Find Academic Supervisors

Gilad LermanView profile

Professor

• Machine Learning
• Data Analysis
• Computational Harmonic Analysis
• +4 more

Gilad Lerman is a Professor at the School of Mathematics, University of Minnesota, and serves as Director of the Data Science Lab at both the Minnesota Center for Industrial Mathematics (MCIM) and Institute for Mathematics and its Applications (IMA). His office is located at 533 Vincent Hall, 206 Church Street SE, Minneapolis, MN 55455. Research Focus: Dr. Lerman specializes in computational harmonic analysis, high-dimensional data analysis, statistical learning, and machine learning. His work includes robust optimization techniques, non-convex modeling, and applications in computer vision and bioinformatics. Key methodologies involve geometric data analysis, variational methods, and spectral techniques for large-scale datasets. Publications: His recent works demonstrate consistent focus on robust machine learning, including subspace recovery, Riemannian data assimilation, graph neural networks, and applications in geophysics/computer vision. Theoretical rigor combines with practical implementations across domains. Student Advising & Grants: He has supervised 16+ PhD dissertations and 3 MSc theses, with graduates now in academia and industry (Google, NVIDIA, Mayo Clinic). Current research is supported by NSF and NGA funding. Laboratory Leadership: Directs the IMA Data Science Lab, focusing on industrial mathematics collaborations and developing algorithms for real-world data challenges.

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +3 more

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

Stefan BalkeView profile

Researcher

• Music Information Retrieval
• Deep Learning
• Jazz Music Analysis
• +3 more

Stefan Balke is a Researcher at the AudioLabs Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), focusing on wind music research and Music Information Retrieval (MIR). Previously, he held a PostDoc position at the Institute of Computational Perception, JKU Linz (2018–2019) and later worked as a Data Scientist and Team Lead in industry. He temporarily served as a professor at Hochschule Weserbergland in 2023/24. Education: PhD (Dr.-Ing.) in MIR from FAU (2018), Electrical Engineering studies at Leibniz Universität Hannover (2008–2013). Research Interests: Music Information Retrieval, Deep Learning, Jazz and Wind Music Analysis, Dataset Development. His work includes creating datasets like ChoraleBricks (wind music) and JSD (jazz structure analysis), and tools like trackswitch.js for audio visualization. Grants & Projects: Secured 35k€ funding for his orchestra through the 'Engagiertes Land' program (2024). Collaborated on exhibits such as 'The Listening Machine' (Ars Electronica) and 'Con Espressione!' (mathematics of music exhibition). Contributions: Active on GitHub with repositories for datasets (choralebricks, jsd) and tools. Co-developed web-based audio tools and contributed to open-source projects like Sonic Visualiser and librosa.

Dena BazazianView profile

Lecturer

• Robotics
• Machine Vision
• Artificial Intelligence
• +3 more

Dr. Dena Bazazian is a Lecturer in Robotics and Machine Vision at the University of Plymouth's School of Engineering, Computing and Mathematics. Previously, she held positions as a Senior Research Associate at the University of Bristol's Visual Information Laboratory, Research Scientist at CTTC (Barcelona), and postdoctoral researcher at Universitat Autònoma de Barcelona, where she earned her PhD in 2018. Her international research includes visiting positions at Barcelona Supercomputing Center (2024), NAVER LABS Europe (2019), and University of Florence (2017). Her research focuses on robotic vision, underwater technologies, and AI-driven applications. Key interests include: 3D reconstruction and neural rendering techniques Anomaly detection using graph neural networks Underwater perception systems for marine environments Intergenerational co-design of immersive technologies Point cloud processing and geometric deep learning Publication analysis reveals strong emphasis on computer vision applications across robotics, industrial inspection, heritage preservation, and marine technology. Recent works increasingly integrate human-centered approaches with XR technologies and underwater systems. She currently supervises 8 PhD students across robotics, healthcare, and environmental monitoring domains: Offshore wind turbine maintenance drones (EPSRC-UDLA funded) Autonomous sea vessel swarms for climate forecasting (ARIA funded) AI for marine biodiversity monitoring (Cefas funded) Elderly fall detection systems (URS General funded) Healthcare anxiety detection through visual analysis (EPSRC DTP HMT funded) Dr. Bazazian leads modules including Artificial Vision and Deep Learning and Computer Vision . She co-organizes workshops including the Deep Learning for Geometric Computing series (CVPR/ICCV) and Women in Computer Vision workshops (CVPR/ECCV).

René QuiniouView profile

Researcher

• Machine Learning
• Data Mining
• Online Monitoring
• +4 more

René Quiniou is a Researcher at INRIA, affiliated with the DREAM team within the IRISA laboratory in Rennes, France. His work focuses on Online Monitoring , Change Detection , Machine Learning , and Data Mining , with applications in agriculture, environmental science, and healthcare. He leads research on extracting temporal patterns from sequences and developing adaptive learning algorithms for dynamic environments. Key research areas include incremental learning, anomaly detection in cybersecurity, and health monitoring systems. His publications span topics like multimedia data analysis, bovine disease detection, and intrusion detection in unlabeled data streams. He collaborates extensively with teams in computer science and applied mathematics, contributing to projects such as flood event clustering and cardiac surveillance systems. Articles emphasize practical applications of machine learning and data mining, such as symbolic time series representation (1d-SAX) and hierarchical skyline queries for database optimization. His work bridges theoretical advancements with real-world challenges in agriculture, healthcare, and cybersecurity.

Jonathan F. KominskyView profile

Assistant Professor

• Cognitive Science
• Developmental Psychology
• Vision Science
• +3 more

Jonathan F. Kominsky is an Assistant Professor of Cognitive Science at Central European University in Vienna, serving as Principal Investigator (PI) of the Causal Cognition Lab and co-PI of the CEU Cognitive Development Center. His research focuses on understanding how the mind represents causal relationships and employs tools from vision science, cognitive psychology, and developmental psychology to explore causal perception and its development. He created PyHab , an open-source system for infant gaze coding and stimulus presentation, enhancing reproducibility in developmental studies. Education: Ph.D. in Developmental Psychology from Yale University (2016). Research Interests: Causal perception, counterfactual reasoning, causal representations in infancy, and the interplay between causality and memory. His work bridges perceptual and cognitive mechanisms, examining how causal judgments form and influence decision-making and social evaluation. Labs/Teams: Leads the Causal Cognition Lab and collaborates with the CEU Cognitive Development Center. His projects emphasize methodological rigor and interdisciplinary approaches to developmental research.

Mulugeta Weldezgina AsresView profile

Researcher

• Data Science
• Machine Learning
• Deep Learning
• +4 more

Mulugeta Weldezgina Asres is a Researcher at the Department of Information and Communication Technology , University of Agder, Norway. His roles include developing large-scale anomaly detection systems for CERN detectors and advancing AI-driven industrial data analytics. He holds a Ph.D. in machine learning for non-intrusive load monitoring (NILM) and has prior academic experience at Mekelle University (Ethiopia) and Politecnico di Torino (Italy). Education: B.Sc. (Gold Medal) and M.Sc. (Gold Medal) in Computer Engineering from Mekelle University, followed by doctoral research at the Center for Artificial Intelligence Research (CAIR), Norway. His research focuses on time-series analysis, anomaly detection, IoT, and Industry 4.0 applications. Key research areas include multivariate time-series modeling, deep learning for anomaly prediction in high-energy physics detectors, and AI-powered industrial monitoring. He has contributed to projects at CERN, Telecom Italia, and automotive manufacturing, emphasizing data-driven solutions. Awards include Gold-Medal Awards for academic excellence in both B.Sc. and M.Sc. His work bridges theoretical machine learning with practical applications in energy efficiency, telecommunications, and particle physics instrumentation. Publications span IEEE journals and conferences, focusing on anomaly detection frameworks, NILM algorithms, and predictive modeling for industrial systems. Collaborations include institutions like CERN, Politecnico di Torino, and Midori srl, an energy efficiency startup.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +2 more

Haw-Shiuan Chang is a Research Fellow in the Department of Computer Science at the University of Massachusetts Amherst, based in Room 366 of the Computer Science Building. His research focuses on advancing natural language processing and artificial intelligence through innovative modeling techniques. His primary research interests include: Natural Language Processing (core focus) Machine Learning optimization Large Language Model efficiency Text generation factuality and diversity Computational creativity measurement Recent publications (2021-2025) reveal a strong emphasis on resolving fundamental limitations in language models, particularly addressing entropy extrapolation, softmax bottlenecks, and multi-mode distribution representation. His work bridges theoretical modeling with practical applications in story generation, recommendation systems, and scientific document processing. No scientific awards or mentoring activities are documented in the provided materials. Research is conducted within the Computer Science department infrastructure, with no specific lab or team affiliations mentioned.

Yury KorolevView profile

Lecturer

• Applied Analysis
• Inverse Problems
• Mathematical Imaging
• +3 more

Yury Korolev is a Lecturer in the Department of Mathematical Sciences at the University of Bath. His research focuses on applied analysis, inverse problems, variational methods, mathematical imaging, and machine learning. He actively organizes workshops, including the Second Workshop on Machine Learning in Infinite Dimensions at ETH Zurich (2025), sponsored by ETH Zurich and the EPSRC Hub ProbAI. His work emphasizes infinite-dimensionality in machine learning, operator learning, and neural networks for partial differential equations (PDEs). He leads multiple funded projects from institutions like the Royal Society and EPSRC, addressing operator learning in inverse problems and regularization theory. Korolev is open to supervising PhD students in machine learning theory, inverse problems, and imaging. Key projects include 'Operator Learning in Inverse Problems' (Royal Society, 2024–2026) and 'Regularisation Theory in the Data Driven Setting' (EPSRC, 2022–2024). His research integrates mathematical rigor with applications in imaging, computational methods, and theoretical foundations of AI. Collaborations span academia and industry, emphasizing interdisciplinary problem-solving.

• Information Network Analytics
• Decentralized Machine Learning
• Data Mining
• +5 more

Sarunas Girdzijauskas is a Professor at the Department of Computer Science within KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science (EECS), where he leads the Graph Analytics and Network Learning group (GALE) . He also holds a Senior Researcher position at RISE (Research Institutes of Sweden) and has served as Program Director for EECS's doctoral program in Information and Communication Technology (2019-2022) and Deputy Director of Third-Cycle Education at EECS. PhD from Ecole Polytechnique Fédérale de Lausanne (EPFL) in 2016 Docent in Computer Science at KTH (2016) Coordinated multiple large-scale projects: H2020 Marie Curie RAIS, Marie Curie ITN iSocial Research Focus : His work spans decentralized machine learning systems, graph analytics, and blockchain infrastructure. Key areas include: Information network analysis for social and technical systems Federated learning with adaptive personalization Graph partitioning and distributed algorithms Privacy-preserving models with differential privacy Knowledge distillation for evolving graph representations Scalable solutions for big data and IoT botnet detection Article Trends : Recent work demonstrates convergence of graph neural networks with federated learning, emphasizing privacy (differential privacy in 5/15 articles), robustness (out-of-distribution detection in 3 articles), and decentralized infrastructure (blockchain, gossip protocols, and P2P overlays). The articles frequently address temporal modeling, knowledge distillation, and heterogeneous graph representations. Scientific Leadership : Coordinated H2020 Marie Curie European Training Network 'RAIS' (2018-2023) Coordinated Marie Curie ITN 'iSocial' (2012-2017) Activity Leader for EIT Digital Innovation Activities Teaching & Advising : Has supervised/examined over 60 master theses and currently advises 8 PhD students, including Adam Orucu (Main Supervisor, 2024-), Filip Cornell (Main Supervisor, 2020-), and Ahmed Emad Samy Youssif (Main Supervisor, 2019-). Former supervisees include 9 PhDs like Dr. Edvin Listo Zec (2025) and Dr. Debaditya Roy (2024).

Paolo FavaroView profile

Professor

• Computer Vision
• Computational Photography
• Machine Learning
• +4 more

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.

Fredrik LindstenView profile

Associate Professor

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +7 more

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Yinghan ChenView profile

Associate Professor

• Bayesian Statistics
• Educational Measurement
• Cognitive Diagnosis Models
• +3 more

Yinghan Chen is an Associate Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR), within the College of Science. Their research focuses on Bayesian statistics, cognitive diagnosis models, and educational measurement, with applications in public health and behavioral science. Key areas include latent variable modeling, health disparities among Asian American populations, and statistical methods for educational interventions. Dr. Chen’s work bridges theoretical statistical methodologies with practical applications in education and healthcare. Recent publications explore topics such as Bayesian hierarchical modeling for cognitive diagnostic assessments, multimodal data integration, and health behavior analysis using frameworks like the transtheoretical model. Their research also addresses algorithm optimization for network motif detection and model reduction techniques in psychometrics. No scientific awards or grants are explicitly listed in the provided materials. They currently advise no formally listed students but contributes to academic advising within their department. The full description provides additional details on their affiliations, educational background, and research trajectory.

Petar VeličkovićView profile

Lecturer

• Geometric Deep Learning
• Graph Representation Learning
• Algorithmic Reasoning
• +3 more

Petar Veličković is an Affiliated Lecturer at the University of Cambridge's Department of Computer Science and Technology and an Associate of Clare Hall. He holds a PhD from Trinity College, Cambridge, and is a Senior Staff Research Scientist at Google DeepMind. His work focuses on geometric deep learning, particularly graph representation learning and its applications in algorithmic reasoning and mathematical conjectures. Education: PhD in Computer Science (University of Cambridge, Trinity College), supervised by Pietro Liò. Research Interests: Geometric Deep Learning, Graph Neural Networks, Symmetry-Invariant Architectures, and Applications in Real-World Systems (e.g., Google Maps Travel-Time Predictions and Mathematical Theorem Discovery). Notable Contributions: First author of Graph Attention Networks and Deep Graph Infomax , which have been widely adopted in machine learning. His work has been featured in Nature , Science , and major tech publications. Awards: Recognized as an ELLIS Scholar in the Geometric Deep Learning Program. Teaching: Co-teaches an MPhil course on geometric deep learning at the University of Cambridge.

Helge RhodinView profile

Professor

• 3D Computer Vision
• Machine Learning
• Computer Graphics
• +3 more

Helge Rhodin is a full professor at Bielefeld University (since March 2024) and an affiliate at the University of British Columbia (UBC). His research focuses on advancing 3D computer vision, machine learning, and computer graphics to enable non-intrusive human-computer interaction, particularly in augmented reality (AR) and virtual reality (VR) environments. He previously held positions as an Assistant Professor at UBC, a postdoctoral researcher at EPFL, and a PhD candidate at the Max-Planck-Institute for Informatics under Christian Theobalt. Education & Career: PhD in Computer Science, Max-Planck-Institute for Informatics (2016) Postdoctoral Researcher, EPFL (2016–2020) Assistant Professor, UBC (2020–2024) Full Professor, Bielefeld University (2024–present) Research Interests: Rhodin’s work spans 3D human pose estimation, neural rendering, AR/VR applications, and generative models. Key contributions include real-time motion capture systems (e.g., XNect), self-supervised learning frameworks, and synthetic biomarker development for movement disorders. Publications: His work has been published at top venues like CVPR, ICCV, and SIGGRAPH, with notable achievements like the Best Paper Award at CRV 2023 and an IEEE VR Honorable Mention in 2019. Research trends emphasize human-centric computer vision and interdisciplinary applications in healthcare and sports analytics. Awards & Recognition: Best Paper Award at CRV 2023 IEEE VR Best Journal Paper Honorable Mention (2019) Advising & Labs: He leads a multidisciplinary team at Bielefeld, previously mentoring over 30 students at UBC. His group focuses on developing open-source tools and frameworks for 3D vision and AR. Current research directions include mirror-aware neural humans and scalable neural rendering techniques.