Prof. Patrick Jenny is a Full Professor at the Department of Mechanical and Process Engineering and Head of the Institute of Fluid Dynamics at ETH Zurich. His research focuses on computational fluid dynamics (CFD), numerical methods for turbulent and multiphase flows, and reservoir simulation. He has held positions at ChevronTexaco and Cornell University, and received the National Latsis Prize 2005. PhD in CFD from ETH Zurich (1997) Postdoctoral work at Cornell University (1997–1999) Senior Researcher at ChevronTexaco (1999–2003) Research interests include: turbulent reactive flows, PDF modeling, multi-scale reservoir simulation, and data assimilation in engineering systems. He teaches courses on fluid dynamics, turbulence, and computational methods. Over 100 peer-reviewed publications span topics like fracture modeling, LES/RANS coupling, and particle-laden flows. His work bridges academia and industry, addressing challenges in energy systems, environmental engineering, and numerical algorithms. Winner: National Latsis Prize 2005 Led over 20 PhD projects and collaborates with institutions globally. His lab develops open-source tools for CFD and energy systems analysis.
Benny Sudakov is a Professor of Mathematics at ETH Zurich, where he conducts research in combinatorics. He has previously held positions at UCLA, Princeton University, and the Institute for Advanced Study. His work is supported by the SNSF grant 200021_196965. Research Interests: His primary research areas include Extremal Graph and Hypergraph Theory, Ramsey Theory, Random Structures, and the application of Algebraic and Probabilistic Methods in Combinatorics, with strong connections to Theoretical Computer Science. He investigates fundamental structural properties of discrete systems, such as the existence of regular subgraphs, extremal configurations, and the behavior of random combinatorial objects. The recent popular science articles on his work highlight a consistent trend of solving long-standing open problems in extremal combinatorics using sophisticated probabilistic and algebraic techniques. His research spans topics like equiangular lines, graph decompositions, and the emergence of cycles in sparse graphs, demonstrating a deep focus on the interplay between structure and randomness. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: He has advised numerous Ph.D. students, many of whom have gone on to become professors at top universities (e.g., Oxford, Stanford, CMU, ETH, Princeton). His research is currently funded by the Swiss National Science Foundation (SNSF). He has organized workshops and seminars, such as the Theory of Combinatorial Algorithms Mittagsseminar at ETH and a workshop at UCLA on Extremal and Probabilistic Combinatorics. Labs and Teams: He is a key member of the combinatorics group at ETH Zurich and co-organizes the Theory of Combinatorial Algorithms Mittagsseminar, a central forum for research discussions in discrete mathematics at the institution.
Jana Mareckova is an Assistant Professor of Econometrics at the Swiss Institute for Empirical Economic Research (SIEW), part of the School of Economics and Political Science (SEPS) at the University of St. Gallen. She joined the university in 2020 after completing a postdoc at SEW-HSG following her PhD from the University of Konstanz (2019). Her research focuses on causal machine learning, shrinkage methods, regularization techniques, and labor economics. She explores applications in labor market outcomes and fairness, leveraging econometric tools to address real-world economic questions. Education: PhD in Econometrics, University of Konstanz (2019); Postdoc at SEW-HSG (pre-2020). Research interests include shrinkage estimation for categorical regressors, causal inference via machine learning, and predicting economic outcomes using noncognitive skills. Her work bridges statistical theory with practical policy analysis, as seen in her 2021 Journal of Econometrics publication on shrinkage methods. Recent projects emphasize causal forests and comprehensive frameworks for policy evaluation. No scientific awards are listed, though her contributions to causal ML and econometric methods are notable. She has no documented advising or grant information. Her research is affiliated with SIEW, focusing on empirical economic research.
Prof. Dan Olteanu is a full professor at the Department of Informatics, University of Zurich, leading the Data Systems and Theory (DaST) group. He holds visiting professorships at the University of Oxford and is an emeritus fellow of St Cross College. His academic journey includes a PhD from Ludwig Maximilian University (2005), postdoctoral roles at Saarland University and Cornell University, and prior faculty positions at Oxford (2007–2020). He has also worked in industry with companies like LogicBlox and RelationalAI, focusing on database systems and AI. Education: Bachelor’s in Computer Science, Politehnica University of Bucharest (2000) PhD in Computer Science, Ludwig Maximilian University (2005) Professional Roles: Full Professor, University of Zurich (since 2020) Visiting Professor, University of Oxford Emeritus Fellow, St Cross College Editorial Roles: ACM TODS, VLDBJ, SIGMOD Conference Chair: ICDT Council (since 2022) His research focuses on data systems theory, including query optimization, probabilistic databases, factorized databases, and in-database machine learning. He co-authored the seminal book Probabilistic Databases (2011) and has pioneered algorithms for efficient machine learning over relational data and incremental maintenance of analytical workloads. His work emphasizes scalable, theoretically grounded solutions for real-world data challenges. Awards: ICDT 2019 Best Paper Award ACM SIGMOD 2018 Distinguished PC Member Award ERC Consolidator Grant (2016) Oxford Outstanding Teaching Award (2009) Grants & Funding: Supported by Google, Microsoft Azure, Amazon AWS, EPSRC, and the European Commission. His research bridges academia and industry, with contributions to commercial systems like LogicBlox and RelationalAI. Labs & Teams: Heads the DaST group at Zurich, focusing on data systems theory and applications. Collaborates widely in the database and AI communities.
Shayan Aziznejad is a Senior ML Scientist at Distran, working on the intersection of machine learning and acoustic imaging. He was previously an ML researcher at Daedalean AI (October 2022–December 2024) and a Ph.D. candidate at Ecole Polytechnique Fédérale de Lausanne (EPFL) , where he focused on mathematical optimization and signal processing under Prof. Michael Unser. His academic background includes dual B.Sc. degrees in Electrical Engineering and Pure Mathematics from Sharif University of Technology . Research Focus: Machine learning, neural network certification, wavelet analysis, Hessian-Schatten regularization, and sparse modeling. Scientific Recognition: Swiss National Science Foundation Postdoc Fellowship (2021) Best Student Paper Award at ICASSP (2019) Gold Medalist at Iranian National Mathematics Olympiad (2011) Academic Contributions: Authored 15+ publications in top-tier journals (SIAM, IEEE, etc.) and conferences (ICASSP, EUSIPCO), with a focus on Lipschitz-regularized models, spline-based optimization, and inverse problems. Advising Experience: Supervised 11+ students across master's theses, summer internships, and semester projects, including Eliana Renzo, Joaquim Campos, and Haojun Zhu. Email: shayan.aziznejad@gmail.com
Laurent Condat is a Senior Research Scientist at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, where he conducts research in optimization algorithms and their applications. He is affiliated with the College of Engineering, Department of Computer Science, and has previously held research positions at CNRS in France, working at GREYC in Caen and GIPSA-Lab in Grenoble. Dr. Condat received his PhD in 2006 from Grenoble Institute of Technology, followed by a 2-year postdoc in Munich, Germany. He was recruited as a permanent researcher by CNRS in 2008 and has been on leave from CNRS since November 2019 to work at KAUST. In February 2025, he was promoted to 'chargé de recherche hors classe' (senior research scientist) by CNRS. His research focuses on deterministic and stochastic optimization algorithms, convex relaxations, and applications to machine learning, signal and image processing. His work spans theoretical foundations of optimization methods to practical implementations for distributed and federated learning systems. He has developed several influential algorithms including RandProx, TAMUNA, and LoCoDL that address communication efficiency in distributed optimization. His recent publications demonstrate strong trends in communication-efficient distributed optimization, with particular emphasis on federated learning, compression techniques, and local training methods. His work bridges theoretical optimization with practical machine learning applications, showing consistent innovation in algorithmic design for large-scale problems. Best reviewer award at AISTATS 2025 Meritorious Service Award from Mathematical Programming Stanford's list of world's top 2% most influential scientists Dr. Condat has co-supervised PhD students including Daniele Picone and Julien Baderot. He serves as an Associate Editor for IEEE Transactions on Signal Processing and has presented his work at numerous international conferences including plenary talks at major optimization workshops. His research is supported through KAUST funding and collaborative projects with researchers worldwide.
Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.
Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.
Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.
Marco Maggi is Associate Professor of Comparative Literature and Literary Theory at the University of Italian Switzerland (USI), affiliated with the Faculty of Communication, Culture and Society. He co-directs the Master's program in Italian Language, Literature, and Civilization and previously served as Vice Dean (2020–2022). His editorial roles span multiple international series, including Crossovers: New Perspectives on CompLit and Diomira. Studi teorici sull'intermedialità , and he curates the Lea Ritter Santini Collection at the Natalino Sapegno Foundation. Marco Maggi's research delves into Literary intermediality, particularly between literature and visual culture Comparative literature, focusing on European Baroque and modernity Reception studies of classical texts in contemporary contexts Cultural transfer mechanisms in historical and artistic frameworks His 2020–2022 Images in Question project at USI's Faculty of Communication, Culture and Society exemplifies his commitment to interdisciplinary visual analysis. His publications portfolio, Walter Benjamin and Dante (2017) Visual Modernity of The Betrothed (2019) Intermediate Forms (2025) reveals a trajectory from Baroque textual theory to contemporary ecocriticism projects exploring natural inscriptions. Scientific recognition includes Italian national scientific qualification (2017) and fellowships at Naples' Italian Institute for Philosophical Studies (1999–2001) and Paris' Institut d'Études littéraires (2005). As curator of documentary exhibitions like Affioramenti (2018), and regular contributor to L'Indice dei Libri del Mese and antinomie.it , he bridges academic research with public intellectual discourse through frequent media appearances and cultural curation.
Georgios Moschidis is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Chair of Mathematical General Relativity (CMGR) and the Mathematics Section of the School of Basic Sciences (SB-SMA). He holds dual positions within the Department of Mathematics (MATH) and the SMA-ENS unit, focusing on advanced mathematical physics and geometric analysis. His research interests center on mathematical general relativity, differential geometry, and partial differential equations, with a strong emphasis on spacetime stability problems, trapped surface formation, and geometric analysis in curved spacetimes. He teaches advanced courses such as Differential Geometry IV (General Relativity) and Analysis IV, reflecting his expertise in theoretical physics and advanced mathematics. Moschidis' work includes groundbreaking studies on the instability of anti-de Sitter (AdS) spacetime, ergosphere instabilities, and scalar wave dynamics in black hole geometries. His publications span topics from Vlasov systems to geometric inequalities in Gauss spaces, demonstrating interdisciplinary rigor. He advises doctoral student Abraham Gabriel Dorsaz and maintains active research through the CMGR lab (https://www.epfl.ch/labs/cmgr/). His research is supported by EPFL's academic framework, and he contributes to both teaching and doctoral supervision within the School of Basic Sciences.
Pascal Mark Gygax is a faculty member at the University of Fribourg , specifically within the Faculty of Philosophy under the Department of Psychology . His academic career spans psycholinguistics, cognitive psychology, and gender studies, focusing extensively on how language structures influence gender perceptions and cognitive biases. With a research profile marked by experimental methodologies, Gygax has published 64 works, including a 2021 book Does the Brain Think Masculine? that critically examines the psycholinguistic impacts of gender-fair language reforms. Role: Researcher at Department of Psychology, University of Fribourg Key Collaborations: Sandrine Zufferey, Ute Gabriel, Anton Öttl Research Focus: Gygax investigates the cognitive mechanisms behind gender representation in language, particularly how grammatical gender systems (e.g., French, German) perpetuate male-dominated mental models. His work explores: Experimental validation of gender-neutral pronouns Discourse connective processing across age groups Impact of language reform on occupational stereotypes Cross-linguistic comparisons of gender perception Implicit bias in performance evaluation (e.g., magic tricks) Recent Publications (2023-2025) demonstrate methodological rigor in studying: Neological intuition in Romance languages Temporal focus and spatial cognition Connective mastery in L2 contexts Participant variability in experimental linguistics Gender ratio norms across global languages Pragmatic constraints on causal connectives Scientific Contributions: While no explicit awards are listed, Gygax's work has significantly advanced: Development of experimental paradigms for gender perception studies Creation of multilingual datasets on role noun norms Validation of facial feedback mechanisms through collaborative research Understanding of discourse coherence in educational contexts Advising & Collaborations: Though specific students aren't named, Gygax collaborates with: Development of the Routledge Handbook of Experimental Linguistics Leadership in SNSF-funded projects Contributions to debates on inclusive language policies
Tao Lin is a Tenure-Track Assistant Professor and Principal Investigator of LINs Lab at Westlake University, School of Engineering. He leads cutting-edge research in deep learning optimization, generalization, and robustness, particularly in distributed and federated settings. Prior to this, he was a Ph.D. student at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, under the supervision of Prof. Martin Jaggi and Prof. Babak Falsafi. Doctor of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2017–2022) Master of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2014–2017) Bachelor of Engineering (with honors), College of Electrical Engineering, Zhejiang University, China (2010–2014) His research focuses on the intersection of optimization and generalization in deep learning, leveraging theoretical and empirical insights into loss landscapes and training dynamics to design efficient and robust learning and inference methods. This includes work on decentralized and federated learning under noisy, heterogeneous, and hardware-constrained environments. His work spans algorithmic innovation, theoretical analysis, and practical system integration. The recent publications from his lab demonstrate a strong trend in advancing federated learning, efficient inference for large language models, multimodal foundation models in pathology, and robust training under distribution shifts. Key themes include communication efficiency, model personalization, gradient tracking, and hardware-aware learning. His group has published at top venues including NeurIPS, ICML, ICLR, CVPR, and ECCV, with several papers receiving oral or spotlight presentations. ECCV Best Paper Candidate, 2024 Top 2% Scientists Worldwide 2024 (Stanford University) Doctoral Program Thesis Distinction Award, EPFL, 2022 Outstanding Performance Bonus, EPFL, 2021–2022 Top Reviewer: NeurIPS, ICML, AISTATS He advises multiple Ph.D. and master’s students, including Yongxin Guo, Futing Wang, Peng Sun, and Yuxuan Sun, whose work has been accepted at premier conferences. He has secured competitive grants as PI and participant, including the National Natural Science Foundation of China for Excellent Young Scientists Fund (Overseas) and the Science and Technology Innovation 2030 – Major Project. He also contributes to the community through service as an area chair (NeurIPS, ICML), reviewer for top journals and conferences, and organizer of workshops and academic events. His open-source contributions, such as Post-local SGD, have been integrated into PyTorch. Tao Lin teaches graduate courses such as Research Methodology of Computer Science and Technology and Deep Learning at Westlake University. He is actively involved in academic governance, serving on committees for student seminars, academic exchange, doctoral studies, and teaching leadership. The LINs Lab runs a regular research seminar on Deep Learning and Optimization, fostering a collaborative and dynamic research environment.
Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.
Franck Iutzeler is a Professor of Applied Mathematics at Université de Toulouse, working within the Statistics & Optimization team of the Institut Mathématique de Toulouse and teaching in the Department of Mathematics. He previously served as an Assistant Professor at Université Grenoble Alpes from 2015 to 2023 and completed his Habilitation à Diriger des Recherches in 2021. His research focuses on the intersection of optimization, statistics, and optimal transport theory to develop robust data-driven models. Key areas include numerical optimization, statistical learning, stochastic programming, and optimal transport. He is particularly interested in distributionally robust optimization using Wasserstein metrics and has developed the skwdro Python library for implementing these methods. Iutzeler's recent publications demonstrate a strong focus on Wasserstein Distributionally Robust Optimization (WDRO), with multiple papers in top venues like NeurIPS and SIAM Journal on Optimization. His work bridges theoretical guarantees with practical implementation, particularly through the skwdro library which provides efficient code for WDRO in machine learning applications. ANR JCJC grant for project STROLL: Harnessing Structure in Optimization for Large-scale Learning Co-PI of ANITI chair on Trust and Responsibility in Artificial Intelligence led by JM. Loubes and J. Bolte Iutzeler actively supervises PhD students including Yu-Guan Hsieh (awarded Université Grenoble Alpes's PhD award), Gilles Bareilles, Waïss Azizian, and Victor Mercklé. He has secured research funding through the ANR (MAD project on Automatic Differentiation) and ANITI. His current research includes statistical fairness using optimal transport theory and automatic differentiation for stochastic optimization. He leads the development of the skwdro library for Wasserstein Distributionally Robust Optimization and is involved with ANITI (Toulouse's AI Cluster), where he also took responsibility for the 2nd year of the Master SID in Data Science & Engineering in September 2024.
