow at Ulm University's Faculty of Mathematics and Economics, Department of Mathematics and Economics, and serves as Chief Executive Officer at LeverageData GmbH in Ulm, Germany. His academic work spans game theory, machine learning applications in economics, and microeconomic analysis of digital markets. Dr. Kies completed his Diplom Wirtschaftsmathematik (equivalent to M.Sc. in Business Mathematics) at Ulm University between 2006 and 2012, followed by doctoral studies under Prof. Sebastian Kranz from 2012 to 2020. His teaching experience at Ulm University from 2013-2018 included seminars on Competition Policy, Empirical Industrial Organization, and sustainable cooperation using game theory and simulation. His research focuses on the intersection of economics and computational methods. Key areas include game theory applications, particularly the Iterated Prisoner's Dilemma; machine learning techniques for economic forecasting; and microeconomic analysis of digital markets and competition policy. His publications demonstrate how computational approaches can provide new insights into traditional economic questions, with particular emphasis on reinforcement learning algorithms, time series analysis, and digital market structures. Dr. Kies's scholarly work shows consistent application of computational methods to solve complex economic problems, with publications spanning game theory, machine learning applications in economics, and digital market analysis. His research demonstrates methodological innovation through the development of novel approaches for analyzing economic interactions using computational techniques.
Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.
Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Her work bridges relational machine learning with biological applications , focusing on theoretical improvements to deep learning through complex network analysis. Education: Studied Mathematics and Physics at TU Darmstadt, followed by a PhD at ETH Risk Center under Frank Schweitzer and Hans J. Herrmann. Postdoctoral Experience: Worked at Harvard's Biostatistics Department (2019-2021) and ETH Zurich's Machine Learning Institute (2017-2019). Her research explores graph neural networks , sparse training , and biologically informed models , aiming to enhance algorithm robustness and interpretability. Recent publications analyze: 2025 : Implicit sparsification, hyperbolic regularization, and network balance techniques. 2024 : Spectral pruning, gene regulatory modeling, and dynamic rescaling for GNNs. Scientific recognition includes: Zurich Dissertation Prize for systemic risk research (2016). CSF Best Contribution Award for maize trade analysis (2016). She collaborates with institutions like Harvard T.H. Chan School of Public Health and ETH Zurich, focusing on trustworthy information processing and empirical security at CISPA.
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
Fabian David Schmidt is a Research Associate and Doctoral Student at the CAIDAS Chair for NLP at Julius-Maximilians-Universität Würzburg. He works on multilingual representation learning and sample-efficient cross-lingual transfer, co-advised by Prof. Dr. Goran Glavaš (University of Würzburg) and Ivan Vulić (University of Cambridge). Research Interests: His work focuses on cross-lingual transfer methods, low-resource NLP, and robust knowledge editing in LLMs. He also explores vision-language benchmarks, process mining, and semantic encoders for information retrieval. Key areas include Robust Cross-Lingual Transfer Sample-Efficient Training Vision-Language Integration LLM Evaluation Publication Trends: Fabian's recent publications emphasize multilingual and cross-lingual NLP advancements, including sliced fine-tuning for NER, model averaging for robustness, and domain adaptation. His 2025 work extends into vision-language tasks and LLM generalization across cultures. He also contributes to spoken language understanding benchmarks. Labs & Teams: Affiliated with the WüNLP group and the CAIDAS Chair at the University of Würzburg, collaborating with international researchers on cross-lingual NLP and LLM optimization.
Volker Tresp is a Professor at the Ludwig-Maximilians-Universität München (LMU) and a leading researcher in machine learning for relational structured domains . His work bridges cognitive AI , knowledge graphs , and quantum computing . He is a PI in the Munich Center for Machine Learning (MCML) and co-director of the ELLIS program on Semantic, Symbolic, and Interpretable Machine Learning . His research interests focus on temporal knowledge graphs , foundation models , multimodal learning , and quantum machine learning . Recent projects include WebPilot (multi-agent web task execution) and FedBiP (federated learning with diffusion models). His work on PyKEEN and RESCAL has advanced knowledge graph embeddings . Volker Tresp's scientific contributions are recognized through ELLIS Fellowship (2020) , Siemens Inventor of the Year (1996) , and Best Paper Awards at ISWC 2021 and IEEE ICHI 2020 . His students have published extensively at top AI venues like AAAI , CVPR , and ECCV . Awards and Honors: ELLIS Fellow (2020) Siemens Inventor of the Year (1996) Best Paper Award, ISWC 2021 Student Best Paper Award, ISWC 2017 Best Paper Runner-up, PKDD 2005
Shiqing Liu is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering and the Cognitronics & Sensor Technology Group within the Center for Cognitive Interaction Technology (CITEC). Based at office CITEC 3-204, Liu contributes to the university's research in the Socio-Technical World domain, focusing on technologies that enable agents to act and communicate in complex environments. Dr. Liu's research spans several cutting-edge areas in artificial intelligence and optimization. Their work primarily focuses on graph neural networks, combinatorial optimization, and federated learning systems. They have made significant contributions to applying machine learning techniques to solve complex optimization problems including vehicle routing, facility location, and neural architecture search. Their research bridges theoretical computer science with practical applications in distributed systems and privacy-preserving technologies. An analysis of Dr. Liu's recent publications reveals a strong trajectory in developing unified frameworks that combine graph-based learning with combinatorial optimization. Their work increasingly addresses challenges in federated settings where data privacy and distribution heterogeneity present significant obstacles. The research demonstrates a progression from single-objective optimization problems toward more complex multi-objective scenarios, with growing emphasis on practical implementation constraints and real-world applicability. Dr. Liu is actively involved in the Cognitronics & Sensor Technology research group at CITEC, which is part of Bielefeld University's strategic focus on the Socio-Technical World. This center investigates how humans, robots, and AI systems can effectively interact and collaborate in complex environments, aligning with the university's broader mission of "Transcending Boundaries" between disciplines and between science and society.
Jacques Wainer is a Professor at the University of Campinas (Brazil), with expertise spanning Machine Learning, Medical Informatics, and Workflow Systems. His research focuses on algorithm optimization (e.g., SVM hyperparameters, imbalanced data strategies), medical applications (diabetic retinopathy detection), and educational technology (Python vs C in programming education). He has published extensively in top journals like Expert Systems with Applications and Journal of Machine Learning Research, and collaborates with institutions globally. His work bridges theoretical advancements and practical implementations, emphasizing reproducibility and real-world impact. Research interests include: Machine Learning methodologies, medical image analysis, workflow systems, educational computing, and bibliometric studies. Notable contributions include Bayesian model comparisons, nested cross-validation critiques, and low-memory face verification systems for mobile devices. His interdisciplinary approach addresses challenges in healthcare, education, and computational efficiency.
Enrique S. Quintana-Ortí is a Professor at the Technical University of Valencia and Jaume I University , Spain, specializing in Computer Science of Systems and Computers . His work bridges High-Performance Computing (HPC) , Parallel Computing , and Deep Learning , with a focus on optimizing Matrix Algorithms for modern architectures. Key research areas: Quantized Inference , GEMM-Based Convolutions , GPU Acceleration , and Performance Portability across ARM, RISC-V, and NVIDIA processors. Recent projects include RED-SEA (European interconnect solutions), GreenLightningAI (decoupled AI systems), and Ginkgo (GPU-based linear algebra frameworks). His publications (2023–2025) emphasize edge computing , mixed-precision techniques , and energy-efficient AI . Collaborative efforts span institutions like Xilinx , Fujitsu , and co-authors such as Adrián Castelló , Héctor Martínez , and Francisco D. Igual .
Dileep Kumar is a Professor in the Department of Communications Engineering at the University of Oulu's Faculty of Information Technology and Electrical Engineering. With a publication record spanning over three decades from 1994 to 2025, his research bridges wireless communications and medical imaging domains. Professor Kumar's research interests focus on wireless communication systems (particularly mmWave, 5G/6G networks, and SWIPT technology) and medical imaging applications . His work demonstrates exceptional versatility across theoretical signal processing and practical implementations. In wireless communications, he has pioneered research in multi-point connectivity for reliable mmWave systems, latency-aware network design, and energy-efficient 6G architectures. In medical imaging, his contributions include advanced MRI analysis techniques, compressed sensing applications, and automated segmentation methods for knee cartilage assessment. His publication portfolio reveals a strong trend toward interdisciplinary research, with recent work (2021-2025) increasingly integrating machine learning techniques into both wireless communication optimization and medical image analysis. The publications span high-impact journals including IEEE Transactions on Wireless Communications, BMC Medical Imaging, and IEEE Access. Professor Kumar maintains extensive collaborations with international researchers, particularly with Antti Tölli and Onel L. Alcaraz López at University of Oulu (wireless communications focus) and with Akash Gandhamal and Sanjay N. Talbar (medical imaging focus).
Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. Her research focuses on sequential decision making and theoretical reinforcement learning (RL), particularly in non-stationary environments, bandit problems, and principled learning algorithms. She has received prestigious awards including the Emmy Noether Award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , respectively. Claire has previously worked as a Research Scientist at DeepMind (London) and as a part-time Applied Scientist at Amazon (Berlin). Education : PhD in Machine Learning from Telecom ParisTech (2017), under Prof. Olivier Cappé. Research Interests include: Sequential Decision Making Bandit Problems (Combinatorial, Delayed Feedback, Sparse Actions) Reinforcement Learning Theory Meta-Learning and Lifelong Learning Optimization Algorithms Game-Theoretic Approaches to PCA Publications highlight trends in non-stationary environments, contextual bandits, and theoretical foundations of RL and bandit algorithms. Her work spans applications in scientific discovery, statistical testing, and optimization. Scientific Awards : Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 Advising and Grants : Claire leads a research group with ongoing PhD and postdoc opportunities through IMPRS-IS and ELLIS doctoral programs. Her projects receive funding from the European Research Council and DFG, with focus on continual learning and adaptive AI systems. Labs/Teams : She coordinates the Tübingen Women in Machine Learning (TWiML) initiative and co-leads the Women in Learning Theory (WiML-T) website. Her group emphasizes diversity, inclusivity, and collaborative research in theoretical machine learning.
