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Laurent CondatView profile

Researcher

• Optimization
• Convex Relaxation
• Stochastic Algorithms
• +5 more

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.

• Applied Analysis
• Numerical Analysis
• Data Science
• +6 more

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
• +3 more

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.

• Distributed Computing
• Algorithms
• Parallel Processing
• +3 more

Dr. Manuela Fischer is a Lecturer at the Department of Computer Science at ETH Zürich. Her research focuses on distributed computing, algorithms, and parallel processing, with a particular emphasis on graph theory and massively parallel computation. She has contributed to advancements in algorithms for sparse graphs, approximation techniques in semi-streaming models, and distributed systems optimization. Her work often explores theoretical computer science challenges, including algorithm design and computational complexity. Dr. Fischer’s publications span topics such as exponential speedups in MPC frameworks, deterministic symmetry breaking in graphs, and efficient connectivity algorithms in distributed environments. These contributions highlight her expertise in leveraging parallel and distributed computing paradigms to solve complex computational problems. No scientific awards or grants are explicitly mentioned in the provided information. She is affiliated with ETH Zürich and contributes to teaching and research in computer science, though specific educational qualifications or lab affiliations are not detailed in the text.

• Financial Risk Management
• Econometrics
• Algorithmic Contract Standards
• +3 more

Prof. Henriette Elise Breymann is a Professor in the Department of Finance, Risk Management and Econometrics at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW). Her research focuses on financial risk modeling, algorithmic contract standards (ACTUS), big data analytics, and regulatory technology (RegTech). She has led numerous projects, including Data Driven Financial Risk and Regulatory Reporting, Strengthening Swiss Financial SMEs through Reinforcement Learning, and Explainable AI in Credit Risk Management. Her work intersects finance, physics, and computational methods, with contributions to energy economics (e.g., renewable energy systems), high-frequency financial data analysis, and quantum chaos applications. She co-authored Unified Financial Analysis: The Missing Links of Finance (2009) and pioneered the ACTUS framework for standardizing financial contracts. Projects: 12+ leadership roles in applied finance, risk modeling, and energy systems. Key Expertise: Regulatory reporting, stress testing, and data-driven financial systems. ACTUS Standard: Global initiative for granular financial contract representation. Her research spans interdisciplinary collaborations, including the Risk- and Finance-Lab, and has been presented at major conferences like the Society for Economic Measurement. She advises on regulatory innovations and financial resilience strategies for SMEs.

Radu VintanView profile

Researcher

• Theoretical Computer Science
• Online Algorithms
• Approximation Algorithms
• +5 more

Radu Vintan is a Doctoral Assistant and PhD student at École polytechnique fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, affiliated with the Institute of Computer Science and the Theory of Computation Laboratory 2 (THL2). He is part of the Doctoral Program in Computer and Communication Sciences (EDIC). Education: Bachelor's and Master's in Computer Science, Technical University of Munich (TUM), Germany PhD in Computer Science, École polytechnique fédérale de Lausanne (EPFL), Switzerland (ongoing) His primary research interests lie in theoretical computer science , particularly online algorithms and approximation algorithms . He also has experience in machine learning and software engineering . His work often involves algorithmic design and analysis for graph and network problems. The recent publications reflect a strong focus on online edge coloring and network update algorithms , with contributions to top-tier conferences such as FOCS, STOC, SODA, and INFOCOM. These works explore both theoretical limits and practical algorithmic solutions, often bridging deterministic and randomized approaches. Scientific Awards: No awards mentioned. Advising and Grants: Radu Vintan is advised by Professor Ola Svensson. There is no mention of him advising students or receiving independent grants. His research is conducted within the Theory group at EPFL, supported through his Doctoral Assistant position. Laboratories and Teams: He is an active member of the Theory of Computation Laboratory 2 (THL2) at EPFL, contributing to foundational algorithmic research in collaboration with leading experts in theoretical computer science.

Gilles VilmartView profile

Senior Lecturer

• Numerical Analysis
• Geometric Numerical Integration
• Multiscale Methods
• +3 more

Gilles Vilmart is a Senior Lecturer (Maître d'enseignement et de recherche) at the University of Geneva's Department of Mathematics since 2013. His research focuses on numerical analysis of geometric and multiscale methods for deterministic and stochastic differential equations, emphasizing geometric numerical integration and numerical homogenization. He holds a PhD (2008, double doctorate from University of Rennes 1 and Geneva) and a Habilitation (2013, ENS Rennes). Former affiliations include the Swiss Federal Institute of Technology Lausanne (EPFL), University of Rennes 1, and INRIA Rennes. He organizes the numerical analysis seminar with Martin Gander and Bart Vandereycken, and co-founded the CAMAS Consulting platform. He is a member of Swiss mathematical society, Swiccomas, and SMAI (France). Key achievements include the Young Researcher First Prize 2013 from Brittany Region for Mathematics. His work includes developing numerical methods for stochastic and multiscale systems, with software contributions like PIROCK and multirevolution integrators. He advises PhD students and organizes workshops on numerical analysis and complex systems.

• Quantum Computing
• Quantum Error Correction
• Superconductivity
• +3 more

Dr. Christoph Hellings is a Lecturer at the Department of Physics at ETH Zürich, specializing in quantum technologies and superconducting circuits. His research focuses on quantum error correction, quantum hardware implementation, and the development of scalable quantum systems. He contributes to advancements in superconducting qubit architectures, leakage reduction mechanisms, and fault-tolerant quantum computing protocols. His work bridges theoretical models with experimental validation, particularly in areas like stabilizer code decoding, lattice surgery, and device-independent randomness amplification. Hellings collaborates on projects involving multi-module quantum processors, photonic cluster states, and real-time feedforward systems. His experiments frequently address practical challenges such as magnetic flux control, inter-chip coupling optimization, and mitigation of loss mechanisms in superconducting circuits. Key research themes include improving qubit coherence times, enhancing gate fidelity through dynamic control, and advancing quantum error correction techniques for surface codes. Recent studies explore the integration of leakage reduction units and the characterization of quantum processor performance under realistic conditions.

Maximilian ProbstView profile

Lecturer

• Graph Algorithms
• Convex Optimization
• Dynamic Algorithms
• +3 more

Maximilian Probst is a Lecturer at ETH Zürich's Department of Computer Science, affiliated with Rasmus Kyng's Algorithms and Optimization Group. He holds a Ph.D. from the University of Copenhagen (2020) and has conducted research at MIT. His research focuses on graph algorithms, dynamic networks, and optimization, with notable contributions to expander decomposition and network flow algorithms. Probst has received prestigious awards including the 2023 Frontiers of Science Award and the 2022 FOCS Best Paper Award. Education: Ph.D. in Computer Science (2020), University of Copenhagen; M.Sc. in Computer Science, University of Copenhagen. Supervisors: Christian Wulff-Nilsen and Mikkel Thorup. Research Interests: Graph Algorithms (Dynamic, Expander-based), Network Flows, Convex Optimization, and Algorithm Design. His work emphasizes theoretical guarantees for real-world graph problems, particularly in dynamic environments. Publications: Probst's work spans foundational algorithmic breakthroughs, including nearly-linear-time maximum flow algorithms and dynamic graph sparsification techniques. His research bridges theory and practice, with applications in routing, congestion balancing, and expander decomposition. Awards: Frontiers of Science Award (2023) FOCS Best Paper (2022) ESA Best Student Paper (2018) Teaching: Co-instructor for courses like 'Advanced Graph Algorithms and Optimization' and 'Advanced Algorithms' at ETH Zurich. Lab/Team: Member of the Algorithms and Optimization Group, collaborating closely with Rasmus Kyng and leading Ph.D. students in theoretical computer science.

Laura GrigoriView profile

Professor

• Numerical Linear Algebra
• High Performance Computing
• Deterministic and Randomized Algorithms
• +4 more

Laura Grigori is a Full Professor and Chair of High Performance Numerical Algorithms and Simulations at EPFL's School of Basic Sciences (SB) Department of Mathematics (MATH). Her research focuses on numerical linear algebra, high performance computing, and tensor computations, with applications in astrophysics and molecular simulations. She leads the HPNalgs lab and teaches courses in numerical analysis and HPC. Her awards include the SIAM Supercomputing Career Prize (2024) and SIAM Fellow distinction (2020). She advises four PhD students and has authored numerous papers on communication-avoiding algorithms, randomized methods, and parallel linear algebra techniques. Her work addresses scalability challenges in scientific computing and large-scale data analysis. Labs/Teams: HPNalgs Lab (https://www.epfl.ch/labs/hpnalgs/) Grants: ERC Synergy Grant (2019) for Extreme-scale Computational Chemistry