Find Academic Supervisors

Raymond Ka Wai WongView profile

Associate Professor

• Statistical Learning
• Causal Inference
• Functional Data Analysis
• +4 more

Raymond Ka Wai Wong is an Associate Professor and Director of the PhD Program in the Department of Statistics at Texas A&M University. He holds a PhD in Statistics from the University of California, Davis (2014), an MPhil from The Chinese University of Hong Kong (2010), and a BSc with minors in Mathematics and Risk Management Science (2008). His research focuses on causal inference, functional data analysis, low-rank modeling, reinforcement learning, and statistical learning with applications in astronomy, brain imaging, and genomics. Wong's professional roles include Associate Editor for Journal of Computational and Graphical Statistics , Journal of the American Statistical Association , and member of the Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He has secured grants from NSF, NASA, and NIH, including leadership in projects like Virtual Assistant for Spacecraft Anomaly Treatment and phenomic selection in maize hybrids. His awards include Top Reviewer distinctions at NeurIPS (2023) and ICML (2020), and a 2016 Discussion Paper in the Annals of Applied Statistics. He has advised numerous doctoral students, with notable advisees receiving awards such as the ICSA Student Paper Award and Emanuel Parzen Fellowship. Wong’s recent work emphasizes methodological advancements in reinforcement learning (e.g., distributional off-policy evaluation) and matrix/tensor completion under informative missingness. His research bridges theoretical statistics with practical applications in interdisciplinary domains such as neuroscience, agriculture, and space exploration.

• Image processing
• Remote sensing
• Estimation of model parameters
• +7 more

Max Mignotte is a Full Professor at the Department of Computer Science and Operations Research , Faculty of Arts and Sciences, Université de Montréal. He leads research in Image Processing, Remote Sensing, and Bayesian Inference , focusing on applications in medical imaging, computer vision, and data fusion. His work includes developing advanced algorithms for saliency estimation, multimodal change detection, and 3D reconstruction. Research Interests: Image processing techniques for medical diagnostics, remote sensing analysis, and machine learning-driven solutions for visual attention and segmentation. His methodologies often integrate Bayesian models and fusion strategies to address complex problems in computer vision and biomedical applications. Key Projects: Leading the Multi-Dimensional Scaling Maps for Saliency Estimation initiative. Developing Bayesian fusion models for remote sensing applications. Advancing 3D biplanar reconstruction of human limbs using statistical models. Grants & Teams: Principal investigator on CRSNG-funded projects, including New unsupervised Bayesian and energy-based models (2022–2028) and Bayesian fusion models (2016–2023). Collaborates with the Laboratoire de traitement d’images and interdisciplinary teams in biomedical research.

Debashis MondalView profile

Associate Professor

• Spatial statistics
• Computational science
• Machine learning
• +3 more

Debashis Mondal is an Associate Professor of Statistics & Data Science at Washington University in St. Louis, part of the Arts & Sciences School. He holds a PhD from the University of Washington (2007) and BSTAT/MSTAT from the Indian Statistical Institute. His research focuses on spatial statistics, computational science, machine learning applications in ecology and environmental sciences, and methodological advancements in statistical modeling. Education: PhD in Statistics, University of Washington, Seattle (2007) MSTAT in Statistics, Indian Statistical Institute BSTAT in Statistics, Indian Statistical Institute (2000) Research Interests: Spatial statistics and environmental applications Machine learning and computational methods Data-driven approaches in ecology and microbiology Methodological development for high-dimensional and spatial-temporal data Professional Recognition: Recipient of NSF Career Award (2013), Elected ISI Member (2016), and numerous service awards. His work bridges statistical theory and environmental science, with publications in top journals like Annals of Applied Statistics and Biometrika. Advising and Grants: Mentor to multiple PhD students (e.g., Si Liu, Paul Logan) and has supervised research leading to postdocs and industry roles. His grants include NSF funding for methodological research in spatial statistics. Professional Activities: Organized major conferences (e.g., Midwest Statistics Colloquium, International Indian Statistical Association Conference) and serves on editorial boards and award committees for leading statistical societies.

Haziq JamilView profile

Assistant Professor

• Statistical Theory
• Bayesian Statistics
• Latent Variable Models
• +7 more

Haziq Jamil is an Assistant Professor in Statistics at Universiti Brunei Darussalam (UBD) within the Faculty of Science, Department of Mathematics. He concurrently serves as a Visiting Fellow at the London School of Economics and Political Science (LSE) Department of Statistics (2024-2027) and will transition to King Abdullah University of Science and Technology (KAUST) as a Research Specialist in August 2025, taking leave from UBD. His academic journey includes a PhD in Statistics (2018) and MSc in Statistics (2014) from LSE, and a BSc & Master in Mathematics, Statistics, Operational Research and Economics (2010) from Warwick University. His research spans statistical theory, methods, and computation with strong social science applications. Core interests include latent variable models, Gaussian processes, Bayesian statistics, and spatio-temporal modeling, particularly applied to Brunei's housing market and psychometric testing. He pioneered I-prior regression methodology using Fisher information kernels and developed bias-reduction techniques for Item Response Theory models. His publication record shows consistent output in high-impact journals with increasing focus on Brunei-specific applications since 2022. Haziq's 15 most recent publications (2022-2025) demonstrate methodological innovation in Bayesian computation, latent variable modeling, and spatial statistics, with growing emphasis on real-world applications in Brunei's housing market and educational assessment. Key trends include development of sparse Gaussian process models for property valuation, spatio-temporal analysis of Brunei's real estate, and bias-adjustment methods for psychometric models – reflecting his dual expertise in theoretical statistics and practical implementation. Teaching Excellence Award in Sciences (Universiti Brunei Darussalam, 2023) Arnold Zellner Thesis Award Honourable Mention (American Statistical Association, 2020) In-Service Training Scheme Scholarship (Brunei Public Service Commission, 2013-2018) Supreme Commander of Royal Brunei Armed Forces Scholarship (2006-2010) Best Student Award 2005 (Persekutuan Guru-Guru Melayu Brunei) Haziq serves as Graduate Programme Coordinator for Mathematics (2021-2025) and Faculty Liaison Committee member at UBD. His consultancy includes defense-related data analysis for Brunei's Ministry of Defence (2021-2022). He leads the Brunei R User Group (2024-2026) and maintains active research collaborations through the Bayesian Computational Statistics and Modelling (BAYESCOMP) group at KAUST. Current projects include open-source statistical tables, I-prior methodology R packages, and quantitative text analysis of Brunei's legislative council meetings. He directs multiple ongoing research initiatives including the Brunei housing market dataset covering 30,000+ transactions across three decades, Hamiltonian Monte Carlo educational tools, and quantitative analysis of Brunei's legislative proceedings. His work bridges theoretical statistics with practical applications in urban planning, defense analytics, and educational assessment within Brunei's unique socio-economic context.

Andriy NoretsView profile

Professor

• Bayesian Econometrics
• Dynamic Discrete Choice Models
• Nonparametric Estimation
• +3 more

Andriy Norets is a Professor in the Economics Department at Brown University. His research focuses on Bayesian econometrics, dynamic discrete choice models, and nonparametric estimation methods. He has contributed to advancements in econometric theory, particularly in areas such as posterior consistency, adaptive estimation, and computational methods for complex models. Key research themes include Bayesian nonparametric models, instrumental variable regression, and the development of efficient computational algorithms for variable dimension models. Norets has published extensively in top journals like Econometrica , Journal of Econometrics , and Annals of Statistics . His work emphasizes methodological rigor and practical applications, such as analyzing stock market trading activity and improving inference in nonstandard econometric problems. He also collaborates with co-authors like Ulrich Müller, Justinas Pelenis, and Debdeep Pati on projects spanning from theoretical foundations to applied econometric problems. Norets teaches courses including Undergraduate Econometrics and Graduate Econometric Theory II, demonstrating his commitment to both research and education in economic methodology.

Piotr ZwiernikView profile

Associate Professor

• Graphical Models
• Covariance Matrix Estimation
• Convex Analysis
• +7 more

Piotr Zwiernik is a Professor Agregat in the Department of Economics and Business at Universitat Pompeu Fabra (UPF), Barcelona, and a member of the BSE Data Science Center. He is currently on leave from the Department of Statistical Sciences and the Department of Mathematics at the University of Toronto. His research bridges statistics, algebraic geometry, and machine learning, focusing on graphical models, covariance estimation, tensors, and algebraic methods in statistics. Research Interests: Graphical Models Covariance Matrix Estimation Convex Analysis Tensors Algebraic and Combinatorial Methods in Statistics Statistical Learning Theory His recent publications span high-impact journals such as the Annals of Statistics , Biometrika , and Journal of the Royal Statistical Society . The work demonstrates a consistent trend in developing mathematically rigorous frameworks for understanding dependence structures, latent variable models, and high-dimensional inference, often leveraging tools from algebraic geometry and convex optimization. Key themes include total positivity, tensor methods, and the geometry of statistical models. Scientific Recognition: Editorial Board Member, Journal of the Royal Statistical Society Series B (JRSS-B) Editorial Board Member, Biometrika Editorial Board Member, Scandinavian Journal of Statistics Editorial Board Member, Algebraic Statistics Zwiernik actively mentors students and is seeking PhD candidates with strong mathematical backgrounds at UPF and the Institute of Mathematics of UPC. He has led the development of several open-source R packages, including golazo , MTP2binary , and StructuralEM , facilitating research in graphical models and latent structures. His work is supported by a network of collaborations with leading statisticians and has significant theoretical and applied implications in data science. Laboratories and Research Groups: Statistics@UPF BSE Data Science Center

Søren HøjsgaardView profile

Associate Professor

• Statistics
• Graphical Models
• Bayesian Networks
• +7 more

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.

• Molecular Biology
• Microbiology
• Environmental Science
• +4 more

Isabelle Ewers is a researcher in Eukaryotic Microbiology at the Faculty of Biology, University of Duisburg-Essen, Germany. She specializes in phylogenetic placements for taxonomic assignment of environmental DNA, particularly focusing on colpodean ciliates and their evolutionary and ecological characteristics. Her research compares phylogenetic placement methods (EPA-ng, GAPPA) with pairwise alignment approaches (VSEARCH) to resolve taxonomic assignments in environmental sequencing data. This includes analyzing operational taxonomic units (OTUs) from Neotropical rainforest soils, evaluating likelihood weight ratios for placement accuracy, and measuring expected distances between placement locations to quantify uncertainty. Key findings include demonstrating that phylogenetic placement outperforms pairwise alignment for low-similarity sequences and establishing frameworks for interpreting multi-placement scenarios in microbial eukaryotes. Her work has implications for improving metabarcoding workflows and understanding protist diversity in understudied ecosystems.

• Environmental Engineering
• Bushfire Risk Modeling
• Natural Hazards Management
• +3 more

Dr. Amelie Jeanneau is a Grant-Funded Researcher (A) at the University of Adelaide, affiliated with the School of Architecture and Civil Engineering within the Faculty of Sciences, Engineering and Technology. She specializes in spatiotemporal modeling applied to environmental challenges such as bushfire risk, natural hazards, urban planning, and soil erosion. Her research emphasizes collaboration with stakeholders like farmers and local governments to translate scientific insights into practical solutions. Dr. Jeanneau is eligible to supervise Masters and PhD students as a co-supervisor. Her research interests focus on modeling complex environmental systems, including wildfire dynamics, fuel management strategies, erosion processes, and the integration of GIS and remote sensing technologies. Recent work highlights innovative approaches to quantify wildfire likelihood, assess regional wildfire risk using earth observation data, and develop decision-support frameworks for natural hazard mitigation. Notable contributions include studies on climate change impacts on bushfire risk, wind erosion modeling in agricultural zones, and the application of novel indices for multi-scale wildfire analysis. Her interdisciplinary approach bridges technical expertise with real-world policy and management applications, aiming to enhance environmental resilience and sustainable practices. Dr. Jeanneau’s current projects involve advancing computational methods for burn probability estimation, optimizing fuel management strategies, and enhancing soil erosion prediction through integrated modeling frameworks. She actively engages in academic collaborations and contributes to the broader discourse on environmental risk reduction and sustainable land management.

• Data Assimilation
• Numerical Multiscale Methods
• Stochastic Systems
• +3 more

Dr. John Maclean is a Lecturer in Data Science and Statistics at the University of Adelaide, affiliated with the School of Computer and Mathematical Sciences within the Faculty of Sciences, Engineering and Technology. His research focuses on Data Assimilation (DA) and Numerical Multiscale Methods, with particular interests in coherent structure DA, projected DA, non-Gaussian measurement error modeling, and surrogate-based DA techniques. He also explores projective integration and patch dynamics for stiff systems and spatially heterogeneous problems. His work addresses challenges in combining uncertain model forecasts with data, accelerating simulations of complex systems, and designing efficient statistical surrogates. Notable contributions include methodologies for adaptive moving patches in multiscale simulations and theoretical insights into stochastic processes and uncertainty quantification. Dr. Maclean is actively involved in supervising postgraduate research students and is open to mentoring those interested in his research areas. His publications span topics from data assimilation algorithms to environmental and computational modeling, reflecting his interdisciplinary approach to applied mathematics and computational science.

Hugo Maruri-AguilarView profile

Lecturer

• Algebraic Statistics
• Computer Experiments
• Statistical Data Analysis
• +4 more

Dr Hugo Maruri-Aguilar is a Lecturer in Statistics at Queen Mary University of London, affiliated with the School of Mathematical Sciences and the Centre for Probability, Statistics and Data Science. His research focuses on algebraic statistics, computer experiments, and statistical modeling using likelihood and penalized likelihood techniques. He explores design of experiments through algebraic methods and develops emulators for computer experiments using polynomial models regularized by smoothness. His research interests include Lasso regression, topological data analysis, and applications in music generation and healthcare. Recent work emphasizes model selection and sparse polynomial prediction. He has collaborated on projects ranging from biomarker prognostic models in heart failure to AI-driven music composition. Dr Maruri-Aguilar has contributed to over 15 peer-reviewed publications across journals like Statistical Papers , Annals of the Institute of Statistical Mathematics , and Technometrics . His methodologies address challenges in optimal design, computational topology, and interdisciplinary applications in music and medicine.

Kaie KubjasView profile

Associate Professor

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +5 more

Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.

Erich SchikutaView profile

Associate Professor

• Cloud Computing
• Information and Database Systems
• Parallel and Distributed Computing
• +3 more

Erich Schikuta is an associate professor at the University of Vienna's Faculty of Computer Science and heads the Research Group Workflow Systems and Technology. He has held multiple administrative roles, including Vice Dean and Curricular Commission Head. His research focuses on Cloud Computing, Parallel/Distributed Systems, Neural Networks, and Database Systems, with over 200 peer-reviewed publications. Education: Studied Computer Science, Business Informatics, and Mathematics at the University of Technology Vienna and University of Vienna. Holds titles including Dipl.-Ing., Dr., and Berufstitel Universitätsprofessor (2009). Research interests emphasize Utility Computing, Cloud Resource Optimization, and Neural Network Simulation. Notable contributions include ranking 25th globally in Cloud Computing productivity and developing frameworks like N2Sky for neural network-as-a-service. Key awards include the 2011 Best Paper Award and 2010 Best Teacher Award. Active in international collaborations, he serves as a European Commission evaluator (FP5-FP7/H2020) and chairs program committees. Labs/Teams: Leads the Research Group Workflow Systems and Technology. Involved in projects like BIG-Business (Grid Infrastructure) and 'Änderung in kollab.Prozessen' (2011-2016).

• Coding Theory
• Discrete Mathematics
• Blockchains
• +5 more

Dr. Son Hoang Dau is a Senior Lecturer in Computer Science at the School of Computing Technologies, RMIT University, Australia. His research focuses on coding theory, discrete mathematics, blockchains, and their applications in secure distributed systems and quantum computing. He actively supervises PhD and Master’s students in areas like fraud detection on blockchain networks and quantum annealing for satellite communication optimization. Research Interests: - Coding Theory & Error-Correcting Codes - Blockchain Security & Decentralized Systems - Quantum Computing & Optimization - Privacy-Preserving Algorithms - Distributed Storage Systems Dr. Dau's recent work includes groundbreaking studies on blockchain fraud detection, secure erasure coding in distributed storage, and quantum annealing for satellite beam placement. His contributions bridge theoretical computer science with practical applications in cybersecurity and telecommunications. Supervision Projects: - Machine Learning methods for Fraud Detection on Blockchain networks - Efficient Repair of Reed-Solomon Codes - Robust Computer vision for surveillance and threat detection Collaboration Network: Engages with global researchers in cryptography, distributed systems, and quantum computing through publications in top venues like IEEE Transactions and ACM Conferences.

• Financial Econometrics
• Time Series Analysis
• Large Dimensional Financial Data Analysis
• +4 more

Professor Ruijun Bu is a Professor of Econometrics at The University of Liverpool Management School (ULMS). He holds a Bachelor’s degree in Engineering from Tongji University, China, and a Master’s in Finance and a Ph.D. in Economics and Finance from The University of Liverpool. His research focuses on financial econometrics, time series analysis, large-dimensional data, nonparametric statistics, quantitative finance, and energy economics. He has held significant roles, including Director of Research for the Economics Group at ULMS and Founder/Director of the Econometrics and Big Data research cluster (2012–2021). He has secured grants from the ESRC, British Academy, and others. His work has been published in top journals like the Journal of Econometrics and Energy Economics . Bu has collaborated with institutions such as Princeton University (as a Visiting Research Fellow) and international universities on topics like regime-switching models and energy economics. He is an Associate Editor of Economic Modelling (2020–present) and has contributed to academic committees and teaching modules in econometrics and financial economics. Education: Bachelor’s in Engineering, Tongji University Master’s in Finance, University of Liverpool Ph.D. in Economics and Finance, University of Liverpool Research Interests: Financial Econometrics Time Series Analysis Large-Dimensional Data Analysis Non- and Semi-Parametric Statistics Quantitative Finance Empirical Finance Energy Economics Grants and Awards: Dissecting Systemic Risks in Large Economic Sectors (British Academy, 2022–2024) Modelling Interest Rate Dynamics (ESRC, 2012–2014) Teaching and Advising: Supervised theses on econometric models and transformed diffusion applications. Teaches modules like ECON311 (Time Series Econometrics) and ECON308 (Quantitative Financial Economics). Labs and Collaborations: Led the Liverpool Advanced Methods for Big Data Analytics (LAMBDA) Research Centre. Collaborations with Professors at institutions like Keele University, University of Lille, and Sungkyunkwan University.