Find Academic Supervisors

TAN Tiow SengView profile

Associate Professor

• Computational Geometry
• GPU Computing
• Algorithm Design
• +2 more

TAN Tiow Seng is an Associate Professor at the School of Computing, National University of Singapore . He holds a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign (1993), an M.Sc. in Computer Science from NUS (1988), and a B.Sc. in Mathematics & Computer Science from NUS (1984). Research Interests : Design of geometric algorithms for GPU acceleration Applications in interactive graphics, visualization, and game development Development of robust GPU software for geometric computation Selected Publications demonstrate expertise in Delaunay triangulation, Voronoi diagrams, convex hulls, and GPU-based distance transforms. His team pioneered GPU-accelerated mesh refinement techniques and parallel recurrence optimization. Scientific Awards : NUS/SOC Teaching Excellence Award (1999, 2004, 2022) C. W. Gear Outstanding Graduate Student Award (UIUC, 1992) National University Overseas Graduate Scholarship (1988–1992) Data Processing Managers’ Association Award (1984) Industry Contributions : Holds five US/Singapore patents Chairman/co-founder of G Element Pte Ltd , a graphics/visualization company Served as expert panel member for Media Development Authority (MDA) funding evaluations

Abbas KhaliliView profile

Professor

• High-dimensional statistics
• Distributed learning in big data
• Neural Networks
• +5 more

Abbas Khalili is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. His research focuses on statistical methodology in data science, particularly high-dimensional statistics, distributed learning in big data, latent variable models (e.g., finite mixtures, hidden Markov models), and time series analysis. He holds a PhD from the University of Waterloo and BSc/MSc degrees from Isfahan University of Technology. Education: PhD in Statistics, University of Waterloo (supervised by Jiahua Chen) MSc in Mathematical Sciences, Isfahan University of Technology BSc in Mathematical Sciences, Isfahan University of Technology His research interests include post-selection inference, neural networks, sparse network analysis, and change point detection. His work has been funded by NSERC (Canada) and Fonds de recherche du Québec-Nature et technologies. Recent articles focus on mixture models, regularization in autoregressive systems, and hub structure analysis in networks. His work bridges theoretical advancements with practical applications in drug discovery and network science. Dr. Khalili advises students in PhD, MSc, and undergraduate projects in his research areas. He can be contacted at abbas.khalili@mcgill.ca .

Y. Samuel WangView profile

Assistant Professor

• Causal Discovery
• Graphical Models
• High-Dimensional Statistics
• +3 more

Y. Samuel Wang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. He holds a PhD in Statistics from the University of Washington and a BS in Applied Mathematics and Economics from Rice University. Prior to academia, he worked as a management consultant and served as a postdoctoral researcher at the University of Chicago’s Booth School of Business. His research focuses on causal discovery, graphical models, mixed membership models, and high-dimensional data analysis, with applications in network science, environmental studies, and healthcare. Education: PhD in Statistics, University of Washington BS in Applied Mathematics & Economics, Rice University Research Interests: Development of interpretable statistical methods for causal inference and graphical model structures High-dimensional data analysis, particularly in non-Gaussian settings Applications in collaborative networks, environmental microbiology, and healthcare outcomes Recent Research Trends: Recent publications emphasize causal discovery under latent confounding, functional graphical models, and gender dynamics in scholarly collaborations. Methodological contributions include robust high-dimensional inference techniques and computational tools for cyclic structural equation models. Professional Activity: Licensed on GitHub, Google Scholar, and ORCID GitHub repositories include projects on causal discovery (highDNG), gender homophily analysis (genderHomophily), and mixed membership models (mixedMem)

Muirne PaapView profile

Associate Professor

• Psychometrics
• Clinical Diagnostics
• Item Response Theory
• +4 more

Dr. Muirne Paap is an Associate Professor with Ius Promovendi at the Faculty of Behavioural and Social Sciences, University of Groningen. She specializes in psychometrics, with expertise in test theory, item response theory, and computerized adaptive testing. Her research focuses on enhancing clinical decision-making through advanced measurement methodologies. Education: PhD in Psychiatry from the University of Oslo (2011), MSc in Clinical Psychology and Psychometrics from the University of Groningen (2006, cum laude). Research Interests: Development of reliable clinical assessment tools Computerized adaptive testing (CAT) applications in healthcare Measurement of personality disorders and quality of life Notable Projects: SEALS Project (2024–2028): Methodology for progressive tests in education PersoniCAT Project (2019–2022): Adaptive diagnostic interviews for personality pathology Awards: FRIPRO Young Research Talents Grant (2018) Member of the Young Academy Groningen (2021) Teaching: Test theory, item response theory, and applied statistics at undergraduate and master's levels. Collaborations: Extensive international partnerships, including with Oslo University Hospital, Harvard Medical School, and the Netherlands Cancer Institute.

• Mathematical Statistics
• Non-Euclidean Data Analysis
• Algebraic Statistics
• +3 more

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry. His research interests include: Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces. Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models. Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors. Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport. Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives. Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis. No scientific awards listed. No advisees or grants mentioned. Labs or teams are not specified in the provided information.

• Visualization
• Computer Graphics
• Flow Visualization
• +3 more

Tino Weinkauf is a Professor of Visualization and Head of the Division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm. His work bridges computer science and applied mathematics, with a focus on visualization and topological data analysis. He leads research in visualizing complex data from fields like fluid dynamics, neurobiology, and human-computer interaction. Education: Ph.D. in Computer Science (not explicitly stated in provided texts, but inferred from career trajectory). Research interests include flow visualization, topological methods for data analysis, and interactive visualization techniques. He develops tools like the TopoInVis Toolkit (TTK) and contributes to infrastructure such as the Swedish Research Infrastructure for Visualization Support (InfraVis). His work emphasizes applications in turbulence modeling, biomedical imaging, and user-centered design. Teaching: Responsible for courses such as Advanced Topics in Visualization and Computer Graphics , Information Visualization , and Introduction to Visualization and Computer Graphics . Supervises degree projects in Computer Science and Engineering across specializations like Machine Learning and Interactive Media Technology. Publications focus on topological data analysis, flow segmentation, and algorithm optimization. Notable projects include binary segmentation of turbulent flows and interactive reward tuning systems for preference elicitation. Labs/Teams: Leads the Division of Computational Science and Technology at KTH, fostering interdisciplinary research in computational methods and visualization technologies.

Steve MaddockView profile

Senior Lecturer

• Facial Modelling and Animation
• Augmented Reality
• Virtual Reality
• +3 more

Dr Steve Maddock is a Senior Lecturer in Computer Graphics and Acting Head of the Visual Computing research group at the University of Sheffield's School of Computer Science. He holds a Class I Degree in Computer Science (University of Sheffield), a PGCE in Mathematics (11-18), and a PhD in computer graphics modeling and animation, all from the University of Sheffield. With over 30 years of experience in computer graphics software development, he has contributed to the computer games industry through a six-month secondment at Gremlin/Infogrames. His research focuses on facial modeling and animation, augmented/virtual/mixed reality applications, and sketch-based interfaces. Key areas include 3D computer graphics, real-time rendering, and human-robot collaboration systems. Maddock has led and co-led several grants, including projects on game software engineering, rail network surveillance, and heritage visualization using immersive technologies. He is a member of INSIGNEO, Sheffield Robotics, and the Cultural Industries Research Network. Publications highlight contributions to facial analysis for medical diagnostics, style transfer techniques for games, and safety zone visualization in robotics. His work integrates interdisciplinary approaches, combining computer science with fields like biology and robotics. Maddock's Visual Computing research group explores cutting-edge solutions in graphics, virtual environments, and computational tools for real-world applications.

Anthony ConstantinouView profile

Senior Lecturer

• Causal Machine Learning
• Decision Systems
• Bayesian Networks
• +6 more

Anthony Constantinou is a Senior Lecturer at Queen Mary University of London, part of the School of Electronic Engineering and Computer Science. He leads the MInDS Research Group and the Bayesian AI Lab, focusing on causal machine learning for decision-making under uncertainty. His research spans healthcare, defense, sports, economics, and gaming, with a strong emphasis on Bayesian networks and causal inference. Research Interests: Causal machine learning, decision systems, Bayesian networks, uncertainty quantification, and their applications in healthcare, military defense, sports analytics, and economics. He collaborates with academia and industry to advance AI-driven decision-making systems. Grants: Noted for securing funding such as the Engineering and Physical Sciences Research Council grant (EP/S001646/1) for Bayesian AI research. His work emphasizes practical applications in diverse fields through collaborative projects. Labs & Teams: Head of the MInDS Research Group and Bayesian AI Lab, fostering interdisciplinary research in AI and decision systems.

• Privacy-Preserving Algorithms
• Differential Privacy
• Federated Learning
• +6 more

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Libera Università di Bolzano (2018) and a Diploma in Media Informatics from the University of Munich (2014). His research focuses on developing algorithms and data structures for managing large and sensitive datasets, with a particular emphasis on privacy-preserving techniques like differential privacy and federated learning. He has held postdoctoral roles at the University of Warwick and King's College London before joining QMUL in 2023. Research Interests: Privacy-preserving algorithms, differential privacy, federated learning, data management systems, randomized algorithms, and efficient query processing. His work bridges theoretical foundations with practical applications, aiming to enable secure data sharing while preserving individual privacy. Teaching: Leads undergraduate modules in Database Systems and Operating Systems at QMUL. His teaching emphasizes foundational concepts in computer science through rigorous coursework and practical projects. Grants and Funding: Currently supervises a PhD studentship titled 'Privacy-Preserving Algorithms: Unlocking Data Sharing for Medical Sciences & Machine Learning', funded by QMUL and open to UK home students. The role involves exploring privacy-preserving frameworks for collaborative data analysis. Professional Contributions: Serves as a reviewer for top-tier conferences (NeurIPS, ICML, SIGMOD, ICDE) and journals (IEEE TKDE, Data & Knowledge Engineering). Actively involved in conference organization, including NeurIPS Area Chair (2024) and ICDT Proceedings Chair (2024). Labs and Collaborations: Affiliated with the Centre for Fundamental Computer Science at QMUL, fostering interdisciplinary research in theoretical and applied computing. Engages with industry partners on privacy-enhancing technologies and data management solutions.

Ferrante NeriView profile

Professor

• Machine Learning
• Artificial Intelligence
• Optimization
• +5 more

Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.

Sabin TabircaView profile

Senior Lecturer

• Artificial Intelligence
• Data Analytics
• Algorithmics
• +6 more

Dr. Sabin Tabirca is a Senior Lecturer at the School of Computer Science and Information Technology, University College Cork (UCC). He holds a BSc from Bucharest University and a PhD from Brunel University. His research focuses on Artificial Intelligence, Data Analytics, Algorithmics, Interactive Media, and HCI, with applications in computational cancer modeling, mobile health (mHealth), and parallel computing. He coordinates MPT Activities and is affiliated with the CRR Group and BCRI Centre. Notable awards include UCC's President Award for Innovation in Teaching (2007), IT@Cork Leader Award (2008), and UCC Staff Recognition Award (2013). His teaching includes modules like Parallel and Grid Computing, Mobile Application Design, and Graphics for Interactive Media. Dr. Tabirca has supervised over 70 MSc students and numerous PhD candidates, many of whom now hold academic and industry roles globally. His research includes developing mHealth apps for cystic fibrosis patients, 3D cancer visualization tools, and frameworks for mobile parallel computing. Publications span mHealth design pipelines, cancer prediction models, and mobile gaming for health education. He actively engages in interdisciplinary projects, blending computer science with medical and biological applications.

Quan ZhouView profile

Assistant Professor

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +7 more

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Yubai YuanView profile

Assistant Professor

• Complex Network Analysis
• Causal Inference
• Optimal Transport
• +7 more

Yubai Yuan is an Assistant Professor of Statistics at the Pennsylvania State University, affiliated with the Department of Statistics within the Eberly College of Science. He holds a PhD from the University of Illinois Urbana-Champaign (2020) and completed postdoctoral research at UC Irvine. His research focuses on network science, causal inference, and statistical machine learning, with applications to neuroscience and social systems. Notable awards include the 2022 NSF-Simons Center Fellow Award and the 2019 ASA Student Paper Award. Education: PhD in Statistics (UIUC, 2020), MS in Statistics (Sun Yat-sen University, 2016), BS in Mathematics (Shandong University, 2012). Research interests span complex network analysis, optimal transport, active learning, and mediation analysis. Current projects include de-confounding causal inference and hypergraph modeling. Teaching includes courses on probability theory and statistical modeling at Penn State. Advises PhD students Yuanchen Wu (active learning on graphs) and Siyu Huang (latent network structures). Collaborates with the Center for Social Data Analytics and organizes workshops in statistical network science. Publications emphasize methodological advancements in network analysis, causal pathways, and data integration. Recent work addresses disaster response via social media data and neuronal activity analysis using optimal transport frameworks.

Yunxiao ChenView profile

Associate Professor

• Multivariate Statistics
• Social Statistics
• Latent Variable Models
• +5 more

Dr. Yunxiao Chen is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), where he co-leads a psychometric lab with Professor Irini Moustaki. Previously, he was an Assistant Professor at Emory University (2016–2018) and earned his PhD in Statistics from Columbia University (2016). His research focuses on developing statistical and computational methods for social data science, addressing challenges in high-dimensional data analysis, latent variable models, and educational assessment. Education: PhD in Statistics, Columbia University, 2016 Research Interests: High-dimensional factor models (matrices, tensors, counting processes) Dynamic behavioral data analysis Sequential decision theory in personalized learning Statistical inference for large-scale item response data Applications in education, psychology, and marketing Publications: Recent work includes advancements in factor analysis, change-point detection, and DIF statistical inference Key journals: Journal of the American Statistical Association , Psychometrika , Journal of Machine Learning Research Awards: 2024 Psychometrics Society Best Reviewer Award 2022 Early Career Award 2018 NCME Loyd Dissertation Award Advising & Grants: Accepts PhD students in statistical methodology Funded by National Academy of Education/Spencer Fellowship (2018–2020) and IEA R&D grants (2022–2023) Labs & Teams: Runs LSE’s psychometric lab focused on educational measurement Collaborates with interdisciplinary teams on machine learning applications

Seth SullivantView profile

Professor

• Algebraic Statistics
• Computational Algebra
• Combinatorics
• +3 more

Seth Sullivant is a Distinguished Professor in the Department of Mathematics at North Carolina State University (NC State), within the College of Sciences. His research focuses on algebraic statistics, computational and combinatorial algebra, and mathematical phylogenetics. He holds a Ph.D. in Mathematics from the University of California, Berkeley (2005). His expertise spans interdisciplinary areas, including algebraic approaches to statistical problems, combinatorial methods in phylogenetics, and the development of algebraic tools for graphical models. He is affiliated with research groups in Algebra and Combinatorics, Mathematical Biology, and Symbolic Computation at NC State. Recent research trends in his publications emphasize the application of algebraic geometry and combinatorics to statistical models, phylogenetic network analysis, and identifiability problems in systems biology. His work bridges abstract mathematical concepts with practical statistical methodologies, addressing challenges in data analysis and model interpretation. No specific scientific awards are listed in the provided texts. His advising and grant activities are not detailed here, though his extensive publication record suggests active research collaborations. He is part of research teams focused on advancing algebraic methods in statistics and computational biology.