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.
Prof. Peter Müller is a Full Professor at the Department of Computer Science at ETH Zurich since 2008. Previously, he held positions as Assistant Professor at ETH Zurich (2003-2008), Researcher at Microsoft Research Redmond (2007-2008), and IT project manager at Deutsche Bank. He earned his Diploma in Computer Science from Technical University of Munich (1996) and his Dr. rer. nat. from University of Hagen (2001) with a dissertation on modular verification of object-oriented programs. His research focuses on enabling correct software development through programming languages, verification methods, and tools. Key areas include formal verification for Rust and Go programs (Prusti and Gobra projects), separation logic, security protocols, and distributed systems verification. Müller's work emphasizes practical verification techniques for real-world systems, including secure router implementations and smart contract verification. Recent research trends highlight advancements in hyperproperties, modular reasoning for iterators and closures in Rust, and formal validation of verification tools. His methodologies bridge theoretical foundations with industrial applications, addressing challenges in concurrency, memory safety, and security assurance. Notable contributions include the SCION internet architecture, the Prusti verifier for Rust, and formal verification frameworks for distributed systems. His work often integrates rigorous mathematical foundations with scalable software engineering practices.
Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.
Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement
Malvina Nissim is a leading researcher in computational linguistics and NLP at the University of Groningen's Department of Artificial Intelligence, with a focus on multilingual modeling, bias mitigation, and human evaluation frameworks. Key Contributions : Developed CALAMITA (Italian LLM benchmark), IT5 models for Italian language processing, and ReproHum framework for NLP evaluation reproducibility Research Pillars : Multilingual reasoning consistency, perspective-based text analysis, and figurative language modeling Her work spans activation steering techniques, cross-lingual transfer learning, and the creation of specialized language resources like the EurekaRebus dataset and MAGPIE idiom corpus. She pioneered methods for gender bias measurement in BERT and developed the SocioFillmore tool for perspective visualization. Recent publications explore model uncertainty as MCQ difficulty proxy, Italian headline generation benchmarks, and multilingual multi-figurative language detection. She actively participates in teaching initiatives like the "NLP with Bracelets" workshop for Italian high school students. Scientific Awards : ACL Best Paper Award (2025) EMNLP Outstanding Reviewer (2023) EVALITA Leadership Recognition (2024) She advises PhD students in model bias analysis and has contributed to the development of the Dutch Abusive Language Corpus (DALC) and the ReproNLP reproducibility framework. Her collaborations span institutions in Italy, Netherlands, and international NLP communities.
Christoph Müller is a Full Professor of Energy Science and Engineering at ETH Zürich's Department of Mechanical and Process Engineering. He leads the Laboratory of Energy Science and Engineering, focusing on sustainable energy generation, heterogeneous catalysis, and granular systems. His research integrates experimental methods like Magnetic Resonance Imaging (MRI) and Discrete Element Modelling (DEM) with mathematical modeling to address industrial energy challenges. Education: Dipl.-Ing. from Technical University of Munich (2004), PhD in Chemical Engineering from the University of Cambridge (2008). Notable awards include the Danckwerts-Pergamon Prize (2009) and DAAD Scholarship (2005). He teaches courses such as Thermodynamics I and Thermo- and Fluid Dynamics. Research interests span CO₂ capture via chemical looping, catalytic hydrogenation, and granular flow dynamics. Recent work explores catalyst design for propane dehydrogenation, MXene-based ammonia synthesis, and MgO-based CO₂ sorbents. His lab employs advanced techniques like operando X-ray absorption spectroscopy to study catalyst behavior under reaction conditions. Key achievements include developing stable PtGa propane dehydrogenation catalysts and advancing understanding of Na₂CO₃-promoted CO₂ sorbents. His work on fluidized bed hydrodynamics via MRI contributes to reactor design optimization. Müller's interdisciplinary approach bridges fundamental science and industrial application, addressing global energy sustainability challenges.
Francesca Da Lio is a Professor at the Department of Mathematics, ETH Zurich, where she has held a titular professorship since 2014. Her research focuses on nonlinear elliptic and parabolic partial differential equations (PDEs), with applications in stochastic and deterministic optimal control, homogenization, front propagation, and geometric analysis. She has pioneered work on conformally invariant variational problems and nonlocal PDEs, including fractional harmonic maps and stability analysis for critical points. PhD in Mathematics (1998) and Summa Cum Laude Degree in Mathematics (1994) from University of Padova. Her research explores the interplay between nonlinearity and non-locality, particularly in problems arising from geometry, mathematical finance, and physics. She has led major Swiss National Fund (SNF) projects, including grants for geometric analysis and conformally invariant variational theory. Her work on 3-commutators, integrability by compensation, and Morse index stability has advanced the understanding of harmonic maps and elliptic systems. Francesca Da Lio has mentored numerous PhD, postdoctoral, and Master/Bachelor students, including Dominik Schlagenhauf, Jerome Wettstein, and Ali Hyder. She has served on hiring committees for full professorships at ETH Zurich and co-organized international conferences such as 'Recent Advances in Nonlocal and Nonlinear Analysis' and 'Topics in Sub-Elliptic PDEs.' Scientific Awards: Italian Scientific Qualification as Full Professor in Mathematical Analysis (2013). She contributes to editorial boards, including Advances in Calculus of Variations , and participates in academic services like refereeing for SNF projects and international journals.
Yuning Jiang is a Visiting Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Automatic Control Laboratory (LA3) within the School of Engineering (STI). He teaches the doctoral course Optimal Control for Dynamic Systems and contributes to research in distributed optimization, model predictive control (MPC), and smart grid technologies. His work bridges theoretical advancements in control systems with practical applications in power networks and autonomous systems. Current research emphasizes scalable solutions for AC optimal power flow, real-time MPC for embedded systems, and robust optimization under uncertainty. His research interests span Optimal Control , Power Systems , Smart Grids , and Federated Learning . Notable contributions include distributed algorithms for large-scale power systems and privacy-preserving co-simulation frameworks. Recent publications focus on microservice deployment in satellite-terrestrial networks and real-time pricing mechanisms for vehicle-to-grid (V2G) integration. Yuning holds a position in the EDEE-ENS unit under EPFL’s Academic Affairs division (VPA-AVP-DLE), reflecting his role in academic administration and teaching infrastructure. His lab, the Automatic Control Laboratory, focuses on cutting-edge research in control theory and its interdisciplinary applications.
Boris Buffoni is a Senior Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences, Institute of Mathematics, specifically within the Chair of Partial Differential Equations. He maintains his office at MA C2 605 (MA Building), Station 8, 1015 Lausanne, Switzerland, and can be contacted at boris.buffoni@epfl.ch or +41 21 693 49 87. His academic role spans both teaching responsibilities across multiple mathematics programs and active research in theoretical and applied mathematics. Dr. Buffoni's research program centers on the calculus of variations applied to Lagrangian and Hamiltonian systems, with significant contributions to optimal transportation in Lagrangian dynamics and hydrodynamics. His work explores semi-global minimization methods for quasi-linear elliptic variational problems and the variational approach to capillary-gravity water waves and their energetic stability. Additional research foci include local bifurcation and center-manifold theory for elliptic PDEs, the configurations of infinite elastic cylinders under compression or traction, and the analytic theory of global bifurcation with applications to gravity waves and their secondary bifurcations. The trajectory of his recent publications reveals a deepening focus on three-dimensional water wave phenomena, particularly steady rotational flows, gravity-capillary solitary waves, and advanced mathematical techniques for analyzing these complex systems. His 2025 publications demonstrate continued innovation in applying Kato's approach to locally coercive problems and developing the theoretical foundations of global bifurcation. The consistent application of variational methods and bifurcation theory across his work represents a unifying theme in addressing challenging problems in fluid dynamics and nonlinear partial differential equations. Dr. Buffoni has received research support including an EPSRC grant (GR/L41059) for work on 'Multibump localised solutions for spatially homogeneous partial differential equations,' reflecting the significance of his contributions to the field. His teaching portfolio at EPFL includes foundational courses such as Analysis II, Functional Analysis I, and Partial Differential Equations of Evolution, where he imparts knowledge of differential and integral calculus of real functions of several variables, linear functional analysis, and fundamental techniques for solving evolution equations.
Nargiz Humbatova is a postdoctoral researcher at the Testing Automated (TAU) research group within the Software Institute (SI) at Università della Svizzera italiana (USI). She holds a PhD from USI (2023), an MSc in Advanced Computing from the University of Bristol, and a BSc in Mathematics from Moscow State University. Her research focuses on mutation testing of deep learning systems, fault localization, and program repair, with a particular emphasis on real-world fault analysis and testing methodologies for AI systems. Education: PhD in Informatics, Università della Svizzera italiana (2023) MSc in Advanced Computing, University of Bristol BSc in Mathematics, Moscow State University Research interests include mutation testing techniques, test input prioritization, deep learning fault benchmarks, and the application of large language models (LLMs) to fault localization and repair. She has contributed to the ERC-AdG project PRECRIME, dedicated to testing AI-based systems. Her work emphasizes practical validation through empirical studies and real-world fault injection. Her publications span topics such as mutation testing pipelines (e.g., muPRL), spectral analysis of neural activation values, and the development of tools like DeepCrime for deep learning testing. These articles highlight advancements in evaluating and improving the robustness of AI systems through rigorous testing frameworks. Labs/Teams: Member of the TAU (Testing Automated) research group at USI’s Software Institute, collaborating on interdisciplinary projects at the intersection of software engineering and artificial intelligence.
David Steurer is an Associate Professor in the Department of Computer Science at ETH Zurich, where he leads the Institute for Theoretical Computer Science. His research bridges theoretical computer science, mathematics, and practical applications in machine learning and statistics. Steurer has made significant contributions to the understanding of sum-of-squares methods, semidefinite programming, and high-dimensional estimation problems. Steurer earned his B.Sc. & M.Sc. in Computer Science from Saarland University in 2006, followed by a Ph.D. in Computer Science from Princeton University in 2010 under the supervision of Sanjeev Arora. His doctoral dissertation, "On the Complexity of Unique Games and Graph Expansion," received an Honorable Mention for the ACM Doctoral Dissertation Award in 2011. Steurer's research focuses on algorithm design through mathematical programming relaxations, particularly semidefinite programming and the sum-of-squares method. His work addresses computational complexity of high-dimensional estimation problems including tensor decomposition, clustering, Gaussian mixture models, and stochastic block models. He also investigates algorithmic aspects of robustness and differential privacy, especially for estimation tasks. His theoretical contributions have practical implications for machine learning and data analysis in the presence of noise and adversarial conditions. Analysis of Steurer's recent publications reveals a consistent trajectory in advancing the sum-of-squares framework for high-dimensional estimation and robust statistics. His work demonstrates how sophisticated mathematical programming techniques can provide certifiable guarantees for challenging problems in machine learning. The research spans theoretical foundations while maintaining relevance to practical applications, particularly in scenarios involving corrupted or noisy data. A notable trend is the extension of sum-of-squares methods to handle increasingly complex statistical models while providing rigorous performance guarantees. ERC Consolidator Grant (2019) Michael and Sheila Held prize (2018, with Raghavendra) Invited speaker at International Congress of Mathematicians (2018) STOC Best Paper Award (2015) Alfred P. Sloan Research Fellowship (2014) NSF CAREER Award (2014) Microsoft Research Faculty Fellowship (2014) FOCS Best Paper Award (2010) Steurer has advised numerous PhD students and postdocs who have gone on to prominent positions, including Sam Hopkins (now faculty at MIT), Jonathan Shi, and Aaron Potechin. His research is supported by multiple prestigious grants, including an ERC Consolidator Grant, an NSF CAREER Award, and a Microsoft Research Faculty Fellowship. He has served on numerous program committees for top theoretical computer science conferences and has been recognized for his service to the community through roles such as internal member of the Scientific Advisory Committee of the Institute for Theoretical Studies at ETH Zurich. As head of the Institute for Theoretical Computer Science at ETH Zurich, Steurer leads a research group focused on advancing the theoretical foundations of computer science with particular emphasis on mathematical optimization methods. His team explores the boundaries of what can be efficiently computed in high-dimensional settings, with applications spanning machine learning, statistics, and data science. The group maintains strong connections with both theoretical and applied researchers across ETH and the broader academic community.
Florent Krzakala is a Full Professor at École polytechnique fédérale de Lausanne (EPFL) in Switzerland, holding positions across multiple departments including the School of Basic Sciences (SB), School of Engineering (STI), and specifically within the Department of Physics (IPHYS) and Department of Electrical Engineering (IEM). He leads the Information, Learning and Physics Laboratory (IdePHICS) and maintains an office at ELD 239, Station 11, 1015 Lausanne. His research bridges statistical physics and computational disciplines, with significant contributions to understanding the theoretical foundations of machine learning and optimization problems. Dr. Krzakala received his MSc in Physics from Orsay, France in 1999, followed by a PhD in Statistical Physics from Orsay, Paris XI, France in 2002, and completed a postdoctoral position at Roma La Sapienza in 2004. This strong foundation in physics has informed his interdisciplinary approach to computational problems. His research interests span Statistical Physics, Machine Learning, Probability and Statistics, Computer Science, Information Theory, Inference on Graphs, Random Constraint Optimization, and Computational Optics. Krzakala's work focuses on applying methods from statistical physics to problems in theoretical computer science, probability, and machine learning. He investigates how concepts from disordered systems and phase transitions can illuminate computational barriers in optimization and inference tasks. His research has particular relevance for understanding the behavior of neural networks, compressed sensing, and high-dimensional statistical models. Analysis of his recent publications reveals a strong trend toward understanding the fundamental limits of learning in high-dimensional settings, with particular emphasis on phase transitions, statistical-to-computational gaps, and the theoretical properties of deep learning architectures. His work frequently bridges rigorous mathematical analysis with practical machine learning applications, demonstrating how insights from statistical physics can inform algorithm design and theoretical understanding in AI. Krzakala actively mentors the next generation of researchers, supervising numerous PhD students whose work continues to advance these interdisciplinary fields. His laboratory serves as a hub for researchers exploring the intersection of physics and computation, fostering collaborations across traditional disciplinary boundaries. He teaches advanced courses including Fundamentals of Inference and Learning, Statistical Physics, and Statistical Physics for Optimization & Learning, which examine the connections between physical principles and computational methods. His educational materials, including lecture notes on statistical physics methods in optimization and machine learning, have become valuable resources for students and researchers worldwide. As founder and scientific advisor of the startup Lighton, Krzakala has also demonstrated a commitment to translating theoretical insights into practical applications, particularly in the realm of optical computing for machine learning tasks.
Myrto Limnios is a Bernoulli Instructor at the Institute of Mathematics at École Polytechnique Fédérale de Lausanne (EPFL), holding dual appointments as Lecturer in the School of Basic Sciences - Mathematics Section and Scientist in the Mathematics Department - Geometry section. Her office is located at CM 1 618 (Centre Midi), Station 10, 1015 Lausanne, Switzerland. Dr. Limnios's academic journey began with her PhD at Centre Borelli, ENS Paris-Saclay, Université Paris-Saclay, under the supervision of Prof. Nicolas Vayatis and Ioannis Bargiotas. Her doctoral thesis, Rank Processes and Statistical Applications in High Dimension , established her expertise in statistical methodology. Following her PhD, she completed a postdoctoral fellowship at the Copenhagen Causality Lab of the University of Copenhagen. Her research program focuses on nonparametric statistics, statistical learning theory, and stochastic processes with biomedical applications. She specializes in causal learning methods for event processes using conditional local independence testing (collaborating with Niels Richard Hansen) and concentration results for k-sample Rank and U-processes (with Prof. Stephan Clémençon of Télécom Paris). Her work bridges theoretical advances with practical implementations in epidemiology, neuroscience, and medical diagnostics. Analysis of her recent publications reveals a strong trend toward developing ranking-based statistical methods with applications to anomaly detection, two-sample testing, and causal inference. Her work demonstrates increasing sophistication in handling high-dimensional event processes while maintaining theoretical rigor in statistical guarantees. Among her notable achievements is the Bernoulli Instructorship at EPFL and organizing the Women in Mathematics conference at EPFL in May 2025, which hosted over 60 participants from Switzerland, France, and Spain. She was also honored with a research visit to the Simons Institute for the Theory of Computing at UC Berkeley in November 2024, hosted by Prof. Peter Bartlett. Dr. Limnios teaches advanced statistical methods at EPFL, including Regression Methods (covering linear regression, analysis of variance, diagnostics, and variable selection) and Empirical Processes (focusing on controlling nonasymptotic behavior of estimator collections). Her GitHub activity shows active development of statistical software for independence testing and anomaly ranking. She maintains active collaborations with the Copenhagen Causality Lab, Télécom Paris, and biomedical research groups, particularly through her work on postural control analysis for Parkinsonian syndromes and epidemiological modeling of infectious diseases.
Christian Hilbes is a Lecturer at the School of Engineering of Zurich University of Applied Sciences (ZHAW). He serves as Deputy Head of the Institute for Applied Mathematics and Physics (IAMP) and co-leads the research focus on Safety-Critical Systems. Active projects include leadership roles in Autonomous Predictive Interlock Systems , Personnel Safety Systems , and Triggering Conditions for Autonomous Cars . He specializes in STPA (Systems-Theoretic Process Analysis) , UML-based modeling , and Dynamic Flowgraph Modeling for safety-critical applications. Email: christian.hilbes@zhaw.ch His research integrates safety analysis with emerging technologies like autonomous systems and nuclear facilities, contributing to publications at MIT STAMP Workshops and in journals like Nuclear Engineering and Design . He actively collaborates on European Spallation Source (ESS) safety frameworks and develops domain-specific languages for STPA.
Christoph Hertrich is a tenure-track professor for Applied Discrete Mathematics at University of Technology Nuremberg, where he conducts research at the intersection of discrete mathematics, theoretical computer science, and machine learning. His work particularly focuses on applying polyhedral geometry and combinatorial optimization techniques to neural network theory, with significant contributions to understanding the computational complexity and expressivity of neural networks. Hertrich received his BSc and MSc degrees from TU Kaiserslautern (2013-2018) working with Sven O. Krumke, followed by his PhD at TU Berlin (2018-2022) under the supervision of Martin Skutella. His doctoral thesis, titled "Facets of Neural Network Complexity," laid foundational work for his current research direction. Prior to joining UTN, he held postdoctoral positions at Université libre de Bruxelles (2023-2024) with a Marie Skłodowska-Curie fellowship under Samuel Fiorini, and at LSE London (2022-2023) with László Végh. He also served as a substitute professor for discrete mathematics at Goethe-Universität Frankfurt during the winter semester of 2023/24. Hertrich's research interests center on the mathematical foundations of neural networks, with particular emphasis on polyhedral geometry approaches. His work explores computational complexity questions related to neural network training and architecture, expressivity bounds, and connections to combinatorial optimization problems. He has made significant contributions to understanding the relationship between neural network depth and function representation, the complexity of counting linear regions in ReLU networks, and the application of extended formulations to neural network theory. His approach combines rigorous theoretical analysis with practical implications for neural network design and optimization. His recent publication record reveals a strong trend toward establishing fundamental theoretical limits and connections between deep learning and discrete mathematics. A significant portion of his work examines computational complexity of various neural network problems, often proving hardness results or establishing bounds on expressivity. He has also developed novel connections between polyhedral combinatorics and neural network architecture, demonstrating how techniques from operations research can inform deep learning theory. Marie Skłodowska-Curie fellowship Since February 2025, Hertrich has been supervising PhD student Moritz Stargalla at UTN. His research has been supported by prestigious fellowships including a Marie Skłodowska-Curie fellowship during his postdoctoral period in Brussels. He is organizing a workshop on "Polyhedral Geometry for Neural Networks" in March 2026 in Nuremberg, highlighting his leadership in this emerging interdisciplinary field.
