Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.
Andrea Tagliasacchi is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), where he holds the Visual Computing Research Chair. He is also a part-time (20%) Staff Research Scientist at Google DeepMind in Toronto and holds an associate professor (status only) appointment in the Department of Computer Science at the University of Toronto. Education: PhD in Computing Science – Simon Fraser University (NSERC Alexander Graham Bell Fellow) Postdoctoral Research – École Polytechnique Fédérale de Lausanne (EPFL) MSc in Computer Science – Politecnico di Milano (Gold Medalist) His research lies at the intersection of computer vision, computer graphics, and machine learning, with a focus on 3D visual perception. Key areas include neural radiance fields (NeRF), 3D Gaussian splatting, inverse rendering, and geometric deep learning, with applications in robotics, augmented reality, and autonomous systems. His work emphasizes robust and efficient scene understanding and reconstruction from visual data. His recent publications, appearing in top venues like CVPR, SIGGRAPH, NeurIPS, and ECCV, demonstrate a strong emphasis on neural fields, 3D reconstruction, and generative modeling. Trends include improving rendering efficiency, enhancing robustness to noise and distractors, and enabling controllable and 3D-aware generation. His group has made significant contributions to Gaussian splatting, NeRF optimization, and diffusion-based 3D/4D synthesis. Scientific Awards: 2024 CVPR Best Paper Award (Honorable Mention) 2020 CVPR Best Student Paper Award 2015 SGP Best Paper Award NSERC Alexander Graham Bell Canada Graduate Scholarship MITACS Best Paper Award (SIGGRAPH Asia 2009) NSF Best Poster Award (SGP 2012) He has advised numerous PhD and MSc students, many of whom are now researchers at leading institutions and companies. His research has been supported through collaborations with Google, Intel, and academic partners. He serves the community as a Senior Area Chair for CVPR 2025, Associate Editor for IEEE TPAMI (2024–2026), Guest Editor for IEEE TPAMI on 3D GenAI, and Program Chair for 3DV 2024. He leads a vibrant research lab at SFU focused on pushing the boundaries of 3D scene understanding with machine learning.
Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.
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.
Prof. Bryan Ford leads the Decentralized/Distributed Systems (DEDIS) lab at EPFL. He focuses on secure decentralized systems, including blockchain technology, privacy, and systems security. He earned his Ph.D. from MIT and held faculty positions at Yale University and EPFL. His work spans distributed consensus protocols, peer-to-peer networking, and privacy-preserving systems. Key projects include QuePaxa (timeout-free consensus), UIA (global connectivity for mobile devices), and MedCo (secure healthcare data sharing). He advises numerous PhD students and contributes to open-source projects like Bitcoin collective signing and privacy networks like Riffle. Education: Ph.D., MIT; Postdoctoral work at Yale Research interests include blockchain scalability, consensus algorithms, and cryptographic privacy. His lab develops systems like TRIP for coercion-resistant voting and F3B to mitigate blockchain front-running. His work on NAT traversal and peer-to-peer protocols (e.g., STUN/ICE) remains foundational in network architecture. He emphasizes practical, auditable security solutions such as CertiKOS and atomic cross-chain transactions (Atom). Notable contributions: CoSi (collective signing), OmniLedger (sharded blockchain), and privacy-preserving protocols like PURBs (Protected Unsealable Recursive Boxes). His lab collaborates with Swiss Post to audit e-voting systems and designs democratic cryptocurrencies like PoPCoin.
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.
Bernhard Schölkopf is a Professor and director at the Max Planck Institute for Intelligent Systems in Tübingen. He previously served as a director at the Max Planck Institute for Biological Cybernetics (2001-2011) and held industrial positions at AT&T Bell Laboratories, Microsoft Research, and Biowulf Technologies. He is also an honorary professor at the Technical University of Berlin since 2002. Education : Studied physics, mathematics, and philosophy in Tübingen and London; received a doctorate in computer science from the Technical University of Berlin (1997). His research spans machine learning , artificial intelligence , and causal inference , with interdisciplinary applications in computational neuroscience and theoretical computer science . He has contributed to foundational work in kernel methods, support vector machines, and causal modeling. Scientific Awards : Körber Prize 2019
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 .
Kord Eickmeyer is a Lecturer at Technische Universität Darmstadt in the Department of Mathematics, specializing in the mathematical logic group. He holds a PhD in mathematics from Humboldt University Berlin and has held postdoctoral positions at TU Darmstadt (2011–2017) and the National Institute of Informatics in Tokyo (2011–2013). His research focuses on finite model theory, graph structure theory, and computational complexity, particularly in descriptive and parameterized complexity, as well as randomization and derandomization techniques. Research interests include exploring the boundaries of computational complexity through logical frameworks, analyzing graph structures for efficient algorithm design, and investigating the role of randomness in computation. His work bridges theoretical computer science and mathematical logic, with applications in algorithm design and formal methods. Publications span topics from model-checking on ordered structures to gap-planar graphs and randomized logics. Collaborations include prominent institutions like the National Institute of Informatics and Humboldt University Berlin. No scientific awards are explicitly listed, but his extensive academic contributions reflect a strong research trajectory. Advising and grants are not detailed in the provided text, though his academic career includes supervision roles during his PhD and postdoctoral phases. His involvement with the mathematical logic group at TU Darmstadt highlights collaborative research efforts in foundational areas of computer science and mathematics.
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
Ignasi Sau Valls is a Directeur de Recherche (DR2) at CNRS, affiliated with the LIRMM laboratory at Université de Montpellier, France. He is a member of the AlGCo team, focusing on algorithms for graphs and combinatorics. His academic background includes dual degrees in Mathematics and Telecommunications Engineering from UPC (Barcelona), a PhD in joint supervision between UPC and Projet Mascotte (Sophia Antipolis), and a postdoctoral position at the Technion (Israel). He has been with CNRS since 2010 and was promoted to his current role in October 2024. He also served as a Visiting Professor at UFC (Brazil) from 2016–2017. His research interests lie primarily in Graph Theory and Parameterized Complexity , with a focus on structural graph properties, kernelization, and algorithm design. He has made significant contributions to problems involving minor-closed graph classes, treewidth, and graph modification. His work bridges theoretical foundations with algorithmic applications, particularly in discrete optimization and network problems. The recent articles highlight a strong trend in parameterized algorithms, especially for graph modification, kernelization, and structural graph problems. Topics such as hitting minors, dynamic programming on tree decompositions, and edge contractions reflect a deep engagement with structural parameterizations and fixed-parameter tractability. His publications frequently appear in top-tier journals like SIAM Journal on Computing, Journal of Combinatorial Theory, and Algorithmica, as well as major conferences such as ICALP, SODA, and IPEC. Best paper award of Track C of ICALP'10 Best student paper award of WG'09 Ignasi Sau has been a principal investigator of the ANR JCJC project ELIT (ANR-20-CE48-0008-01), funded with 169k€ from 2021 to 2026. He serves as an editor for DMTCS and Information and Computation , and has held significant organizational roles, including PC member of numerous conferences (MFCS, WG, IPEC, COCOON) and as co-chair and main organizer of WG 2019, ICGT 2022, and JCALM 2023. He has delivered invited courses at international schools in France, Argentina, and Brazil. He is actively involved in the research community through editorial duties, conference organization, and collaborative research. His lab affiliation is the AlGCo team at LIRMM, a leading group in algorithmic graph theory and combinatorics.
Maks Ovsjanikov is a Professor in the Computer Science Department at École Polytechnique, France , and a Visiting Research Scientist at Google DeepMind. His research focuses on mathematically principled approaches for geometric data analysis and synthesis, including learning on surface meshes, 3D point clouds, and graphs. Key Collaborations: Google DeepMind, Sanofi, Dassault Systèmes Research Themes: Non-rigid shape matching, 3D reconstruction, transfer learning, learning on geometric data, functional maps, deep learning for scientific discovery Recent Article Trends emphasize geometric deep learning, with publications at top venues like SIGGRAPH Asia, ICCV, and CVPR. Topics include surface reconstruction, functional maps, 3D keypoint detection, and diffusion models for shape matching. Scientific Honors include: ERC Consolidator Grant (VEGA Project, 2023) ERC Starting Grant (2017) ACM SIGGRAPH 2023 Test-of-Time Award Best Paper Awards at 3DV 2021 and 3DV 2022 Student Advisees have received prestigious awards, such as the IP Paris Best PhD Thesis Award (Souhaib Attaiki, 2023) and GdR IG-RV Runner-Up (Nicolas Donati, 2024). The GeomeriX Team at École Polytechnique drives his group's research, supported by the VEGA and AIGRETTE projects.
Yen-Chi Chen is an Associate Professor in the Department of Statistics at the University of Washington. He also holds positions as a Data Science Fellow at the UW eScience Institute and as a co-investigator and statistician at the National Alzheimer's Coordinating Center. His academic career spans multiple interdisciplinary fields including statistics, data science, and astrostatistics. Chen's educational background includes a Ph.D. from Carnegie Mellon University, where he received prestigious awards including the Umesh K. Gavasakar Thesis Award (2017) and the William S. Dietrich II Presidential Ph.D. Fellowship Award (2015). His research focuses on nonparametric statistics, topological data analysis, missing data methodologies, cluster analysis, manifold learning, and applications in large-scale structure analysis and astrostatistics. Chen has made significant contributions to the development of statistical methods for analyzing cosmic web structures, GPS data, and causal inference with continuous treatments. His work bridges theoretical statistics with practical applications in astronomy, neuroscience, and public health. Analysis of his recent publications reveals a strong emphasis on developing novel statistical frameworks for complex data structures, particularly focusing on density-based methods, manifold learning, and approaches that address challenges in missing data and causal inference without standard assumptions. ASA Noether Early Career Scholar Award, American Statistical Association (2022) CAREER Award, National Science Foundation (2022-2027) Umesh K. Gavasakar Thesis Award, Carnegie Mellon University (2017) William S. Dietrich II Presidential Ph.D. Fellowship Award, Carnegie Mellon University (2015) Chen has advised numerous graduate students across multiple publications, with a focus on developing new statistical methodologies. His research has been supported by major funding agencies including the National Science Foundation and the National Institutes of Health. He is actively involved in several research groups including the UW Geometric Data Analysis Group, the UW Center for Statistics and the Social Sciences, and the National Alzheimer's Coordinating Center.
Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.
