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.
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.
Rhenish Friedrich Wilhelm University of BonnGermany
Prof. Waldemar Kolanus leads the Molecular Immunology and Cell Biology department at the University of Bonn's Life & Medical Sciences Institute (LIMES) . His research bridges immunoregulation , stem cell dynamics , and metabolic stress responses in immune cells. Unit 2 member at LIMES Principal investigator in SFB 704 and ImmunoSensation Cluster Leads a multidisciplinary lab with postdocs, PhD students, and technical staff His work focuses on intracellular signaling pathways connecting immune activation to tissue homeostasis, particularly through: Cytohesin proteins in integrin-mediated adhesion and migration TRIM71 in stem cell regulation and congenital hydrocephalus High-salt environments affecting macrophage function Publication trends show expertise in immune cell migration , genetic models , and chemical inhibition , with frequent use of mice and zebrafish for in vivo studies. Key articles explore: TRIM71's dual role in auditory development and germ cell maintenance Cytohesin family's Golgi regulation and insulin signaling Ruxolitinib's off-target migration inhibition of dendritic cells Contact details: Address: LIMES Institute, Carl-Troll-Straße 31, Bonn Email: kolanus.sekretariat@uni-bonn.de Phone: +49 228 73-62788
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.
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 .
Luigi Acerbi is an Associate Professor in the Department of Computer Science at the University of Helsinki, where he leads the Machine and Human Intelligence research group. He is also an active member of the Finnish Center for Artificial Intelligence (FCAI) and ELLIS (European Laboratory for Learning and Intelligent Systems). His research focuses on probabilistic machine learning and computational neuroscience, particularly on developing efficient methods for statistical inference, Bayesian models of perception, and resource-constrained rationality. His work bridges machine learning and cognitive science, with applications in Bayesian optimization, simulation-based inference, and image completion. The recent publications highlight a strong trend toward unifying probabilistic conditioning across diverse tasks using transformer-based meta-learning frameworks like the Amortized Conditioning Engine (ACE). These works emphasize amortized inference, flexible latent variable modeling, and the integration of prior knowledge at runtime, enabling efficient and scalable Bayesian methods for complex problems. Scientific Affiliations: University of Helsinki, Department of Computer Science Finnish Center for Artificial Intelligence (FCAI) ELLIS (European Laboratory for Learning and Intelligent Systems) Education: PhD in Computational Neuroscience, Doctoral Training Centre, Edinburgh, UK Advisor: Sethu Vijayakumar and Daniel Wolpert Visiting work at Computational and Biological Learning Lab, Cambridge Postdoctoral Experience: Alex Pouget’s lab, University of Geneva, Switzerland Wei Ji Ma, New York University, USA Collaboration with the International Brain Laboratory Luigi Acerbi mentors PhD students including Daolang Huang and Nasrulloh Loka, and collaborates widely with researchers such as Samuel Kaski. He has contributed to open-source tools like PyVBMC and is involved in community initiatives such as the EurIPS conference. His work is supported by grants from the Research Council of Finland, Business Finland, and the UKRI Turing AI World-Leading Researcher Fellowship. He leads a research lab focused on amortized probabilistic inference, with ongoing projects including PriorGuide and Stacked VBMC, aiming to make Bayesian methods more practical and accessible for real-world scientific and engineering applications.
Weierstrass Institute for Applied Analysis and StochasticsGermany
Gianni Dal Maso is a Professor of Mathematical Analysis at the International School for Advanced Studies (SISSA) in Trieste, Italy. He has been a faculty member at SISSA since 1985, first as Associate Professor and then as Full Professor since 1987. He has held several leadership positions at SISSA including Head of the Sector of Functional Analysis and Applications (1993-1998, 2001-2010), Deputy Director (2010-2015), and Coordinator of the Mathematics Area (2016-2020). His educational background includes: 1973-1977: Undergraduate student in Mathematics at the University of Pisa and Scuola Normale Superiore 1977: Degree in Mathematics with honors at the University of Pisa (thesis: "Gamma-limits of set functions," advised by Ennio De Giorgi) 1977: "Diploma" in Mathematics from the Scuola Normale Superiore 1977-1981: Post-graduate Research Fellowship in Mathematics ("Perfezionamento") at the Scuola Normale Superiore Dal Maso's research focuses on the Calculus of Variations, with particular emphasis on semicontinuity and relaxation problems, Gamma-convergence, and more recently, free discontinuity problems and their applications to mechanics. His work bridges pure mathematical analysis with practical applications in material science, particularly in plasticity and fracture mechanics. He has developed mathematical frameworks for understanding crack propagation, material failure, and the behavior of solids under stress, contributing significantly to both theoretical foundations and practical modeling approaches in these areas. His extensive publication record shows a clear evolution from foundational work in Gamma-convergence (culminating in his influential book "An Introduction to Gamma-Convergence" in 1993) toward increasingly sophisticated models of material behavior, particularly in fracture mechanics and plasticity. Recent work demonstrates continued innovation in handling complex discontinuities, non-local effects, and multi-scale phenomena in material science applications. Among his notable scientific recognitions: 1982: Stampacchia Prize, awarded by the Scuola Normale Superiore 1990: Caccioppoli Prize, awarded by the Italian Mathematical Union 1996: Medaglia dei XL per la Matematica, awarded by the Accademia Nazionale delle Scienze detta dei XL 2003: Prize of the Minister for the Cultural Heritage for Mathematics and Mechanics, awarded by the Accademia Nazionale dei Lincei 2005: Prize Luigi and Wanda Amerio, awarded by the Istituto Lombardo Accademia di Scienze e Lettere Dal Maso has supervised 42 PhD students at SISSA, demonstrating a strong commitment to academic mentorship. His research has been significantly supported by multiple National Research Projects (PRIN) in Italy, and notably by an ERC Advanced Grant "Quasistatic and Dynamic Evolution Problems in Plasticity and Fracture" (QuaDynEvoPro) from 2012-2017, where he served as Principal Investigator. This major project focused on nonlinear evolution problems in plasticity and fracture, with three main research directions: plasticity with hardening and softening, quasistatic crack growth, and dynamic fracture mechanics. His scholarly activities extend to editorial service, with membership on the boards of numerous prestigious journals including Archive for Rational Mechanics and Analysis, SIAM Journal on Mathematical Analysis, and Journal of Convex Analysis. He has also been active in the mathematical community through membership in scientific committees and academies, including the Accademia Nazionale dei Lincei since 2014.
Professor Dejan Gajic is a mathematician specializing in mathematical physics and general relativity at Leipzig University's Faculty of Physics and Earth Sciences. He leads research on black hole dynamics, particularly extremal black holes, and was awarded a €1.5M ERC Starting Grant (2023) to study their mathematical properties. His work bridges partial differential equations and theoretical physics. Education: PhD in Mathematics from the University of Cambridge (201?), followed by research positions at Imperial College London, the University of Cambridge, and Radboud University. His research focuses on Einstein’s equations, black hole stability, and gravitational collapse. Research Interests: Mathematical theorems on extremal black holes’ dynamical properties, wave equations in curved spacetime, and asymptotic behavior of gravitational fields. Current projects include the ExBHGravRad initiative, exploring rapid-rotation black holes’ physical phenomena. Key Achievements: ERC Starting Grant (2023), selection from >2600 applicants. Plans include establishing a research group in Leipzig and enhancing the Center for Mathematical Physics (joint with Max Planck Institute for Mathematics in the Sciences). Labs/Teams: Leading the new black hole mathematics research group at Leipzig, collaborating with the Max Planck Institute. Aims to host international scholars and elevate Leipzig’s global profile in mathematical physics.
F. Ömer Ilday is a distinguished physicist and Alexander von Humboldt Professor at Ruhr University Bochum since July 2023, holding a joint appointment in the Faculty of Electrical Engineering and Information Technology and Faculty of Physics and Astronomy. His pioneering work in ultrafast laser technology has transformed non-linear laser-matter interactions, with applications spanning precision manufacturing, medical surgery, and nanofabrication. Education: PhD in Physics, Cornell University (2003) Postdoctoral Research Scientist, Massachusetts Institute of Technology (2003-2005) Ilday's research centers on ultrafast laser development and materials science, focusing on GHz-repetition-rate burst-mode systems, nonlinear laser lithography, and self-organization phenomena. His interdisciplinary approach bridges photonics, plasma physics, and materials engineering to enable breakthroughs in nanostructuring, silicon processing, and laser-based manufacturing. Current work emphasizes developing high-power laser sources and exploring fundamental laser-matter interaction mechanisms for next-generation applications. His recent publications (2023-2025) reveal dominant trends in high-repetition-rate burst-mode lasers (up to 50 GHz), ablation efficiency optimization, and nonlinear laser lithography for 3D silicon structuring. These works demonstrate strong convergence between fundamental physics and industrial applications, particularly in medical surgery, nanofabrication, and materials synthesis, with increasing emphasis on self-organization principles in laser systems. Scientific awards: Turkish Academy of Sciences Outstanding Young Scientist Award (2006) Marie Curie International Reintegration Grant (2006) ERC Consolidator Grant (2014) - Turkey's first ERC Advanced Grant (2022) Election to Academia Europaea Election to Turkish Academy of Sciences Membership in Turkish and American Physical Societies Ilday has secured major competitive grants including two ERC awards and a Marie Curie fellowship, directing research teams at Bilkent University's Ultrafast Optics & Lasers Laboratory (UFOLAB) which developed technologies adopted globally. At RUB, he is establishing the Center for Complex Laser-Matter Interactions as an interdisciplinary hub fostering collaborations between photonics, plasma research, and materials science, with explicit goals for spin-off company formation and transdisciplinary innovation in manufacturing technologies. As founding director of UFOLAB at Bilkent University, Ilday developed laser systems deployed by research institutions worldwide and established Turkey's first laser company. His RUB center integrates electrical engineering and physics expertise to advance complex laser-matter interaction research, focusing on self-organizing laser systems, nanostructuring techniques, and applications in semiconductor manufacturing and medical technology through close industry partnerships.
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.
Martin Grohe is a Professor at the School of Logic and Theory of Discrete Systems , part of the Department of Computer Science at RWTH Aachen University . His research spans Algorithms and Complexity , Logic , Database Theory , Graph Theory , and Machine Learning , with a focus on integrating logical frameworks into computational models. His recent work explores graph neural networks , Weisfeiler-Leman algorithms , and parameterized complexity , as seen in publications on isomorphism testing , database repairing , and probabilistic query evaluation . While no specific scientific awards are mentioned, his contributions to graph theory and machine learning are widely recognized through numerous peer-reviewed publications.
Weierstrass Institute for Applied Analysis and StochasticsGermany
Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.
Heidelberg Institute for Theoretical StudiesGermany
Daniela M Witten is a Professor of Statistics and Biostatistics at the University of Washington, holding the Dorothy Gilford Endowed Chair in Mathematical Statistics. Her research focuses on developing statistical machine learning methods for high-dimensional data, with a particular emphasis on unsupervised learning and theoretical foundations. Witten earned her BS in Math and Biology with Honors and Distinction from Stanford University in 2005 and her PhD in Statistics from Stanford University in 2010 under Robert Tibshirani. Her academic journey established her expertise in bridging mathematical theory with biological applications. Her research program centers on high-dimensional statistical learning , where she develops methods for unsupervised learning and graphical modeling when features outnumber observations. She pioneers statistical models for neural activity through collaborations with the Allen Institute for Brain Science and Princeton University, addressing functional connectivity and neuron sub-population identification. Her groundbreaking work on selective inference solves the "double-dipping" problem in hypothesis generation and testing, enabling valid inference after hierarchical clustering and regression trees. Additionally, she advances multi-view data analysis to integrate complementary data sources like clinical and genomic measurements. Applications span genomics, neuroscience, microbial ecology, and pathology, demonstrating her commitment to solving real-world biomedical challenges. Her 2025 publications reveal a cohesive trend toward developing theoretically rigorous inference frameworks for high-dimensional settings, with emphasis on linear regression validity, semi-supervised efficiency, Gaussian decomposition, and PCA variance quantification—showcasing her signature blend of methodological innovation and practical applicability. Witten's exceptional contributions are recognized through extensive honors: Presidents’ Award, Committee of Presidents of Statistical Societies (COPSS) (2022) Mortimer Spiegelman Award, American Public Health Association (2019) Simons Investigator Award (2018-2023) Sloan Research Fellowship (2013-2015) NSF CAREER Award (2013-2018) NIH Director’s Early Independence Award (2011-2016) 23 major awards including named lectureships, fellowships, and editorial leadership As a dedicated mentor, she has guided students like Olivia McGough (NSF GRFP winner), Dwight (Zichun) Xu (ASA Nonparametrics Student Paper Award winner), Yiqun Chen (Hopkins Biostat faculty), and Anna Neufeld (Williams College faculty). Her research is sustained by major grants from NIH, NSF, and Simons Foundation. Witten co-authored the seminal textbook "Introduction to Statistical Learning" and currently serves as Joint Editor of the Journal of the Royal Statistical Society, Series B (2023-2025), shaping the field through both scholarship and community leadership.
Prof. Dr. Sören Laue is a Professor of Machine Learning at the University of Hamburg's Department of Informatics. His research focuses on optimization algorithms, machine learning frameworks, and high-performance computing. He leads the Machine Learning research group and developed the GENO optimization framework and the Matrix Calculus toolset. His work emphasizes GPU acceleration, tensor operations, and scalable solutions for classical machine learning problems. Projects: GENO solver (Python-based optimization), Matrix Calculus (derivative computation), and SQL-based tensor operations. Key Research Themes: Optimization frameworks, GPU computing, neural network scalability, and algorithm design. Selected recent publications highlight contributions to tensor calculus benchmarks, GPU-optimized machine learning pipelines, and novel optimization methods. His work bridges theoretical foundations and practical software tools for the machine learning community.
Prof. Dr. Andreas S. Schulz is a faculty member at Technische Universität München (TUM), holding a chair in the Department of Mathematics and the Department of Business and Economics. He previously served as the Patrick J. McGovern Chair of Management and Professor of Mathematics at MIT. His research focuses on mathematical optimization, algorithm design, and their applications in logistics, production, healthcare systems, and online advertising. He has held visiting professorships at institutions such as the Sauder School of Business (UBC) and ETH Zurich. Prof. Schulz’s research bridges operations research, theoretical computer science, and economics. He develops analytical methods to solve complex decision-making problems in business, including scheduling, resource allocation, and network optimization. A key interest is applying mathematical approaches to enhance healthcare delivery and system efficiency. Education: PhD in Operations Research (MIT), prior academic roles at MIT and visiting institutions. Key Achievements: Alexander von Humboldt Professorship (2014), Humboldt Research Award (2010), Glover-Klingman Prize (2006). Research Themes: Robust optimization, approximation algorithms, scheduling theory, and algorithmic game theory. His publications span topics like integer programming, optimal transport, and congestion games. He collaborates across disciplines, emphasizing practical applications of theoretical insights.
