جستجوی استاد راهنمای دانشگاهی

• Cryptography
• Cryptanalysis
• Stream Ciphers
• +۵ مورد دیگر

Prof. Dr. Willi Meier serves as a Lecturer for mathematics and cryptology at the Institute for Sensors and Electronics within the School of Engineering and Environment at FHNW (University of Applied Sciences and Arts Northwestern Switzerland) in Windisch, Switzerland. With an extensive publication record spanning over three decades (1988-2025), he maintains an active research profile in cryptographic analysis. Dr. Meier's research primarily focuses on cryptanalysis of symmetric cryptographic primitives, with particular expertise in stream ciphers, block ciphers, and hash functions. His work frequently employs algebraic techniques, differential cryptanalysis, and mathematical modeling approaches to analyze cryptographic security. Recent research has centered on analyzing modern ciphers like Grain, Keccak, RIPEMD-160, and various lightweight cryptographic designs, often developing novel attack methodologies such as coefficient grouping and algebraic meet-in-the-middle approaches. His publication trends over the last five years show consistent high productivity in top-tier venues including CRYPTO, EUROCRYPT, ASIACRYPT, and IACR Transactions on Symmetric Cryptology. The research spans both theoretical advancements in cryptanalytic techniques and practical applications to real-world cryptographic standards. A significant portion of his recent work involves collaborations with international researchers, particularly Fukang Liu, Takanori Isobe, and Santanu Sarkar, reflecting his integration within the global cryptographic research community. Dr. Meier's work has practical implications for cryptographic standardization and implementation security, with analyses of protocols used in telecommunications (TETRA), lightweight IoT applications, and post-quantum cryptographic candidates. His research continues to contribute to the fundamental understanding of symmetric cryptographic primitives and their security margins.

Lenka Zdeborováمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

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.

Nicolas Zuffereyمشاهده پروفایل

استاد

• Operations Management
• Supply Chain Management
• Optimization
• +۴ مورد دیگر

Nicolas Zufferey is a Full Professor of Operations Management at the University of Geneva, Switzerland, where he has served since 2008. He leads research in optimization methods for complex systems, focusing on applications in supply chain management, production planning, inventory control, and transportation logistics. His affiliations include the Research Institute of Management and collaborations with CIRRELT (Transportation & Logistics) and GERAD (Decision Analysis). Education: PhD in Operations Research (EPFL, 2002), MSc/BSc in Mathematics (EPFL) Prior Experience: Postdoc at University of Calgary (2003–2004), Assistant Professor at Université Laval (2004–2007) Research Interests: His work emphasizes developing advanced metaheuristics (e.g., VNS, Tabu Search, PSO) for challenging optimization problems. Key domains include: Multi-objective scheduling with resource constraints Inventory deployment under uncertainty Network design for supply chains and transportation systems Publications: Over 150 peer-reviewed articles across journals like European Journal of Operational Research , Transportation Research , and INFORMS Journal on Computing . Recent work addresses electric vehicle routing, drone integration in delivery systems, and robust decision-making under uncertainty. Collaborations: Engaged with 35+ universities and 27 private companies globally. Active in applying operations research to industrial problems (e.g., Swiss railways, luxury watch production, pharmaceutical networks).

Kaspar Riesenمشاهده پروفایل

پژوهشگر

• Pattern Recognition
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

Philipp Baumannمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Philipp Baumann is a Professor at the University of Bern, affiliated with the Group for Business Analytics, Operations Research and Quantitative Methods. His research focuses on optimization algorithms, clustering techniques, and operations research applications. He has contributed to advancements in constraint-based clustering, fault-tolerant facility location, and fair-capacitated clustering, leveraging mathematical programming and heuristic methods. His work bridges theoretical optimization and practical applications in industries like printing and healthcare. Research interests include combinatorial optimization, algorithm design for machine learning, and large-scale data mining. Notable contributions include the MPFCC algorithm for fair clustering and FT-KMEANS for fault-tolerant systems. His recent publications emphasize scalable solutions for real-world scheduling and resource allocation challenges. No scientific awards are explicitly mentioned. He advises students on thesis guidelines (Bachelor and Master levels) but no specific advisees are listed. His research group collaborates on projects involving mathematical programming in operations and finance, with applications to production scheduling and portfolio optimization.

• Algorithmic Graph Theory
• Computational Complexity
• Structural Graph Theory
• +۳ مورد دیگر

Narmina Baghirova is a Researcher at the Department of Informatics within the Interfaculty Informatics Department at the University of Fribourg. She holds a bachelor’s degree in Mathematics from the University of Primorska (Slovenia), a master’s in Mathematical Sciences from the same institution, and has completed exchange semesters at Universidad del País Vasco (Spain) and Eötvös Loránd University (Hungary). She is currently pursuing a PhD under Prof. Bernard Ries and Dr. David Schindl. Her research focuses on Algorithmic Graph Theory, Computational Complexity, and Combinatorial Optimization. Key areas include k-community structure detection in graphs, proportionally dense subgraphs (PDS), and parameterized complexity analysis. Recent work explores efficiently solvable cases for these problems and their applications in social network modeling. Her publications (2022–2025) span theoretical computer science, discrete mathematics, and bioinformatics, addressing graph partitioning, phylogenetic algorithms, and structural graph properties. She has contributed to both journal articles and conference proceedings. Baghirova collaborates with international researchers and is affiliated with the Department of Informatics at the University of Fribourg, where she contributes to research on computational complexity and graph-theoretic problems.

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.

Norbert Trautmannمشاهده پروفایل

استاد

• Combinatorial Optimization
• Project Management
• Production Planning
• +۳ مورد دیگر

Prof. Dr. Norbert Trautmann is a Full Professor of Quantitative Methods in Business Administration at the University of Bern, leading the Group for Business Analytics, Operations Research, and Quantitative Methods. He holds a PhD from the University of Karlsruhe (2000) and a Habilitation degree in Operations Research from the University of Karlsruhe (2004). His research focuses on Combinatorial Optimization , Project Management , Production Planning , and Portfolio Optimization . Prof. Trautmann has extensive professional experience, including roles as Head of the Department of Business Administration (2010–2012), and leadership in academic societies like the Swiss Operations Research Society (President since 2017). He has authored over 100 peer-reviewed publications and supervised numerous PhD students, including Philipp Baumann, Adrian Zimmermann, and Tom Rihm. His research has led to awards such as the 2010 IEEE Honourable Mention Award and the 2001 German Society of Operations Research Doctoral Thesis Award. Key projects include scheduling optimization for assessment centers, production planning in process industries, and portfolio tracking models for small investors.

• 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.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

Emo Welzlمشاهده پروفایل

استاد

• Theoretical Computer Science
• Combinatorial Algorithms
• Algorithm Design
• +۱ مورد دیگر

Emo Welzl is a Professor at ETH Zürich's Department of Computer Science, leading the Theory of Combinatorial Algorithms Group within the Institute of Theoretical Computer Science. His work focuses on foundational aspects of algorithms, combinatorics, and computational geometry. He maintains active research collaborations and supervises doctoral students through his group. His publications span journals, conference proceedings, and miscellaneous academic contributions. Affiliations: ETH Zürich | Institute of Theoretical Computer Science | Theory of Combinatorial Algorithms Group Research Interests: Algorithmic theory, combinatorial optimization, geometric algorithms, and their applications. Doctoral students under his supervision are listed on his official page, showcasing his mentorship in advancing theoretical computer science. Despite the lack of explicit awards listed here, his academic standing at ETH Zürich underscores significant contributions to the field.

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +۱۱ مورد دیگر

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

Thomas Weberمشاهده پروفایل

استاد

• Credit collections
• Decision making under risk
• Behavioral economics
• +۸ مورد دیگر

Thomas Weber is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , holding the Chair of Operations, Economics, and Strategy (OES) within the College of Management (CDM) . He serves as Director of the Doctoral Program in Management of Technology and contributes to academic governance through roles in committees such as the CDM Academic Evaluation Committee. PhD Students: Zhang Ru, Han Jun, Mark Michael, Razeghian Jahromi Maryam Email: thomas.weber@epfl.ch His research spans behavioral economics, risk analysis, and optimization in dynamic systems, with a focus on sharing economy applications, inventory management, and cryptocurrency market dynamics. Recent publications address robust decision-making frameworks, self-exciting point processes, and economic implications of information endogeneity. Key teaching activities include: Information: Strategy & Economics Innovation & Entrepreneurship in Engineering Microeconomics

Maryam Kamgarpourمشاهده پروفایل

استادیار

• Control Theory
• Game Theory
• Mechanism Design
• +۶ مورد دیگر

Maryam Kamgarpour is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Engineering. She previously held faculty positions at the University of British Columbia and ETH Zürich. Her work bridges stochastic control , multiagent learning , and game theory , focusing on safety-critical systems. Education: PhD in Engineering from UC Berkeley, BSc in Applied Science from University of Waterloo. Research Interests: Control under uncertainty, game theory, mechanism design, mixed-integer optimization, and applications to transportation, robotics, power grids, and healthcare. Her recent publications emphasize safe reinforcement learning , multirobot coordination , and stochastic trajectory planning , with applications to aircraft navigation and energy systems. She has received the European Union ERC Starting Grant, NASA High Potential Individual Award, and IEEE Transactions on Control of Network Systems Outstanding Paper Award. Scientific Awards: ERC Starting Grant (2016-2021) NASA High Potential Individual Award (2010) NASA Excellence in Publication Award IEEE Outstanding Paper Award (2022) PhD Students: Jordan Philip Christopher Maddux Anna Maria Ni Tingting Ren Kai Salizzoni Giulio Schlaginhaufen Andreas Vaishampayan Saurabh Dilip Vallat Gabriel Rémi Former EPFL student: Guo Baiwei