Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.
Ralf Herbrich serves as Professor of Computer Science at the University of Potsdam and Hasso Plattner Institute (HPI) since 2022, chairing the Artificial Intelligence and Sustainability research group. With 25 years of combined industry and academic experience, he previously held research leadership roles at Microsoft, Facebook, Amazon, and Zalando. His educational foundation includes a Diploma in Computer Science (1997) and PhD in Theoretical Statistics (2000), both from Technical University of Berlin. Herbrich's research spans 17 interconnected AI domains: Approximate Computing Bayesian Inference and Decision Making Game Theory Information Retrieval Natural Language Processing Computer Vision Distributed Systems Learning Theory Knowledge Representation and Reasoning Computer Gaming Computational Advertising Recommender Systems Network Science Battery Management Kernel Methods Knowledge Bases Machine Learning Theory He champions a people-first methodology emphasizing real-world problem solving through 'working backwards' and 'customer-obsession', with over 80 peer-reviewed publications across these fields. His leadership prioritizes researcher development over project outcomes and favors scientific dialogue over solitary study. Herbrich actively mentors researchers within his AI and Sustainability group but specific advisee names aren't documented in source materials. His team focuses on applying rigorous research to large-scale sustainability challenges through principled engineering approaches.
Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.
Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.
PD Dr. Daniel Werner Meyer-Massetti is a Privatdozent (Part-Time Lecturer) at the Department of Mechanical and Process Engineering , ETH Zürich. His research focuses on stochastic methods for fluid dynamics and multiphase transport problems in complex systems. Primary Affiliation: ETH Zürich, Department of Mechanical and Process Engineering Email: meyerda@ethz.ch His work bridges theoretical and applied research in turbulence, porous media, and combustion. Key contributions include: Stochastic particle-based frameworks for fractured subsurface flows Turbulence modulation in droplet-laden flows Uncertainty quantification in heterogeneous reservoirs Computational tools like the Netflow Python library His recent publications demonstrate methodological advancements in: Modeling inertial particle clustering in turbulence Simulating evaporation dynamics in reactive flows Developing non-local transport formulations Quantifying dispersion mechanisms in porous media Validating kinematic turbulence models Creating adaptive simulation strategies He collaborates with research groups including the Coletti Group , Jenny Group , and Supponen Group , while maintaining connections to the Haller Group and Noiray People as a former member.
Prof. Dr. Rebecca Buller is a leading researcher at the ZHAW School of Life Sciences and Facility Management , specializing in biocatalysis and enzyme engineering. Her work bridges computational methods with industrial applications, focusing on sustainable chemical synthesis and food safety. Research Interests : Biocatalysis, enzyme design, halogenation reactions, mycotoxin reduction, 3D bioprinting inks, machine learning-driven enzyme optimization. Key Projects : National Competence Center of Research Catalysis , Microbial Epimerases for Drug Synthesis , Reduction of Mycotoxins in Food Streams , and Excelzyme (university-industry collaboration). Publications highlight advancements in: Temperature-induced enzyme stabilization (2025) Computational protein design (2024) Mutational path enrichment (2024) Mycotoxin detoxification (2024) Industrial biocatalysis (2023-2024) Collaborations span institutions like the University of Basel, ETH Zurich, and industry partners in Switzerland. Her work integrates computational analysis, directed evolution, and real-world applications in pharmaceuticals and food safety.
Paul Schneider is a Full Professor in the Faculty of Economic Sciences at the University of Italian Switzerland (USI), where he has been a faculty member since 2012. He is affiliated with the Institute of Finance (IFin) and the Euler Institute (EUL), contributing to interdisciplinary research in quantitative finance and econometrics. His research focuses on financial econometrics, asset pricing, and statistical methods in finance, with an emphasis on extracting latent market information under minimal assumptions. He integrates techniques from engineering, mathematics, and data science to develop robust models for financial markets. His work spans risk premia, ambiguity in investment decisions, nonlinear pricing, and model-free recovery methods. His recent publications (2023–2024) in journals such as Review of Finance , Management Science , and SIAM Journal on Mathematics of Data Science highlight trends in adaptive learning, empirical scenario generation, constrained likelihood estimation, and optimal investment under ambiguity . These reflect a strong focus on data-driven, computationally efficient, and theoretically sound approaches to financial modeling. Adaptive joint distribution learning Fast empirical scenarios Optimal Investment under Ambiguity Constrained polynomial likelihood Dispersion of Beliefs and Sentimental Recovery Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: While no formal list of advisees is provided, Paul Schneider has collaborated extensively with researchers such as Damir Filipovic, Fabio Trojani, and Christian Wagner, suggesting a strong mentorship and collaborative role. He has contributed to funded research projects, particularly in financial modeling and econometrics, though specific grant names are not detailed. Labs and Research Teams: He is actively involved with the Institute of Finance (IFin) and the Euler Institute at USI, which support interdisciplinary research in finance, mathematics, and data science. He has also developed computational tools such as the KDM R package for kernel density machines, indicating engagement with data science and open research practices.
Masashi Sugiyama is a Professor at the Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, where he has been serving since 2014. He concurrently serves as the Director of the RIKEN Center for Advanced Intelligence Project (AIP) since 2016, leading research groups focused on fundamental AI technologies, AI applications, and social issues of AI. His academic journey began at Tokyo Institute of Technology, where he earned his Bachelor, Master, and Doctor of Engineering degrees in Computer Science in 1997, 1999, and 2001 respectively, before becoming an Assistant Professor and later Associate Professor at the same institution. Sugiyama's research primarily focuses on statistical machine learning, with particular expertise in weakly supervised learning, learning from noisy supervision, and learning under distribution shift. His work has established important theoretical frameworks for density ratio estimation, covariate shift adaptation, and non-stationary environments. He has developed numerous practical algorithms including KLIEP (Kullback-Leibler importance estimation procedure), uLSIF (unconstrained least-squares importance fitting), and LSDD (least-squares density difference), which have become standard tools in the machine learning community. Analysis of his recent publications reveals a strong emphasis on reliable and robust machine learning techniques that can handle imperfect supervision and data distribution changes. His work spans theoretical foundations, algorithm development, and practical applications across various domains including bioinformatics, anomaly detection, and dimensionality reduction. The recurring themes in his research include direct density ratio estimation methods, importance weighting techniques, and mutual information-based approaches for various machine learning tasks. Award for Science and Technology from the Japanese Minister of Education, Culture, Sports, Science and Technology (2022) Japan Academy Medal (2017) Japan Society for the Promotion of Science Award (2017) Faculty Award from IBM (2007) Nagao Special Researcher Award from the Information Processing Society of Japan (2011) Young Scientists' Prize for the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan (2014) Sugiyama has served as program co-chair for major machine learning conferences including NeurIPS 2015, AISTATS 2019, and ACML 2010 and 2020. He has received prestigious fellowships including the Alexander von Humboldt Foundation Research Fellowship (2003-2004) and the European Commission Program Erasmus Mundus Scholarship (2006). He is the (co-)author of influential machine learning monographs including Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman & Hall, 2015), and Machine Learning from Weak Supervision (MIT Press, 2022). At RIKEN AIP, Sugiyama leads research initiatives focused on developing robust AI systems that can operate reliably in real-world conditions with imperfect data. His laboratory maintains an active software development effort, providing open-source implementations of many of his research contributions, which has significantly influenced both academic research and practical applications of machine learning.
Victor Kristof is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the INDY2 laboratory. His work spans interdisciplinary domains, combining Natural Language Processing (NLP) , Machine Learning , and Social Process Modeling to analyze legislative dynamics, vote prediction, and environmental perception. Research Focus: Kristof develops interpretable models for democratic transparency, including aligning interest group positions with parliamentary speeches. He pioneered methods for predicting legislative edit acceptance using matrix factorization and NLP. His work on Swiss referendum prediction integrates historical data with real-time analysis via the Predikon platform . Methodological Contributions: He applies Bayesian statistics , time-dynamic pairwise comparison models , and active learning algorithms to diverse problems, from carbon footprint perception to sports analytics. His War of Words framework reveals ideological patterns in EU law-making, while his Player Kernel model improves football match prediction. Labs & Collaborations: Based at EPFL's Laboratory of Dynamic Information and Networks (INDY2) , he collaborates with researchers like Matthias Grossglauser and Patrick Thiran. His datasets on legislative edits and carbon perception have advanced transparency studies.
Dr. Anne Christine Obermann is a Senior Researcher and Lecturer at the Swiss Seismological Service (SED), ETH Zurich. She holds a PhD in seismology from ISTerre (Grenoble, France), completed in 2013 under Professors Michel Campillo and Eric Larose. Her research focuses on seismic monitoring techniques, including ambient noise interferometry and coda wave analysis, with applications to volcanic eruptions, earthquake precursors, and crustal deformation studies. She has applied these methods to case studies such as Piton de la Fournaise volcano in Réunion Island and the 2008 Wenchuan earthquake in China. Education: PhD in Seismology (ISTerre, Grenoble, 2013) Key Techniques: Probabilistic inversion for crustal deformation localization, numerical modeling of coda wave sensitivity Her work bridges numerical analysis and field applications, addressing challenges in resolving subsurface changes linked to hazardous events. Recent studies include 3D sensitivity kernel implementations for concrete fracturing analysis under tension.
Lénaïc Chizat is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) within the School of Basic Sciences and Institute of Mathematics. He chairs the Dynamics of Learning Algorithms (DOLA) laboratory and teaches advanced courses in machine learning and computational optimal transport, focusing on mathematical analysis of neural networks and measure transportation theory. His research centers on optimal transport theory and its applications to deep learning, with emphasis on Wasserstein geometry, entropic regularization, and gradient flow dynamics. He investigates implicit regularization in neural networks, convergence properties of learning algorithms, and the infinite-width limits of deep architectures. His work bridges theoretical mathematics with practical machine learning challenges, particularly in computational aspects of modern supervised learning. Analysis of his 15 most recent publications (2023-2025) reveals a strong thematic focus on entropic optimal transport, where he has made fundamental contributions to Sinkhorn algorithm convergence in continuous settings and Wasserstein barycenter computation. His research consistently explores the mathematical foundations of deep learning, especially training dynamics, min-max optimization, and the role of initialization in neural network scaling. Chizat currently advises PhD student Wang Guillaume Yitian and leads the DOLA laboratory, which develops theoretical frameworks for understanding learning algorithm dynamics through the lens of optimal transport and measure-valued optimization.
Mark Cieliebak is a Professor for Speech and Text Processing at the Zurich University of Applied Sciences (ZHAW), School of Engineering, where he leads the Natural Language Processing (NLP) research group at the Centre for Artificial Intelligence. He has held this position since December 2018, following prior roles as a lecturer at ZHAW (2012-2018). His educational background includes a Dr. sc. techn. in Computer Science from ETH Zürich (1999-2003) and a Diplom in Computer Science from Universität Dortmund (1990-1999). His research focuses on Natural Language Processing , Text and Speech Analysis , and Machine Learning applications . Primary domains include dialogue systems evaluation, low-resource speech recognition (particularly Swiss German), automated text generation assessment, and NLP for social media analysis. He also explores educational applications through flipped classroom methodologies and machine learning operations. Cieliebak's publications (2020-2024) demonstrate strong emphasis on NLP evaluation frameworks, speech corpus development for dialects, and generative AI validation. Recurring themes include robustness testing, bias mitigation in automated metrics, and multilingual processing challenges. His work frequently involves large-scale dataset creation and ensemble learning techniques. Scientific Awards: SwissNLP Award 2023 Best Paper Award - Honorable Mention at EMNLP 2020 ZHAW Teaching Award 2015 He leads numerous research projects including: Unified Model for Text Generation Evaluation End-to-End Swiss German Speech Translation Holistic Analysis of Misinformation in Social Networks Automated News Processing (AutoNews) Chatbot Development for Language Learners He co-founded SpinningBytes AG (2015-present) and maintains extensive industry collaborations. As head of the NLP research group, he oversees doctoral candidates and master's students, though specific advisee names aren't listed.
Prof. Dr. Aurelien Lucchi is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Basel, Faculty of Science. His research group focuses on the intersection of optimization and machine learning, particularly in advancing theoretical understanding and algorithmic design for deep learning systems. His research interests include: Stochastic and non-convex optimization Deep learning theory and generalization Kernel methods and spectral analysis Transformer architectures and training dynamics Batch Normalization and initialization effects Modeling optimization via stochastic differential equations (SDEs) The recent publications (2023–2025) highlight a strong focus on theoretical machine learning, especially in characterizing optimization landscapes, generalization in kernel methods, and the role of noise and adaptive methods in training. There is a clear trend toward using advanced mathematical tools—such as random matrix theory, SDEs, and curvature analysis—to explain phenomena in deep learning. His group actively publishes in top venues including NeurIPS, ICML, ICLR, and AISTATS. Scientific awards and recognitions include: SNF Consolidator Grant (1.7M CHF) Prof. Lucchi leads an active research group with postdoctoral fellows and ongoing projects, including work on quantum machine learning and noise-adaptive optimization. He has secured competitive research funding and mentors early-career researchers. His group has received recent paper acceptances at ICLR 2025, AISTATS 2025 (oral), and NeurIPS 2024, indicating strong momentum in theoretical and algorithmic machine learning. He previously held a scientific research position at ETH Zurich (2014–2021) and earned his PhD from EPFL. The group is currently involved in two major ongoing projects: Designing and Training Hybrid Hierarchical Quantum Neural Networks with Quantum Advantage Noise-Adaptive Optimization Methods and their Robustness Properties
Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.
Jonathan Dong is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) within the School of Engineering. He works at the Biomedical Imaging Laboratory (LIB), focusing on advanced imaging techniques and computational models. PhD Students: Hu Zhiyuan, Liu Yan His research spans biomedical imaging, computational optics, and machine learning applications in imaging inverse problems. Recent work emphasizes phase retrieval, optical reservoir computing, and quantum information in microscopy. Key article trends show expertise in MRI classification , optical tomography , deep learning for imaging, and scattering media analysis . Collaborations include technical development in optoacoustics and super-resolution microscopy. Contact: jonathan.dong@epfl.ch | Office: BM 4141, EPFL, Station 17, Lausanne
