Lutz Schubert is a researcher at the Institute of Computer Science , University of Cologne. He focuses on efficient distributed and parallel execution environments for heterogeneous systems, notably the MyThOS operating system in collaboration with Brandenburg University of Technology, Cottbus-Senftenberg. His interdisciplinary work bridges computer science and digital archaeology , addressing modeling of non-deterministic events from sparse excavation data and exploring human behavior constraints in archaeological contexts. Research Interests Design of modular, scalable operating systems for distributed systems Optimization of execution environments for heterogeneous hardware Application of complex systems modeling to digital archaeology Statistical and probabilistic methods for archaeological interpretation Autonomic resource distribution and adaptation in computing Publication Trends : His work spans operating systems , parallel computing , and digital humanities , with recent emphasis on probabilistic reasoning in archaeology and adaptive OS design for multicore architectures. Labs & Collaborations : He collaborates with Brandenburg University of Technology on MyThOS and leads research in computational archaeology as chair of Computer Applications and Quantitative Methods in Archaeology (CAA) , Germany.
Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.
Holger Fröning is a full professor at Heidelberg University’s Institute of Computer Engineering (ZITI), where he leads the Hardware and Artificial Intelligence (HAWAII) Lab. His research focuses on embedded machine learning , high-performance computing , and hardware-software co-design , with emphasis on resource efficiency, power optimization, and emerging architectures like analog , photonic , and resistive memory systems. He has held leadership roles including Managing Director of ZITI (2023–present) and Dean of Studies for Computer Science (2019–2022) , and has collaborated with institutions such as NVIDIA Research, Chinese Academy of Sciences, and Graz University of Technology. Research Trends : His recent publications explore Bayesian neural networks , green machine learning , analog computing noise mitigation , and GPU/FPGA optimization . Articles highlight photonic computing for AI , memory-efficient training , and hardware-aware DNN compression . Scientific Awards : 2025 HiPEAC Paper Award (Nature Computational Science) 2014 Google Faculty Research Award Multiple Best Paper Awards (IPDPS, ICPP, ECML-PKDD workshops) Leadership & Service : Organized workshops (WEML, ITEM, F4HD), chaired tracks at EuroPar and ISC, and served on program committees for ICPR, ECAI, and FPL. Education & Affiliations : PhD and MSc from University of Mannheim (2007/2001). Sponsors include DFG, FWF, FFG, NVIDIA, SAP, and XILINX.
Matthias Feurer is a Thomas Bayes Fellow and interim professor at the Chair of Statistical Learning and Data Science, funded by the Munich Center for Machine Learning (MCML) at Ludwig Maximilian University of Munich. He is a member of the Department of Statistics at LMU Munich, working under Prof. Dr. Bernd Bischl. His academic background includes: PhD in Computer Science from Albert-Ludwigs-Universität Freiburg, supervised by Prof. Dr. Frank Hutter M.Sc. in Computer Science from the University of Freiburg B.Sc. in Computer Science and Media from the Media University Stuttgart Feurer's research focuses on simplifying machine learning usage through Automated Machine Learning (AutoML). His work encompasses hyperparameter optimization, meta-learning, and model selection, with increasing emphasis on multi-objective AutoML that considers factors beyond predictive performance such as interpretability, deployability, and fairness. He actively develops open-source tools to advance the field. His recent publications demonstrate a strong trajectory in practical AutoML systems, with growing attention to tabular machine learning, foundation models integration, and addressing real-world constraints in optimization. His work consistently bridges theoretical advances with practical implementations through several widely-used open-source projects. Notable achievements include: 1st place in the warmstarting-friendly leaderboard of the BBO NeurIPS challenge Winner of the 2nd AutoML challenge Winner of the kdnuggets blog contest on AutoML Feurer is actively mentoring and teaching, having advertised PhD positions focused on AutoML, optimization, and benchmarking. He co-founded the Open Machine Learning Foundation supporting OpenML.org. His upcoming move to TU Dortmund as an assistant professor in AutoML and Optimization signals continued growth in his academic career while maintaining his research focus on making machine learning more accessible and rigorous.
Antonia Holzapfel is a doctoral researcher at the Institute for Data Science in Mechanical Engineering (DSME) of RWTH Aachen University. Supervised by Prof. Sebastian Trimpe, her work focuses on safe and explainable machine learning (XAI) for robotics and time-series modeling, with applications in mechanical engineering and medical technology. Bachelor's in Mechanical Engineering (Design Engineering specialization) Master's in General Mechanical Engineering (Simulation Technology and Medical Technology specializations) Her research addresses safety in learning processes , including Bayesian optimization for quadcopters and concept extraction for model interpretability. Her Friedrich-Wilhelm Award -winning thesis (2024) explored safe online learning in time-varying environments, presented at the L4DC conference. She contributes to DFG-funded projects on data-driven process modeling in forming technology. Key scientific contributions include: Advancing safe Bayesian optimization for autonomous systems Developing explainable AI methods for time-series analysis Improving model robustness through internal representation patterns Scientific awards: Friedrich-Wilhelm Award for outstanding Master's thesis (2024) Antonia's work bridges machine learning safety with practical applications in robotics and industrial processes.
Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.
Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.
Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.
Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.
Michael Schaub is a tenure-track Assistant Professor in the Department of Computer Science at RWTH Aachen University, specializing in Computational Network Science. His research focuses on analyzing complex systems through network and graph models, integrating dynamical systems, control theory, and machine learning. He leads the Computational Network Science group, advancing methodologies for higher-order network models like simplicial complexes and hypergraphs. Schaub holds a PhD from Imperial College London and has held postdoctoral positions at MIT and Oxford. He is an ERC Starting Grant recipient (2022) and a Marie Curie Fellow, recognized for contributions to network dynamics and topological data analysis. Education: PhD in Mathematics, Imperial College London (2011-2015) MSc in Biomedical Engineering, Imperial College London (2010) BSc in Electrical Engineering, ETH Zurich (2007-2010) Research Interests: Schaub’s work spans interdisciplinary applications of network science, including biological systems, social networks, and technical infrastructures. Key areas include: Higher-order network models (hypergraphs, simplicial complexes) Graph signal processing and dynamics on networks Community detection and dynamical systems analysis Topological data analysis and machine learning Grants & Awards: ERC Starting Grant (2022): HIGH-HOPeS project Marie Skłodowska-Curie Fellowship (2017-2019) Junior Fellow, German Informatics Society (GI) Member of Junges Kolleg (North Rhine-Westphalia Academy) Labs & Teams: Leads the Computational Network Science Lab at RWTH Aachen, collaborating internationally on projects like the ELLIS Society and the European Laboratory for Learning and Intelligent Systems (ELLIS). Active in organizing workshops (e.g., Toponets, SIAM MDS).
Dominik Schnaus is a PhD Student at the Computer Vision Group within the School of Computation, Information and Technology at the Technical University of Munich. His research focuses on computer vision and deep learning, particularly in vision-language correspondence and uncertainty estimation in neural networks. Research Interests: 3D/4D reconstruction, vision-language models, neural network uncertainty, robotics Contact: dominik.schnaus@tum.de
Dr. Moritz Ziegler is a geomechanics researcher affiliated with the Technical University of Munich (TUM) and the Assistant Professorship of Geothermal Technologies . He previously worked at GFZ Potsdam (2017–2023) and earned his PhD in Geophysics from the University of Potsdam (2014–2017). His research focuses on geomechanical numerical modeling , uncertainty quantification , and 3D stress field analysis , with applications to geothermal energy, seismic hazard, and rock mechanics. Education: Bachelor of Science in Geosciences (2008–2011), Freie Universität Berlin Master of Science in Geophysics (2011–2014), University of Potsdam PhD in Geophysics (2014–2017), University of Potsdam & GFZ Potsdam His work integrates advanced computational methods ( Altair Hypermesh , Dassault Systèmes Abaqus , Python , Matlab ) to address complex geomechanical problems. Recent publications analyze stress gradients in the North Alpine Foreland Basin, fault-stress interactions, and physics-based machine learning in geomechanics. He has contributed to software development (DOuGLAS v1.0) and calibration tools (FAST Calibration v2.4). Scientific awards or honors are not explicitly mentioned in the provided text. However, his research has been published in leading journals such as Geophysical Journal International , Solid Earth , and Pure and Applied Geophysics .
Ke Xu is a Professor in the Department of Computer Science at Tsinghua University's School of Information Science and Technology. With extensive research contributions in network security, privacy-preserving technologies, and machine learning applications for networking, Professor Xu has established himself as a leading researcher in computer science. Professor Xu's research interests span network security, privacy-preserving technologies, machine learning for networking, federated learning, internet protocols, encrypted traffic analysis, blockchain applications, and AI in networking. His work bridges theoretical foundations with practical implementations, focusing on real-world security challenges and network optimization problems. He has developed novel frameworks for secure network operations, privacy-preserving data sharing, and efficient AI deployment in distributed environments. Professor Xu's publication record shows a clear trend toward integrating artificial intelligence with traditional networking challenges. His recent work explores federated learning security, encrypted traffic analysis using deep learning, and novel approaches to network security that leverage machine learning techniques. The interdisciplinary nature of his research spans computer networking, security, privacy, and artificial intelligence. Professor Xu has received recognition for his contributions to network security and privacy-preserving technologies through publications in top-tier venues including IEEE journals, ACM conferences, and security symposia. His work has appeared in IEEE Transactions on Dependable and Secure Computing, IEEE/ACM Transactions on Networking, and security conferences like CCS and NDSS. Professor Xu actively collaborates with researchers across institutions, supervising students and junior researchers in exploring cutting-edge problems in network security and AI. His research has been supported by significant grants focusing on network security, privacy, and intelligent networking infrastructure. He leads projects that address fundamental challenges in secure communication, privacy-preserving data analysis, and intelligent network management. Professor Xu is involved with research laboratories focusing on network security and intelligent systems at Tsinghua University. His team works on developing practical security solutions, privacy frameworks, and AI-enhanced networking protocols that address real-world challenges in today's increasingly connected world.
Prof. Dr.-Ing. Gerhard Müller is a Full Professor at the Chair of Structural Mechanics within the TUM School of Engineering and Design at Technical University of Munich (TUM). Since 2004, he has held this distinguished position, and since 2014, he has served as Executive Vice President for Academic and Student Affairs at TUM. His research focuses on structural dynamics and vibroacoustics, with specific expertise in dynamic soil-structure interaction, sound radiation analysis, and seismic risk assessment. Professorship: Structural Mechanics University: Technical University of Munich School: TUM School of Engineering and Design Department: Chair of Structural Mechanics in Civil Engineering Prof. Müller's research spans multiple domains, including: Structural Dynamics : Examining building and vehicle vibrations, seismic soil-structure interaction, and advanced model order reduction techniques Vibroacoustics : Investigating sound radiation from vibrating structures and developing acoustic metamaterials for noise control Computational Methods : Pioneering hybrid deterministic-statistical approaches, Wave Based Methods (WBM) for saturated elastodynamic structures, and parametric model order reduction His recent publications demonstrate expertise in: Wave propagation analysis in poroelastic media Bayesian parameter updating for structural models Acoustic metamaterials for vibration control Advanced numerical methods for seismic risk assessment Hybrid ITM-FEM approaches for soil-structure interaction Energy flow analysis in timber structures Awarded the Spindler Prize in 1984 , Prof. Müller also holds significant academic leadership roles: President of European Association for Structural Dynamics (EASD) Chairman of Bavarian-French University Center (BayFrance) Active member of ASIIN accreditation agency and Bavarian Chamber of Engineers Previously served as Dean of Civil Engineering and Surveying at TUM (2010-2014) He leads the Structural Dynamic Lab (formerly Vibroacoustics Lab) and has developed interactive web apps for engineering education. His work bridges theoretical advancements with practical applications in construction acoustics, transportation noise control, and geothermal energy infrastructure analysis.
