Dr. Joseph Renes is a Lecturer at the Department of Physics, ETH Zürich, specializing in Quantum Information Theory. His research focuses on quantum error correction, quantum thermodynamics, and quantum metrology, with applications in fault-tolerant computing and quantum communication. Key research contributions include the development of quantum codes (e.g., Reed-Muller codes, LDPC codes), tensor network decoding algorithms, and theoretical frameworks for quantum thermodynamics. His work bridges fundamental quantum information theory with practical implementations, emphasizing robustness against noise and resource optimization. Recent publications highlight advancements in quantum error correction protocols, decoding algorithms leveraging graph neural networks, and foundational studies on uncertainty relations and coherent thermodynamics. Dr. Renes actively contributes to the academic community through course instruction (e.g., Quantum Information Processing I) and collaborations on cutting-edge quantum technologies.
Prof. Daniela Rupp is an Assistant Professor at the Department of Physics at ETH Zürich, where she leads the Nanostructures and Ultrafast X-Ray Science group. Her research focuses on imaging and understanding transient states and dynamics in nanoscale systems using advanced X-ray and optical techniques. Key areas include coherent diffractive imaging of nanoparticles, ultrafast plasma dynamics, and the development of novel imaging frameworks like SPRING. Her work bridges physics, materials science, and computational methods to explore phenomena at femtosecond timescales and nanometer resolutions. Her laboratory, located at the Laboratorium für Festkörperphysik in Zürich, employs cutting-edge X-ray facilities to study systems such as helium nanodroplets, xenon clusters, and free nanoparticles. Research interests span structural dynamics, energy transfer mechanisms, and the interplay between light and matter at the nanoscale. Recent advancements include real-time visualization of nanoplasma formation and the application of machine learning for diffraction image analysis. Publications highlight innovations in imaging frameworks, ultrafast dynamics studies, and interdisciplinary approaches to photon science. Ongoing projects aim to advance single-shot diffraction techniques and explore applications in materials characterization. The group collaborates with Swiss and international research infrastructures to address future photon science needs through strategic roadmap initiatives.
Dr. Flavia Timpu is a Researcher affiliated with the Experimental Quantum Information Professorship at ETH Zürich's Institute of Quantum Electronics. Her work focuses on quantum optics, nonlinear optics, and advanced photonics materials such as lithium niobate and barium titanate. She explores applications in quantum photonics, including trapped ion qubit systems and metasurface-based optical devices. Her research bridges nanotechnology and materials science to advance optical technologies. Key research areas include: Quantum information systems and photonics integration Nonlinear optical materials and resonant phenomena Ferroelectric and semiconductor nanostructures Optical microscopy techniques for material characterization Recent work emphasizes broadband photon pair generation, parametric down-conversion in microcubes, and enhanced electro-optic modulation using metasurfaces. Her studies contribute to foundational understanding of Mie resonances and phase-matching mechanisms in nanoscale systems. Flavia Timpu's research is supported by the Institute for Quantum Electronics, part of ETH Zürich's renowned physics and engineering programs. She collaborates on projects involving ultraviolet photonics and hybrid nanostructure design.
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. 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
