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Florian DörflerView profile

Professor

• Distributed Control Systems
• Optimization
• Cyber-Physical Systems
• +7 more

Florian Dörfler is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and Deputy Head of the Automatic Control Laboratory. He holds a Ph.D. in Mechanical Engineering from the University of California, Santa Barbara (2013), and a Diplom in Engineering Cybernetics from the University of Stuttgart (2008). Prior to ETH, he was an Assistant Professor at UCLA (2013–2014). His research focuses on distributed control and optimization in complex systems, including smart grids, robotic coordination, and social networks. Key areas include power grid stability, online feedback optimization, and data-driven control. He has advised students who received or were finalists for prestigious awards at major conferences such as the European Control Conference and American Control Conference. Notable awards include the IFAC Manfred Thoma Medal (2020), European Control Award (2020), and IEEE Circuits and Systems Best Paper Award (2016). His work spans theoretical advancements and practical applications, with contributions to stability analysis, decentralized control, and energy systems. Led the Automatic Control Laboratory and contributes to ETH Zurich’s broader efforts in cyber-physical systems. Active in academic service, including editorial roles and conference organization. His lab develops cutting-edge solutions for grid resilience, distributed optimization, and networked systems.

Robert W. SumnerView profile

Adjunct Professor

• Computer Graphics
• Mesh Processing
• Inverse Kinematics
• +3 more

Robert W. Sumner is an Adjunct Professor at ETH Zurich and Associate Director at Disney Research Zurich. He specializes in computer graphics, focusing on mesh deformation, inverse kinematics, and game programming. His work bridges academic research and industry applications, particularly in 3D modeling and animation. Affiliations: Disney Research Zurich, ETH Zurich Education: PhD in Computer Science from MIT (2005), MSc from MIT (2001) Research Interests: Developing algorithms for shape manipulation, deformation transfer, and game development. Notable contributions include the ETH Game Programming Laboratory course and foundational work on mesh-based inverse kinematics. Key Projects: Curvature-domain shape processing, embedded deformation techniques, and deformation transfer between 3D models.

• Mixed Integer Optimization
• Mixed Integer Linear Programming
• Mixed Integer Convex Optimization
• +5 more

Robert Weismantel serves as Full Professor at ETH Zürich's Department of Mathematics and Deputy Head of the Institute for Operations Research. His research establishes foundational frameworks for Mixed Integer Optimization across linear, convex, and nonlinear domains, with significant theoretical contributions to algorithm design and computational complexity. His primary research domains include: Mixed Integer Linear Optimization using cutting plane methods based on lattice-free polyhedra Mixed Integer Convex Optimization combining polytope shrinking techniques with convex programming Mixed Integer Nonlinear Optimization focusing on convex relaxations for chemical engineering applications Polynomial Optimization in fixed dimensions with combinatorial substructure analysis Professor Weismantel's methodological innovations bridge discrete and continuous optimization, particularly through Mirror-Descent Methods and Lipschitz-continuous function minimization over integer points. His work demonstrates practical applications in chemical engineering through the SFB/TR 63 InPROMPT research collaboration. With an extensive supervision record spanning over two decades, he has guided 17 doctoral students to completion and mentored two successful habilitations. Current advisee Sabrina Bruckmeier continues this legacy of training optimization specialists. His research group maintains active projects including Mixed Integer Convex Minimization with Timm Oertel, Integer Polynomial Optimization with Kevin Zemmer, and Mixed-Integer Nonlinear Optimization applications with Martin Ballerstein and Dennis Michaels. The team operates within ETH's Institute for Operations Research, contributing to both theoretical advances and practical implementations of optimization algorithms.

Rico ZenklusenView profile

Professor

• Combinatorial Optimization
• Theoretical Computer Science
• Network Optimization
• +3 more

Prof. Rico Zenklusen is a Full Professor at the Department of Mathematics and Deputy Head of the Institute for Operations Research at ETH Zurich. He previously held positions at Johns Hopkins University and conducted postdoctoral research at MIT and EPFL. His research focuses on Combinatorial Optimization, including algorithm design for complex optimization problems using structures like matroids, submodular functions, and polyhedral methods, with applications in Theoretical Computer Science and Graph Theory. Education: PhD in Mathematics from ETH Zurich Master's degree in Mathematics from EPFL Research Interests: Combinatorial Optimization, Network Design, Submodular Maximization, Matroid Theory, and Applications in Operations Research. His work emphasizes efficient algorithms for optimization problems with real-world applications, such as train engine scheduling, operating room management, and rolling stock scheduling for railways. Grants & Awards: ERC Consolidator Grant (ICOPT) Swiss National Science Foundation Support Students & Advising: Advises PhD and master’s students in optimization and related fields. Current and past students include Adam Kurpisz, Etienne Bamas, and Vera Traub. Visit the group page for project opportunities. Labs & Collaborations: Leads the Zenklusen Group, collaborating on interdisciplinary projects with industries like BLS Cargo (train engine optimization) and the SBB (rolling stock scheduling). Active in ETH Zurich's AI Center for complex system research.

Benjamin GreweView profile

Associate Professor

• Artificial Intelligence
• Neural Networks
• Neuroscience
• +3 more

Prof. Dr. Benjamin Grewe is an Associate Professor at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on the intersection of artificial intelligence, neuroscience, and neural networks, with a particular emphasis on cortical hierarchies, continual learning, and biologically plausible algorithms. He leads projects involving deep feedback control, synaptic connectivity analysis, and neural ensemble dynamics. Grewe teaches courses such as Learning in Deep Artificial and Biological Neuronal Networks and Reinforcement Learning Basics , integrating theoretical and applied perspectives. His work bridges computational models with biological insights, contributing to advancements in medical robotics, process control, and AI safety. His research interests span neural network architectures , continual learning , and biological neuronal systems . Recent projects explore synaptic plasticity in cortical microcircuits and the application of AI to surgical planning and industrial automation. Grewe’s publications reflect a multidisciplinary approach, addressing challenges in both technical and biological domains. No scientific awards are explicitly mentioned in the provided texts. His advising and grant activities remain unspecified in the available data. His lab, part of the Neural and Intelligent Systems group, focuses on developing biologically inspired algorithms and neural interfaces.

Hans-Andrea LoeligerView profile

Professor

• Signal Processing
• Information Theory
• Communications
• +6 more

Prof. Hans-Andrea Loeliger is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and serves as Deputy Head of the Signal and Information Processing Laboratory. With a career spanning over two decades at ETH Zurich since 2000, he has established himself as a leading researcher in signal processing, information theory, and related fields. His work bridges theoretical foundations with practical applications in communications, electronics, and machine learning. Loeliger's research interests encompass a broad spectrum of topics including signal processing, information theory, communications, system theory, electronics, machine learning, quantum systems, error correcting codes, and neural computation. His work on factor graphs and message passing algorithms has been particularly influential, providing a unifying framework for various signal processing techniques. His recent publications demonstrate continued innovation in areas such as NUV priors, control-bounded analog-to-digital conversion, and neural network applications. His publication record shows consistent high-impact contributions across multiple disciplines, with recent work focusing on the intersection of statistical signal processing, machine learning, and circuit design. The trend in his research demonstrates an evolution from foundational work in factor graphs and information theory toward increasingly sophisticated applications in machine learning, neural computation, and practical circuit implementations. Fellow of the IEEE Loeliger has supervised numerous PhD students and master's candidates through ETH Zurich's Signal and Information Processing Lab, though specific student names aren't listed in the provided text. His teaching responsibilities include courses such as Discrete-Time and Statistical Signal Processing, Electronic Circuits & Signals Exploration Laboratory, and Introduction to Estimation and Machine Learning. His research has been supported by various grants enabling the development of novel signal processing techniques and their implementation in practical systems. The Signal and Information Processing Laboratory under his leadership serves as a hub for interdisciplinary research connecting theoretical signal processing with applications in communications, imaging, and neural systems. The lab maintains strong connections with both academic and industrial partners, facilitating the translation of theoretical advances into practical implementations.

• Biochemical Engineering
• Chemical Engineering
• Fluid Dynamics
• +5 more

Dr. Xiaomei Li is a researcher at the Institute for Chemical and Bioengineering (ICB) at ETH Zurich in Switzerland. She is affiliated with the Professorship for Biochemical Engineering , contributing to interdisciplinary research in chemical and bioengineering disciplines. Her work focuses on droplet dynamics, surface engineering, and material coatings to enhance liquid repellency and control electrostatic phenomena. She is based at Vladimir-Prelog-Weg 1-5/10, Zürich, Switzerland, and can be reached via email . Her research interests span experimental and computational methods to study droplet behavior on surfaces. Key topics include: Electrostatic forces and charge deposition in sliding drops, Dynamic friction and viscous forces in fluid-solid interactions, Development of adaptive materials like pH-responsive polymers and superhydrophobic coatings, Integration of machine learning for feature extraction in droplet motion analysis, Surface modification via plasma treatments and nanoscale polymer layer synthesis. Recent studies (2023–2025) emphasize understanding how charge generation affects droplet splashing and motion. Earlier work (2021–2022) explored biomimetic surface designs and recovery mechanisms of polymer surfaces after water exposure. No scientific awards or grants are explicitly mentioned in the provided text. While no formal advisees are listed, her position within the ICB suggests involvement in mentoring and collaborative research teams. She is part of a lab focused on advancing surface engineering techniques for industrial and biomedical applications.

• Power System Identification
• Bayesian Estimation
• Microgrid Control
• +3 more

Pulkit Nahata is a Senior Scientist at ETH Zurich's Power Systems Laboratory and Project Lead for the NCCR Automation Energy Moonshot initiative. His research bridges power systems, control theory, and renewable integration, with industrial experience from Alstom Switzerland. Key research areas include Bayesian identification of distribution grids, passivity-based microgrid control, and cyber-security for DC networks. His publications demonstrate consistent focus on: adaptive grid modeling under uncertainty, decentralized voltage stabilization algorithms for microgrids with nonlinear loads, and resilient control architectures. Major collaborations include the NCCR Automation consortium with Walenstadt utility, advancing renewable integration in Swiss grids. His industry background informs applied methodologies for grid modernization challenges.

Paul TackleyView profile

Professor

• Mantle Dynamics
• Planetary Dynamics
• Plate Tectonics
• +3 more

Prof. Paul Tackley is a Full Professor of Geophysical Fluid Dynamics at ETH Zurich’s Department of Earth and Planetary Sciences, serving as Deputy Head of the Institute of Geophysics. His research focuses on mantle dynamics, planetary evolution, and numerical simulation techniques using high-performance computing. He holds a BA (1st Class Honors) in Natural Sciences from the University of Cambridge (1987), an MSc in Geophysics from Caltech (1991), and a PhD in Geophysics from Caltech (1994). His work bridges computational geophysics and planetary science, emphasizing mantle convection, lithospheric dynamics, and exoplanet modeling. Notable honors include Fellowships from Academia Europaea (2021), the American Geophysical Union (2017), and the Packard Foundation (1997). He has led major initiatives, including the Division on Geodynamics of the European Geosciences Union (2017–2021) and international workshops since 2008. His research group explores topics like Venusian tectonics, planetary differentiation, and mission-critical instrumentation for ESA’s EnVision Venus mission. Recent articles highlight advancements in isotopic heterogeneity tracking, bridgmanite grain-size effects, and Venusian surface-atmosphere coupling via the VenSpec instrument suite. His work underscores interdisciplinary approaches to understanding Earth and other terrestrial bodies.

• Computational Geometry
• Algorithm Design and Analysis
• VLSI Computer-Aided Design
• +3 more

Evanthia Papadopoulou is a Full Professor of Computer Science at the University of Lugano (Università della Svizzera italiana) since 2016. Previously, she was an Associate Professor at USI (2008–2016), a Research Staff Member at IBM T.J. Watson Research Center (1996–2008), and an Assistant Professor at Athens University of Economics and Business (2004–2008). She holds a BS in Mathematics from the University of Athens, an MS in Computer Science from the University of Illinois at Chicago, and a PhD in Computer Science from Northwestern University (1995). Her research focuses on computational geometry, algorithm design, and their applications in VLSI CAD and geometric computing. Key areas include Voronoi diagrams, proximity algorithms, triangulations, and geometric optimization. She has received the IBM Outstanding Innovation Award (2006) and the Technical Accomplishment for IBM Research (2006) for her work on VLSI critical area analysis using Voronoi diagrams. As part of the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), her work integrates theoretical and applied aspects of computational geometry. Her contributions span algorithmic frameworks, geometric data structures, and practical applications in VLSI layout analysis and manufacturing. Her recent research emphasizes high-order Voronoi diagrams, subdivision methods for geometric optimization, and certified approximation algorithms. These advancements address challenges in geometric computing, such as farthest-color Voronoi diagrams and dynamic geometric problems.

• Parallel Computing
• Java Virtual Machine (JVM) Optimization
• Performance Profiling
• +5 more

Walter Binder is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a MSc, PhD, and venia docendi from Vienna University of Technology. Previously, he was a senior researcher at the Artificial Intelligence Laboratory, EPFL. His research focuses on program transformations, virtual execution environments, aspect-oriented programming, profiling, and resource management. Education: MSc from Vienna University of Technology PhD from Vienna University of Technology Venia docendi (Habilitation) from Vienna University of Technology Research Interests: His work spans JVM optimization, parallel computing, and dynamic program analysis. Notable contributions include tools like Renaissance (a JVM benchmark suite), S2S (SQL-to-Stream translator), and Akkaprof (profiler for actor-based systems). He explores topics such as thread management, vectorization, and performance profiling in distributed systems. Articles Trends: Recent work emphasizes JVM performance (e.g., Java Vector API, Native Image optimizations), parallel execution policies (NAS benchmarks), and adaptive runtime systems (e.g., MPR framework). He also investigates big data systems (Spark, Node.js) and runtime verification tools. Grants & Labs: Leads projects on JVM profiling, polyglot runtimes, and large-scale program analysis. His lab develops tools like NodeMOP for Node.js and AccStream for stream processing systems.

• Reservoir Computing
• Machine Learning
• Neural Networks
• +4 more

Lyudmila Grigoryeva is a Professor at the University of St. Gallen. Her research focuses on reservoir computing, machine learning, and nonlinear dynamical systems. She has contributed to advancing theoretical frameworks for recurrent neural networks, including memory capacity analysis, approximation bounds, and time-series forecasting. Key collaborations include work with Juan-Pablo Ortega Lahuerta and Lukas Gonon on reservoir systems and stochastic processes. Research Themes: Infinite-dimensional reservoirs, geometric invariant learning, and parallel-in-time solvers. Applications: Macroeconomic forecasting, chaotic attractor learning, and PDE solving. Her recent work addresses foundational questions in reservoir computing, such as memory capacity evaluation and universal approximation properties. She has also explored interdisciplinary applications in econometrics and computational neuroscience.

• Human Factors
• Aviation Psychology
• Human-Automation Interaction
• +5 more

Dr. Ruth Esther Häusler Hermann serves as Team Leader for Human Factors at the Zurich University of Applied Sciences (ZHAW) School of Engineering, where she directs research on human-automation interaction in safety-critical systems. Her work integrates engineering and psychological methodologies to enhance performance in aviation, nuclear safety, and industrial automation contexts through empirical field studies and experimental design. Her academic foundation includes a PhD and Master's in Psychology from the University of Bern (2000-2006), supplemented by higher education didactics certification at ZHAW in 2011. Professional roles span the Swiss Federal Office of Energy (2014-present), Federal Office of Civil Aviation (2004-2010), and co-founding Human Factors Solutions GmbH (2005-present). Häusler Hermann's research program emphasizes: Objective measurement of cognitive states using eye-tracking and psychophysiological parameters Evidence-based training systems with real-time performance feedback Human factors in automation design for aviation and nuclear safety Root cause analysis methodologies for incident investigation Human-robot collaboration frameworks for industrial applications Analysis of her recent publications reveals a strategic focus on artificial situational awareness systems, with increasing integration of AI in air traffic control and cockpit interfaces. Her work consistently bridges laboratory experimentation with real-world implementation, prioritizing human-centered design in high-stakes environments where automation failures could have catastrophic consequences. She leads the Horizon 2020 AISA Consortium project on human-machine distributed situation awareness and serves on EASA's Human Factors Advisory Group. Current research includes field studies on AI-based conflict detection for air traffic controllers and novel augmented reality assistance systems validated through controlled cockpit experiments. As head of ZHAW's Human Factors research group, she maintains active collaborations with the European Association for Aviation Psychology (EAAP) and Switzerland's Nuclear Safety Commission. Her team operates at the intersection of cognitive psychology, systems engineering, and data science, developing measurement protocols that translate lab findings into practical safety interventions for aviation authorities and industrial clients.

• Virtual Reality Applications
• Digital Transformation
• Affective Computing
• +5 more

Prof. Dr. Thomas Keller is a Professor at the Zurich University of Applied Sciences (ZHAW) , affiliated with the School of Management and Law and the Centre of Information Systems - People & Technology . His research focuses on Virtual Reality (VR) , Digital Transformation , and Human-Computer Interaction , with particular emphasis on educational applications and inclusive technology design. PhD in Information Systems, University of Zurich (2004-2008) MSc in Information Systems, University of Zurich (1998-2002) Certificate in Electrical Engineering, ETH Zurich (1996-1998) MSc in Electrical Engineering, ETH Zurich (1996-1998) Thomas Keller's research explores VR applications in vocational training , digital legacy solutions, and affective computing . He develops immersive learning environments for apprentices, language learners, and children with special needs, while investigating responsible innovation through speculative science fiction prototyping. His recent publications demonstrate a clear trend in VR for education (6/15), digital transformation (5/15), and inclusive technology (4/15). Topics span VR training effectiveness , AI ethics , and blockchain visualization . As a deputy project leader and co-project leader, he has developed technology-supported stress management programs for healthcare workers and immersive scenarios for responsible innovation. His work also includes VR applications for children with special needs and digital estate solutions .

• Embedded Systems
• FPGA
• High Performance Computing
• +5 more

Alberto Dassatti is a Full Professor at HES-SO University of Applied Sciences Western Switzerland and Director of the Reconfigurable & Embedded Digital Systems (REDS) Institute. He is affiliated with the School of Engineering and Management of the Canton of Vaud. His work focuses on embedded systems, FPGA applications, high-performance computing, and topological data analysis. He has led and collaborated on numerous projects funded by institutions like Innosuisse and HES-SO Rectorat, totaling over CHF 1.2 million in grants. His research spans computational storage systems, medical sensor technologies, and signal processing frameworks for wireless communication. Key projects include developing the CarbonViz energy visualization tool, advancing topological methods for AI reliability, and creating the BB-Sensors system for neonatal vital monitoring. He has contributed to open-source frameworks like giotto-tda and gr-dnn, emphasizing interdisciplinary collaboration between hardware and software domains. His publications span topics such as FPGA-GPU communication, real-time embedded systems optimization, and heterogeneous computing architectures. He actively participates in conferences like NeurIPS and IEEE SOC, showcasing innovations in reconfigurable computing and energy-efficient systems.