Patrick Schnider is a lecturer in the Department of Mathematics and Computer Science at the University of Basel and the Department of Computer Science at ETH Zürich. His academic journey includes postdoctoral positions at ETH Zürich under Prof. Bernd Gärtner and Prof. Emo Welzl, and at the University of Copenhagen with Prof. Karim Adiprasito. He holds a PhD in Theoretical Computer Science from ETH Zürich between 2015 and 2020. Education: Bachelor's and Master's degrees in Mathematics from ETH Zürich. PhD in Theoretical Computer Science under Prof. Emo Welzl. Research focuses on combinatorial and topological methods in discrete geometry and high-dimensional data analysis, including topological methods for mass partitions, combinatorial depth measures, geometric transversals, computational geometry, and topological properties of solution spaces. Recent work explores geometric algorithms, topological data analysis, and combinatorial optimization, with notable contributions to clustering algorithms, persistent homology applications, and fair division problems. No scientific awards explicitly mentioned. Collaborations include the Theory of Combinatorial Algorithms Group at ETH Zurich and research groups led by Prof. Gärtner and Adiprasito. Other interests include music, playing clarinet in a wind orchestra, and conducting in various orchestral projects.
Thomas Bolander is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on logic, artificial intelligence, algorithms, and graph theory. He is affiliated with the Algorithms, Logic and Graphs research group and is based at Richard Petersens Plads in Lyngby, Denmark. He serves as an Editor for the Artificial Intelligence journal (since July 2022), demonstrating his leadership in academic publishing within his field. Despite extensive scholarly contributions, no specific scientific awards or student advisees are listed in the provided data. His professional profile includes 76 publications and 8 research projects, reflecting a deep engagement with theoretical and applied computer science. The provided data also highlights 56 press/media mentions, though specific details are not elaborated here.
Michael Kaufmann is a Lecturer and Project Manager at the Lucerne School of Computer Science and Information Technology, part of Lucerne University of Applied Sciences and Arts. He has held this position since 2016 and has extensive experience in academic and industry roles, including roles as a Board Member of the FMsquare Foundation and Coordinator of the Research Team Data Intelligence. His academic journey includes a Habilitation in 2023 from FernUniversität in Hagen, Germany, focusing on 'Emergent Knowledge Engineering in Big Data Management,' and a PhD in Computer Science from the University of Fribourg in 2012. His research and teaching focus on databases, big data management, and data science, with notable contributions to database security, fuzzy logic applications, and semantic analysis. He has led numerous research projects, including IFZ FinTech, Netted Letters, and MinimalTools, exploring topics such as decision intelligence and data quality assessment. His work often bridges theoretical advancements with practical applications in industries like tourism, finance, and social media analytics. Despite his prolific output, no specific academic awards or student advisees are explicitly mentioned in the provided materials. Education highlights include a Habilitation thesis on big data management frameworks and a PhD on inductive fuzzy classification in marketing analytics. His teaching spans specialized courses like Database Modeling, NoSQL Systems, and Data Science. Professional experience prior to academia includes roles as a Business Analyst, Data Architect, and Data Warehouse Analyst at firms like PostFinance and Mobiliar. Key research projects include analyzing tourism hotspots using travel blog data, GDPR-compliant social network analysis, and developing interactive research environments. His publications emphasize practical applications of databases and machine learning, with a focus on ethical AI frameworks and scalable in-database analytics. Kaufmann’s work often integrates interdisciplinary methods, such as combining fuzzy logic with marketing analytics and natural language processing for knowledge extraction.
Jan S Hesthaven is a Professor of Mathematics and Dean of the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds adjunct professorships at Brown University and the Technical University of Denmark. His research focuses on computational methods for partial differential equations, model order reduction, and high-order numerical methods like discontinuous Galerkin schemes. He leads the Chair of Computational Mathematics and Simulation Science (MCSS) and founded SCITAS, EPFL's Scientific IT and Application Support unit. Education: M.Sc. (1991) and Ph.D. (1995) in Numerical Analysis from Technical University of Denmark, with postdoctoral training at Brown University and NASA Langley. He earned a Dr.Techn. (2009) for contributions to nodal discontinuous Galerkin methods. Research interests include computational fluid dynamics, seismic monitoring of porous media, and machine learning integration with PDE solvers. Notable contributions include physics-informed neural networks, reduced basis methods for acoustics, and surrogate models for gravitational waves. He has received prestigious awards like the SIAM Fellowship (2014) and the Philip J. Bray Teaching Award (2004). Leadership roles: Dean of SB (2017–present), Director of CCV at Brown (2006–2013), and Deputy Director of ICERM (2010–2013). His work bridges fundamental mathematics with applications in engineering, physics, and environmental science. Grants and collaborations include NSF funding for model reduction and NASA partnerships. He supervises interdisciplinary projects on seismic data analysis and acoustic simulations. SCITAS, under his leadership, provides high-performance computing support across EPFL.
Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.
Dr. Lucas Slot is a Lecturer at the Department of Computer Science, ETH Zurich, specializing in theoretical computer science, computational complexity, and optimization algorithms. His research focuses on polynomial optimization, sum-of-squares hierarchies, and semidefinite programming, with applications to algorithmic design and complexity analysis. Recent work includes studies on computational thresholds in stochastic block models, convergence rates of optimization hierarchies, and kernel-based methods for high-dimensional inference. His contributions span theoretical foundations and algorithmic advancements in mathematical programming and geometric data analysis. Lacking explicit mentions of academic awards or grants, Dr. Slot’s scholarly activities emphasize computational and mathematical challenges in optimization and discrete geometry. No student advisees are listed in the provided materials.
Prof. Angelika Steger is a Full Professor in the Department of Computer Science at ETH Zurich, leading research in theoretical computer science since 2003. She holds a Master's in Applied Mathematics from Stony Brook University (1985) and a PhD from the University of Bonn (1990). Her career includes roles at Kiel, Duisburg, and TU München before joining ETH. She is a Leopoldina member (2007), ICM speaker (2014), and Collegium Helveticum Fellow (2009+). Research focuses on probabilistic methods, randomized algorithms, graph theory, and combinatorial optimization. She has contributed to understanding discrete structures, neural networks, and algorithmic resilience. Awards include recognition in both computer science and mathematics circles. Her work bridges theoretical foundations with applications in AI, neuroscience, and distributed systems.
Maciej Besta is a leading researcher at ETH Zurich's Institute for Computing Platforms, where he heads research initiatives at the Scalable Parallel Computing Lab (SPCL) and contributes to the ETH Future Computing Laboratory (EFCL). Working under the mentorship of Professor Torsten Hoefler, he has established himself as a prominent figure in high-performance computing, graph processing, and large language models. Position: Researcher at Institute for Computing Platforms, ETH Zurich Research Leadership: Head of Sparse Graph Computations and Large Language Models Research at SPCL Collaboration: Leads project management for SPCL's contributions to ETH Future Computing Laboratory Besta's research spans multiple abstraction levels, from hardware and network topologies to middleware, algorithms, and programming models. His primary focus areas include graph-enhanced language models, graph neural networks, graph databases, and sparse models, with applications across various computational settings. He approaches these problems through rigorous performance modeling and formal reasoning, emphasizing both scalability and practical implementation. His recent publications reveal a clear trend toward integrating graph structures with language models and AI systems. Besta has pioneered work on graph databases, knowledge graphs of thoughts, and higher-order graph neural networks, while maintaining his strong foundation in high-performance computing and network topology design. His research bridges traditional HPC with cutting-edge AI, creating novel approaches for efficient large-scale computation. IEEE TCSC Award for Excellence in Scalable Computing (Early Career, 2023) Multiple Best Paper Awards at Supercomputing conferences (2022, 2023) ACM SIGHPC Doctoral Dissertation Award (2022) ETH Medal for outstanding doctoral thesis (2021) Fellow of The Explorers Club (2022) Besta actively mentors ETH Zurich students through semester projects, Bachelor's, and Master's theses, focusing on graph processing and related computer science challenges. His mentorship extends beyond technical guidance, incorporating lessons from his extensive polar and mountaineering expeditions that emphasize mental resilience, efficient risk management, and leadership. He has supervised numerous student projects that have resulted in high-impact publications at top-tier conferences. As a core member of the Scalable Parallel Computing Lab, Besta collaborates with researchers across ETH Zurich and international institutions. His unique approach integrates insights from extreme environment expeditions into research methodology, creating a distinctive framework for tackling complex computational problems. The lab's work under his leadership spans theoretical modeling, practical implementation, and real-world deployment of high-performance systems.
Hans-Joachim Böckenhauer is a Lecturer at the Department of Computer Science at ETH Zürich, specializing in theoretical computer science with a focus on algorithms, online algorithms, and computational complexity. His research explores algorithmic optimization in problems like knapsack, graph exploration, and parameterized complexity. Affiliation: ETH Zürich, Department of Computer Science Research Interests: Online Algorithms, Graph Theory, Algorithm Design, Computational Complexity His work emphasizes the theoretical foundations of algorithms, including advice complexity and reoptimization strategies. Recent contributions address online knapsack variants, graph exploration with limited memory, and parameterized problem-solving.
Dr. Ndaona Chokani is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. Their research focuses on aerothermodynamics, mechanical engineering, and process engineering, with a recent emphasis on interdisciplinary applications in computer vision and image processing. Affiliated with the Professur f. Aerothermodynamik, their work spans theoretical and applied domains. While explicit educational background details are not provided, their academic career includes contributions to fields such as fluid dynamics and process systems engineering. Research interests are inferred from departmental affiliation and recent publications, emphasizing computational methods in mechanical systems and image quality assessment. Recent publications highlight work in perceptual quality metrics for images and videos, immersive systems (VR/360 content), and machine learning applications in 3D pose estimation and generative models. Key areas include diffusion models for low-light imaging, spatial-temporal geometric networks, and benchmarking frameworks for image harmonization. No awards or grants are explicitly listed in the provided text. Their research often involves collaborations on datasets like Salient360! and tools for gaze data analysis in 3D environments. Future work appears to focus on improving perceptual metrics and expanding applications in healthcare and immersive technologies.
Cansin Yaman Evrenosoglu is a Principal Expert at the Research Center for Energy Networks (FEN) at ETH Zürich, Switzerland. His academic journey includes a B.S. and M.S. in Electrical Power Engineering from Istanbul Technical University (1998–2001) and a Ph.D. in Electrical and Computer Engineering from Texas A&M University (2006). Prior to FEN, he held roles such as Assistant Professor at Virginia Tech (2011–2013) and University of Nevada Reno (2008–2011), and Principal Scientist at ABB Corporate Research (2013–2019). His research focuses on electric power grids, including design, operation, and protection of transmission/distribution systems. Key areas include secure integration of renewable energy sources, high-penetration scenarios with energy storage systems, electric vehicles, solar PVs, and heat pumps. He is a Senior Member of IEEE and served as Editor for the IEEE Transactions on Smart Grid (2014–2019). Evrenosoglu has supervised numerous students, including PhD graduates Meghana M. Reddy and Alexandra Weinberger, and has contributed to projects like the Arrangements and Drawings initiative. His work spans academic and industrial collaborations, emphasizing infrastructure planning and smart grid technologies.
Dr. Blazhe Gjorgiev is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. His research focuses on power systems reliability, energy transition modeling, and grid risk engineering. He leads the Reliability and Risk Engineering group, addressing challenges in grid resilience, renewable integration, and cascading failure mitigation. His work bridges machine learning applications with physical grid systems, emphasizing data-driven approaches for infrastructure optimization. Key research interests include: Power grid reliability and vulnerability analysis Renewable energy integration and policy implications Data-driven modeling of transmission and distribution systems Machine learning for fault detection and grid operation Recent publications highlight advancements in anomaly detection for power line inspection, European energy transition policies, and graph-based power grid benchmarking. His work often intersects with socio-technical issues, such as energy inequities and infrastructure resilience in aging systems. Dr. Gjorgiev collaborates on projects like the Nexus-e platform for energy-economic assessments and the Cascades-Risk model for expansion planning. His research has practical implications for grid modernization, policy design, and sustainable energy systems.
Tianyi Zhang is a Researcher at the Professorship for Theoretical Computer Science, ETH Zurich, located at OAT Z 29, Andreasstrasse 5. His research focuses on advancing fundamental algorithms in graph theory, with particular expertise in dynamic graph problems, efficient spanner constructions, edge coloring optimizations, and shortest-path computations. Dr. Zhang develops both theoretical frameworks and practical implementations for complex computational challenges. His core research areas include the design of near-linear and subquadratic time algorithms for graph optimization problems, fault-tolerant network structures, streaming-optimized graph coloring, and geometric graph embeddings. Recent work emphasizes breakthroughs in Vizing's theorem implementations, dynamic set cover deamortization, and space-efficient distance oracles. Dr. Zhang's publications demonstrate consistent innovation in algorithm efficiency for planar graphs, Euclidean spaces, and dynamic network settings. His 2023-2025 articles reveal concentrated efforts on: 1) Optimizing edge coloring through multi-step Vizing chains and streaming adaptations, 2) Enhancing spanner constructions for doubling metrics and planar environments, and 3) Developing failure-resistant path algorithms with improved time/space complexity. These contributions address scalability challenges in large-scale network processing. He collaborates within the Theoretical Computer Science research group at ETH Zurich, contributing to the institution's leadership in algorithmic innovation. No information about awarded grants, supervised students, or educational background is available in the source materials.
Fabio Widmer is a PhD researcher at the Institute for Dynamic Systems and Control (IDSC) within ETH Zürich's Department of Mechanical and Process Engineering. He received his B.Sc. and M.Sc. degrees in mechanical engineering from ETH Zürich in 2015 and 2017, respectively, with distinction, focusing on electric mobility and energy flows during his studies. His academic background includes: B.Sc. in Mechanical Engineering, ETH Zürich (2015) M.Sc. in Mechanical Engineering, ETH Zürich (2017) Widmer's research centers on model-based optimization of thermal and energy management systems for electrified public transport vehicles. His work spans electric buses, hydrogen hybrid vehicles, and charging infrastructure optimization. He has made significant contributions to understanding energy-comfort trade-offs in HVAC systems, developing optimization methods for charging strategies, and creating online-capable control algorithms for hydrogen vehicles. His research combines theoretical modeling with practical applications, utilizing dynamic programming, model predictive control, and scenario-based optimization approaches to address real-world transportation challenges. Widmer has received recognition for his academic achievements, including two Outstanding Bachelor Awards and a scholarship from ETH Zürich's Excellence Scholarship and Opportunity Programme. His research has resulted in numerous publications in high-impact journals such as Control Engineering Practice, Energy, and Energies, demonstrating both theoretical rigor and practical relevance to sustainable transportation systems. As part of his research activities, Widmer has contributed to projects including ISOTHERM and Swiss eBus Plus. He was also actively involved with ETH's formula student team AMZ, where student teams develop and compete with self-developed electric race cars, reflecting his hands-on approach to electric mobility research.
Micha Wasem is an Associate Professor at the Fribourg School of Engineering and Architecture (HES-SO), affiliated with the Institute for Intelligent and Secure Systems. His research spans mathematical modeling, geometric analysis, and statistical methods with applications in engineering contexts. Current projects include FRISAM-Swisslos (statistical game analysis) and metamodeling techniques for geotechnical applications. His publications demonstrate expertise in computational mathematics, algebraic structures, and geometric equilibria. Teaching responsibilities include mathematics courses across engineering programs including civil, electrical, and mechanical engineering disciplines.
