Dr. Marcel Dettling is a Group Lead in Data Analysis and Statistics at the ZHAW School of Engineering , focusing on predictive analytics, applied statistics, and complex data analysis. He also serves as a Lecturer at ETH Zurich , teaching advanced statistical methods. Education : PhD in Mathematics (2000-2004), ETH Zurich Postdoc in Applied Statistics (2004-2006), Johns Hopkins University His research spans predictive analytics (regression, classification, time series), data mining, and applications in health economics, transportation safety, social sciences , and business analytics . Recent work includes pharmaceutical cost group analysis for Swiss healthcare and predictive maintenance for marine vessels. Selected publications highlight his expertise in flight trajectory modeling , deep learning error mitigation , and statistical frameworks for rehabilitation finance . His projects address diverse fields like crowdworking in nursing, energy optimization for shipping, and customer behavior prediction.
Michael Gastpar is a full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the Laboratory for Information in Networked Systems (LINX). He previously held faculty positions at the University of California, Berkeley (2003-2011, earning tenure in 2008) and Delft University of Technology. His research spans information theory, signal processing, communications, and systems neuroscience. His research interests focus on network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. Recent work demonstrates a strong shift toward exploring the theoretical foundations of modern machine learning, particularly investigating transformer architectures from an information-theoretic perspective. His research group at EPFL explores how information theory principles can provide fundamental limits and novel approaches for contemporary machine learning problems. His recent publications reveal a clear trend toward bridging classical information theory with modern machine learning. The 15 most recent papers show increasing focus on theoretical analysis of transformers, rate-distortion frameworks for language models, universal prediction methods, and applications of information measures to machine learning theory. This represents a strategic evolution from his earlier work on sensor networks and physical-layer network coding toward foundational questions in artificial intelligence. Scientific Awards: IEEE Fellow 2013 Communications Society & Information Theory Society Joint Paper Award Information Theory Society Distinguished Lecturer (2009-2011) ERC Starting Grant (2010) Okawa Foundation Research Grant (2008) NSF CAREER award (2004) 2002 EPFL Best Thesis Award Professor Gastpar has advised over 20 PhD students who have gone on to successful careers in both academia and industry. His research has been generously supported by major grants including an ERC Starting Grant "ComCom" (2011-2016) and ongoing support from the Swiss National Science Foundation. He has served in significant editorial roles, including as Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11) and as Technical Program Committee Co-Chair for the IEEE International Symposium on Information Theory in 2010 and 2021. He leads the Laboratory for Information in Networked Systems (LINX) at EPFL, which brings together researchers working at the intersection of information theory, machine learning, and networked systems. The lab maintains strong connections with both theoretical research communities and practical applications in communications and neuroscience.
Giorgia Ramponi is an Assistant Professor (tenure-track) in Artificial Intelligence for Cyber-Physical Systems at the University of Zurich (UZH), leading the Autonomous Learning and Predictive Intelligence Lab. She holds affiliations with ETH Zurich’s AI Center and Chalmers University of Technology. Previously, she was a postdoctoral researcher at ETH AI Center, sponsored by Google Brain. Her research focuses on machine learning and mathematical modeling, particularly Reinforcement Learning (RL), Multi-Agent Learning, and Imitation Learning. Notable contributions include work on non-cooperative Markov Decision Processes, mean-field games, and batch IRL for multiple intentions. Publications highlight advancements in constrained MDPs, policy optimization, and applications of RL in cyber-physical systems. Awards include the IBM Best Student Award (2016) and a Hassler Research Grant (2024). She advises students on topics ranging from RL theory to social network analysis. Her academic journey includes a PhD (Politecnico di Milano, 2021) and MSc/BSc (La Sapienza, Rome) with honors. She has taught courses on AI, data science, and machine learning, and contributed to open-source projects like GAN_Time_Series.
David Basin is a Full Professor at the Department of Computer Science, ETH Zurich, and heads the Information Security Group. He has held academic positions since 2003, including roles at the University of Freiburg (1997–2002) and the Max-Planck-Institut für Informatik (1992–1997). His research focuses on Information Security, including methods and tools for secure systems, formal verification, and cryptographic protocols. He is Editor-in-Chief of the ACM Transactions on Privacy and Security and Springer's Information Security and Cryptography book series. Basin founded the Zurich Information Security Center (ZISC) in 2003 and led it until 2011. Education: B.Sc. in Mathematics (Reed College, 1984), Ph.D. (Cornell University, 1989), and Habilitation (University of Saarbrücken, 1996). Research interests span formal methods for security protocol verification, privacy-preserving systems, and cryptographic implementations. He has contributed to foundational work on security protocols, including the Tamarin verification framework. His work addresses real-world systems like payment protocols (EMV), DNS security, and database isolation guarantees. Awards: ACM Fellow (2018) for contributions to Information Security and Formal Methods, IEEE Fellow. He has organized numerous conferences, including IEEE S&P, Euro S&P, and ACM CCS. Labs/Teams: Leads the Information Security Group at ETH Zurich and co-founded Anapaya Systems, a startup focused on network security solutions. His team develops tools like VeriMon (formally verified monitoring) and Tamarin for protocol analysis.
Professor Stefan Goedecker is a distinguished faculty member in the Department of Physics at the University of Basel, Faculty of Science. He holds the position of Professor of Computational Physics and leads an active research group focused on developing advanced computational methods for materials science and quantum physics. Dr. Goedecker received his physics education at the Technical University Munich and the College of William and Mary, followed by a Ph.D. from EPFL Lausanne. His postdoctoral training included positions at Cornell University and the Max-Planck Institute in Stuttgart. In 2003, he was appointed Professor of Computational Physics at the University of Basel, where he has established himself as a leading researcher in computational methods development. His research interests center on computational physics with emphasis on electronic structure calculations, atomistic simulations, and the development of novel algorithms for materials science applications. His work has strong interdisciplinary connections spanning physics, mathematics, material sciences, chemistry, and computer science. Current research directions include machine learning applications in catalysis, fourth-generation neural network potentials for molecular chemistry, and methods for quantifying material synthesizability. Analysis of his recent publications reveals a strong focus on advancing computational methods for electronic structure calculations, with particular emphasis on machine learning potentials, molecular dynamics optimization, and accurate modeling of material properties. His work bridges theoretical physics with practical applications in materials science and nanotechnology, with increasing integration of artificial intelligence techniques into traditional computational physics frameworks. Machine learning for Catalysis (Ongoing) Fourth-Generation Neural Network Potentials for Molecular Chemistry (Completed) Towards Quantifying the Synthesizability of Materials (Completed) Professor Goedecker's research group operates within the Department of Physics at the University of Basel, which is part of the NCCR SPIN initiative focused on silicon-based quantum computing development. The department hosts over 20 research groups with more than 180 teaching staff members, creating a vibrant research environment for computational physics and quantum technologies.
Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.
Wenzel Jakob is an Associate Professor and leader of the Realistic Graphics Lab at EPFL's School of Computer and Communication Sciences , currently on sabbatical at the University of Tokyo until Fall 2025. His work bridges inverse graphics , physically based rendering , and compiler/systems research , with a focus on developing robust differentiable rendering frameworks. Key research themes include: Backpropagation through rendering algorithms for inverse problems Material appearance modeling and optical measurement systems Compiler design for differentiable rendering pipelines Manifold sampling techniques and light transport derivatives His group created Mitsuba renderer , Dr.Jit , and Instant Meshes (recipient of the SGP Software Award). Recent publications (2021–2024) explore volumetric rendering, SDF-based differentiable systems, and efficient Monte Carlo estimators. Awards include the ACM SIGGRAPH Significant Researcher Award , Eurographics Young Researcher Award , and ERC Starting Grant . Teaching roles (2016–2024) span Advanced Computer Graphics and Numerical Methods for Visual Computing courses at EPFL.
Fabio Sigrist is a Professor of Applied Statistics and Data Science at the Institute of Financial Services Zug (IFZ) , part of the Lucerne University of Applied Sciences and Arts . He also holds a Senior Scientist and Lecturer position at the Seminar for Statistics, ETH Zurich . His career spans academic research, industry consulting, and project leadership in finance and data science. PhD in Statistics (2013), ETH Zurich MSc in Mathematics with distinction (2008), ETH Zurich MEd in Mathematics Education (2008), ETH Zurich Sigrist’s research focuses on integrating Machine Learning with Spatial Statistics for applications in Financial Econometrics and Credit Risk . His work includes developing novel algorithms like GPBoost and KTBoost , advancing spatio-temporal modeling , and applying tree-based boosting to financial problems. Projects such as CreHos (credit risk in hospitality) and NISMO (interpretable real estate modeling) highlight his interdisciplinary approach. His publications address challenges in large-scale spatial data , loss given default modeling , and stock volatility prediction . He contributes to software development with tools like spate (R package) and varycoef (spatially varying coefficients).
Dr. Edouard Boujo is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering (STI) and working in the Institute of Mechanical Engineering (IGM) and Laboratory of Fluid Mechanics and Instabilities (LFMI) . He also teaches in the SGM-ENS department of the School of Engineering. Scientist at EPFL STI IGM LFMI Lecturer at EPFL STI-SGM SGM-ENS His research focuses on Fluid Dynamics with expertise in Flow Stability , Flow Control , Aeroacoustics , Thermoacoustics , Fluid-Structure Interaction , and Coating Flow Dynamics . He employs advanced mathematical modeling and computational methods to study complex fluid behaviors. Recent publications highlight his work on stochastic modeling of fluid instabilities, adjoint-based optimization of flow systems, and nonlinear dynamics of coating flows. His 15 most recent papers cover topics ranging from symmetry-breaking bifurcations to spin coating optimization and noise-induced transitions in fluid systems. Dr. Boujo actively collaborates with institutions across Europe and New Zealand, mentoring PhD student Atharva Lagwankar . He has received research funding from the Swiss National Science Foundation for two PhD theses and contributes to major fluid dynamics conferences like the European Fluid Dynamics Conference and APS Division of Fluid Dynamics meetings. His laboratory work at LFMI involves experimental and computational studies of fluid instabilities, with applications in aerospace, mechanical engineering, and industrial coating processes. He develops adjoint-based control methods for optimizing flow systems and reducing drag in various fluid configurations.
Suliana Manley is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences and the Laboratory of Experimental Biophysics . She also holds teaching and research roles in EPFL's School of Life Sciences and Swiss Plasma Center , focusing on interdisciplinary biophysical studies. Education : PhD in Physics (2004), Harvard University Bachelor's in Physics & Mathematics (1997), Rice University Manley's research centers on super-resolution fluorescence imaging , single-molecule tracking , and quantitative biophysics . Key themes include: Understanding protein assembly dynamics at cellular membranes Elucidating viral assembly mechanisms (e.g., HIV-Gag) Developing 3D imaging algorithms and high-density data reconstruction tools like PALMsiever and FALCON Quantifying nanoscale organization in systems like telomeres and centrioles Her work bridges optical physics , computational image analysis , and cellular biology , with notable Nature and PNAS publications. Collaborations span bioengineering , genetics , and medical research . Scientific Awards : Featured in Nature Methods Research Highlights (3x) Very Important Paper and Cover Article (ChemBioChem, 2012) Postdoctoral Fellow, NIH and MIT Advising & Collaborations : Current PhD students in biophysics, cellular biology, and bioengineering Former students: Anna Archetti, Aleksandr Benke, Andrea Callegari, and others Co-founder of tools for high-density super-resolution microscopy and live-cell imaging Labs & Teams : Leads the Laboratory of Experimental Biophysics at EPFL, integrating physics-based methods into biological questions. The lab focuses on quantitative imaging , computational modeling , and software development for nanoscale analysis.
Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).
Zhendong Su is a full professor in the Department of Computer Science at ETH Zurich since August 2018. Previously, he held a full professorship at UC Davis from 2003 until June 2019. He earned his Ph.D. in Computer Science from UC Berkeley and dual Bachelor’s degrees in Computer Science and Mathematics from UT Austin in 1995. Affiliations: ETH Zurich: Full Professor (since 2018) UC Davis: Full Professor and Chancellor’s Fellow (2003–2019) IEEE Fellow, ACM Fellow, and Member of Academia Europaea His research focuses on programming languages, compilers, software engineering, computer security, and education technologies . Key contributions include compiler validation (e.g., Project Yin-Yang for SMT solvers and DBMS testing), testing tools like SQLancer, and educational innovations such as the Algot visual programming language. Recent work emphasizes secure AI (e.g., CipherSteal for TEE-shielded models) and compiler reliability (e.g., Artemis/Apollo for JIT validation). He has pioneered techniques like metamorphic testing and equivalence modulo inputs (EMI) for compiler validation, uncovering thousands of bugs in GCC/LLVM and SMT solvers. Awards: ICSE MIP Award (2022), ACM SIGSOFT Impact Paper (2018), NSF CAREER Award, and multiple industrial awards. His students have won IEEE TCSE Rising Star and SIGSOFT Impact Paper awards, securing roles at top universities and companies like Google and NVIDIA. Service: Steering committee member of ISSTA and ESEC/FSE, ACM Distinguished Speaker, and Associate Editor for ACM TOSEM. Program chaired ISSTA 2012 and co-chaired FSE 2016. Labs/Teams: Leads research groups on compiler validation, secure AI, and education technologies. Projects include Yin-Yang (SMT testing), SQLancer (DBMS fuzzing), and Algot (visual programming for education).
Vincent Dufour-Décieux is a researcher at the Professorship for Energy and Process Systems Engineering at ETH Zürich , focusing on developing computational methods for material screening in separation processes and global net-zero transitions. He earned his Master's in Materials Chemistry from Ecole Polytechnique (France) and a PhD in Materials Science from Stanford University , where he pioneered statistical methods combining Kinetic Monte Carlo and random graph theory to study planetary diamond formation. Research Highlights: Application of Classical Density Functional Theory (cDFT) for 100x faster adsorption property predictions in porous materials Development of science-based definitions for "hard-to-abate" emissions to guide climate action prioritization Integration of Coulombic interactions in cDFT for CO2 adsorption accuracy Article Trends : His work spans computational materials science (cDFT, random graph theory) and climate policy analysis, with recent publications in Joule , AIChE Journal , and Physical Review E . These studies emphasize scalable solutions for carbon capture, material screening efficiency, and accurate thermodynamic modeling. Collaborations : Active in international conferences (FOA15, MolMod, Gordon Research Conference) and cross-institutional projects with teams at Stanford, ETH Zürich, and industry partners.
Dr. Olga Vysotska is a Researcher affiliated with the Professorship for Robotic Systems at ETH Zurich's Department of Mechanical and Process Engineering. Her work focuses on advancing robotic systems through research in sensor-based navigation, SLAM (Simultaneous Localization and Mapping), and autonomous systems. She holds a doctoral degree and is based in Zurich, Switzerland. Her email is olga.vysotska@inf.ethz.ch. Research Interests: Olga's research spans robotics, computer vision, and autonomous navigation. She specializes in LiDAR-based place recognition, SLAM algorithms, and cross-modal localization using 3D scene graphs. Her work addresses challenges such as environmental changes, sensor fusion, and data association in dynamic environments like agriculture and underground exploration. Key themes include robust localization, loop closure detection, and adaptive algorithms for real-world robotic applications. Publications Overview: Olga's recent work emphasizes diffusion-based LiDAR place recognition (2025), 4D spatial-temporal mapping for agricultural robots (2023), and SceneGraphLoc for cross-modal localization (2024). Her research trends highlight innovation in sensor integration, algorithmic robustness, and practical applications in challenging environments. Earlier contributions include exploration of catacombs with mobile robots (2013) and SLAM enhancements using public map data (2017). Grants & Advising: While specific grants or student advisement details are not listed, her active publication record suggests involvement in funded research projects. Her work often collaborates with industry and academic partners to advance robotic autonomy in complex scenarios. Labs/Teams: As part of the Robotic Systems Professorship, she likely contributes to ETH Zurich's robotics labs focused on SLAM, sensor systems, and autonomous navigation. Her projects may intersect with the Department's broader initiatives in mechanical and process engineering.
Christiane Barz is a Professor of Mathematics at the University of Zurich's Institute for Business Administration since 2016. Previously, she held academic roles at the UCLA Anderson School of Management, the Chicago Booth School of Business, and the Technical University (TU) Berlin. Her research focuses on stochastic dynamic systems, Markov decision processes, and their applications in revenue management. She emphasizes making mathematical tools accessible and practical for real-world problem-solving, particularly in optimizing decision-making under uncertainty. Education includes a degree in industrial engineering and a doctorate from the University of Karlsruhe (TH), Germany. Her career path includes postdoctoral research at the University of Chicago's Booth School of Business and roles as an Assistant Professor at UCLA. She combines academic excellence with balancing family life, advocating for gender equity in STEM fields. Her research explores risk-sensitive decision-making frameworks, dynamic pricing models for transportation and healthcare, and optimizing resource allocation in complex systems. Recent work includes applications in FlixBus, air cargo networks, and improving patient admission scheduling in hospitals. Barz's teaching philosophy prioritizes demystifying mathematics for students, encouraging critical engagement rather than fear of complexity. She collaborates with industry partners to apply operations research methods to real-world challenges, emphasizing both theoretical rigor and practical relevance.
