Maximilian Egger is a Doctoral Researcher at the Institute for Communications Engineering under Prof. Antonia Wachter-Zeh at the Technical University of Munich (TUM). His research focuses on distributed machine learning, privacy-preserving computing, and information theory. He holds an M.Sc. in Electrical Engineering and Information Technology (2022, TUM) and a B.Eng. in Electrical Engineering (2020). He has conducted research stays at École Polytechnique Fédérale de Lausanne (2024) and Imperial College London (2023). Egger has received several awards, including the DAAD Scholarship (2023) and the VDE Award Bavaria (2020). His work emphasizes secure federated learning, Byzantine-resilient systems, and efficient distributed algorithms. He is affiliated with the Chair of Coding and Cryptography and actively contributes to advancements in decentralized learning systems. Recent publications highlight breakthroughs in privacy preservation, channel capacity estimation, and scalable federated edge learning.
Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.
Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.
Max Planck Institute for Evolutionary AnthropologyGermany
Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.
Prof. Jalal Etesami is an Assistant Professor in the Department of Computer Science at Technical University of Munich (TUM), leading the Decision Sciences & Systems group. He holds a Ph.D. in Industrial and Systems Engineering from the University of Illinois at Urbana-Champaign and was a Postdoctoral Fellow at EPFL in Switzerland. His research focuses on machine learning, causal inference, multi-agent systems, and game theory, with applications to systemic risk modeling and market design. He teaches advanced courses such as Causal Inference in Time Series , Algorithmic Game Theory , and Optimization, Learning, and Market Design . Notable contributions include work on causal structure learning, stochastic optimization, and non-Gaussian causal models. Recent research explores causal effect identification under confounding, neural networks for market analysis, and optimal experiment design. Prof. Etesami’s work appears in top venues like NeurIPS, AAAI, and IEEE journals. He actively contributes to the academic community, organizing seminars and workshops on topics ranging from causal reasoning to computational social choice.
Max Planck Institute for Intelligent SystemsGermany
Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. She holds an Emmy Noether award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , focusing on theoretical reinforcement learning and non-stationary environments. Education: PhD from Telecom ParisTech (2017) Post-doctoral researcher at University of Magdeburg (2018) Her research bridges sequential decision making, bandit problems, and reinforcement learning theory. Recent work explores lifelong learning, distributional RL, and game-theoretic approaches to PCA, emphasizing mathematical rigor and algorithmic innovation. Recent publications highlight trends in non-stationary RL , continual learning , and bandit algorithms with complex feedback structures. Key subfields include meta-learning, adaptive control, and theoretical guarantees in dynamic programming. Scientific Awards: Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award & Oral Presentation, ICLR (2021) She mentors PhD and master's students in theoretical machine learning, with current advisees including Nicolas Nguyen, Onno Eberhard, and Ziyad Sheebaelhamd. Her lab actively recruits candidates in bandit algorithms and RL theory through the IMPRS-IS and ELLIS doctoral programs. Claire co-leads diversity initiatives like Tübingen Women in Machine Learning and Women in Learning Theory, advocating for inclusivity in AI research. She has organized workshops at ICML and EWRL, and contributed to union activism in the tech industry.
Katharina Eggensperger is an Early Career Research Group Leader at the University of Tübingen , leading the AutoML for Science group within the Cluster of Excellence Machine Learning for Science . She previously completed her Ph.D. at the University of Freiburg under Frank Hutter and Marius Lindauer (2022), and actively contributes to the AutoML community through open-source tool development and competition leadership. Co-developer of AutoML.org tools Faculty member of IMPRS-IS Chair for multiple AutoML workshops/conferences (2019-2025) Her research focuses on automated machine learning (AutoML) with specific attention to: AutoML Systems Hyperparameter Optimization Tabular Machine Learning Scientific Applications of ML She has organized multiple AutoML schools and conferences, including serving as Program Chair for AutoML 2024 and Non-archival Track Chair for AutoML 2025. Her work emphasizes making machine learning accessible through automation while maintaining scientific rigor and interpretability, particularly for tabular data applications. Katharina actively recruits PhD students through IMPRS-IS and collaborates with institutions like the University of Freiburg and Cyber Valley .
Claire Vernade is a Group Leader at the University of Tübingen in the Cluster of Excellence Machine Learning for Science. She leads an active research group focused on theoretical aspects of sequential decision making, with particular expertise in bandit problems and reinforcement learning theory. Her work bridges theoretical foundations with practical applications in scientific discovery. Her research interests span sequential decision making, bandit problems, theoretical Reinforcement Learning, Learning Theory, and principled learning algorithms. She has made significant contributions to understanding non-stationary environments, lifelong learning frameworks, and the theoretical foundations of bandit algorithms. Her work on "Eigengame: PCA as a Nash Equilibrium" received an Outstanding Paper Award at ICLR 2021. Dr. Vernade has been awarded prestigious grants including an Emmy Noether award (2022) for her FoLiReL project and an ERC Starting Grant (2024) for her ConSequentIAL project. Her current ERC project explores the role of Reinforcement Learning in developing Continual Learning agents, with applications to scientific domains like drug discovery and micro-chemistry. Emmy Noether award under the AI Initiative call (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 She currently supervises three PhD students and actively recruits postdocs and PhD candidates through the IMPRS-IS and ELLIS doctoral programs. Her group collaborates extensively with the broader machine learning community, organizing workshops like FoRLaC at ICML 2024 and serving as co-chairs for tutorials at major conferences. Dr. Vernade is also deeply committed to diversity and inclusion in machine learning, co-leading initiatives like Women in Learning Theory and Tübingen Women in Machine Learning.
Thorsten Joachims is a Professor at Cornell University with a focus on machine learning, recommendation systems, and algorithmic fairness. His work spans conferences like ICML, NeurIPS, KDD, and SIGIR, emphasizing counterfactual learning, contextual bandits, and ethical AI. Key Research Themes: Fairness in rankings, reinforcement learning, position bias estimation, and NLP applications to recommendation systems. Recent Publications: 2025 work on policy decomposition for contextual bandits, 2024 studies on fairness under uncertainty, and 2023 papers on LLM steerability and bias mitigation. Awards: Recipient of the ACM SIGKDD 2020 Innovation Award . Collaborators: Regularly works with Adith Swaminathan, Tobias Schnabel, Yuta Saito, Ashudeep Singh, and Yi Su.
Dr. Setareh Maghsudi is a Professor in the Learning Technical Systems group at the Faculty of Electrical Engineering and Information Technology at Ruhr-University Bochum. She joined Ruhr-University Bochum in August 2023 after serving as an Assistant Professor at the University of Tübingen (2020-2023) and at the Technical University of Berlin (2017-2020). Her academic journey began with an M.Sc. from Kiel University (2008-2010), followed by her Ph.D. and postdoctoral work at Technical University of Berlin (2011-2015), Yale University (2016-2017), University of Manitoba (2015-2016), and Kyushu University (2019). Dr. Maghsudi's research focuses on the application of machine learning to communication networks and distributed systems, with particular emphasis on bandit algorithms, federated learning, and resource allocation in dynamic environments. Her work bridges theoretical machine learning with practical networking challenges, developing algorithms that can adapt to non-stationary environments with partial information. She has made significant contributions to multi-armed bandit frameworks for wireless communications, edge computing, and network optimization. Her recent publications (2023-2025) demonstrate a strong trend toward addressing challenges in integrated sensing and communication (ISAC), federated learning for edge networks, and non-stationary decision-making problems. The publications show expertise spanning theoretical machine learning foundations, wireless communications engineering, and practical implementation for real-world networked systems. Her work increasingly incorporates causal reasoning and robustness considerations into learning frameworks for communication systems. Dr. Maghsudi leads the Learning Technical Systems research group at Ruhr-University Bochum, where she supervises PhD students and postdoctoral researchers working at the intersection of machine learning and communication systems. Her research is supported by various grants focusing on AI for future communication networks. Current projects include developing AI-driven solutions for next-generation communication systems with emphasis on robustness, efficiency, and adaptability in dynamic environments.
Joachim Baumeister is a Professor at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. While his primary employment since September 2010 has been at denkbares GmbH, a company specializing in knowledge-based systems, he continues to regularly give lectures at the university. His research focuses on Semantic Information Systems, Knowledge Graphs, Deep Learning applications, Natural Language Processing, and Knowledge-based Configuration for Industry 4.0. Professor Baumeister's work bridges theoretical AI research with practical industry applications, particularly in knowledge-based configuration systems and semantic technologies. His recent publications (2020-2024) reveal a strong emphasis on product configuration systems, semantic knowledge representation, regulatory document processing, and knowledge-based systems. His research has evolved from foundational work on semantic wikis and knowledge engineering to more recent applications involving deep learning and large language models, demonstrating adaptability to emerging technologies while maintaining focus on practical knowledge representation problems. Professor Baumeister's work demonstrates significant contributions to case-based reasoning, knowledge configuration, and semantic technologies, with applications spanning regulatory compliance, industrial configuration systems, and document processing. His current research areas include: Semantic Information Systems and Knowledge Graphs Deep Learning for Image Recognition and Language Understanding Knowledge-based Configuration for Industry 4.0 Natural Language Processing Intelligent Personal Assistants and Chat Bots Though specific students aren't listed in the provided information, Professor Baumeister actively invites students to contact him regarding projects, bachelor theses, and master theses in his areas of expertise. His work at denkbares GmbH focuses on the design, implementation, and evolution of knowledge-based systems and semantic information systems.
Dimitrije Marković is a Researcher in the Department of Psychology at Technische Universität Dresden, affiliated with the Faculty of Science. His work bridges cognitive neuroscience, theoretical neuroscience, and machine learning, focusing on computational models of human decision-making and adaptive behavior. He employs concepts from information theory and probability to develop and test experimental predictions from these models. Education: PhD in Physics (2013), Goethe University Frankfurt, advised by Prof. Claudius Gros Diploma in Theoretical and Experimental Physics (2007), Belgrade University Undergraduate studies in Physics (2002–2007), Belgrade University Research Focus: His research emphasizes understanding neurophysiological and computational mechanisms underlying human decision-making, particularly in dynamic environments. He explores topics like adaptive learning, Bayesian inference, and neural modeling of uncertainty and agency perception. Publications Trends: Recent work includes advancements in active inference frameworks, machine learning models for decision-making, and applications to mental health (e.g., depression). His 2020–2025 publications highlight innovations in scalable models (e.g., AXIOM), Bayesian methods, and interdisciplinary approaches linking neuroscience with AI. Professional Experience: Postdoc at TU Dresden (2014–present) Guest Researcher, Max-Planck Institute for Human Cognitive and Brain Sciences (2013–2015) Postdoc, University Clinic Jena (2013–2014) Labs/Teams: Affiliated with TU Dresden’s Chair for Neuroimaging, focusing on neuroimaging techniques and collaborative projects in computational psychiatry and AI-driven neuroscience.
Dominik Baumann is an Assistant Professor at the Department of Electrical Engineering and Automation at Aalto University in Espoo, Finland. His research focuses on the interplay of systems and control theory with machine learning and communication networks, with a current emphasis on causal inference in control systems. Education: Diploma in Electrical Engineering from TU Dresden, Germany (2016) PhD from KTH Stockholm, Sweden (2020), supervised by Sebastian Trimpe (Max Planck Institute) and Karl H. Johansson Postdoctoral positions at RWTH Aachen University (1 year) and Uppsala University with Thomas Schön (1 year) Dr. Baumann's research bridges theoretical foundations with practical applications in robotics, wireless networks, and decision-making systems. His work spans safe reinforcement learning, event-triggered control systems, causal inference in dynamical systems, and ergodicity economics perspectives on long-term decision-making. He applies mathematical rigor to address challenges in resource-constrained environments, particularly focusing on safety guarantees and computational efficiency for real-world implementation. His recent publication record demonstrates a strong trajectory in safe learning-based control, with increasing focus on ergodicity economics in reinforcement learning, multi-agent coordination, and human-robot interaction. The research consistently balances theoretical guarantees with practical implementation constraints, particularly in bandwidth-limited wireless control systems and robotics applications. Scientific Awards: Best Paper Award for 'Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks' at ACM/IEEE International Conference on Cyber-Physical Systems (2019) Dr. Baumann maintains extensive international collaborations, evidenced by numerous seminar invitations worldwide including Oxford University, ETH Zürich, University College London, and institutions across Asia. His research program addresses fundamental challenges in cyber-physical systems with applications in industrial automation, robotics, and the Internet of Things, securing research funding for projects focused on safe learning in control systems and wireless cyber-physical systems. His research group at Aalto University develops both theoretical foundations of learning-based control and practical algorithms for real-world deployment, with active software repositories on GitHub related to predictive triggering, causal structure identification, and ergodic reinforcement learning.
Prof. Anja Klein is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. She leads the Communications Technology group, focusing on cutting-edge research in wireless communications, edge computing, and UAV-enabled systems. Her work emphasizes optimization techniques, machine learning applications, and energy-efficient network designs. Key areas of expertise include integrated sensing and communication (ISAC), multi-agent systems, and resilient digital infrastructure. Her research spans topics such as UAV-assisted communication networks, risk-aware optimization, and decentralized learning in mobile edge computing. Notable contributions include advancements in beamforming algorithms for UAV systems, age of information minimization, and sustainable resource allocation strategies. Prof. Klein collaborates extensively on projects like the MAKI initiative, exploring future wireless network transitions and protocol-independent architectures. Her publications highlight innovations in network resilience, such as Safehaul for mmWave self-backhauling and techniques for handling imperfect feedback channels in status update systems. She also investigates socio-technical challenges, including user preferences in data forwarding and incentive mechanisms for video streaming in multi-hop networks. Her work bridges theoretical foundations with practical applications in 5G/6G networks, IoT, and smart city technologies.
Wouter M. Koolen is a Professor of Mathematical Machine Learning at the University of Twente and a Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. Appointed to his professorship on June 1, 2022, he delivers his expertise across both institutions with offices in Enschede and Amsterdam. He is actively engaged in academic leadership through his organization of the Machine Learning Theory Research Semester Programme at CWI in Spring 2023 and serves as an ELLIS Scholar since December 2020. Dr. Koolen's research spans machine learning theory with particular focus on pure exploration in multi-armed bandit models , game tree search algorithms , and provably accelerated learning in both statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His work bridges theoretical foundations with practical applications, especially in safe statistical testing using e-values. He maintains active collaborations through INRIA-CWI associate teams 6PAC with Inria Lille and 4TUNE with Inria Paris and Grenoble. His recent publications reveal a strong emphasis on developing theoretically sound methods for statistical inference that maintain validity under optional stopping and continuation, representing a significant shift from traditional p-value based approaches. This work has important implications for fields requiring rigorous statistical guarantees in adaptive experimental settings. NWO VENI grant recipient QUT Vice-Chancellor's postdoctoral research fellowship awardee ELLIS Scholar (elected December 2, 2020) Member of ACM Future of Computing Academy Professor Koolen has supervised numerous PhD students to completion, including Hongwei Wen, Clément Lezane, and Tyron Lardy in 2025, and formerly Rianne de Heide who won the VVSOR Willem R. van Zwet award. He has served on program committees for major conferences including COLT, ICML, and ALT, and actively organizes workshops on cutting-edge topics in machine learning theory. His research group at CWI hosts regular reading groups and seminar series, fostering a vibrant theoretical machine learning community in the Netherlands.
