جستجوی استاد راهنمای دانشگاهی

Alexander Heinleinمشاهده پروفایل

استادیار

• Numerical methods for partial differential equations
• Scientific computing
• Domain decomposition methods
• +۶ مورد دیگر

Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.

• Machine Learning
• Data Science
• Mathematical Physics
• +۳ مورد دیگر

Rostyslav Hryniv serves as Professor of Applied Mathematics and Statistics at Ukrainian Catholic University (UCU), holding dual leadership roles as Deputy Dean for Research and Head of the Machine Learning Laboratory. His academic profile uniquely bridges rigorous mathematical physics with cutting-edge artificial intelligence applications, reflecting UCU's interdisciplinary research vision. Hryniv's educational foundation includes dual PhDs in Mathematics (1996, 2007) and a higher doctorate (dr hab.) from the Institute of Mathematics of the Polish Academy of Sciences (2009). His postdoctoral trajectory features prestigious appointments as a Humboldt Research Fellow at the University of Bonn (2003-2005) and PIMS Postdoctoral Fellow at the University of Calgary (1998-1999), complemented by teaching roles across institutions in Ukraine, Canada, Poland, and the United States. His research demonstrates a strategic evolution from foundational work in spectral theory and inverse problems to contemporary machine learning. Early career contributions focused on Schrödinger operators with singular potentials, trace formulas, and quantum scattering phenomena. Recent publications (2021-2025) reveal a decisive pivot toward applied AI, including Ukrainian NLP benchmarks, symmetry-based 3D reconstruction, and minimal solvers for computer vision. This transition exemplifies his commitment to leveraging deep mathematical insights for real-world technological challenges. Analysis of his publication trends indicates a clear interdisciplinary trajectory: while maintaining mathematical rigor, his work increasingly addresses Ukrainian language technology and geometric deep learning. The Machine Learning Laboratory under his direction has become a regional hub for AI innovation, particularly in developing resources for low-resource languages and symmetry-aware computer vision systems. Hryniv's scholarly recognition includes: Humboldt Research Fellowship (University of Bonn, 2003-2005) PIMS Postdoctoral Fellowship (University of Calgary, 1998-1999) As Head of UCU's Machine Learning Laboratory, Hryniv leads research initiatives spanning Ukrainian NLP, 3D vision, and quantum machine learning. The lab's recent focus on symmetry-based algorithms and Ukrainian language resources positions it at the forefront of culturally contextual AI development in Eastern Europe, while maintaining connections to his foundational work in mathematical physics through quantum-inspired computing approaches.

• Natural Language Processing
• Machine Learning
• Uncertainty Estimation
• +۴ مورد دیگر

Wilker Aziz is an assistant professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning research group. His academic journey includes a PhD from the University of Wolverhampton (2014), followed by research positions at the University of Sheffield and ILLC before joining the UvA faculty in January 2019. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Aziz investigates how to design models and algorithms that learn to represent, understand, and generate language data, with particular emphasis on uncertainty estimation in language models, probabilistic inference techniques, and developing novel decoding algorithms for text generation. His work addresses fundamental problems in language modeling, machine translation, syntactic parsing, textual entailment, text classification, and question answering. Aziz's recent publications (2022-2024) demonstrate a strong focus on uncertainty in natural language generation, with multiple papers accepted at top-tier conferences including EACL and EMNLP. His research shows how language models can better align with human uncertainty patterns and how to properly evaluate model confidence when humans themselves disagree on annotations. Best paper award at Coling 2020 for 'Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation' Outstanding Area Chair award at ICLR 2022 Active program committee member for major ML/NLP conferences including NeurIPS, ICML, ACL, EMNLP, and ICLR Aziz actively supervises PhD and MSc students, with a current cohort of five ongoing PhD candidates and numerous recent graduates. His Probabilistic Language Learning group develops theoretical frameworks and practical applications for uncertainty-aware natural language processing. He has contributed to open-source projects including PET (Post-Editing Tool), grasp (Randomised Semiring Parsing), and mosesdecoder, demonstrating his commitment to advancing the field through shared research tools.

• Remote Sensing
• Earth Observation
• Machine Learning
• +۵ مورد دیگر

Begüm Demir is a Professor and Head of the Remote Sensing Image Analysis (RSiM) Group at the Technische Universität Berlin , Faculty of Electrical Engineering and Computer Science. Her work focuses on leveraging machine learning and deep learning techniques for scalable processing and information discovery from Earth observation (EO) data, particularly multi-modal satellite imagery. Research Highlights : Development of the BigEarthNet dataset, Agora-EO ecosystem for EO data literacy, and projects like TreeSatAI and IDEAL-VGI integrating EO data with wireless communication and volunteered geographic information. Grants & Collaborations : Principal Investigator for TU Berlin-Huawei Wireless Joint Innovation Center (2021), BIFOLD (Berlin Institute for the Foundations of Learning and Data) project (2020), and ERC-funded BigEarth (2017). Scientific Awards : 2018 IEEE GRSS Early Career Award ERC Starting Grant (2017) Leadership & Outreach : Active in organizing workshops, summer schools (e.g., GFZ Potsdam), and serving as Guest Editor for journals like IEEE Geoscience and Remote Sensing Letters and MDPI Remote Sensing.

Víctor Elviraمشاهده پروفایل

مدرس ارشد

• Bayesian Inference
• Sequential Monte Carlo Methods
• Importance Sampling
• +۵ مورد دیگر

Víctor Elvira is a Senior Lecturer at the University of Edinburgh, specializing in Bayesian inference, sequential Monte Carlo methods, and computational statistics. He serves as an Associate Editor for IEEE Transactions on Signal Processing and organizes major academic events like the Sequential Monte Carlo Workshop (SMC 2024) and Summer School in Bayesian Filtering (SSBF 2024) . Key research areas: Bayesian filtering, importance sampling, state-space modeling, and machine learning applications Recent work focuses on sparse graphical models, differentiable particle filters, and Monte Carlo optimization Scientific Awards : 2025 EURASIP Early Career Award 2024 ELLIS Fellow 2023 Leverhulme Research Fellow 2021 Alan Turing Fellow 2018 Fulbright & Marie Curie Fellow 2018 IEEE Senior Member He has delivered PhD courses on Monte Carlo methods and Bayesian filtering in Madrid (2025) and Barcelona (2025), and maintains open-access software implementations for publications like Sparse Graphical Linear Dynamical Systems and Graphical Inference in Linear-Gaussian State-Space Models .

• Machine Learning
• Support Vector Machines
• Evolution Strategies
• +۴ مورد دیگر

Christian Igel is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen. He serves as Director of the SCIENCE AI Centre and co-lead of the Pioneer Centre for Artificial Intelligence. Igel holds a Doctoral degree from Bielefeld University (2002) and a Habilitation from Ruhr-University Bochum (2010). Previously, he was a Juniorprofessor at Ruhr-University Bochum (2003-2010). His research focuses on fundamental and applied machine learning, including: kernel methods, evolutionary optimization, deep neural networks, PAC-Bayesian analysis, and applications supporting sustainable development goals. He maintains extensive collaborations across disciplines, particularly in environmental monitoring and medical informatics. Igel's recent publications demonstrate strong interdisciplinary focus with applications in: Environmental science (tree cover mapping, carbon stock quantification) Healthcare (sleep staging, mental health epidemiology) Core ML theory (monotonic networks, PAC-Bayesian bounds) His work consistently appears in high-impact venues including Nature, ICML, and NeurIPS. Awards and Honors: ELLIS Fellow (European Lab for Learning and Intelligent Systems) Editorial Roles: Editor, German Journal on Artificial Intelligence (KI) Associate Editor, Evolutionary Computation Journal (ECJ) Associate Editor, Artificial Intelligence Journal (AIJ)

Or Litanyمشاهده پروفایل

استادیار

• 3D computer vision
• Generative AI
• Machine Learning
• +۳ مورد دیگر

Or Litany is an Assistant Professor at the Technion - Israel Institute of Technology and a Senior Research Scientist at NVIDIA, specializing in 3D computer vision and generative AI . They are an ELLIS scholar and actively recruiting Postdoctoral and PhD candidates for their new lab. 2023 Azrieli Faculty Fellow Taub Fellow Education: PhD in Computer Science from Tel-Aviv University under Prof. Alex Bronstein B.Sc. in Physics and Mathematics from Hebrew University Postdoctoral research at Stanford University (with Prof. Leonidas Guibas) and at FAIR (with Prof. Jitendra Malik) Research Interests focus on: 3D scene understanding and motion modeling Neural field representations for vision Generative AI for autonomous vehicle simulation Conference Leadership: Chairing Research Interaction sessions at 3DV 2026 Area Chair for ICCV 2025 Workshops: Co-organizing CVPR 2025 workshops on Open-Vocabulary 3D Scene Understanding and Neural Fields Beyond Conventional Cameras Scientific Contributions include: Emergent spatial-temporal decomposition via self-supervision Data-driven simulation for autonomous vehicle perception

• Human-Centered Computing
• Gaze Estimation
• Embodied Visual Intelligence
• +۶ مورد دیگر

Xucong Zhang is a tenured Assistant Professor at the Intelligent Systems Department , Faculty of EEMCS , Delft University of Technology (TU Delft) . He also serves as a Guest Scientist at the Max Planck Institute for Intelligent Systems and is an EL LIS Scholar . He joined TU Delft in 2021 and was granted a permanent position in 2023. PhD from Max Planck Institute for Informatics (2013–2018) Postdoc at ETH Zurich (2018–2021) Research Interests : Xucong’s work centers on human-centered computing , combining gaze estimation and holistic human motion modeling (gaze, face, hand, full-body) to advance human-AI interaction , human-robot collaboration , and avatar generation . His healthcare research integrates computer vision with medical applications to support clinical practice. Publications Trends : Xucong’s recent work focuses on gaze estimation (e.g., UniGaze , PrivateGaze ), 3D kinematics (e.g., GazeNeRF ), and human-robot interaction (e.g., GazeHTA , SpeechCAT ). His research spans multi-camera systems , adversarial learning , and domain adaptation . Best Paper Honorable Mention, UIST 2017 Best Paper Honorable Mention, UIST 2016 EL LIS Scholar title (2023) Teaching & Funding : He leads the BSc Image Processing course and co-teaches MSc Computer Vision by Deep Learning . Xucong has secured 1.5M EUR in funding, including a DFG UGaze Programme grant (2026–2029) and sole PI for the Honda Research Institute + Netherlands TKI grant (2023–2028). Labs & Collaborations : Xucong coordinates the INSYGHT Lab at TU Delft and collaborates with institutions like Honda Research Institute Japan and ETH Zurich . His lab’s Cutting-Edge Multi-Camera System supports multi-modal data capture for social interaction analysis.

Frédéric Grouillerمشاهده پروفایل

پژوهشگر

• Multimodal Imaging
• EEG-fMRI
• Cognitive Neuroscience
• +۶ مورد دیگر

Frédéric Grouiller serves as Head of the CIBM MRI HUG-UNIGE Clinical MR Imaging Section at the University of Geneva since October 2023, holding a Researcher position focused on advanced neuroimaging methodologies and clinical applications. His academic foundation includes: Master in Engineering in Electronics and Computer Science (ESEO, Angers, 2005) Master’s research degree in Medical Imaging (University of Angers, 2005) PhD in Neurosciences (Grenoble Institute of Neurosciences, 2008) Dr. Grouiller’s pioneering work centers on multimodal imaging integration , particularly EEG-fMRI for spatio-temporal brain dynamics characterization. His research drives pre-surgical brain mapping innovations and pioneers Virtual Reality-bimodal neurofeedback systems for pediatric neurological/psychiatric therapies. This bridges cognitive neuroscience with clinical translation , emphasizing real-time data processing for epilepsy, tumor, and neonatal applications. He directs operational and methodological development at the Brain and Behaviour Laboratory’s MRI facility, building on prior roles in the Functional Brain Mapping Laboratory (University of Geneva) and Swiss Center for Affective Sciences (CISA). His leadership spans technical management of 3T/7T MRI systems and collaborative clinical research infrastructure.

• Neurobiology
• Evolution
• Genetics
• +۵ مورد دیگر

Richard Benton, a Professor at the University of Lausanne, is a leading researcher in neurobiology, evolution, and genetics. His work focuses on the structure, function, and evolution of chemosensory systems in insects, particularly Drosophila species. He has made groundbreaking contributions to understanding Ionotropic Receptors (IRs) and Odorant Receptors (ORs) in olfaction, gustation, and thermosensation. His academic journey includes a PhD from the University of Cambridge (2003) and post-doctoral training at Rockefeller University (EMBO/Heley Hay Whitney Fellow). He joined the Center for Integrative Genomics in 2007 as Assistant Professor, advancing to Full Professor by 2018. His research employs multidisciplinary approaches (bioinformatics, genetics, electrophysiology) to unravel neural circuit evolution and sensory adaptation. Research Themes: Chemosensory receptor evolution and function Neurodevelopmental mechanisms in olfactory circuits Drosophila sechellia as a model for behavioral evolution His lab has produced 15 recent publications spanning molecular evolution, neural circuitry, and ecological adaptation. Key awards include the EMBO Gold Medal (2016), National Latsis Prize (2015), and multiple ERC grants. His team includes postdocs and students working on insect neurogenetics, behavioral assays, and computational methods.

• Computational (neuro)anatomy
• medical image analysis
• magnetic resonance imaging
• +۷ مورد دیگر

Mheitxell Bach Cuadra serves as Senior Lecturer at the University of Lausanne's School of Biology and Medicine and Head of the CIBM Signal Processing CHUV-UNIL Trustworthy Medical Image Analysis Section since 2020. Based at CHUV Radiology Department, she leads the Medical Image Analysis Laboratory (MIAL) with deep integration into Lausanne's biomedical research ecosystem. Educational background includes MSc in Electrical Engineering (Universitat Politècnica de Catalunya, 1998) and PhD from EPFL (2003), followed by postdoctoral work at EPFL until 2005. Her research spans computational (neuro)anatomy , medical image analysis , and 7T MRI applications with focus on machine learning-driven solutions. Key specialties include image reconstruction for fetal imaging, segmentation techniques for multiple sclerosis studies, human thalamus mapping, and ocular tumor analysis. The MIAL laboratory develops clinically validated tools emphasizing algorithmic trustworthiness and reproducibility. As section head, she coordinates CIBM's signal processing initiatives and fosters collaborations between engineers, radiologists, and neurologists. Her leadership supports translational research from algorithm development to clinical implementation within the CHUV-UNIL infrastructure.

Giovanni Chiarionمشاهده پروفایل

پژوهشگر

• Biomedical Engineering
• Brain-Computer Interfaces
• EEG Signal Processing
• +۳ مورد دیگر

Giovanni Chiarion is a postdoctoral researcher at the Center for Biomedical Imaging (CIBM), affiliated with the Electrical Neuroimaging Section of CHUV-UNIL. He holds a Bachelor’s degree in Biomedical Engineering from the University of Padua and Master’s/Ph.D. degrees in Biomedical Engineering from Politecnico di Torino. Education: University of Padua (B.Sc.), Politecnico di Torino (M.Sc., Ph.D.) His research spans Brain-Computer Interface (BCI) development for motor-impaired patients, multi-scale neuronal connectivity analysis using EEG and in vitro networks, and spike sorting algorithm optimization for overlapping neural signals. Current work investigates pupillometric responses to auditory surprise and regularity violation detection. Scientific contributions include a grant-funded BCI project combining sensorimotor rhythms with pupillometric methods, and innovations in resolving overlapping spike waveforms for synchronized neuronal activity studies. Key award: University of Torino research grant for binary response BCI system

Tomas Rosمشاهده پروفایل

مدرس ارشد

• Neurofeedback
• Brain-Computer Interface
• EEG
• +۴ مورد دیگر

Tomas Ros serves as Senior Lecturer at the Department of Clinical Neuroscience, University of Geneva, and Research Staff Scientist at the Center for Biomedical Imaging (CIBM) within the EEG HUG-UNIGE Clinical and Translational Neuroimaging Section under Prof. Serge Vulliémoz. His career bridges physics, neuroscience, and clinical applications with focus on translational neuroimaging. His academic foundation includes: BSc in Physics from Imperial College of Science and Technology (London, UK) MSc in Neuroscience from Louis Pasteur University (Strasbourg, France) PhD in Psychology from Goldsmiths College, University of London (2010) Dr. Ros pioneered research in EEG neurofeedback , discovering immediate post-session brain changes during his doctoral work. His expertise spans psychiatric applications (PTSD, ADHD) and neurological rehabilitation (post-stroke), utilizing advanced signal processing within brain-computer interface frameworks. Current work focuses on methodological refinement of neurofeedback protocols for clinical deployment, emphasizing cross-disciplinary integration of engineering and medicine. His scientific recognition includes the Brain Star Award from the Canadian Institutes of Health Research for PTSD neurofeedback research. As Principal Investigator of a Swiss National Science Foundation grant, he leads methodological development of EEG neurofeedback for psychiatric/neurological conditions. Prior funding includes Leenards Foundation support for stroke rehabilitation and Synapsy NCCR program involvement for ADHD studies. At CIBM, he contributes to the Clinical and Translational Neuroimaging Section's mission of advancing 7T MRI and EEG technologies for clinical neuroscience, collaborating with multidisciplinary teams to translate neuroimaging innovations into therapeutic applications.

Abele Michelaمشاهده پروفایل

پژوهشگر

• Intervention Science
• Neurofeedback
• Biofeedback
• +۶ مورد دیگر

Abele Michela is a Postdoctoral researcher at the Center for Biomedical Imaging (CIBM), EEG HUG-UNIGE Clinical and Translational Neuroimaging Section, University of Geneva, supervised by Tomas Ros. His work focuses on intervention science through neuro- and bio-feedback, with applications in police training and clinical rehabilitation. His educational background: PhD, Radboud University Nijmegen (2024): Focused on in-action HRV biofeedback in VR for police officers under threat. Master's, University of Geneva: Centered on EEG neurofeedback for treatment of stroke and attention-related disorders. Bachelor's in Biotechnology, EPFL. Research interests include Intervention Science, Neurofeedback, Biofeedback, Physiological and EEG Signal Analysis, Dynamical Systems, Virtual Reality Design, Police Training, Philosophy of Science, and Education. His work integrates technical signal processing with real-world applications to enhance human performance and health. Based at University of Geneva's Campus Biotech, he is part of the Clinical and Translational Neuroimaging Section, which fosters collaboration between Geneva University Hospitals (HUG) and the University of Geneva (UNIGE).

Oscar Dabrowskiمشاهده پروفایل

پژوهشگر

• MRI
• deep learning
• image processing
• +۳ مورد دیگر

Oscar Dabrowski is a Postdoctoral Researcher at the Center for Biomedical Imaging (CIBM) MRI section, affiliated with the University of Geneva (UNIGE) and Geneva University Hospitals (HUG), where he joined the Clinical MR Imaging Section on February 1, 2024 to advance MRI image quality through computational innovation. His academic foundation includes: BSc from HEPIA (EIG) in 2013 MSc in Computer Science from University of Geneva in 2017 PhD in Computer Science from University of Geneva in 2023 His research integrates deep learning with MRI physics to solve artifact correction challenges, specializing in k-space motion estimation and NUFFT-based reconstruction to eliminate hallucinations while preserving diagnostic integrity. This work directly enhances neuroimaging reliability through signal processing and model-driven AI approaches. Dr. Dabrowski actively contributes to education as a teaching assistant for computer programming courses and participant in the "Advanced MRI techniques in neuroimaging" curriculum, though no research grants or student advisees are documented. He operates within CIBM's Clinical MR Imaging Section at HUG Radiology, collaborating across UNIGE, HUG, EPFL, and CHUV to pioneer 7T MRI applications in clinical settings.