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Afonso S. Bandeira is a Professor in the Department of Mathematics (D-MATH) at ETH Zurich, where he conducts research at the intersection of mathematics, statistics, and computer science. He maintains strong affiliations with several interdisciplinary research centers including the Institute For Operations Research (IFOR), the Max Planck ETH Center for Learning Systems, the ETH Foundations of Data Science, and the ETH AI Center, with a courtesy appointment at D-ITET. Bandeira actively teaches courses including Mathematics of Signals, Networks, and Learning, and Mathematics of Data Science. His research focuses on High Dimensional Probability, Random Matrices, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. Bandeira's work often explores the theoretical foundations of data science, examining phase transitions in statistical problems, computational barriers, and the geometry of high-dimensional spaces. He maintains a research group blog called Randomstrasse101 that emphasizes open problems in his field. Analysis of his recent publications reveals a strong focus on matrix and tensor concentration inequalities, synchronization problems, nonconvex optimization landscapes, and computational-statistical tradeoffs in high-dimensional inference. His work bridges theoretical mathematics with practical applications in machine learning and data analysis, particularly examining where computational limitations arise in statistical problems. Bandeira actively mentors students and researchers, currently supervising several doctoral candidates including Daniil Dmitriev, Konstantin Donhauser, Anastasia Kireeva, Kevin Lucca, Chiara Meroni, Gil Kur, Petar Nizic-Nikolac, and Almut Roedder. He emphasizes that students working with him should participate in the DACO seminar and group meetings, and encourages prospective students to have completed his Mathematics of Signals, Networks, and Learning or Mathematics of Data Science courses. He leads a research group focused on the mathematics of data science, with regular group meetings and seminars. Bandeira has developed comprehensive lecture notes including 'A Tour Through the Mathematics of Signals, Learning, and Networks' (2025) and 'Mathematics of Machine Learning' (2021), and previously authored 'Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science' (2015).

• High Dimensional Probability
• Random Matrices
• Mathematical Statistics
• +3 more

Rasmus Kyng is an Assistant Professor in the Department of Computer Science at ETH Zurich, where he has been since 2019. His research focuses on fast algorithms for graph problems, convex optimization, and their applications in machine learning. He has received grants from the Swiss National Science Foundation, including project grants and a starting grant. Education: B.A. in Computer Science from the University of Cambridge (2011), PhD in Computer Science from Yale University (2017), advised by Daniel A. Spielman. Postdoctoral positions included Harvard University (2018–2019) and a research fellowship at the Simons Institute, UC Berkeley (2017). Research Interests: Development of nearly linear-time algorithms for fundamental graph problems (e.g., maximum flow, minimum-cost flow), dynamic graph algorithms, discrepancy theory, and fine-grained complexity. His work bridges numerical linear algebra and combinatorial optimization, emphasizing practical implementations such as the Laplacians.jl package. Awards: FOCS Best Paper Award (2022), Inaugural ICBS Frontiers of Science Award (2022), Machtey Award (Best Student Paper, FOCS 2017). Teaching: Advanced Graph Algorithms and Optimization (ETH Zurich, 2020–2023), Algorithms, Probability, and Computing (ETH Zurich, 2020–2022). Supervised numerous PhD students and mentored postdocs in theoretical computer science. Labs/Teams: Co-leads a research group with Maximilian Probst Gutenberg, focusing on dynamic graph algorithms and optimization. Collaborations include work on sparsification, spectral graph theory, and machine learning applications.

• Theoretical Computer Science
• Algorithms
• Convex Optimization
• +4 more

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

• Statistical Physics
• Machine Learning
• Computational Biology
• +3 more

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.

• Signal Processing
• Machine Learning
• Computational Imaging
• +5 more

Prof. Daniel Kressner is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL), holding positions in the School of Basic Sciences (SB), Mathematics Institute (MATH), and the Numerical Algorithms and High-Performance Computing (ANCHP) group. He also leads the SMA-ENS unit within the SB-SMA division. His research focuses on numerical linear algebra, high-performance computing, and tensor approximation methods, with applications in scientific computing and data science. Education details are not explicitly listed, but his career at EPFL includes leadership in key research groups and doctoral programs. He supervises multiple doctoral students, including Alice Cortinovis, Peter Effenberger, and others. Research interests emphasize low-rank methods, matrix equations, and efficient algorithms for large-scale problems. Recent work includes advancements in randomized algorithms, tensor networks, and preconditioning techniques for eigenvalue problems. His publications span high-impact journals like Siam Journal on Matrix Analysis and Applications and Numerical Linear Algebra with Applications, addressing topics such as compressed sensing, multigrid methods, and distributed signal processing. Prof. Kressner advises doctoral candidates and contributes to the Program doctoral Mathématiques (EDMA-GE) committee. His lab, ANCHP, develops software tools for hierarchical matrices and tensor computations, such as the hm-toolbox for HODLR and HSS matrices.

• Numerical Linear Algebra
• High-Performance Computing
• Matrix Equations
• +4 more

Bart Vandereycken is an Associate Professor in the Mathematics Department at the University of Geneva, specializing in numerical analysis and scientific computing. His research focuses on large-scale and high-dimensional problems solved using low-rank matrix and tensor techniques, with applications in numerical linear algebra, optimization, and nonlinear eigenvalue problems. He previously held positions as an instructor at Princeton University and postdoctoral researcher at EPF Lausanne and ETH Zurich, and earned his PhD from KU Leuven in 2010. His research interests include Riemannian optimization algorithms, multilevel preconditioning, and machine learning applications. He serves as an associate editor for SIAM Journal on Matrix Analysis and Applications and Linear Algebra and its Applications . Bart organizes the Numerical Analysis seminar with colleagues, and advises students interested in numerical analysis or numerical linear algebra. Recent work emphasizes convexity structures in matrix decompositions, robust preconditioning techniques, and scalable low-rank algorithms for high-dimensional PDEs. His 2024–2025 publications explore advancements in Riemannian optimization schemes, subspace iteration methods, and distributed computing applications of matrix decompositions. Key themes include improving convergence guarantees and developing geodesic-based optimization frameworks for challenging numerical problems.

• Numerical Analysis
• High-Dimensional Problems
• Low-Rank Matrix and Tensor Techniques
• +4 more

Nicolas Boumal is an Assistant Professor in Mathematics at EPFL, Switzerland, holding dual affiliations in the Continuous Optimization Chair (OPTIM) within the Institute of Mathematics (MATH) and the SMA Education Group. He leads research in optimization on Riemannian manifolds, non-convex optimization, and statistical estimation. His work bridges geometry, numerical analysis, and applications in cryo-electron microscopy and synchronization problems. Education: PhD in Mathematical Engineering from UCLouvain (2014), postdoctoral research at Inria Paris and Princeton University. Prior to EPFL, he was an Instructor and Assistant Professor at Princeton’s Mathematics Department. Research Interests : Non-convex optimization landscapes, optimization on manifolds, low-rank matrix optimization, synchronization, phase retrieval, and computational methods in cryo-EM. His ERC Starting Grant (GEOSYM, 2022–2027) focuses on geometric and symmetric optimization techniques. Grants & Awards : ERC Starting Grant (2021), SIAM Optimization Prize (2018 for student co-author), and several conference best paper awards. Teaching : Courses include Continuous Optimization (MATH-329), Algebra Linéaire (MATH-111), and graduate-level Optimization on Manifolds (MATH-512). He authored the textbook An Introduction to Optimization on Smooth Manifolds (Cambridge University Press, 2023). Labs & Teams : Heads the OPTIM lab at EPFL, collaborating with global researchers in optimization and applications. His group develops the Manopt toolbox for manifold optimization.

• Optimization
• Non-convex Optimization
• Riemannian Manifolds
• +5 more

Peter Schwendner is a Professor at the School of Management and Law , Zurich University of Applied Sciences . His work bridges finance, machine learning, and sustainable investing, with a focus on robust portfolio construction and risk management. Role: Head, Institute of Wealth & Asset Management Location: Winterthur, Switzerland Research Interests include: Explainable Machine Learning Portfolio Optimization Correlation Regimes Quantitative Finance Digital Assets Sustainable Finance Recent Article Trends highlight synthetic correlation matrix generation, biodiversity risk assessment, and blockchain applications. His work frequently integrates GPU computing, Monte Carlo simulation, and network analysis to enhance financial modeling. Labs & Teams : He leads the Institute of Wealth & Asset Management and collaborates with institutions like Munich Reinsurance Company and European Stability Mechanism on financial risk frameworks.

• Financial Engineering
• Machine Learning in Finance
• Portfolio Management
• +6 more

Renato Renner is a Full Professor of Theoretical Physics and Head of the Institute for Theoretical Physics at ETH Zürich. He specializes in Quantum Information Science, Quantum Thermodynamics, and the Foundations of Quantum Physics. Born in Lucerne, he earned his physics degrees from EPF Lausanne and ETH Zurich, with a PhD focused on quantum cryptography. After postdoctoral work at the University of Cambridge, he joined ETH Zurich in 2007, progressing through academic ranks to Full Professor by 2015. His research group, the Research Group for Quantum Information Theory, explores cutting-edge topics like quantum key distribution, device-independent protocols, and the thermodynamic limits of quantum processes. His work bridges theoretical physics and applied cryptography, with contributions to quantum security, entropy accumulation, and foundational questions in quantum mechanics. Notable projects include the Space QUEST mission proposal to test quantum decoherence due to gravity and the development of rigorous security frameworks for quantum communication. Renner’s publications span foundational quantum theory, cryptographic protocols, and interdisciplinary applications of quantum information principles.

• Quantum Information Science
• Quantum Thermodynamics
• Foundations of Quantum Physics
• +2 more

Miklós Abért is a Professor at the MTA Alfréd Rényi Institute of Mathematics. His research focuses on measured and asymptotic group theory, with specific interests in spectral theory of graphs and groups, Benjamini-Schramm convergence, invariant random subgroups, homology growth, sofic entropy, and cellular automata. He leads the ERC Consolidator Grant research group "Asymptotic invariants of discrete groups, sparse graphs and locally symmetric spaces" (2015–present). Award of the Academy (2023) Rényi Prize (2017) János Bolyai Research Fellowship (2011) Erdős Pál Prize (2010) Grünwald Géza Memorial Medal (2003) His recent work explores the interplay between physical network structures, stochastic processes, and group-theoretic invariants, with applications to spectral radius analysis, torsion growth, and unimodular random trees. He has organized major conferences on measured group theory and collaborated extensively with leading mathematicians.

• Measured group theory
• Asymptotic group theory
• Spectral theory of graphs and groups
• +7 more

Dr. Edward Mazenc is a Research Fellow in Theoretical Physics at ETH Zürich, affiliated with the group of Prof. Matthias Gaberdiel. His work focuses on gauge/string duality, spacetime emergence, and the AdS/CFT correspondence. Previously, he was a Kadanoff Fellow at the University of Chicago and completed his PhD at Stanford under Prof. Sean Hartnoll. His research bridges quantum information theory, random matrix theory, and topological string models. Education: PhD in Theoretical Physics, Stanford University (2017) Masters in Mathematics, University of Cambridge (Maths Tripos) Undergraduate Physics, Massachusetts Institute of Technology (MIT) Research interests include deriving exact equivalences between matrix integrals and topological strings, exploring connections between random matrices and moduli space geometry, and extending these results to full AdS/CFT frameworks. His recent work with Prof. Rajesh Gopakumar established foundational results in the simplest gauge/string duality. Key contributions span theoretical cosmology (de Sitter microstates), quantum gravity deformations, and interdisciplinary applications like N95 mask decontamination using thermal methods.

• Gauge/String Duality
• Quantum Gravity
• AdS/CFT Correspondence
• +4 more

Michael Kapralov is an Associate Professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) , where he leads research at the Theory Group . His work spans multiple departments including the Laboratory of Theory of Computation 4 and the Doctoral Program in Computer Science and Communications . Kapralov's research focuses on theoretical computer science , particularly sublinear algorithms for big data analysis , with applications in streaming , sketching , sparse recovery , and Fourier sampling . University: EPFL School: School of Computer and Communication Sciences Department: Theory Group Academic Rank: Associate Professor Education: Kapralov earned his Ph.D. in Computer Science from Stanford iCME under the supervision of Ashish Goel . He subsequently held postdoctoral positions at the Mit CSAIL Theory of Computation Group with Piotr Indyk and as a Herman Goldstine Postdoctoral Fellow at IBM T. J. Watson Research Center . Ph.D.: Stanford iCME (2012), advisor: Ashish Goel Postdoctoral: MIT CSAIL (2012-2014), IBM Watson (2014) Research Interests: Kapralov's work addresses fundamental challenges in processing large-scale data through rigorous mathematical models. His contributions include advancements in sublinear algorithms , streaming complexity , spectral sparsification , sparse Fourier transforms , and differential privacy . He has developed techniques for dimension-independent signal processing , kernel ridge regression , and graph spanners , with theoretical guarantees and practical implications for machine learning and data analysis. Scientific Awards: Kapralov received the ERC Starting Grant SUBLINEAR (2018-2023) and the Gene H. Golub Dissertation Award (2012). Advising: He has supervised numerous Ph.D. students and postdoctoral researchers, including Ekaterina Kochetkova , Grzegorz Gluch , Kshiteej Sheth , and Amir Zandieh , many of whom have taken academic or industry positions at institutions like UC Berkeley, National University of Singapore, and Google Zurich. Collaborations and Teaching: Kapralov co-organizes the Turing Course for high school students, leads the Reading Group on Foundations of Deep Learning , and contributes to academic initiatives such as Theory Coffee and the Swiss Winter School on Theoretical Computer Science . He teaches courses like Sublinear Algorithms for Big Data Analysis and Algorithms II , focusing on advanced algorithm design and analysis.

• Theoretical Computer Science
• Sublinear Algorithms
• Big Data Analysis
• +9 more

Ashkan Nikeghbali is a Professor of Quantitative Finance at the University of Zurich's Department of Finance, holding a part-time academic position. His research focuses on advanced topics in probability theory, random matrix theory, and their applications to quantitative finance, particularly in credit risk modeling and stochastic processes. He has contributed to areas such as Mod-Gaussian convergence, asymptotic analysis of random matrices, and statistical mechanics. His work often bridges pure mathematics and practical financial applications, addressing challenges in portfolio risk management and optimization. Research interests include the interplay between probability theory and algebraic structures, with notable studies on characteristic polynomials of random matrices, stochastic processes, and their implications in mathematical physics. He explores topics like high-dimensional optimization, graphons, and permutons, contributing to both theoretical advancements and applied financial models. His publications emphasize precise deviation analysis, approximation schemes, and the application of advanced mathematical techniques to understand complex systems. Despite no explicit mention of awards or grants, his extensive publication record highlights sustained contributions to probability and financial mathematics. No specific advising roles or student mentorship details are provided in the available texts.

• Quantitative Finance
• Probability Theory
• Random Matrix Theory
• +3 more

Vincent Tassion is an Associate Professor at the Department of Mathematics , ETH Zurich , Switzerland. His research focuses on probability theory and stochastic processes , with particular emphasis on percolation theory , Ising models , and random walks on transitive graphs . He supervises Master's theses in discrete probability, requiring a strong background in probability and attendance in advanced probability courses. Over the years 2017-2024, he taught courses such as Probability Theory , Stochastic Processes , and Percolation Theory , often collaborating with colleagues like J. Bertoin, A.-S. Sznitman, and W. Werner. His research explores critical phenomena in statistical physics, geometric properties of random structures, and probabilistic methods in mathematical physics. He leads a research group within the department and offers thesis opportunities in topics like branching processes, random matrices, and Boolean functions.

• Probability Theory
• Stochastic Processes
• Percolation Theory
• +5 more

Elisa Gorla is a Full Professor of Applied Mathematics at the Institute of Mathematics within the Faculty of Science at the University of Neuchâtel, Switzerland, where she has been employed since October 2012. She previously held positions as a Swiss National Science Foundation Professor at the University of Basel (2009-2012), Lecturer at the University of Zurich (2008-2009), and Postdoctoral fellow at the University of Zurich (2004-2008). Her educational background includes a Ph.D. in Mathematics from the University of Notre Dame (2004), an M.Sc. in Mathematics from the University of Notre Dame (2001), and an M.Sc. (Laurea) in Mathematics with highest honors (110/110 cum Laude) from the University of Genoa, Italy (1999). Professor Gorla's research spans the intersection of commutative algebra, coding theory, and cryptography. Her work focuses on algebraic structures for cryptographic applications, network coding, and the theoretical foundations of error-correcting codes. She has made significant contributions to understanding the complexity of polynomial system solving, determinantal ideals, and rank-metric codes. Her research has evolved from pure commutative algebra toward increasingly applied directions, particularly in cryptographic applications and coding theory for modern communication systems. Her recent publications demonstrate a strong focus on multivariate cryptography, rank-metric codes, and the algebraic foundations of modern cryptographic systems. She has published extensively on topics including Gröbner bases complexity, generalized weights of codes, and algebraic structures for post-quantum cryptography. Prix Leon Du Pasquier - Louis Perrier from University of Neuchatel (for best Ph.D. thesis in Faculty of Science in 2016) Featured on AcademiaNet - Expert Database of Outstanding Female Scientists and Scholars since 2012 Member of MEGA Advisory Board since 2015 2018 RTG Lecture Series speaker at University of Clemson Recipient of multiple research grants including European Commission Horizon Europe Doctoral Network Grant Professor Gorla has supervised numerous Ph.D. students including Giulia Gaggero, Flavio Salizzoni, and Alberto Ravagnani (who received the Prix Leon Du Pasquier). She has served as Director of the Mathematics Institute at the University of Neuchâtel since 2021 and is a member of the Board of Trustees of the Swiss Mathematical Society. She organizes the research seminar in coding theory and cryptography across the Universities of Neuchâtel, St. Gallen, and Zurich, fostering a collaborative research environment in these fields.

• Commutative algebra
• Code theory
• Cryptography
• +3 more