Hans Weytjens is a postdoctoral researcher at the Technical University of Munich (TUM) and a guest professor at KU Leuven in Belgium. He holds a Ph.D. in Machine Learning for Predictive and Prescriptive Process Monitoring, an M.Sc. in Business and Information Science, and an MBA in Finance. His current academic role at TUM involves research in the Department of Information System Development and Operation. Ph.D., KU Leuven (2023) M.Sc., KU Leuven (1990-1991) MBA, University of Chicago (1990-1991) Hans Weytjens specializes in Machine Learning applications for Business Processes , with a focus on Prescriptive Process Monitoring , Generative AI , and Autonomous Enterprise systems. His work bridges theoretical advancements with practical implementations in process optimization and AI-driven decision-making frameworks. Hans Weytjens' publications span topics like Event Log Dynamics , Reinforcement Learning , and Time Series Forecasting , reflecting his expertise in integrating Uncertainty Quantification and Visual Analytics into process mining methodologies. At TUM, Hans contributes to the Chair of Information System Development and Operation , advancing research on AI-driven enterprise automation and predictive process analytics.
Thomas Apel is a Professor of Mathematics at Universität der Bundeswehr München (UniBw München), where he maintains an active research program in numerical analysis and computational mathematics. His academic career began at TU Chemnitz (formerly TH Karl-Marx-Stadt), where he completed his PhD in 1991 and habilitation in 1999. He has been supervising PhD students since 2006, with his most recent student completing in 2022, demonstrating his ongoing academic engagement and leadership in the field. Apel's research focuses on challenging mathematical problems involving singularities, optimal control, and specialized mesh techniques. His work bridges theoretical numerical analysis with practical applications for solving complex partial differential equations. He has made significant contributions to the development of anisotropic finite element methods, error estimation techniques, and adaptive algorithms for problems with geometric singularities. His publication record spans over three decades, with continuous contributions to top numerical analysis journals through 2024. His recent work shows increasing focus on pressure-robust methods for fluid dynamics problems, isogeometric analysis for complex geometries, and mathematical modeling of biological processes. The consistent quality and relevance of his research have established him as a leading figure in computational mathematics. Member of the Scientific Committee of the annual Chemnitz Finite Element Symposium Supervised 9 PhD students since 2006 Author of numerous journal articles, books, and conference proceedings Active researcher with publications continuing through 2024 Apel's academic journey reflects a deep commitment to advancing numerical methods for challenging mathematical problems. His work has evolved from foundational contributions to anisotropic finite elements to more recent applications in fluid dynamics, eigenvalue problems, and interdisciplinary mathematical modeling. His sustained research productivity and mentorship of the next generation of numerical analysts demonstrate his enduring impact on the field.
Dr. Marcel Köster is a researcher affiliated with the Ubiquitous Media Technology Lab at the German Research Center for Artificial Intelligence (DFKI) and the Saarland Informatics Campus. His work focuses on GPGPU computing, particle simulations, compilers, and optimization techniques. Email: Marcel.Koester@dfki.de Phone: +49 681 85775 7750 Location: Gebäude D3 1, Room 0.13, Saarbrücken Research Interests Dr. Köster's research integrates GPU computing with simulation algorithms and compiler optimization. He contributes to advancements in parallel processing, domain-specific languages, and scientific visualization through both theoretical exploration and practical implementations. His publications highlight innovative applications of GPU acceleration to heuristic optimization, state generation, and particle simulations. These works demonstrate expertise in thread compaction, shared memory utilization, and warp scheduling. Teaching Experience Dr. Köster has taught multiple courses at HBK Saar, including: Artificial Intelligence (Summer 2019) Grundlagen der Medieninformatik (Winter 2016/17) Physical Simulations on Media Facades (Winter 2015/16) Core Lecture: Compiler Construction (Winter 2013/2014)
Prof. Dr. Florian Elert serves as Professor of Business Administration with a specialization in Insurance at the Hamburg School of Business Administration (HSBA), where he heads the Bachelor's program in Insurance Management and leads the Department of Finance & Accounting. He directs practice-oriented teaching, research, workshops, and industry studies while regularly presenting at sector events as a key thought leader in insurance innovation. His academic foundation includes a Business Administration degree from the University of Cologne focused on Insurance Sciences and a PhD from the University of Leipzig on 'Value-oriented management of insurance companies,' combining theoretical rigor with extensive industry experience as an insurance executive. Elert's research centers on digital disruption in insurance, analyzing how digitalization transforms business models, distribution channels, and risk management practices. He pioneers studies on Generation Y's insurance expectations and digital assistance services, emphasizing practical applications through empirical research and industry collaboration to address evolving market dynamics and consumer behavior shifts. His publication portfolio reveals consistent thematic focus across fifteen recent works, demonstrating deep expertise in InsurTech ecosystems, digital risk management frameworks, and business model innovation. The trajectory shows increasing emphasis on generational consumer analysis and practical digital transformation strategies for insurers, with foundational work on comparison portals evolving into current InsurTech and assistance service research. No personal scientific awards are documented in the source material, though he actively participates in academic recognition processes such as the Excellence Award for outstanding student theses in insurance management. As academic director, Elert oversees thesis supervision and curriculum development while leading major industry-funded research initiatives like the Generation Y study on digital risk management, supported by the Association for the Promotion of Insurance Science in Hamburg and the Hamburg Financial Centre. He organizes international student excursions to London institutions including Lloyd's and AIG to bridge academic learning with global industry practices. He founded the Hamburg Insurance Innovation Days (HIID) platform connecting insurers, distributors, and InsurTechs, co-founded the InsurTech-Werft Hamburg implementation initiative, and established the ITW Institute for Transformation and Further Education in Insurance as a HSBA spin-off where he serves as managing director driving executive education programs for the sector.
Martin Vechev is a Professor in the Department of Computer Science at ETH Zurich, leading the Secure, Reliable, and Intelligent Systems (SRL) Lab. His research bridges programming languages, software analysis, and emerging domains like quantum computing, with significant contributions to static analysis, abstract interpretation, and machine learning for code. His primary research interests include programming languages, static analysis, abstract interpretation, and quantum computing. Vechev has pioneered scalable techniques for software verification, particularly in concurrency and security analysis, and has recently driven innovations at the intersection of programming languages and quantum software development. His work emphasizes practical applications while maintaining theoretical rigor. Analysis of Vechev's recent publications reveals a strategic evolution toward quantum programming languages and machine learning integration. His lab has shifted from traditional static analysis to developing foundational frameworks for quantum circuit synthesis (e.g., Silq, Unqomp) and robust neural network certification, addressing critical challenges in quantum resource management and AI safety. The SRL Lab under Vechev's direction focuses on building intelligent systems that are inherently secure and reliable through advanced program analysis techniques. The lab's research spans theoretical foundations to industrial-strength tools, with strong emphasis on quantum software engineering and the application of machine learning to code understanding and generation.
Weilong Chen is a researcher at the Multiscale Modeling of Fluid Materials group in the School of Mechanical Engineering at Technical University of Munich (TUM). He is affiliated with the Atomistic Modeling Center (AMC) and Munich Data Science Institute (MDSI), working on computational methods for fluid materials and molecular simulations. Education: MSc in Mathematics (2024, Chalmers University, Sweden) and BSc in Aerospace Engineering (2022, National University of Defense Technology, China) His research focuses on AI for Science , particularly Graph Neural Networks , Deep Generative Models (including Flow Matching/Diffusion), and Machine Learning Potentials for applications in Coarse-grained Molecular Dynamics and scalable simulations. Recent work involves developing frameworks like chemtrain-deploy for million-atom molecular dynamics and generative thermodynamics modeling. He actively collaborates on projects at the intersection of machine learning and physics-based modeling, with involvement in workshops and team events. Weilong Chen is available for master's thesis supervision and can be contacted via email.
Yaohua Zang is a Professor at the Technical University of Munich , affiliated with the Department of Data-driven Materials Modeling . Their research bridges computational materials science, machine learning, and mathematical modeling, focusing on advanced methods for solving partial differential equations (PDEs), inverse problems, and optimal control in complex systems. Research Interests include: Physics-informed neural operators for PDEs Stochastic generative modeling for materials design Optimal control in robotics and dynamic systems Bayesian inference and uncertainty quantification High-dimensional numerical analysis Adversarial and weak formulation-based neural networks Recent work highlights physics-aware neural operators (DGenNO), weak adversarial networks for inverse problems, and applications in elastography and robotic assembly. Their scientific contributions span PDEs, materials science, and computational control. Labs & Teams : Part of the Chair of Data-driven Materials Modeling, located in Garching b. München, Germany.
Claus Wimmer is a researcher at the Chair of Thermodynamics within the Faculty of Mechanical Engineering at the Technical University of Munich (TUM), holding the position of Researcher. His work bridges mechanical engineering principles with clinical medicine, specifically focusing on orthopedic applications through interdisciplinary collaboration with medical institutions. His primary research interests include: Artificial Intelligence in Medical Imaging and Diagnosis Orthopedic Surgery and Arthroplasty Optimization Bone Tumor Classification and Analysis Wearable Sensor Technology for Gait Assessment Machine Learning for Surgical Outcome Prediction Digital Health Implementation in Clinical Settings Analysis of Wimmer's recent publications (2024-2025) reveals a concentrated research trajectory in developing AI-driven solutions for orthopedic challenges. Key trends include deep learning applications for bone tumor segmentation in radiological images, predictive modeling of arthroplasty failures using national registry data, and systematic evaluations of wearable technology for postoperative rehabilitation. His work consistently emphasizes practical clinical translation, with frequent collaborations leveraging German healthcare datasets and addressing real-world implementation barriers in medical AI. No scientific awards were documented in the available information. Details regarding student supervision, research grants, laboratory infrastructure, or collaborative research teams were not provided in the source material, though his publication patterns suggest active engagement with clinical partners in orthopedic departments and medical technology development.
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.
Dr. Petra Bevandic is a researcher at the Faculty of Engineering at Universität Bielefeld within the Machine Learning Group . Her work spans key areas in computer vision and machine learning. Primary Affiliation: Faculty of Engineering, Machine Learning Group, Universität Bielefeld Research Interests: Specializes in semantic segmentation and anomaly detection Focus on open-set recognition and domain adaptation Active in diffusion models and garment reconstruction Scientific Contributions: Pioneering work on virtual try-on/try-off systems Developing robust methods for out-of-distribution detection Advancing multi-domain image segmentation techniques
Jonas Geiping is a Professor at the ELLIS Institute Tübingen, leading the Safety- and Efficiency- aligned Learning research group. He has previously worked at the University of Maryland, University of Siegen, and University of Münster. His research focuses on safety and efficiency in machine learning, exploring data poisoning principles, watermarking for generative models, privacy in federated learning, and adversarial attacks against large language models. He also investigates how to make AI systems more efficient through weight averaging, recursive computation, and computational constraint analysis. His recent work includes test-time computation scaling using recurrent depth approaches, adversarial attacks on LLMs beyond jailbreaking, and zero-shot detection of machine-generated text through contrasting LLMs (Binoculars method). He has contributed to arithmetic capabilities in transformers with positional embeddings and scalable LLM training frameworks like AxoNN. His research addresses critical questions about the intersection of safety and efficiency in AI systems: Can models reason well without sacrificing safety? How do computational constraints affect safety guarantees? Can systems be designed where intelligence and safety reinforce each other?
Anna Midlenko serves as an Instructor in the Department of Medicine at Nazarbayev University School of Medicine, bringing extensive clinical expertise in surgical oncology since joining in 2017. Her work bridges clinical practice and academic research in cancer treatment. Her educational foundation includes: Doctor of Medicine, Ulyanovsk State University, Russia (with honors) Residency in General Surgery (2007-2009) Internship in Surgical Oncology (2009-2010) Ph.D. in Surgical Oncology, Bashkir State Medical University, Ufa, Russia (2012) Dr. Midlenko's research centers on breast cancer biology and treatment innovations, with specific focus on genetic mechanisms, early detection methodologies, elderly patient care protocols, and oncoplastic surgical techniques. She actively develops AI-driven diagnostic tools using thermal imaging to improve accessibility of breast cancer screening. Her recent publications (2023-2024) reveal two dominant research trajectories: computational approaches applying physics-informed neural networks and deep learning to thermography-based detection, and population-level studies examining breast cancer epidemiology and genetic biomarkers within Kazakhstan's healthcare system. As Co-Principal Investigator for the colorectal cancer biomarker project (2019-2020), she contributes to translational research while mentoring through clinical teaching workshops including the University of Pittsburgh Master Class. Her conference participation spans oncology congresses in Salzburg and Shanghai, focusing on gastrointestinal cancers and breast pathology diagnostics.
Ludwig Lautenbacher is a researcher at the Chair for Computational Mass Spectrometry at Technische Universität München (TUM). He contributes to projects democratizing machine learning (ML) applications in proteomics research. University: Technische Universität München Academic Rank: Researcher Email: ludwig.lautenbacher@tum.de Research Interests: Ludwig focuses on integrating machine learning with proteomics, emphasizing accessibility, interoperability, and reproducibility of ML models. His work addresses computational challenges in predicting peptide properties, fragment intensities, and retention times, while developing open-source resources like Koina and ProteomicsDB . Recent Publications (2021–2024) demonstrate expertise in: Temporal proteomics for drug mechanism analysis Deep learning for TMT-labeled peptide identification Fragment ion intensity prediction Open-source platform development Mass spectrometry data interpretation Collaborative software integration (Skyline, EncyclopeDIA, FragPipe) Laboratory Affiliation: Works in the research group of Prof. Dr. Mathias Wilhelm at TUM, contributing to computational mass spectrometry advancements.
Mayank Kejriwal is a Research Assistant Professor in the Department of Industrial and Systems Engineering at the University of Southern California (USC) and a Research Lead at the USC Information Sciences Institute (ISI). His work focuses on applying AI technologies for social good, particularly through knowledge graphs and neuro-symbolic AI. Education: PhD in Computer Science, University of Texas at Austin Research Interests: Dr. Kejriwal's primary research is in knowledge graphs (KG), neuro-symbolic AI, and complex systems. He explores how AI can address real-world issues such as human trafficking, crisis response, and healthcare. His work bridges theory and application, combining symbolic reasoning with modern machine learning techniques. He is also active in computational social science, network science, and AI ethics, with a strong emphasis on human-centered computing. Scientific Awards: USC Graduate Student Mentorship Award (2021) AAAS Early Career Award for Public Engagement with Science Finalist (2021) Yahoo! Faculty Research Engagement Program Recipient (2019) Copper Black Award for Creative Achievement, Mensa Foundation (2019) Key Scientific Challenge Award, Allen Institute for AI (2018) International Best Dissertation Award, Semantic Web Science Association (2017) Grants & Funding: His research has been funded by DARPA, corporate sponsors, and philanthropic organizations. He has led multiple projects under the MEMEX and other federal programs aimed at AI for social impact. Teaching & Mentorship: He teaches courses such as ISE 540: Text Analytics and ISE 599: Applied Predictive Analytics. He has received the USC Graduate Student Mentorship Award for his dedication to student development.
Johannes Schmid, M.Sc., is a Research Associate at the Chair of Vibroacoustics of Vehicles and Machines at the Technical University of Munich . His work focuses on integrating machine learning with computational acoustics, particularly in physics-informed deep learning and uncertainty quantification for vibroacoustic systems. Research Interests Physics-informed deep learning for acoustic modeling Data-driven surrogate modeling for dynamic systems Uncertainty quantification in industrial applications Stochastic Finite Element Methods Interactive acoustics apps for education Publications Schmid has authored/co-authored over 15 publications in computational acoustics, with recent work on neural networks for boundary integral methods, metamaterial design, and hybrid machine learning techniques. His research spans automotive applications, noise control, and educational tools. Laboratory Affiliation He is affiliated with the Chair of Vibroacoustics of Vehicles and Machines , contributing to projects on vehicle acoustics, fluid-structure interaction, and deep learning applications in engineering.
