Marco Bagatella is a doctoral researcher at the Institute of Machine Learning (ETH Zürich), focusing on advanced machine learning and artificial intelligence research. His work spans reinforcement learning, behavioral cloning, and causal inference, often addressing challenges in generalization, exploration, and policy adaptation. Contact: marco.bagatella@inf.ethz.ch Specializes in Reinforcement Learning (including offline and multi-task settings) Expertise in causal modeling and counterfactual data augmentation Investigates graph neural networks for biological systems Active contributor to AI/ML publications (15+ recent works) His research explores problem space transformations, optimal transport for zero-shot imitation learning, and temporal logic-based exploration. Current projects focus on improving policy robustness and adaptability across diverse domains. Scientific awards: No formal honors listed yet. Collaborates with ETH Zurich's machine learning teams on cutting-edge algorithm development and theoretical analysis.
Prof. Stelian Coros is an Associate Professor at the Department of Computer Science, ETH Zürich, and Head of the Institute for Intelligent Interactive Systems. His research focuses on robotics, computational design, and control systems, with applications in robotic manipulation, simulation, and autonomous systems. His work integrates principles from computer science, mechanical engineering, and artificial intelligence to advance the capabilities of robots in real-world environments.
Alexandre Alahi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Visual Intelligence for Transportation (VITA) laboratory. He is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), the Institute of Infrastructure (IIC), and also contributes to diversity initiatives at ENAC. His research focuses on integrating computer vision, machine learning, and robotics to develop socially-aware AI for transportation and autonomous systems. University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Architecture, Civil and Environmental Engineering Department: Institute of Infrastructure, IIC Research Lab: Visual Intelligence for Transportation (VITA) Alexandre's research interests center on computer vision, machine learning, robotics, and AI safety, particularly in human trajectory prediction, depth estimation, and socially-aware autonomous navigation. He investigates how AI can understand and predict human behavior in complex environments to improve safety in mobility systems. His work bridges theoretical advances with real-world applications in autonomous driving, urban planning, and healthcare. His recent publications span a wide array of topics including omnidirectional stereo matching, trajectory forecasting, cross-view localization, AI security, and depth estimation. These works demonstrate a strong trend toward building generalizable, robust, and socially-compliant AI systems, with increasing focus on uncertainty quantification, safety certification, and real-world deployment. The integration of multimodal data and the development of foundation models are recurring themes. Alexandre has received numerous scientific accolades, including: Top 100 Most Influential Scholar in Computer Vision (2022–2023) Editor’s Choice Award, Image and Vision Computing (2021) Honorable Mention, ICCV Workshop (2019) CVPR Open Source Award (2012) ICDSC Challenge Prize (2009) Top 20 Swiss Venture Leaders (2010) He has advised numerous PhD students whose theses cover diverse topics such as human motion prediction, person re-identification, trajectory forecasting, and AI security. His lab has secured significant recognition and funding, enabling impactful research with real-world applications. Alexandre has also co-founded startups like Visiosafe, demonstrating strong industry engagement and technology transfer. The VITA lab fosters interdisciplinary collaboration, working across computer vision, robotics, transportation engineering, and human-centered AI. The team develops datasets, benchmarks, and open-source tools to advance the field and promote reproducibility.
Marc Langheinrich is a Full Professor and Dean at the Faculty of Computer Science, Università della Svizzera italiana (USI), where he also leads the Research Group for Ubiquitous Computing. His academic journey includes a PhD from ETH Zurich and prior roles at NEC Research and the University of Washington. He is affiliated with the Institute of Information Systems (SYS) at USI. Education: PhD (Dr. sc.) in Computer Science, ETH Zurich, Switzerland (2005) Master’s degree (Dipl.-Inf.) in Computer Science, University of Bielefeld, Germany (1997) Marc Langheinrich's research centers on privacy, security, and usability in ubiquitous and pervasive computing environments. His work explores how communication protocols, user interfaces, and system designs can protect personal data while enabling seamless interactions with smart devices. Key interests include lifelogging, memory augmentation, IoT privacy, and ethical design frameworks. He investigates technical and human factors in privacy-aware systems, often integrating insights from human-computer interaction and social computing. His recent publications demonstrate a strong focus on privacy-preserving machine learning (e.g., federated learning), mobility modeling, affective computing, and dataset development (e.g., LAUREATE). The research spans theoretical frameworks and practical implementations, often involving wearable sensing, multimodal data, and real-world user studies. Scientific Awards: Best Paper Award at DIS '18 for Roaming Objects: Encoding Digital Histories of Use into Shared Objects and Tools Best Paper Honorable Mention at MUM '16 for Design and evaluation of a wearable AR system for sharing personalized content on ski resort maps Marc Langheinrich has supervised numerous PhD and Master’s students, including Anton Fedosov, Evangelos Niforatos, and Matias Laporte. He has led multiple funded research projects in privacy, IoT, and human-computer interaction. He serves as Editor-in-Chief of IEEE Pervasive Magazine and on the steering committee of the IoT Conference Series, reflecting his leadership in the academic community. He leads the Research Group for Ubiquitous Computing at USI, which focuses on privacy-aware systems, digital memory augmentation, and interactive public displays. The group develops technologies that enhance user control over personal data while enabling novel applications in smart environments.
Dr. Thomas Möllenhoff is a post-doctoral researcher at RIKEN AIP's Approximate Bayesian Inference Team. He earned his PhD in Informatics from the Technical University of Munich in 2020, where he focused on nonconvex optimization methods for image processing and computer vision. Education: PhD in Informatics (Technical University of Munich, 2020) His research bridges Bayesian principles with deep learning advancements. Recent work involves Sharpness-aware minimization (SAM) and uncertainty estimation in neural networks. He received recognition for his contributions to computer vision (CVPR 2016) and Bayesian deep learning (NeurIPS 2021 challenge). Scientific Awards: Best Paper Honorable Mention at CVPR 2016 First-place at NeurIPS 2021 Challenge on Approximate Inference in Bayesian Deep Learning
Emtiyaz Khan is a Research Professor and Team Leader of the Adaptive Bayesian Intelligence team at the RIKEN Center for Advanced Intelligence Project (AIP) in Japan. His research focuses on developing AI systems that can autonomously learn to perceive, act, and reason throughout their lives, bridging the gap between how living beings and computers learn. Dr. Khan's research interests span multiple areas of machine learning including: Approximate inference and Bayesian statistics Deep learning and neural network optimization Reinforcement learning and active learning Information geometry and signal processing Online learning and continual adaptation His recent work has focused on developing theoretically grounded Bayesian methods for deep learning, with applications in computer vision and decision-making systems. Dr. Khan has made significant contributions to variational inference, Bayesian optimization, and continual learning frameworks that enable AI systems to retain knowledge while learning new tasks. Dr. Khan has received substantial research funding including: (2021-2026, USD 2.23 Million) JST-CREST and French-ANR's grant, The Bayes-Duality Project (2020-2023, USD 167,000) KAKENHI Grant-in-Aid for scientific Research (B), Life-Long Deep Learning using Bayesian Principles (2020-2023, USD 11,000) KAKENHI Grant-in-Aid for Chellenging Research (Exploratory), Linear algebra for continuous learning of large neural networks (2019-2022, USD 237,000) External funding through companies for several Bayes related projects Dr. Khan is actively involved in the machine learning community, having served as General Chair for AISTATS 2026 and given keynotes at major conferences including Bayes Comp 2025 and the 1st EurIPS conference. His work has resulted in multiple papers accepted at top-tier conferences like NeurIPS, with some receiving spotlight presentations.
Alexander Hägele is a researcher at the Laboratoire d'apprentissage automatique et d'optimisation (Machine Learning and Optimization Laboratory, MLO) within the College of Computer and Communication Sciences at École polytechnique fédérale de Lausanne (EPFL). His work focuses on theoretical and practical aspects of machine learning optimization.
Paul Bürkner is a Full Professor of Computational Statistics at TU Dortmund University , focusing on probabilistic (Bayesian) methods. His research sits at the intersection of statistics and machine learning, with applications across quantitative sciences. Key Roles : Developer of the brms R package, member of the Stan and BayesFlow development teams. Research Pillars : Bayesian inference, uncertainty quantification, amortized workflows, simulation-based inference, and probabilistic programming. His lab advances methods for prior specification, model evaluation, and scalable inference, collaborating on applications from cognitive science to ecology. Recent work emphasizes neural superstatistics and BayesFlow for efficient mixture and multilevel models. Students and researchers are encouraged to reach out for collaboration or thesis opportunities. Key Labs/Teams : BayesFlow Development Team Stan Project ELLIS Network (European Laboratory for Learning and Intelligent Systems)
Martin Zach is a postdoctoral researcher at the Center for Biomedical Imaging (CIBM), EPFL , specializing in inverse problems in biomedical imaging. He joined the Mathematical Imaging Section under the supervision of Prof. Michael Unser in September 2024. PhD : Graz University of Technology (2024), advised by Thomas Pock Martin’s research bridges model-based reconstructions and data-driven approaches in imaging, with a focus on MRI reconstruction and diffusion models . His work explores regularization techniques, energy-based priors, and probabilistic modeling. Recent publications highlight advancements in Gaussian mixture models , non-linear inversion , and Langevin sampling for biomedical imaging. His Google Scholar profile reveals a strong focus on inverse problems (2020–2025), with applications in MRI , CT , and quantitative phase imaging . Key methodologies include diffusion models , generative priors , and regularization algorithms , spanning disciplines from machine learning to computational biology . Martin’s current role at EPFL involves collaborative research in mathematical imaging , with affiliations to the BioMedical Imaging (BIG) Group in Lausanne, Switzerland.
Francesco D'Angelo is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the Institute of Computer Science and Communications Systems (IINFCOM). He is part of the Theory of Machine Learning Laboratory (TML) and enrolled in the Doctoral program in computer and communication sciences (EDIC) under the EPFL Doctoral School (EDOC). His research focuses on foundational aspects of machine learning, including sparse attention mechanisms, Bayesian neural networks, uncertainty estimation, and continual learning. He holds the position of Doctoral Assistant at the TML lab, where he contributes to advancing theoretical and practical applications of machine learning algorithms. Research Interests: Francesco's work bridges theoretical computer science with applied machine learning. His key areas include developing robust models for uncertainty quantification, improving generalization in overparameterized networks through regularization techniques like weight decay, and exploring the interplay between transformer architectures and causal structure induction. He also investigates methods to mitigate catastrophic forgetting in continual learning scenarios and applies Bayesian approaches to enhance out-of-distribution detection capabilities. Publications: His recent work explores topics such as sparse attention mechanisms in transformers, the necessity of weight decay in deep learning optimization, and Bayesian methods for uncertainty estimation. These contributions highlight his expertise in both foundational theory and practical algorithm design within the machine learning domain. Labs & Teams: As part of the TML lab, he collaborates on interdisciplinary projects at the intersection of machine learning theory and applications, contributing to advancements in neural network architectures and probabilistic modeling techniques.
Dr. Amir Joudaki is a Researcher affiliated with the Department of Biomedical Informatics at ETH Zürich. His role is part of the Professorship for Data Analytics, focusing on interdisciplinary research at the intersection of machine learning and biomedicine. He is stationed at CAB F 53.1, Universitätstrasse 6, Zurich. His research interests span machine learning, biomedical data analytics, neural network theory, genomics, and bioinformatics algorithms. Notable contributions include work on deep neural network dynamics, batch normalization techniques, and graph-based genomic sequence analysis. Though not explicitly listed in the provided texts, his work suggests involvement in projects related to alignment-free bioinformatics methods, optimization in deep learning, and theoretical aspects of neural networks. Lab/Team Affiliation: Part of the Biomedical Informatics group at ETH Zürich, likely contributing to interdisciplinary data science initiatives in healthcare and genomics.
AmirEhsan Khorashadizadeh is a postdoctoral researcher affiliated with the Paul Scherrer Institute (PSI) and École Polytechnique Fédérale de Lausanne (EPFL) , working in the Computational X-ray Imaging group under Prof. Manuel Guizar Sicairos. His research integrates deep learning and computational imaging , focusing on physics-informed neural networks for applications in tomography , inverse scattering , and cosmological imaging . He collaborates with the Swiss Data Science Center (SDSC) on the CHIP project 'Machine-Learning-assisted Ptychographic nanotomography.' His work spans multiple domains, including medical imaging , Bayesian imaging , and astrophysical signal processing . Publications highlight advancements in scalable image reconstruction , injective flow-based models , and implicit neural representations , with applications in X-ray and cosmological imaging . Scientific Awards Promotion of Young Talent grant from University of Basel for 9-month visiting scholar at University College London (UCL) Top 25 most downloaded paper in IEEE Transactions on Computational Imaging (TCI) from Sept. 2022–Sept.2023
Dr. Timothé Krauth is a researcher at Zurich University of Applied Sciences (ZHAW) School of Engineering within the Aviation Infrastructure department. Holding a Ph.D. in Applied Mathematics from ISAE-Supaero (2021-2024), his work focuses on applying machine learning techniques to air traffic management challenges. He actively contributes to research projects like "Achieving Human-Machine Collaboration with Artificial Situational Awareness" and "Making I-CNS A Reality" for integrated aviation systems. Ph.D. in Applied Mathematics, ISAE-Supaero (2021-2024) His research interests center on aerospace engineering and artificial intelligence, specializing in trajectory modeling, collision risk estimation, and uncertainty quantification in air traffic systems. He has developed deep learning frameworks for flight path analysis and safety prediction. Krauth's publications demonstrate expertise in: Collision risk modeling using variational autoencoders Deep generative modeling for aviation safety Large-scale trajectory dataset creation Adaptive importance sampling techniques Mid-air collision probability assessment Terminal area traffic management As a team member in ongoing projects, he applies these methods to create safer and more efficient aviation systems through integrated communication, navigation, and surveillance technologies.
Roman Bachmann is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Visual Intelligence and Learning Lab (VILAB) within the School of Computer and Communication Sciences (IC). He holds a doctoral status as an Assistant-doctorant in the Programme doctoral en informatique et communications. His research focuses on advanced computer vision, machine learning, and AI-driven multimodal systems, with contributions to generative models, vision-language integration, and 3D scanning technologies. Key research interests include visual personalization (ViPer), multimodal learning (4M series), and scalable vision pipelines (Omnidata). His work bridges theoretical advancements with practical applications in embodied AI, robotics, and creative technologies. He is actively involved in the VILAB, contributing to projects that address challenges in cross-modal understanding and adaptive systems. Roman’s publications reflect a strong emphasis on innovation in AI, with recent work exploring flexible tokenization (Flextok) and task-agnostic vision models (4M-21). His research has implications for robotics, generative AI, and data-driven decision-making in complex environments.
Ernst-Jan Camiel Wit is a Full Professor of Statistics and Data Science at the Università della Svizzera italiana (USI), Lugano, Switzerland. He serves as Director of the Institute of Computing and Vice Dean of the Faculty of Informatics. He holds PhDs in Philosophy (1997, Penn State) and Statistics (2000, University of Chicago). His research focuses on high-dimensional inference, network modeling, and statistical network science with applications in biosciences, ecology, and social sciences. He led the European COST Action COSTNET (2015-2020), uniting 500 researchers across 34 countries. Currently, he directs SNSF projects on sparse network inference and innovation dynamics, and co-leads the EU Periscope project modeling pandemic side-effects. He advises the Dutch Ministry on electoral statistics and co-founded the Data Science and Systems Complexity Center at Groningen. Education: PhD in Philosophy, Penn State University, 1997 PhD in Statistics, University of Chicago, 2000 Research Interests: Network Data Science High-dimensional statistical inference Applications in ecology, epidemiology, and social sciences Grants & Projects: SNSF projects on sparse network inference (2019-2023) and innovation dynamics (2020-2024) EU Periscope project on pandemic side-effects modeling Former leadership of the Medical Statistics Unit at Lancaster and Mathematics Department at Groningen Awards & Leadership: President of the European Bernoulli Society Founding member of the DSSC Center (Groningen) Academic Service: Member, Board of Directors, International Biometrics Society Past President, Dutch Biostatistics Society His Statistical Computing Laboratory at USI develops methodologies for network analysis in life sciences and ecology, collaborating with interdisciplinary teams. The lab includes 2 postdocs and 6 PhD students, focusing on projects like species diversification models, gene therapy safety, and pandemic impact analysis. He teaches advanced courses on text generation and statistical computing, contributing to USI's Data Science and Big Data masters programs.
