جست‌وجوی استاد و پژوهشگر

Jürgen Bernardمشاهده پروفایل

استادیار

• Visual Analytics
• Information Visualization
• Interactive Machine Learning
• +۶ مورد دیگر

Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich (UZH), affiliated with the Digital Society Initiative (DSI). He leads the Interactive Visual Data Analysis (IVDA) Group and holds a joint position in the Department of Informatics within the Faculty of Business, Economics, and Informatics. His research focuses on interactive visual data analysis, explainable machine learning, and human-centered AI interfaces. Bernard completed his PhD at TU Darmstadt in 2015, followed by postdoctoral roles at TU Darmstadt and the University of British Columbia. He has received prestigious awards, including the EuroGraphics Young Researcher Award (2022) and EuroVis Young Researcher Award (2021). His work emphasizes combining human expertise with algorithms in domains like healthcare, climate science, and digital humanities. Key research interests include visual analytics for time-oriented data, interactive machine learning, and applications in healthcare. His projects address challenges such as medical data interpretation, sensor data analysis, and decision-making support systems. Bernard actively contributes to conferences like IEEE VIS and chairs workshops such as VAHC 2023. Education: PhD in Computer Science (2015, TU Darmstadt); Diploma in Computer Science (2009, TU Darmstadt) Grants: SNF Grant on Personalized Visual Analytics (2024), BMW collaboration on manufacturing analytics Labs/Teams: IVDA Group, DSI Health Community

Laura Pozziمشاهده پروفایل

استاد

• Embedded Systems
• Compiler Techniques
• Coarse-Grain Reconfigurable Architectures
• +۳ مورد دیگر

Laura Pozzi is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), Switzerland, since 2015. She previously held positions as Associate Professor (2011–2015) and Assistant Professor (2005–2011) at USI. Prior to joining USI, she was a postdoctoral researcher at EPFL's Processor Architecture Laboratory (2001–2005), a research engineer at STMicroelectronics (2000), and an Industrial Visitor at UC Berkeley (2000). Education: MS and PhD in Computer Engineering from Politecnico di Milano, Italy (1996–2000). Her research focuses on the interaction between compiler and architecture design, particularly in embedded systems , with key areas including approximate computing , coarse-grained reconfigurable arrays (CGRAs) , high-level synthesis (HLS) , and fuzz testing . She has led projects on automated design space exploration, compiler optimizations for reconfigurable architectures, and error estimation in approximate circuits. Recent Publications span topics like SAT-based mapping for CGRAs , grammar-based fuzzing of shell interpreters , and approximate logic synthesis , reflecting her interdisciplinary work bridging hardware/software co-design and software verification. Scientific Awards: Credit Swiss Best Teaching Award IEEE DAC Best Paper Award Leadership Roles: Co-Program Chair, IEEE Symposium on Application Specific Processors (SASP) Editorial Board Member, IEEE Design and Test Students: Current: Rodrigo Otoni (Postdoc), Morteza Rezaalipour (PhD), Riccardo Felici (PhD), Cristian Tirelli (PhD) Alumni: Ilaria Scarabottolo (PhD/Postdoc), Lorenzo Ferretti (PhD/Postdoc), Georgios Zacharopoulos (PhD), Giovanni Ansaloni (PhD/Postdoc), Paolo Bonzini (PhD)

Patric Hagmannمشاهده پروفایل

استاد

• Connectomics
• Neuroimaging
• Diffusion MRI
• +۱۲ مورد دیگر

Patric Hagmann is a Full Professor at the Faculty of Biology and Medicine (FBM) at the University of Lausanne (UNIL), appointed on March 1, 2022. He is based at the Radiodiagnostic and Interventional Radiology Department at CHUV, where he leads a research unit and divides his time between clinical practice as a diagnostic neuroradiologist and advanced research in brain imaging. He is the originator of the concept of the "connectome," a foundational contribution to modern neuroscience. His research focuses on mapping and analyzing brain connectivity using diffusion MRI, functional MRI, and advanced signal processing techniques. He applies these methods to understand brain development in premature infants, aging, and psychiatric conditions such as schizophrenia. His work bridges engineering, neuroscience, and clinical medicine, with strong collaborations at EPFL and within the CHUV. Connectomics Diffusion and Functional MRI Brain Connectivity Analysis Graph Signal Processing Psychosis and Schizophrenia Research Neonatal Brain Development Patric Hagmann has been recognized with the Clinical Science Award from FBM, UNIL (2016). His pioneering PhD thesis at EPFL in 2005 laid the foundation for the field of connectomics. He is actively involved in mentoring, postgraduate education, and academic leadership, including directing the Passerelle program at UNIL. He has collaborated closely with Prof. Jean-Philippe Thiran (EPFL), Prof. Kim Do Cuénod, Prof. Philippe Conus (Psychiatry), and Prof. Anita Truttmann (Neonatology). Since 2008, he has led a cohort study on premature babies to investigate neuronal development. His dual expertise as a physician and engineer enables translational research that impacts both clinical practice and theoretical neuroscience.

Davide La Vecchiaمشاهده پروفایل

استاد

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +۳ مورد دیگر

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.

Suryanarayana Sankagiriمشاهده پروفایل

پژوهشگر

• Discrete Choice Models
• Recommendation Systems
• Blockchain Security
• +۴ مورد دیگر

Suryanarayana Sankagiri is a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, affiliated with the Information and Network Dynamics (INDY1) group under Professor Matthias Grossglauser. Previously, he earned his Ph.D. in Electrical & Computer Engineering (2018-2022) from the University of Illinois at Urbana-Champaign , where he was supervised by Bruce Hajek and participated in the Coordinated Science Lab . He also holds an M.S. in Electrical & Computer Engineering from the University of Illinois (2016-2018) and a B.Tech. in Electrical Engineering from the Indian Institute of Technology Bombay (2012-2016). Education : Ph.D., Electrical & Computer Engineering, University of Illinois (2018-2022) M.S., Electrical & Computer Engineering, University of Illinois (2016-2018) B.Tech., Electrical Engineering, IIT Bombay (2012-2016) Suryanarayana's research focuses on discrete choice models and their application to recommendation systems , with a particular emphasis on learning from choice data and developing novel models for human decision-making. His broader interests include blockchain security under adverse network conditions, network dynamics , probabilistic modeling , and algorithm design . Recent work explores nonconvex matrix factorization and contextual dueling bandits for recommendation systems. His publications span theoretical and applied domains, including high-impact venues like ICML , Stochastic Systems , and IEEE Transactions on Networking . Themes include blockchain efficiency , hidden community detection in preferential attachment graphs, and temporal analysis of Indian classical music. Current projects involve refining recommendation systems through sparse comparison data and designing protocols for resilient blockchain networks. Scientific Awards : zkCapital Paper of the Week (2021) Rambus Fellowship (2021) Mavis Future Faculty Fellowship (2019) List of Teachers Ranked as Excellent (2019) Nomination for IIT Bombay Undergraduate Colloquium (2016) Best Poster Award, IIT Bombay Undergraduate Research Symposium (2013) Suryanarayana has advised no students listed in the provided materials. His work has been supported by fellowships such as the Mavis Future Faculty Fellowship and Rambus Fellowship . He contributes to the INDY1 group at EPFL, which investigates information and network dynamics through interdisciplinary approaches combining probability , network theory , and algorithmic design .

Pedro Abranches De Carvalhoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bioinformatics
• Neural Engineering
• +۳ مورد دیگر

Pedro Abranches De Carvalho is a Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with Prof. Courtine's research group and the Laboratory for Information and Inference Systems (LIONS). He is also enrolled in the Doctoral Program in Electrical Engineering at EPFL. His work spans interdisciplinary research areas, including machine learning, bioinformatics, and computational biology. He holds multiple roles at EPFL: Doctoral Assistant at the UPCOURTINE group (School of Computer and Communication Sciences) Doctoral Assistant at LIONS lab (School of Engineering) Doctoral Student in Electrical Engineering (EDOC program) His research interests focus on applying computational methods to biological systems, such as geometric graph generation using GANs, redox signaling mechanisms, and noncoding RNA roles in leukemia. While no specific grants or awards are mentioned, his work bridges neural engineering and biochemistry. He is part of the Courtine Lab and LIONS, which likely involve collaborations in neural signal processing and machine learning applications in life sciences.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +۷ مورد دیگر

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

• Autonomous Driving
• Computer Vision
• Robotics
• +۵ مورد دیگر

Holger Caesar is a tenured Assistant Professor at Delft University of Technology (TU Delft) in the Intelligent Vehicles Lab . He leads research on scalable approaches for autonomous vehicle perception, prediction, and data annotation, with a focus on sensor fusion, domain adaptation, and minimal supervision. His work has been cited over 15,000 times. Education PhD in Computer Vision, University of Edinburgh Prior studies at KIT Karlsruhe, EPF Lausanne, and ETH Zurich Research Interests span autonomous driving perception, weakly supervised learning, novel view synthesis (NeRFs), diffusion models, and collaborative perception. He emphasizes reducing reliance on manual annotations through active learning and partial labeling techniques. Recent Publications include work on 4D Gaussian Splatting, camera-radar fusion, open-set scene graph generation, and safety benchmarking. These reflect trends toward multi-modal perception, robust sensor fusion, and foundation models for scalable autonomous systems. Scientific Awards ELLIS Europe Scholar (2024) TU Delft Cohesion Grant (100k EUR, 2024) Climate Action Grant (30k EUR, 2024) TKI High Tech Systems Grant (502k EUR, 2022) AiNed XS Grant (80k EUR, 2023) Argoverse Scene Flow Challenge Winner (unsupervised track, 2024) Advising & Grants include EU Horizon funding for the MOSAIC project (1 PhD and 1 Postdoc, 2024), EU KDT Cynergie4MIE grant (450k EUR, 2024), and industrial collaborations with Bosch, Motional, and partners across Europe. Labs & Teams include the Intelligent Vehicles Lab at TU Delft and founding roles in Motional's Data Annotation, Autolabeling, and Data Mining teams. He co-organizes workshops at ICCV, CVPR, and NCCV, and leads the ELLIS Delft Unit seminar series.

Torsten Braunمشاهده پروفایل

استاد

• Machine Learning
• Networking and Telecommunications
• Edge Computing
• +۳ مورد دیگر

Prof. Torsten Braun is a Professor and Head of the Communication and Distributed Systems (CDS) research group at the Institute of Computer Science, University of Bern. His research focuses on advanced networking technologies, edge computing, and machine learning applications in telecommunications. He leads projects addressing challenges in 5G/6G networks, federated learning optimization, and intelligent systems for smart cities. His work integrates theoretical frameworks with practical implementations, emphasizing distributed systems, service-oriented architectures, and IoT security. Key research areas include edge caching strategies for VR/AR applications, trajectory prediction using reinforcement learning, and resilient network design against jamming attacks. Braun has contributed to innovations in vehicular networks (V2X), RAN intelligence, and decentralized machine learning frameworks. He holds leadership roles in developing adaptive resource management systems for edge-cloud environments and has pioneered solutions for energy-efficient federated learning in heterogeneous IoT ecosystems. Publications span topics like spatial-temporal point cloud sensing, mobility-aware service orchestration, and secure positioning systems. His team explores cross-disciplinary applications such as LoRaWAN-based urban heat monitoring and blockchain-inspired public key infrastructures for IoT (Veritaa-IoT). Braun actively engages in standardization and industry collaborations to advance next-generation network architectures.

Andrea Ciniمشاهده پروفایل

پژوهشگر

• Machine Learning
• Graph Deep Learning
• Time Series Forecasting
• +۳ مورد دیگر

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.

Alessandro Facchiniمشاهده پروفایل

استاد

• Logic
• Probability Theory
• AI Ethics
• +۴ مورد دیگر

Alessandro Facchini is a Professor at the Department of Innovative Technologies at SUPSI - University of Applied Sciences Western Switzerland, based in Lugano, Switzerland. He holds a PhD in Computer Science (2010) from the University of Lausanne and the University of Bordeaux, with prior research roles at institutions including the University of Warsaw, University of Amsterdam, and University of California, Santa Cruz. His current research focuses on logic, rationality theories, probability, and the philosophy and ethics of AI, complemented by past work in automata theory and game theory. He has received notable awards such as the FNP Homing Plus Grant (2012) and the Paul Bernays Award (2011). Education PhD in Computer Science (2010), co-supervised by Prof. J. Duparc (U. Lausanne) and I. Walukiewicz (U. Bordeaux) Doctoral Program in Logic and Foundations of Mathematics (2004-2006), University of Barcelona Master in Humanities (Logic, Linguistics, Mathematics) (1998-2003), University of Neuchâtel Research Interests : Facchini’s work bridges formal logic, AI ethics, and foundational questions in probability and quantum theory. His recent projects include the European University on AI in Curricula and SmartH2O, a platform leveraging gamification for water resource management. He explores topics like quantum rational preferences, causal models, and the societal implications of AI systems. Awards & Recognition : FNP Homing Plus Grant (2012) Paul Bernays Award (2011) Teaching & Leadership : Facchini leads courses such as “Ethics of Technology and Engineering” and “Fundamentals of Artificial Intelligence,” emphasizing ethical and legal dimensions of data analytics. He also oversees projects like the Smart UniverCity initiative. Collaborations & Labs : Active member of IDSIA (Istituto Dalle Molle di Studi sull’Intelligenza Artificiale), a leading Swiss AI research institute. His work often intersects interdisciplinary teams addressing environmental, healthcare, and ethical challenges posed by AI.

• Image Processing
• Medical Imaging
• Biological Imaging
• +۳ مورد دیگر

Michaël Unser is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering , leading the Biomedical Imaging Laboratory . He serves as Academic Director for Imaging at EPFL and contributes to cross-departmental teaching in Microengineering , Mathematics , and Life Sciences Engineering . His research spans Image Processing , Medical Imaging , Wavelets , and Spline-based Modeling , with a focus on multiresolution analysis and single-molecule localization microscopy . He has mentored over 30 PhD students and supervised numerous research projects. Recent publications highlight advancements in super-resolution microscopy , deep learning integration , and inverse problem solving for biomedical imaging. His work emphasizes mathematical rigor and open-source software development for accessible bioimaging tools. IEEE Technical Achievement Award (2008) IEEE EMBS Career Achievement Award (2020) Three ERC Advanced Grants (FUNSP, GlobalBioIm, FunLearn) As Academic Director for Imaging , he leads EPFL's cross-disciplinary imaging initiatives. His teaching includes Fundamentals of Image Analysis and Signals and Systems courses.

• 3D Computer Vision
• Geometric Deep Learning
• Topological Deep Learning
• +۱۰ مورد دیگر

Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow in the Department of Computing at Imperial College London. As the Principal Investigator (PI) of the CIRCLE group , his research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with theoretical interests in non-Euclidean inference and deep learning principles. Education: PhD and MSc in Computer Vision from Technical University of Munich (2018), BSc in Computer Science from Sabancı University (2008). Projects: PI for UKRI-EPSRC's UNTOLD (Topological Deep Learning), Royal Society's drug discovery initiative, and EPSRC's GNOMON (Generative Models in non-Euclidean Spaces). Leadership: Area Chair for CVPR, ICCV, and 3DV 2025 Publication Chair. His work bridges differential geometry, algebraic topology, and deep neural networks, with applications in quantum computer vision, 3D/4D generative priors, and medical imaging. Key contributions include novel frameworks for rotation forecasting, graph generation, and topological generalization bounds. Scientific Awards: UKRI Future Leaders Fellowship EMVA Young Professional Award

• Signal Processing
• Machine Learning
• Tracking Algorithms
• +۳ مورد دیگر

Ángel García-Fernández is an Associate Professor at the Polytechnic University of Madrid , specializing in Bayesian inference , multi-target tracking , and nonlinear filtering with applications in signal processing, robotics, and underwater mapping. His work includes the development of Poisson multi-Bernoulli mixture (PMBM) filters, iterated posterior linearization algorithms, and direction-of-arrival (DOA) measurement models.