Find Academic Supervisors

• 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.

Victor AkinwandeView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Victor Akinwande is a doctoral researcher at Carnegie Mellon University's Computer Science Department under advisor J. Zico Kolter. His research focuses on robust machine learning systems, causal inference for global health applications, and advancing vision-language models through generative modeling techniques. He holds an MSc in Information Technology (CMU, 2016–2018) and a BSc in Computer Science from the University of Ilorin (2011–2015). His work spans key areas including adversarial robustness, generalization bounds for prompt-based learning, and causal effect estimation in public health. Notable contributions include HyperCLIP (2024), AcceleratedLiNGAM (2024), and foundational work on subset scanning for anomaly detection in health data (2020–2022). Victor has been recognized with an Outstanding Paper Award (ICLR 2024 workshop) and a Distinguished Paper Award Nomination (AMIA 2022). His research bridges theoretical machine learning advancements with practical applications in global health systems and AI safety.

• Computer Vision
• Artificial Intelligence
• Robotics
• +4 more

Randal C. Nelson is an Associate Professor in the Department of Computer Science at the University of Rochester , where he has been employed since 1988, transitioning from Assistant to Associate Professor in 1994. His research focuses on Computer Vision , Artificial Intelligence , and Robotics , with a particular emphasis on motion analysis, object recognition, and real-time visual systems. Education: Ph.D. in Computer Science from the University of Maryland (1988), thesis on Visual Navigation M.S. in Computer Science from the University of Maryland (1985) B.S. in Physics with Honor from the University of Wyoming (1981), and a second B.S. in Mathematics (1982) Research Interests span Computer Vision , Machine Learning , and Robotics , with key contributions in temporal texture analysis , perceptual grouping hierarchies , and appearance-based 3D object recognition . His work explores how visual behavior can inform intelligent systems, emphasizing efficiency through selective information processing. Recent Publications highlight advancements in tracking objects using temporal cues, recognizing non-rigid motion, and developing robust visual systems for cluttered environments. Collaborations with students like Andrea Selinger, Ramprasad Polana, and Brandon Sanders have produced pioneering studies in 3D recognition and robotic manipulation .

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

Antonio Norelli is a Researcher at the University of Oxford's Department of Computer Science, specializing in cutting-edge topics within Artificial Intelligence and Machine Learning. His work bridges theoretical advancements with practical applications, including AI in games, scientific discovery, and multimodal learning. He has contributed to projects like Olivaw, an Othello-playing AI, and explores foundational AI concepts such as explanatory learning and equivariant neural networks. Research interests span from reinforcement learning in strategic games to latent space translation and representation learning. He investigates how AI can emulate human-like behavior and advance scientific methodologies through automated discovery and hypothesis generation. His contributions also address challenges in zero-shot learning and cross-modal adaptation without extensive training data. Notable research includes analyzing the human-likeness of AI agents in games and quantifying the capabilities of language models. His publications reflect a focus on both technical innovation and ethical considerations in AI development. No scientific awards, grants, or lab affiliations are publicly documented. He has not listed academic advisees, though his work suggests potential collaboration opportunities in AI and machine learning domains.

Sergio DaviesView profile

Senior Lecturer

• Spiking Neural Networks
• Neuromorphic Computing
• Computer Network Security
• +3 more

Dr. Sergio Davies serves as a Senior Lecturer in the Department of Computing and Mathematics at Manchester Metropolitan University, leveraging extensive experience from both academic research and industry leadership. His career spans the University of Manchester's SpiNNaker project, the European Human Brain Project, and strategic roles in hardware/software development before returning to academia in 2019. His educational foundation includes an MSc-equivalent in Telecommunication Engineering from the University "Federico II" in Naples and a Ph.D. in Computer Science from the University of Manchester (2012). MSc-equivalent: Telecommunication Engineering, University "Federico II", Naples Ph.D.: Computer Science, University of Manchester (2012) Davies specializes in spiking neural networks and neuromorphic computing, with pioneering work on the SpiNNaker architecture. His current research focuses on applying these technologies to computer network security and robotics, developing event-driven systems for real-time applications like gesture recognition and intrusion detection. This work bridges theoretical neuroscience with practical engineering challenges, emphasizing low-power, real-time operation. Analysis of his 15 most recent publications (2012-2025) reveals a clear evolution from foundational SpiNNaker development toward applied security and robotics. Recent work integrates event-based vision with neuromorphic hardware for multi-object tracking and gesture recognition, while maintaining core contributions to learning algorithms like STDP. The consistent thread is practical implementation on neuromorphic platforms for security-critical applications. Professional recognition includes: Member of the Institution of Engineering and Technology (MIET) Member of the Institute of Electrical and Electronics Engineers (MIEEE) Fellow of the Higher Education Academy (FHEA) His industry experience as an IT consultant at KPMG and subsequent leadership of hardware/software teams informs his applied research approach. Though specific student advisees aren't listed, his work involves mentoring through the SpiNNaker ecosystem and Human Brain Project collaborations. Current projects focus on neuromorphic security solutions developed through university-industry partnerships. Davies operates within Manchester Metropolitan University's neuromorphic computing research group, maintaining active ties to the original SpiNNaker team at Manchester and the Human Brain Project consortium. His lab focuses on translating theoretical neural models into deployable security and robotics systems using SpiNNaker hardware.

• Data Science
• Artificial Intelligence
• Machine Learning
• +7 more

Mohsen Farid is a Senior Lecturer in Data Science at the College of Science and Engineering. His research spans interdisciplinary areas including artificial intelligence, machine learning, neurotechnology, and healthcare analytics. He has contributed to advancements in genomics data management, video authentication, and neurotechnological solutions for mental health conditions like PTSD. His work frequently integrates computational methods with real-world applications in healthcare and cybersecurity. Key research outputs include studies on storage-optimized genomics systems (2024), neurotechnological interventions for PTSD (2023), and deep learning analyses of histopathology images (2021). His earlier contributions include gait recognition systems (2018) and semantic mapping of legal arguments (2015). Farid’s publications reflect a strong focus on applied data science and its societal impact, particularly in healthcare, security, and biomedical engineering. While no specific awards or grants are explicitly mentioned, his extensive publication record highlights sustained contributions to interdisciplinary data-driven research. His articles often address challenges in data management, pattern recognition, and ethical applications of AI.

Riley CasperView profile

Assistant Professor

• Orthogonal Polynomials
• Integrable Systems
• Spectral Theory
• +4 more

W. Riley Casper is an Assistant Professor of Mathematics at California State University, Fullerton (CSUF). His research focuses on the interplay between noncommutative algebra, algebraic geometry, and integrable systems, with applications to special functions and computational fluid dynamics. He holds a PhD from the University of Washington and dual MS/BS degrees from North Dakota State University. Dr. Casper teaches a wide range of courses at CSUF, including Calculus, Differential Equations, Real/Complex Analysis, Abstract Algebra, and Topology. His work spans both pure and applied mathematics, with recent contributions to matrix-valued differential operators, bispectrality, and prolate spheroidal wave functions. He has collaborated with institutions like Louisiana State University and the University of California, Berkeley on projects blending algebraic methods with computational techniques. Selected publications include studies on fractional differential operators, prolate spheroidal systems, and ocean data assimilation. His research has appeared in journals like Communications in Mathematical Physics and the American Journal of Mathematics. Teaching innovations include advanced problem-solving methodologies and applications of algebraic geometry in undergraduate instruction. Professional service includes organizing conferences (GAP XVI, ICM Satellite) and delivering invited talks at institutions worldwide. His work bridges theoretical mathematics with computational challenges in fluid dynamics and quantum computing.

Jacob KauffmannView profile

Researcher

• Unsupervised Learning
• Explainable AI
• Clustering
• +3 more

Dr. Jacob Kauffmann is a Postdoctoral Researcher at the Technical University of Berlin, specializing in research areas such as Unsupervised Learning, Explainable AI, and Anomaly Detection. His work focuses on enhancing model interpretability in unsupervised techniques and uncovering biases like the Clever Hans effect in machine learning systems. Education: M.Sc. Computer Science, Technische Universität Berlin (2017) Research Interests: Jacob’s research bridges theoretical foundations and practical applications of explainable AI (XAI) in unsupervised learning. He investigates clustering explanations, anomaly detection mechanisms, and kernel-based methods to improve model transparency. His recent work explores dataset shifts via Wasserstein distances and neural network-driven cluster analysis. Publications: Dr. Kauffmann’s articles emphasize XAI advancements, such as explainable anomaly detection and model bias analysis. Key themes include algorithmic transparency, dataset transport phenomena, and deep learning applications in unsupervised tasks. Advising & Grants: No formal advisees listed. Research supported by grants related to XAI and unsupervised learning methodologies (specific grants not detailed).

Thomas SchnakeView profile

Researcher

• Explainable AI
• Natural Language Processing
• XAI for Quantum Chemistry
• +3 more

Thomas Schnake is a postdoctoral researcher at the Machine Learning Lab of the Technical University of Berlin and the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a Ph.D. in Machine Learning from TU Berlin and prior degrees in Mathematics and Scientific Computing from Humboldt University of Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing, and the mathematical foundations of machine learning. He has also gained industry experience at ebuero AG and GFaI e.V. in Berlin. Education: B.Sc. Mathematics & Philosophy, Humboldt University Berlin (2014) M.Sc. Mathematics, Humboldt University Berlin (2018) M.Sc. Scientific Computing, Technical University Berlin (2018) Ph.D. Machine Learning, Technical University Berlin (2024) Research Interests include: Explainable AI for complex domains like quantum chemistry and histopathology Graph neural network interpretability through walk-based explanations High-resolution data synthesis with minimal input Unsupervised anomaly detection in text and energy systems His recent publications (2021-2025) demonstrate contributions to XAI frameworks, graph neural network explanations, and transformer model interpretability. He is affiliated with two prominent institutions and maintains active research in interdisciplinary areas combining mathematics and machine learning.

Laura DietzView profile

Associate Professor

• Information Retrieval
• Natural Language Processing
• Knowledge Graphs
• +3 more

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire. She leads the TREMA lab and focuses on text-based machine learning, information retrieval, and environmental data science. Her academic journey includes postdoctoral roles at Mannheim University and the University of Massachusetts, alongside a PhD from the Max Planck Institute for Informatics. Education: PhD in Computer Science (Max Planck Institute, 2011), followed by postdoctoral research at UMass (2012–2015) and Mannheim (2015–2016). Tenure-track appointment at UNH since 2016. Research Interests : IR+NLP: Integrates knowledge graphs, entity retrieval, and machine learning to automatically generate comprehensive articles. This work is supported by an NSF CAREER Award (2019–2023). Watershed Data Science: Analyzes storm event impacts using time-series forecasting, with grants from the Northeast Big Data Hub. Machine Learning: Develops methods for sequential data analysis across domains like social media and environmental science. Grants & Awards : NSF CAREER Award (Utilizing Fine-Grained Knowledge Annotations) Elite Postdoc Program of BW-Stiftung (2016–2019) Best Paper at JCDL 2018 for Entity-Aspect Linking Service & Leadership : Organized HIPstIR Workshop on non-mainstream IR research Coordinated TREC Complex Answer Retrieval Track (2017–2019) Keynote speaker at ECIR, NAACL, and SPIRE Labs & Teams : Head of TREMA Lab at UNH, focusing on query-driven knowledge synthesis and environmental data science. Collaborates with faculty in Natural Resources and the Environment Department.