جستجوی استاد راهنمای دانشگاهی

Anna-Lena Horlemannمشاهده پروفایل

دانشیار

• Algebraic Coding Theory
• Post-Quantum Cryptography
• Network Coding Theory
• +۳ مورد دیگر

Anna-Lena Horlemann is an Associate Professor for Foundations of Computation at the School of Computer Science, University of St. Gallen. Her research focuses on algebraic coding theory, post-quantum cryptography, and network coding. She leads the Coding Theory and Cryptography research group, actively contributing to NIST's post-quantum standardization efforts. Her work addresses quantum computing threats to current cryptosystems and develops error-correcting codes for reliable data transmission and storage. Education: PhD in Mathematics from the University of Zurich (2013), supervised by Prof. Joachim Rosenthal. Diploma in Mathematics from Ruhr-Universität Bochum. Research interests span coding theory (e.g., rank-metric codes, subspace codes), cryptography (code-based systems, privacy protocols), and neuroinformatics (connectome analysis). Recent publications emphasize Lee metric applications, MDS code classification, and cryptanalysis of Gabidulin-based systems. She mentors doctoral researchers such as Nadja Willenborg and Violetta Weger, focusing on topics like code densities and quantum-resistant algorithms. Her team's work bridges theoretical foundations and practical implementations, with contributions to error-correction resilience and cryptographic security in post-quantum scenarios. No formal awards listed, but actively participates in international conferences (IEEE, CBCrypto) and journal reviews.

Simon Weberمشاهده پروفایل

پژوهشگر

• Unique Sink Orientations
• Computational Complexity
• Combinatorial Algorithms
• +۷ مورد دیگر

Simon Weber is a researcher affiliated with the ETH Zurich (Department of Computer Science). His work focuses on Unique Sink Orientations (USOs) , a combinatorial abstraction of optimization problems like Linear and Quadratic Programming. Simon's research spans three areas: (1) Structure of USOs and their links to Oriented Matroids; (2) Constructions of high-dimensional USOs to analyze algorithm complexity; and (3) Algorithmic improvements for sink-finding. He also explores topics in graph compression, neural networks, and ∃R-complete problems. Key Publications: PhD thesis on USO reductions, ∃R-completeness in neural training, and USO phase analysis. Scientific Contributions: Advances in USO complexity, FPT algorithms for MaxCut, and recognition of geometric hypergraphs and nerves of convex sets. He has supervised multiple theses at ETH Zurich, including topics on USO visualization, MaxCut algorithms, and necklace splitting. His teaching experience includes being a Head Assistant for courses like Geometry: Combinatorics & Algorithms and Topological Data Analysis . Simon's work has been recognized with Best Paper and Best Student Paper Finalist awards at SC19.

Yoav Zemelمشاهده پروفایل

مدرس

• Geometrical statistics
• Point processes
• Shape theory
• +۳ مورد دیگر

Yoav Zemel is a Lecturer at the École polytechnique fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences and Department of Mathematics . He specializes in statistical aspects of optimal transport and related geometric methods. B.Sc. Mathematics & Economics, Hebrew University of Jerusalem (summa cum laude, 2010) M.Sc. Applied Mathematics, EPFL (2012) PhD Mathematical Statistics, EPFL (2017), advised by Victor M. Panaretos His research focuses on geometrical statistics , point processes , shape theory , and optimal transportation . Recent work explores covariance operators, Gaussian processes, and stochastic algorithms in Wasserstein spaces. Publications bridge theoretical advancements with applications in ecology, genetics, and machine learning. Scientific awards include the Robert May Prize , Swiss government scholarship , and multiple Hebrew University honors. He has taught courses on probability, statistical machine learning, and optimal transport at EPFL, Göttingen, and Cambridge.

Dimitar Jetchevمشاهده پروفایل

استاد

• Number Theory
• Arithmetic Geometry
• Mathematical Cryptology
• +۲ مورد دیگر

Dimitar Jetchev is a Swiss National Science Foundation Professor in number theory, arithmetic algebraic geometry, and mathematical cryptology at the School of Basic Sciences, EPFL. He leads the GR-JET research group and holds positions in the Mathematics Section (TAN) and SMA-GE unit. University of California at Berkeley, Ph.D. in Mathematics (2008) Harvard University, B.A. in Mathematics (2004) Jetchev's research spans number theory and cryptography, focusing on elliptic curves, Selmer groups, Euler systems, Heegner points, and complexity analysis of cryptologic algorithms. His work bridges pure mathematics and cryptographic applications like ECC and symmetric key protocols. His recent publications analyze isogeny graphs of abelian varieties, discrete logarithm problems in genus 2, equidistribution phenomena, and Euler systems. These works explore connections between automorphic representations, unitary groups, and cryptologic algorithms. Swiss National Science Foundation Professor Jetchev has advised PhD students including Boumasmoud Mohamed Réda, Dudeanu Alina, and Vuille Marius Lorenz. His research group GR-JET collaborates with institutions like IHES and EPFL's LACAL lab. He works on unitary Shimura varieties, special cycles, and their applications to Iwasawa theory and the Bloch-Kato conjecture. The GR-JET group investigates isogeny-based cryptosystems and algorithmic improvements in discrete logarithm computations.

• Applied Analysis
• Numerical Analysis
• Data Science
• +۶ مورد دیگر

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.

• Distributed Computing
• Parallel Algorithms
• Graph Theory
• +۳ مورد دیگر

Christoph Grunau is a Researcher at ETH Zürich's Theoretical Computer Science department, affiliated with the Professorship for Computer Science. His work focuses on distributed computing, parallel algorithms, graph theory, and network decomposition. He has contributed to advancements in scalable MPC (Massively Parallel Computing) algorithms, efficient parallel derandomization techniques, and deterministic network decomposition methods. His research emphasizes algorithmic efficiency, theoretical guarantees, and applications in distributed systems, quantum computing, and dynamic graph problems. Key contributions include work on graph orientation, dynamic coloring algorithms, and clustering techniques such as k-center and k-means++. His publications span topics like shortest path algorithms with negative edge weights, probabilistic methods for algorithm analysis, and distributed symmetry breaking in sparse graphs. Grunau's research bridges foundational theory with practical distributed computing challenges, addressing scalability and efficiency in both classical and emerging computational frameworks.

Helmut Bölcskeiمشاهده پروفایل

استاد

• Applied Mathematics
• Machine Learning Theory
• Mathematical Signal Processing
• +۳ مورد دیگر

Prof. Dr. Helmut Bölcskei is a Full Professor of Mathematical Information Science at ETH Zurich's Department of Information Technology and Electrical Engineering. He holds a joint affiliation with the Department of Mathematics. His academic journey includes a Dipl.-Ing. and Dr. techn. from Vienna University of Technology, followed by postdoctoral research at Stanford University and industry roles at Iospan Wireless and Celestrius AG. He has been at ETH Zurich since 2002, contributing to applied mathematics, machine learning theory, signal processing, and statistics. Education : 1994: Dipl.-Ing., Vienna University of Technology 1997: Dr. techn., Vienna University of Technology Industry Experience : Co-founder of Iospan Wireless (acquired by Intel) and Celestrius AG His research focuses on applied mathematics , machine learning theory , and data science , with emphasis on neural network approximation, metric entropy, and signal processing. Recent work explores theoretical limits of deep learning and nonlinear system identification. His publications highlight advancements in quantization, compression, and system complexity analysis. Prof. Bölcskei has received numerous accolades, including IEEE Fellow status, the 2010 Vodafone Innovations Award, and the ETH 'Golden Owl' Teaching Award. He served as Editor-in-Chief of the IEEE Transactions on Information Theory (2010–2013) and has held editorial roles in multiple journals. His leadership includes roles on the Board of Governors of the IEEE Information Theory Society and as a delegate for faculty appointments at ETH Zurich. Labs/Teams : Mathematical Information Science Group at ETH Zurich's Department of Information Technology and Electrical Engineering

Qiang Zhangمشاهده پروفایل

پژوهشگر

• Biomechanics
• Sports Medicine
• Rehabilitation Engineering
• +۳ مورد دیگر

Dr. Qiang Zhang is a Researcher affiliated with the Institute of Biomechanics at ETH Zürich, specifically within the Professorship for Movement Biomechanics. His work focuses on biomechanical analysis of human movement, with particular emphasis on musculoskeletal health, sports injury prevention, and rehabilitation engineering. His research integrates advanced sensor technologies, computational modeling, and clinical applications. Research interests include gait analysis in aging populations, muscle synergies in locomotion, ACL injury mechanisms in athletes, and wearable sensor systems for real-time musculoskeletal monitoring. He has contributed to studies on rehabilitation protocols for stroke patients and biomechanical evaluations of orthotic devices. His interdisciplinary approach bridges biomechanics, biomedical engineering, and clinical practice. Publications highlight innovations in strain sensor design, fatigue effects on athletic performance, and systematic reviews of ligament biomechanics. While no formal awards are listed, his work reflects sustained engagement with cutting-edge biomechanical research. Dr. Zhang collaborates with sports medicine and orthopedic teams at ETH, contributing to advancements in injury prevention and rehabilitation technologies.

• Positional games
• Discrete and computational geometry
• Discrete random structures
• +۲ مورد دیگر

Miloš Stojaković is a Full Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Serbia. He has held this position since 2016, following roles as Associate Professor (2011–2016) and Assistant Professor (2006–2011). His research focuses on positional games, discrete and computational geometry, discrete random structures, combinatorial algorithms, and graph theory. He leads the Foundations of Computer Science group since 2018. Stojaković earned his Ph.D. in Computer Science from ETH Zurich (2005), advised by Emo Welzl and Tibor Szabó, and holds M.Sc. and B.Sc. degrees from the University of Novi Sad. He has been recognized with the Dr Z. Đinđić Award (2008) for best young scientist in Vojvodina and the Best Student of University of Novi Sad Award (1998/99). He has advised three Ph.D. students: Mirjana Mikalački, Marko Savić, and Jelena Stratijev. His work spans over 60 publications, including seminal contributions to positional games and computational geometry. He serves on editorial boards of Discrete Mathematics & Theoretical Computer Science and the Novi Sad Journal of Mathematics . Stojaković teaches courses such as Combinatorial Algorithms, Graph Theory, and Theoretical Computer Science at the University of Novi Sad. He has also taught specialized courses on positional games at institutions like the University of Buenos Aires and Eötvös Loránd University Budapest. His research interests emphasize algorithmic and combinatorial aspects of games, geometry, and graph theory.

• Numerical Analysis
• High-Dimensional Problems
• Low-Rank Matrix and Tensor Techniques
• +۴ مورد دیگر

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.

Robert Dalangمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Stochastic Partial Differential Equations
• +۳ مورد دیگر

Robert Dalang is a Full Professor and Chair of Probabilities at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. He is affiliated with the School of Basic Sciences, Department of Mathematics, specifically the Probability Section. His office is located in building MA, room B2 493 at EPFL's Lausanne campus. He also serves as a Full Professor in the Section of Mathematics (SMA) and for the Doctoral Program in Mathematics (EDMA). Professor Dalang's research focuses on probability theory and stochastic analysis. His work primarily centers on: Stochastic partial differential equations (SPDEs), particularly stochastic heat and wave equations Brownian motion and Brownian sheet Lévy processes and Lévy white noise Hitting probabilities and polarity Fractal properties of random fields Stochastic optimization problems His recent publications demonstrate a continued focus on the theoretical properties of solutions to stochastic partial differential equations, including their regularity, hitting probabilities, and asymptotic behavior. He has made significant contributions to understanding the fine properties of solutions to stochastic heat and wave equations, particularly in critical dimensions. Professor Dalang has served on the elected committee for the Pension fund PUBLICA since Fall 2007, being reelected for 2009-2012. His work on this committee has focused on preserving employee interests during the transition from a defined benefits system to a defined contributions system. He has supervised numerous PhD students, including: Candil David Jean-Michel Chen Le Ciccarella Carlo Conus Daniel Dumas Frédéric Humeau Thomas Marie Jean-Baptiste Pu Fei Rossel Jean-Benoît Vinckenbosch Laura Professor Dalang teaches courses including Analysis III, Analysis IV, and Theory of Stochastic Calculus at EPFL.

Peter Fellerمشاهده پروفایل

استاد

• Low-dimensional topology
• Knot theory
• 3-manifolds
• +۱۲ مورد دیگر

Peter Feller is a Full Professor at the Institute of Mathematics, University of Neuchâtel (UniNE) since 2025. Previously, he was an Assistant Professor (2019-2025) and postdoc at ETH Zurich's Geometry, Groups and Dynamics group. Earlier positions include postdoctoral fellowships at Max Planck Institute for Mathematics (Bonn), Boston College, and University of Bern (PhD). His research spans geometry and topology with emphasis on low-dimensional topology, knot theory, 3-manifolds, algebraic geometry connections, and Heegaard-Floer theory. 2010-2014: Doctorate in Mathematics, University of Bern 2008-2010: MSc in Mathematics, University of Bern 2005-2008: BSc in Mathematics, University of Bern Research interests focus on knot concordance , hyperbolizability , mapping class groups , and algebraic embeddings of complex varieties. Publications reveal trends in geometric topology, braid theory, and connections to algebraic geometry through plane curve singularities. His work on the Dehn twist coefficient characterizes irrational rotation behavior in infinite-type surfaces. Current collaborators include PhD student Léo Mousseau and postdoc Miguel Orbegozo Rodriguez. He organizes the biannual Swiss Knots conference on low-dimensional topology and contributes to seminars like the Algebra and Geometry Seminar at UniNE. Research grants acknowledge support from the Simons Foundation.

Bernhard Eggerمشاهده پروفایل

استادیار

• 3D Morphable Models
• Statistical Shape Modeling
• Computer Graphics
• +۷ مورد دیگر

Bernhard Egger is a junior professor (adidas Stiftungsprofessur) at the Chair of Visual Computing, Cognitive Computer Vision Lab at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) . His research bridges human/machine perception of faces and shapes with synthetic data generation. Formerly, he was a postdoc at MIT's Computational Cognitive Science Lab and Computer Science & AI Lab, following a PhD in facial image annotation at the University of Basel. Research Focus: 3D Morphable Models, Statistical Shape Modeling, Inverse Rendering, AI in Mental Health, Medical Imaging. Education: PhD (University of Basel, 2017), MSc/BSc in Computer Science (University of Basel), Teaching Diploma (University of Applied Sciences Northwestern Switzerland). Notable Awards: Best Poster Award (2024, Cognitive Computational Neuroscience) Best Paper Award (CVPR Workshop 2019) Honk Award (SIGBOVIK 2020) Publications Trends: Recent work spans implicit surface modeling (ICLR 2025), 3D scene decomposition (3DV 2025), medical shape models (BVM 2025), and multimodal AI (npj Mental Health 2024). Labs: FAU's Cognitive Computer Vision Lab (advisor to 8+ students/researchers).

• Positional games
• Discrete geometry
• Computational geometry
• +۳ مورد دیگر

Miloš Stojaković is a Full Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Serbia. He has been with the university since 1999, progressing from student to Assistant Professor (2006-2011), Associate Professor (2011-2016), and Full Professor (2016-present). He leads the Foundations of Computer Science group and serves as an editor for Discrete Mathematics & Theoretical Computer Science and the Novi Sad Journal of Mathematics. Ph.D. in Computer Science, ETH Zurich (2005), advisors: Emo Welzl and Tibor Szabó M.Sc. in Computer Science, University of Novi Sad (2001), advisor: Joviša Žunić B.Sc. in Mathematics and Computer Science, University of Novi Sad (1999) Stojaković's research centers on positional games, discrete and computational geometry, discrete random structures, combinatorial algorithms, and graph theory. His work explores theoretical aspects of combinatorial games, particularly Maker-Breaker type games on graphs and other structures. He investigates geometric properties of discrete structures and develops algorithms for geometric problems. His research has applications in theoretical computer science and combinatorics, with significant contributions to understanding game strategies, structural properties, and computational complexity in discrete settings. Stojaković's recent publications (2016-2025) primarily focus on positional games, extending classical results to new settings like posets, generalized saturation, and constructor-blocker variants. He examines computational complexity aspects and structural properties of game outcomes. His work often involves collaborations with leading researchers including Dan Hefetz, Michael Krivelevich, and Tibor Szabó, demonstrating the ongoing vitality of combinatorial game theory and its connections to other areas of discrete mathematics. "Dr Z. Đinđić Award" for the best young scientist in Vojvodina (2008) "Best Student of University of Novi Sad Award" (1998/99) Stojaković has supervised three PhD students to completion: Mirjana Mikalački (2014), Marko Savić (2018), and Jelena Stratijev (2023). His research has been supported by various grants, including the 1 million RSD grant accompanying the Dr Z. Đinđić Award. He has organized multiple workshops including the Novi Sad Workshop on Foundations of Computer Science series (2015, 2017, 2024) and co-organized the Mini-Workshop on Positional Games at Oberwolfach Research Institute (2018) and the Positional Games workshop within A Sparse (Graphs) Coalition (2021). Since 2018, Stojaković has led the Foundations of Computer Science group at the University of Novi Sad. He maintains active international collaborations, having taught courses at Eötvös Loránd University Budapest, University of Buenos Aires, University of Szeged, University of Banja Luka, University of Groningen, and Graz University of Technology. His research group focuses on theoretical aspects of combinatorial games, discrete geometry, and related computational problems, contributing significantly to these fields through publications, workshops, and academic leadership.

Juhan Aruمشاهده پروفایل

دانشیار

• Probability Theory
• Random Geometry
• Statistical Mechanics
• +۳ مورد دیگر

Juhan Aru is an Associate Professor in the Department of Mathematics at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Random Geometry (RGM) group. His research focuses on random geometry, Gaussian Free Fields (GFF), and stochastic processes , with applications to statistical mechanics and mathematical physics. He teaches courses such as Analysis IV, Probability, Gaussian Processes, and Introduction to Random Geometry. Affiliations: SB MATH RGM (Random Geometry Group) EDMA - Enseignement (Mathematics Education) SMA - Enseignement (Mathematics Teaching) His research investigates critical phenomena in 2D statistical physics, including SLE processes, Liouville quantum gravity, and multiplicative chaos. Recent work explores the interplay between GFF properties and geometric/topological structures like excursion decomposition and thick points. He advises PhD students including Philémon Bordereau and Han Xiao, and has directed Guillaume Charles Woessner's thesis. His lab's website is https://www.epfl.ch/labs/rgm/ .