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Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.

• Mobile Robotics
• SLAM (Simultaneous Localization and Mapping)
• Computer Vision
• +۷ مورد دیگر

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.

• Machine Learning
• Explainable AI
• Neural Networks
• +۳ مورد دیگر

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China (USTC) and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal and environmental challenges, such as climate modeling, hydrology, and fairness in AI. Education: Ph.D., University of Minnesota (2020) M.S., State University of New York at Buffalo B.S., University of Science and Technology of China (USTC) Research Interests: Knowledge-Guided Machine Learning Spatiotemporal Data Mining Fairness in AI for Social Good Applications in Environmental Science and Healthcare Publications showcase his work on physics-integrated neural networks, spatiotemporal modeling (e.g., water temperature prediction), and fairness-aware algorithms. His work has been recognized with Best Paper awards at SIAM SDM (2022, 2023). Awards include the Best Applied Data Science Paper Award at SIAM SDM in 2022 and 2023. He teaches advanced machine learning courses, emphasizing theory integration with real-world applications.

• Machine Learning
• Data Mining
• Physics-Guided Neural Networks
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Nadia Polikarpova is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego . She earned her PhD from ETH Zurich in 2014 under Bertrand Meyer , followed by postdoctoral research at MIT CSAIL with Armando Solar-Lezama . Her academic contributions have been recognized with prestigious awards including the 2020 Sloan Fellowship , 2020 Intel Rising Stars Award , and 2020 NSF CAREER Award . Polikarpova's research focuses on program synthesis , program verification , and type systems . She leads the Programming Systems group at UCSD and contributes to the IFIP Working Group 2.8 on Functional Programming since 2022. Her work spans foundational research and practical tools, including projects like Synquid , SuSLik , and Laurel that combine formal methods with machine learning for code generation. Her recent publications in venues like OOPSLA , NeurIPS , and ICFP reveal trends in AI-assisted programming , live programming environments , and formal verification . She has advised numerous PhD and Master’s students including Shraddha Barke , Zheng Guo , and Tristan Knoth , many of whom have moved to prominent academic and industry positions. Notable artifacts from her lab include tools like ColDeco for spreadsheet inspection and Superfusion for eliminating intermediate data structures. 2020 : Sloan Fellow 2020 : Intel Rising Stars Award 2020 : NSF CAREER Award 2021 : Distinguished Paper at POPL 2023 : Distinguished Artifact at PLDI 2023 : Distinguished Paper at OOPSLA Polikarpova actively contributes to academic service, serving on program committees for PLDI , POPL , and OOPSLA , and co-chairing the OOPSLA Review Committee in 2023. She has delivered keynotes at APLAS'20 and PLDI'24 , emphasizing the integration of large language models with formal methods.

• Program Synthesis
• Program Verification
• Type Systems
• +۵ مورد دیگر

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.

• Convex Optimization
• Convex Geometry
• Graph Algorithms
• +۲ مورد دیگر

Christopher Morris is a tenure-track Assistant Professor at RWTH Aachen University and a DFG Emmy Noether fellow. He leads the Learning on Graphs (LoG) research group, focusing on machine learning methods for structured data, particularly graph neural networks. His work bridges machine learning, computer science theory, and discrete mathematics, addressing challenges in generalization, expressivity, and algorithmic efficiency. Education: PhD in Computer Science from TU Dortmund University (advised by Petra Mutzel and Kristian Kersting), postdoctoral research at Mila - Quebec AI Institute (Siamak Ravanbakhsh) and McGill University, and Polytechnique Montréal (Andrea Lodi). Research interests emphasize graph machine learning, including generalization theory, combinatorial optimization, and scalable graph embeddings. Notable contributions include analyzing Weisfeiler-Leman algorithms' impact on GNNs' expressivity and generalization (VC dimension connections). Awards: DFG Emmy Noether Fellowship. Supervises six PhD students in Aachen. Active in teaching, offering courses on graph-based machine learning foundations and applications since 2022. Labs/Teams: LoG group at RWTH Aachen, collaborating with institutions like Mila and Polytechnique Montréal. Erdős number 3 through Petra Mutzel’s collaboration network.

• Machine Learning
• Graph Neural Networks
• Computer Science Theory
• +۳ مورد دیگر

Toby Jia-Jun Li is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame, where he leads the SaNDwich Lab. He also serves as the Director of the Human-Centered Responsible AI Lab in the Lucy Family Institute for Data & Society and is a Faculty Fellow at the Institute for Educational Initiatives (IEI). Previously, he was affiliated with Carnegie Mellon University's Human-Computer Interaction Institute (HCII) and GroupLens Research. Dr. Li's research spans the intersection of Human-Computer Interaction (HCI), End-User Software Engineering, Machine Learning (ML), and Natural Language Processing (NLP), with recent work focusing on addressing societal challenges in the future of work through human-AI collaborative approaches. His work has resulted in over 40 publications at premier venues including CHI, UIST, CSCW, ACL, and ICSE, with 8 papers winning Best Paper or Honorable Mention awards. His recent publications demonstrate a strong focus on human-AI collaboration across various domains, including code understanding, privacy, accessibility, and creative tools. The work shows a trajectory toward increasingly sophisticated integration of human-centered design with AI capabilities, particularly using large language models to enhance human productivity and address societal challenges. Google Research Scholar Award recipient Recipient of Yahoo! Fellowship ($100,000/year) Best Paper Award at UIST 2020 Best Paper Honorable Mention Award at CHI 2021 Best Paper Award at CSCW 2024 Best Paper Award at CHI 2025 Dr. Li actively mentors Ph.D. students and has established collaborations with Google, Microsoft Research, IBM Research, Adobe, Verizon, and J.P. Morgan. His research has been supported by NSF, Google Research Scholar Program, AnalytiXIN Initiative, Yahoo! InMind project, and J.P. Morgan. He is currently recruiting Ph.D. students and undergraduate researchers for his SaNDwich Lab, which focuses on developing interactive systems to empower individuals to create, configure, and extend AI-powered computing systems.

• Human-Computer Interaction
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Lars Rohwedder is an Associate Professor in the Algorithms Group at the University of Southern Denmark (SDU) in Odense. He previously held positions as an Assistant Professor at Maastricht University (Netherlands) and postdoc researcher at EPFL, Lausanne (Switzerland). He earned his Ph.D. in Computer Science from CAU Kiel (Germany), advised by Klaus Jansen, and is a recipient of the 2019 PhD of the year award from Förderverein der TF of Kiel University. His research focuses on algorithms for combinatorial optimization, including approximation algorithms, online algorithms, parameterized algorithms, and integer programming. He has contributed to solving scheduling problems, resource allocation, and optimization under uncertainty. Rohwedder has served on program committees for conferences like MAPSP, SODA, STACS, and ICALP. He is funded by NWO's Open Competition M1 project on quasi-polynomial time algorithms. His teaching includes courses on advanced algorithms, operations management, and optimization at SDU and Maastricht University. Key achievements include a quasi-polynomial approximation for the restricted assignment problem, FPT algorithms for scheduling, and contributions to the Submodular Santa Claus problem. His work bridges theoretical foundations and practical applications, with a focus on algorithmic efficiency and robustness.

• Algorithms
• Approximation Algorithms
• Online Algorithms
• +۳ مورد دیگر

Prof. Dr. Barbara Kraus is the Chair of Quantum Algorithms and Applications at the Technical University of Munich (TUM), affiliated with the TUM School of Natural Sciences. She previously held academic positions at the University of Innsbruck, where she founded her research group in 2010. Education : Physics and Mathematics at the University of Innsbruck; Post-doctoral work at MPI for Quantum Optics and University of Geneva. Her research focuses on foundational problems in quantum information theory, particularly entanglement in multipartite systems, quantum simulation, and verification of quantum processors. She develops theoretical tools for quantum many-body systems and explores applications in quantum computing, emphasizing error characterization and experimental validation. Recent publications highlight advancements in Hamiltonian learning, symmetry-resolved entanglement detection, and multipartite state transformations. Her work bridges theoretical quantum physics with practical implementations, including Rydberg platforms and quantum metrology. Key Awards : START Prize (2010), Ignaz L. Lieben Award (2013), Boltzmann Prize (2011), Südtiroler Sparkasse Research Prize (2019). She supervises doctoral students and postdocs in quantum information theory, with a focus on stabilizer states, quantum networks, and entanglement measures. Her courses at TUM include Quantum Information , Quantum Algorithms , and workshops on entanglement manipulation.

• Quantum Information Theory
• Quantum Computing
• Entanglement and Quantum Correlations
• +۴ مورد دیگر

Sebastian U. Stich is a tenured faculty member at the CISPA Helmholtz Center for Information Security , where he has been since December 2021. He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS) since June 2020. His research focuses on optimization methods for machine learning, collaborative learning algorithms, privacy and security in distributed systems, and theoretical foundations of deep learning. Stich received his PhD in Theoretical Computer Science from ETH Zurich (2014), following a Master's in Mathematics at the same institution (2010-2014). Prior to CISPA, he worked as a research scientist at EPFL (2016-2021) and held positions at ETH Zurich and ICTEAM/CORE. He has been awarded the ERC Consolidator Grant 2024 , Google Research Scholar Award (2023), and Meta Privacy-Enhancing Technologies Research Award (2022). His team includes Dr. Anton Rodomanov (since 2023), Dr. Rotem Mulayoff (since 2024), Xiaowen Jiang (2023), Yuan Gao (2023), and notable alumni like Anastasia Koloskova (defended 2023). Stich actively organizes workshops (e.g., NeurIPS OPT 2024) and serves on editorial boards ( Journal of Optimization Theory and Applications , Transactions on Machine Learning Research ). He teaches advanced courses in optimization at Saarland University and has held visiting positions at MIT. Key scientific contributions include: Developing ProgFed for progressive federated learning (2021) Creating ProxSkip to accelerate communication in federated settings (2022) Formalizing SCAFFOLD with control variates for FL (2020) Introducing RelaySum mechanism for decentralized learning (2021) Proposing Lookahead-Minmax for GAN training (2021) His work addresses fundamental challenges in: Decentralized optimization theory Communication-efficient algorithms Privacy-preserving model training Handling heterogeneous data distributions Stochastic gradient dynamics Second-order optimization methods

• Optimization for Machine Learning
• Collaborative Learning
• Distributed Learning
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Warut Suksompong is an Assistant Professor in the School of Computing at the National University of Singapore (NUS), where he holds the NUS Presidential Young Professorship. Previously, he was a postdoctoral researcher at the University of Oxford, hosted by Edith Elkind, and completed his PhD at Stanford University under Tim Roughgarden. He holds bachelor's and master's degrees from MIT. His research focuses on algorithmic game theory, computational social choice, mechanism design, and interdisciplinary problems at the intersection of computer science, economics, mathematics, and operations research. He is particularly known for contributions to fair division, including envy-freeness, proportionality, and maximin fairness in both divisible and indivisible resource allocation contexts. Key awards include the IJCAI 2021 Distinguished Paper Award for work on dividing land with geometric constraints and the WINE 2021 Best Student Paper Award for research on funding public projects via the Nash product rule. His work bridges theoretical foundations with practical applications, addressing challenges such as connectivity constraints, budget allocation, and social choice mechanisms. He has authored over 100 publications in top conferences and journals, including SIAM Journal on Discrete Mathematics, Artificial Intelligence, and Games and Economic Behavior. His research has been supported by grants from NUS and international collaborations.

• Algorithmic Game Theory
• Computational Social Choice
• Fair Division
• +۴ مورد دیگر

Leo Schwinn is a Lecturer at the Technical University of Munich (TUM) within the Department of Computer Science (I26), working in the Data Analytics and Machine Learning group supervised by Prof. Stephan Günnemann at the TUM School of Computation, Information and Technology. His research focuses on robust machine learning with particular emphasis on data-efficient learning and robustness vulnerabilities of Large Language Models (LLMs). Dr. Schwinn's research interests span multiple critical areas in contemporary machine learning including: Robustness against adversarial attacks in LLMs Embedding space vulnerabilities and defenses Model unlearning and privacy preservation Efficient training methodologies for large models Time-series forecasting with probabilistic frameworks Graph-based machine learning approaches His work bridges theoretical understanding with practical security implications of modern AI systems. Analysis of his recent publications (2023-2025) reveals a strong focus on LLM security, with multiple papers accepted at premier conferences including ICML, CVPR, ICLR, and NeurIPS. His research demonstrates consistent innovation in identifying novel attack vectors while developing practical defense mechanisms, particularly through embedding space manipulation techniques. The work shows increasing sophistication in handling both theoretical aspects of model robustness and practical deployment concerns. His notable scientific achievements include: Receiving the ATE dissertation price for his PhD work at FAU Securing an oral presentation at ICLR 2025 Organizing the ICLR BlogPost Track Becoming a member of ELLIS (European Laboratory for Learning and Intelligent Systems) Dr. Schwinn has served as review process chair for the 2024 Conference on Lifelong Learning Agents (CoLLAs) and actively collaborates with researchers at Mila Quebec AI Institute. His research group at TUM focuses on addressing fundamental challenges in machine learning robustness, particularly as they apply to real-world deployment scenarios where security and reliability are paramount. He maintains active GitHub repositories related to LLM security research, including circuit-breakers-eval and LLM_Embedding_Attack, demonstrating his commitment to open science and reproducible research in the field of AI security.

• Robust Machine Learning
• Large Language Models Security
• Data-Efficient Learning
• +۵ مورد دیگر

Moritz Wiese is a Researcher at the Chair of Theoretical Information Technology within the Department of Electrical and Computer Engineering at Technische Universität München (TUM) . His work focuses on information-theoretic security, quantum communication, and wireless networks, with significant contributions to interference modeling and secure coding schemes. Research Interests : Information-Theoretic Security, Quantum Wiretap Channels, MIMO Systems, 5G/6G Technologies, Physical Layer Security, and Network Coding. Key Grants : Involved in DFG-funded projects (Gottfried Wilhelm Leibniz Prize, UKoloS) and BMBF initiatives (6G-life, QuaDiQua). Collaborations : Worked with Prof. Holger Boche, Christian Deppe, and Rami Ezzine on secure communication and randomness generation. Labs : Active in the ACES Lab and 6G-life research hub, focusing on joint communication/sensing and neuromorphic computing. Publications : Over 20 papers on secure estimation, wiretap channels, and quantum secrecy, including top-tier journals like IEEE Transactions on Information Theory and Journal of Mathematical Physics .

• Information-Theoretic Security
• Quantum Communication
• Wireless Communication Networks
• +۹ مورد دیگر

Atreyi Kankanhalli is a Professor at the National University of Singapore, specializing in Information Systems with a focus on knowledge management, healthcare IT, and digital innovation. Their research spans over three decades, with prolific contributions in top journals like MIS Quarterly, Journal of AIS, and Information & Management. They have co-authored over 150 papers addressing topics such as crowdsourcing, online communities, and the impact of AI on scholarly practices. Notable work includes studies on user adherence to health apps, innovation in public sector data utilization, and the ethical challenges of generative AI in peer review. Kankanhalli has also led research on global virtual teams and digital technologies' role in social justice, reflecting a commitment to both technical and societal dimensions of information systems. Education & Background: While specific degree details are not provided, their extensive publication history and academic roles imply advanced qualifications in Information Systems or related fields. They have collaborated with global researchers across institutions like NUS, University of Illinois, and Singapore Management University. Research Themes: Core areas include digital health interventions (e.g., fitness app adherence), organizational innovation via open data and crowdsourcing, and the socio-technical challenges of AI in academia. Their work often bridges theoretical frameworks with practical applications, such as healthcare decision support systems and policy-driven technology adoption. Impact & Influence: As an editorial board member and frequent conference contributor (e.g., ICIS, PACIS), Kankanhalli shapes the field's research agenda. Their recent focus on generative AI's implications highlights proactive engagement with emerging technologies' ethical and methodological challenges.

• Information Systems
• Healthcare IT
• Knowledge Management
• +۵ مورد دیگر

Prof. Dr. Ingo Scholtes is Chair of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). His research spans network science, graph machine learning, and computational social science, with applications in software engineering, ecology, biology, and physics. He received a Juniorfellowship from the German Informatics Society (2014) and an SNSF Professorship (CHF 1.5Mio, 2018). Current affiliations: JMU Würzburg (since 2021), University of Zurich (2018-2024), Bergische Universität Wuppertal (2019-2021) Research focus: Higher-order network modeling, temporal graph analysis, AI for collaborative systems, causality-aware machine learning His recent publications demonstrate strong trends in temporal network analysis , graph neural networks for time-series, and higher-order models across software engineering and social science domains. He co-chairs multiple international workshops on complex networks and serves as associate editor for EPJ Data Science and Advances in Complex Systems. Key scientific contributions: Foundational work on higher-order network models published in Nature Physics Methodological innovations in temporal network visualization (HOTVis) and path-based analysis (pathpy) As both educator and organizer, he leads the Computational Social Science Section at GI e.V., mentors across disciplines, and develops tools like git2net for collaboration analysis. His work bridges theoretical foundations with practical applications in network science.

• Network Science
• Graph Neural Networks
• Computational Social Science
• +۴ مورد دیگر