Find Academic Supervisors

François ChaletView profile

Professor

• Expanded Animation
• Visual Arts
• Projection Mapping
• +3 more

François Chalet serves as an Artistic Professor at Lucerne University of Applied Sciences and Arts within the Design Film Art school, where he teaches Expanded Animation and has held academic positions since 2005. His professional profile combines academic instruction with an active practice as an independent visual artist, animator, and director spanning nearly three decades since 1997. Chalet's research and creative work centers on expanded animation techniques that transcend traditional screen boundaries. His practice integrates projection mapping, interactive installations, and immersive audiovisual performance, creating innovative experiences that merge digital technology with artistic expression. Recent projects demonstrate increasing sophistication in audience interaction and contextual awareness, particularly through his work with Context-aware AR and full-dome animation environments. As an educator, Chalet has supervised numerous student collaborations, most notably the Reizvoll immersive audio-visual performance that brought together HSLU animation students with the molto cantabile choir. His teaching philosophy emphasizes both technical mastery and conceptual development, encouraging students to explore animation beyond conventional formats and applications. Chalet leads significant research initiatives including Ludic Lab Lucerne (2019-2021), the ETH Zurich collaboration on Context-aware AR (2021-2022), and previous holography research with the National Filmboard of Canada (2018-2019). These projects consistently involve interdisciplinary teams combining animation expertise with music composition, technical development, and interactive design to create cohesive, boundary-pushing works. His professional recognition includes regular participation as a jury member and speaker at internationally relevant conferences and festivals, reflecting his standing within the global animation and visual arts community. Chalet's work continues to evolve toward more sophisticated integration of animation with live performance, audience participation, and responsive environmental systems.

Andrea CiniView profile

Researcher

• Machine Learning
• Graph Deep Learning
• Time Series Forecasting
• +3 more

Andrea Cini is a postdoc researcher affiliated with the Graph Machine Learning Group and the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) at the University of Lugano (USI). He also holds a position as a SNSF postdoc fellow at the University of Oxford under Prof. Michael Bronstein, focusing on machine learning for time series forecasting and graph processing. His research integrates graph deep learning methodologies with spatiotemporal dynamics, emphasizing applications in healthcare, energy systems, and intelligent systems. Education: PhD in Computer Science and Engineering (USI, 2020), supervised by Prof. Cesare Alippi MSc and BSc in Computer Science and Engineering (Politecnico di Milano) Visiting researcher at Imperial College London (Prof. Danilo Mandic) Research interests span graph neural networks , time series forecasting , and spatiotemporal data processing . His work has introduced influential methods such as the Torch Spatiotemporal library, and has been recognized with a best paper award. Recent publications emphasize applications in relational conformal prediction, hierarchical forecasting, and energy grid optimization. Awards include the Best Paper Award for contributions to graph-based forecasting methodologies. Current projects are funded by the Swiss National Science Foundation, exploring graph-based reinforcement learning and spatiotemporal modeling at the University of Oxford. Collaborations include affiliations with the Northernmost Graph Machine Learning group at UiT the Arctic University of Norway. His research bridges theoretical advancements and industrial applications in fields like healthcare dynamics prediction and smart grid optimization.

Julien FageotView profile

Researcher

• Sparsity
• Random Processes
• Approximation Theory
• +7 more

Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.

• Machine Learning
• Probabilistic Modeling
• Neural Networks
• +4 more

Carl Allen is a Laplace Junior Chair in Machine Learning at École Normale Supérieure, Paris, working in the research group of Stéphane Mallat, Giulio Biroli and Garbiele Peyré. Previously, he was a postdoctoral fellow at ETH Zurich and completed his PhD in Machine Learning in 2021 at the University of Edinburgh under the supervision of Professors Tim Hospedales and Iain Murray. His educational background includes a BSc in Mathematics & Chemistry from the University of Southampton, an MSc in Mathematics and the Foundations of Computer Science (MFoCS) from the University of Oxford, and MScs in Artificial Intelligence and Data Science from the University of Edinburgh. Before transitioning to AI/ML research, he spent several years in Project Finance. Allen's research focuses on mathematically understanding mechanisms behind successful machine learning methods, particularly neural networks. He investigates how machine learning models exploit aspects of data distribution from a probabilistic perspective. His current topics include explaining how VAEs disentangle independent factors of data, identifying mathematical models behind self-supervised learning, and deriving probabilistic interpretations of softmax classification. His PhD work investigated neural representations of discrete objects and their relationships, with a main result explaining how word embeddings can seemingly be added and subtracted (e.g., queen ≈ king - man + woman), which received Best Paper (honorable mention) at ICML 2019. His research spans theoretical foundations of machine learning, with particular emphasis on representation learning, disentanglement, and probabilistic modeling of neural networks. His work connects mathematical principles with practical machine learning applications, aiming to develop more interpretable and reliable algorithms. Allen has received notable recognition including a Best Paper honorable mention at ICML 2019 and a research grant from the Hasler Foundation. He has delivered invited talks at prestigious institutions including Harvard Center of Mathematical Sciences & Applications and Astra-Zeneca. His collaborative work spans multiple institutions, including a notable internship at Samsung AI Centre, Cambridge, where he worked at the intersection of representation learning and logical reasoning. His research has significant implications for developing more interpretable, reliable, and theoretically grounded machine learning systems.

• Probabilistic machine learning
• Bayesian experimental design
• Causal inference
• +4 more

Desi R. Ivanova is a research fellow at the University of Oxford's Department of Statistics under the Florence Nightingale Bicentennial Fellowship. Her work bridges probabilistic machine learning, Bayesian experimental design, and LLM evaluation frameworks. She holds a DPhil in Statistics from Oxford's StatML CDT program (2020-2024) and an MMORSE in Mathematics from University of Warwick (2011-2016) with Erasmus exchange at LMU Munich. Research spans causal machine learning and uncertainty quantification Developed CO-BED and Step-DAD frameworks Focus on LLM evaluation methodology and calibration Expert in real-time adaptive experimental systems Her publications demonstrate expertise in Bayesian self-consistency methods, neural data compression, and privacy-preserving dataset merging. Key contributions include improving amortized inference efficiency and developing gradient-based causal experimental designs. Current work emphasizes rigorous statistical evaluation of language models, advocating for appropriate uncertainty quantification when analyzing performance across small datasets. She critiques CLT-based methods for LLM evaluation and proposes more robust frequentist and Bayesian alternatives.

Melih KandemirView profile

Associate Professor

• Machine Learning
• Bayesian Inference
• Stochastic Process Modeling
• +5 more

Melih Kandemir is an Associate Professor of Machine Learning at the University of Southern Denmark, Department of Mathematics and Computer Science. He earned his PhD in 2013 from Aalto University under Prof. Samuel Kaski, followed by postdoctoral work at Heidelberg University (with Prof. Fred Hamprecht) and an assistant professorship at Ozyegin University, Turkey. Prior to joining SDU, he led research at Bosch Center for Artificial Intelligence. Education: PhD (Aalto University, 2013), Postdoc (Heidelberg University). Previous Roles: Assistant Professor (Ozyegin University), Research Group Leader (Bosch CAI). His research focuses on Bayesian inference, stochastic process modeling with deep neural networks, and applications to reinforcement learning and continual learning. He leads the SDU Adaptive Intelligence (ADIN) Lab and is an ELLIS Member, reflecting his standing as a top European AI researcher. His work addresses critical challenges in uncertainty quantification, exploration strategies, and theoretically grounded algorithms for decision-making systems. Recent publications highlight expertise in model-based reinforcement learning (e.g., MOMBO for offline RL), PAC-Bayesian bandits, evidential learning for robust classification, and neural stochastic differential equations. His scientific awards include a Best Paper Award (2017) and ELLIS Membership. He also explores interdisciplinary applications of natural sciences to sustainable technology development.

Jocelyn ChanussotView profile

Professor

• Remote Sensing
• Hyperspectral Imaging
• Signal and Image Processing
• +4 more

Jocelyn Chanussot is a Professor at Grenoble Institute of Technology, holding the AXA Chair of Remote Sensing. He is affiliated with GIPSA-Lab (Laboratoire de traitement du Signal et des Images) at Ense3 (École Nationale Supérieure de l'Énergie, l'Eau et l'Environnement) and maintains a connection with the Chinese Academy of Sciences through his AXA Chair position. His research focuses on advancing remote sensing technologies, particularly in hyperspectral imaging and artificial intelligence applications for environmental monitoring. Professor Chanussot specializes in hyperspectral imaging, which captures information across several hundred wavelengths, allowing for detailed characterization of physical properties in observed scenes. His work develops algorithms to extract meaningful information from complex remote sensing data, with applications spanning natural disaster monitoring, environmental observation, biodiversity assessment, and urban planning. He has pioneered approaches that leverage artificial intelligence, particularly deep learning techniques, to process and analyze large-scale remote sensing datasets. His scholarly output shows a clear trend toward integrating advanced AI techniques with remote sensing data. Recent publications demonstrate growing emphasis on transformer architectures, contrastive learning, generative models, and foundation models specifically adapted for hyperspectral data. His work increasingly addresses multimodal data fusion, anomaly detection, and real-time processing for applications in natural disaster response. Ranked among the 157 most cited French researchers in 2019 by Clarivate Analytics Professor Chanussot actively serves on numerous conference program committees, particularly for SPIE's Image and Signal Processing for Remote Sensing conferences. His research is supported by the AXA Research Fund through his AXA Chair in Remote Sensing, which focuses on developing algorithms for natural disaster monitoring and emergency response. He collaborates extensively with international institutions including UCLA and Stanford University on applications ranging from toxic gas detection to tropical forest biodiversity assessment. He leads research at GIPSA-Lab, focusing on developing advanced tools and algorithms for extracting information from complex remote sensing data. His team works on processing heterogeneous data including aerial photos, multispectral and hyperspectral images, and other environmental measurements to improve natural disaster prediction and response capabilities.

• Probability Theory
• Random Graphs
• Graph Limits
• +3 more

Backhausz Ágnes is a Assistant Professor at Eötvös Loránd University's Faculty of Science , specifically in the Department of Probability Theory and Statistics . She also holds a part-time Researcher position at the Alfréd Rényi Institute of Mathematics . Her academic journey includes habilitation and a PhD in Mathematics, focusing on random graph models and their asymptotic properties. Research Group: Struktúrák limeszei (since 2013, part-time since 2015) Grants: ERC Grant on 'Limits of Discrete Structures' (2014–2019) Ágnes specializes in Probability Theory and Random Graphs , with emphasis on graph limits , factor of i.i.d. processes , and spectral theory . Recent publications analyze epidemic spread on multilayer networks, entropy inequalities, and action convergence in graph operators. Her work bridges theoretical mathematics with applications in network science and stochastic processes. Notable awards include the Grünwald Géza Memorial Medal (2014) from the Bolyai János Matematikai Társulat. She actively contributes to academic service as a Supervisor and Training Lead for the Beyond The Edge Marie Curie Doctoral Network (2024–2027) and serves on program committees for conferences like Eurocomb and the European Girls' Mathematical Olympiad . Her teaching portfolio spans Probability Theory, Stochastic Processes, and Mathematical Statistics at both undergraduate and graduate levels.

Emanuele DelucchiView profile

Professor

• Combinatorics
• Algebraic Topology
• Discrete Mathematics
• +3 more

Emanuele Delucchi is a Professor of Discrete Mathematics at the Department of Innovative Technologies (DTI) of the Scuola Universitaria Professionale della Svizzera Italiana (SUPSI) . He holds a Diploma and Ph.D. in Mathematics from ETH Zurich (2003, 2006) and a Habilitation from Universität Bremen (2011). His academic career includes roles as a Visiting Assistant Professor at SUNY Binghamton (2008–2010), Akademischer Rat at Universität Bremen (2010–2014), and SNSF-Professor at the University of Fribourg (2014–2021). He is an Abilitazione Scientifica Nazionale Italiana as Professore Ordinario (2020) and a member of the Istituto di Studi Avanzati at Bologna University (2019). Delucchi's research focuses on combinatorics, algebraic topology, and discrete mathematics , with emphasis on hyperplane arrangements, matroid theory, and Cohen-Macaulay complexes. He has advised three doctoral students (Sonja Riedel, Elia Saìni, Linard Hoessly) and served as a relator for 14 master’s/diploma and 10 bachelor’s theses. His teaching responsibilities include courses such as Analysis 1 , Discrete Mathematics , and Calculus 1 . His work bridges combinatorial structures with algebraic and geometric methods, contributing to the study of Stanley–Reisner rings, symmetric simplicial complexes, and toric/elliptic arrangements. Key awards include the Abilitazione Scientifica Nazionale and membership at Bologna’s Istituto di Studi Avanzati. His research spans topics like symmetric edge polytopes , group actions on semimatroids , and Cohen-Macaulay properties of combinatorial complexes.

Saverio BolognaniView profile

Lecturer

• Control Systems
• Power Systems
• Optimization
• +3 more

Dr. Saverio Bolognani is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on feedback optimization, control systems, and game theory, with applications to power grids and multi-agent systems. He is affiliated with the Institut für Automatik and teaches courses like 'Game Theory and Control' in the Autumn Semester 2025. His work integrates optimization, stochastic processes, and distributed algorithms to address challenges in energy systems and resource allocation. Recent contributions include PRIME (a feedback optimization framework) and karma-based economies for dynamic resource management. His experimental studies validate real-time control strategies for distribution grids and compressor stations. Research interests span control theory, power systems, game theory, and distributed optimization. He emphasizes practical implementations, such as online feedback optimization for grid flexibility and voltage regulation. His work often bridges theoretical foundations (e.g., Wasserstein space optimization) with real-world applications in smart grids and multi-modal mobility systems. No scientific awards are explicitly listed. His advising and grants involve experimental validation of control systems and participation in collaborative projects. He is involved in labs/teams focused on power distribution networks, reactive power control, and urban motion planning games.

• Mixed Reality
• Context-aware Computing
• Human-Computer Interaction
• +7 more

Kimberly Garcia is a researcher at the University of St. Gallen, focusing on mixed reality systems, contextual understanding, and explainable AI frameworks. Her work bridges human-computer interaction with industrial applications through projects like FoodCoach (automated diet counseling), GEAR (gaze-enabled AR feedback), and BLEARVIS (object identification in ubiquitous environments). Research Focus: Context-aware mixed reality interfaces Knowledge graph construction for environmental and health domains Explainable AI for cyber-physical systems Temporal scene understanding with contextual identifiers Recent Publications Highlight: Cross-domain applications of machine learning in AR systems, sensor fusion for object identification, and semantic tools for greenhouse gas accounting. All publications demonstrate interdisciplinary collaboration with computer scientists and domain experts. Key Collaborations: Regular co-authorship with Simon Mayer and Jannis Rene Strecker on human-AI interaction frameworks and sensor-based systems.

Allison HuetzView profile

Lecturer

• Cultural geography
• History of photography
• History of cinema
• +2 more

Allison Huetz is a doctoral researcher at the University of Geneva since 2015 within the FNS-funded Dispamonde project (Spatial Devices for Spectating the World), supervised by Prof. Jean-François Staszak and Dr. Estelle Sohier. She concurrently serves as a lecturer at the University of Lausanne, teaching master's courses on cinema censorship and bachelor's seminars in social sciences methodology. Her academic foundation includes specialized training in art history and museology at the École du Louvre in Paris, providing critical frameworks for analyzing visual culture and institutional heritage. Huetz's research interrogates spatial transformations in Belle Époque Geneva (1880-1920), particularly how the 1896 Swiss National Exhibition redefined the city as a cultural capital through performance spaces and spectacles. She examines the interplay between urban topography, public morality reforms, and emerging visual technologies, positioning Geneva within transnational networks of geographical imagination. Her interdisciplinary approach synthesizes cultural geography, visual anthropology, and urban history to decode how spatial devices shaped societal perceptions of modernity. Her publications reveal a consistent focus on spectacle as epistemological framework, with recurring themes of geographical representation, emotion visualization, and urban spectacle economies. Methodologically, she bridges archival research with spatial analysis to trace how image production participated in constructing public morality and cosmopolitan identities. Scientific recognition includes: SNSF mobility grant (2017) for research at Humboldt University's Institute for the History of Science Huetz actively contributes to academic knowledge circulation through co-organizing international conferences (Systèmes de simulation du monde, 2016; What do images do in public space?, 2017) and heritage collaborations like the Casino-Théâtre de Genève study. Her teaching demonstrates commitment to training next-generation scholars in visual analysis and qualitative methods. As core member of the Dispamonde research collective, she engages with dynamic interdisciplinary teams across Geneva, Zurich, and Berlin, fostering innovative approaches to historical spatial practices through regular workshops and collaborative publications.

Lénaïc ChizatView profile

Assistant Professor

• Optimal Transport
• Machine Learning
• Deep Learning
• +3 more

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.

Martin JaggiView profile

Associate Professor

• Machine Learning
• Optimization
• Artificial Intelligence
• +3 more

Martin Jaggi is an Associate Professor at EPFL, leading the Machine Learning and Optimization Laboratory (MLO). He holds academic positions within the School of Computer and Communication Sciences (IC), including roles in the SIN, SSC, and EDIC departments. His research focuses on machine learning optimization, federated learning, and large language models. He earned his PhD in Machine Learning and Optimization from ETH Zurich (2011) and a MSc in Mathematics from the same institution. Education: PhD in Machine Learning and Optimization, ETH Zurich (2011) MSc in Mathematics, ETH Zurich Research Interests: Dr. Jaggi's work integrates optimization theory with machine learning applications, emphasizing scalable algorithms and ethical AI. He explores topics like distributed learning systems, privacy-preserving techniques (e.g., federated learning), and model interpretability. His lab develops foundational methods for large language models (LLMs) and their ethical deployment. Teaching & Courses: Optimization for Machine Learning Topics in Machine Learning Systems Advising & Grants: He currently advises 9 PhD students and has supervised 11 past students. His work is supported by grants focusing on federated learning, optimization algorithms, and medical AI applications like Meditron-70b. Collaborations include projects on respiratory disease detection and mental health modeling. Labs & Teams: Academic Director of RCP-GE and oversees MLO lab, which develops open-source tools like FLamby for federated learning benchmarks and Meditron for clinical AI. The lab also advances techniques in distributed optimization and low-precision training.

Benjamin WattsView profile

Researcher

• Soft X-ray spectroscopy
• X-ray microscopy
• Organic materials analysis
• +3 more

Dr. Benjamin Watts is a beamline scientist at the Paul Scherrer Institute's (PSI) Center for Photon Science, specializing in soft X-ray spectro-microscopy for organic materials analysis. He earned his BSc (Professional) in Physics with Honours and PhD in Physics from the University of Newcastle, Australia, followed by postdoctoral research at North Carolina State University and the Advanced Light Source in Berkeley. University of Newcastle, Australia - BSc (Professional) in Physics University of Newcastle, Australia - PhD in Physics His research focuses on advanced X-ray techniques like NEXAFS spectroscopy and STXM microscopy to study nanoscale chemical and magnetic properties in polymer electronics and photonic crystals. He has pioneered 7 nm resolution imaging and developed software tools for data analysis. Recent publications highlight his work in 3D imaging methodologies, contamination control, and data format standardization. He serves as Chair of the NeXus International Advisory Committee, improving data exchange protocols for neutron/X-ray experiments. Benjamin Watts coordinates external user groups at the PolLux beamline, implementing hardware/software upgrades while advancing scientific research through novel imaging capabilities and data infrastructure.