Find Academic Supervisors

• Big Data Management
• Scalable Data Science
• Spatial and Spatio-Temporal Data Processing
• +5 more

Suprio Ray is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick, Canada, leading the Big Data Systems and Analytics Lab. He holds a PhD from the University of Toronto, an M.Sc. from the University of British Columbia, and a B.E. from NIT Trichy. Previously, he worked in industry roles at Oracle, Bell Labs, and Webtech Wireless, and completed a PhD internship at SAP. His research focuses on scalable data systems, spatial/spatio-temporal data management, and modern hardware utilization. Key projects include the Jackpine spatial database benchmark , DaskDB (a scalable data science system), and NUMA-aware query processing . He collaborates across disciplines with ECE and GGE departments, supported by grants from NBIF, NSERC, and industry partners. Research interests span Big Data systems, privacy/security in databases, blockchain analytics, and parallel/distributed computing. He has pioneered techniques like STILT multi-dimensional indexes , privacy-preserving spatial queries (Pystin) , and learned spatial indexes . Over 60 publications appear in top venues like SIGMOD, ICDE, and IEEE BigData. Education: PhD (Computer Science), University of Toronto (2015) M.Sc. (Computer Science), University of British Columbia (2003) B.E. (Computer Science & Engineering), NIT Trichy (2000) Teaching includes courses on big data systems (CS4545/6545), database foundations (CS6585), and data science (CS2545). He advises graduate students in scalable analytics and spatial systems, emphasizing industry-relevant software skills. Key awards include the 2024 ACM SIGSPATIAL best poster award , IBM Best Student Paper (2014) , and Harrison McCain Foundation award (2016) . His work has been recognized with 7 best paper awards and 3 patents. Current projects explore FPGA-accelerated joins , serverless data analytics , and privacy-preserving blockchain queries . Past contributions include the GEMM mobility model (2003) and foundational spatial benchmarks.

• Spatial Statistics
• Spatio-Temporal Analysis
• Image Analysis
• +3 more

Henrike Häbel is a Researcher at Karolinska Institutet, affiliated with the Department of Learning, Informatics, Management and Ethics, Department of Clinical Neuroscience, and Department of Medicine, Huddinge. She holds roles in the Medical Statistics Unit and Tanja Tomson's Prevention group. With a doctoral degree in Mathematical Statistics from Chalmers University of Technology and the University of Gothenburg (2017), she completed a postdoc at the Natural Resources Institute Finland before joining Karolinska Institutet. Her work focuses on statistical consultation, data management, survival analysis, and regression modeling in epidemiological studies. She actively contributes to teaching across multiple academic institutions and participates in the Centre for Bioinformatics and Biostatistics. Key research interests include spatial and spatio-temporal statistics, spatial point processes, and image analysis. Her work spans diverse medical domains such as oncology, cardiovascular disease, and public health. She received the Cramér Prize for Best Thesis in 2018. Current projects include obesity prevention programs (MINISTOP 3.0), rural emergency team dynamics, and long-term outcomes of severe COVID-19. She collaborates across departments and institutions, contributing to multidisciplinary teams like the Eye movements and vision group under Tony Pansell.

Moinak BhaduriView profile

Assistant Professor

• Change-Point Detection
• Stochastic Processes
• Repairable Systems
• +2 more

Dr. Moinak Bhaduri is an Assistant Professor in Mathematical Sciences at Bentley University. His research applies stochastic modeling to change-point detection, spatio-temporal processes, and repairable systems. He holds a Ph.D. from the University of Nevada, Las Vegas. His interdisciplinary work spans finance (market spillovers), environmental science (hurricane interactions), legal analytics (defamation trials), and social dynamics (immigration networks). Publications leverage methods like hidden Markov chains and recurrence rate ratios. No awards or student mentorship details are provided.

Kyran CupidoView profile

Associate Professor

• Actuarial Science
• Spatial Statistics
• Data Science
• +4 more

Kyran Cupido is an Associate Professor in the Department of Mathematics and Statistics at Saint Francis Xavier University. His work bridges spatial statistics with actuarial science, focusing on mortality risk, insurance modeling, and socioeconomic implications of geospatial patterns. PhD in Statistics, Arizona State University, 2020 MSc in Statistics, McMaster University, 2017 BSc in Mathematics and BEd in Intermediate/Senior Education, Brock University, 2015 Research interests center on integrating spatial statistics into actuarial domains. His publications analyze mortality trends, cybercrime patterns, emergency response systems, and social media dynamics through geospatial frameworks. Recent work includes natural hedging techniques for mortality risk and applications of machine learning to emergency logistics. Kyran Cupido has not been explicitly recognized for scientific awards in the provided texts. His academic contributions are documented through publications rather than student advisement details or laboratory affiliations.

Pratheepa JeganathanView profile

Assistant Professor

• Statistical Learning
• Spatial Statistics
• Bayesian Methods
• +3 more

Dr. Pratheepa Jeganathan is an Assistant Professor in the Department of Mathematics and Statistics at McMaster University. Her research focuses on developing statistical methods for multi-view learning, particularly in modeling dependencies across heterogeneous data sources. Applications span molecular microbiology, spatial omics, sensor-based traffic data, and loss reserving. Her methodological work includes generative models, Bayesian sampling, constrained clustering, and spatio-temporal statistics. Education: PhD in Mathematics (Statistics) from Texas Tech University (2016), Postdoctoral Fellowship at Stanford University (2016–2020). Research Interests: Spatial statistics, statistical learning, high-throughput data methods, and statistical theory. Recent work includes analyzing microbiome interventions using transfer functions, studying vaginal microbiota communities, and applying recurrent neural networks to multivariate loss reserving. She has published in journals like PLoS Computational Biology and Genome Biology . Teaching: Instructs courses in data science (STATS 3DA3, CSE 780), statistical research projects, and graduate-level topics in statistics.

• Biostatistics
• Clinical Epidemiology
• Public Health
• +6 more

Jinhui Ma is an academic researcher affiliated with McMaster University, specializing in biostatistics, clinical epidemiology, and public health. Their work focuses on aging populations, bone health in pediatric and geriatric contexts, and application of statistical methods in healthcare research. Research interests include biomarker validation for bone disorders, multimorbidity patterns in chronic disease populations, urban health planning for climate resilience, and methodological advancements in clinical trial design. They contribute to large-scale cohort studies like the Canadian Longitudinal Study on Aging (CLSA), investigating mobility, frailty, and healthcare utilization patterns. Recent work emphasizes machine learning applications for missing data imputation, spatiotemporal analysis of urban heat risks, and validation of predictive models for cardiovascular and bone-related outcomes. Their interdisciplinary approach bridges statistical methodology with clinical and public health challenges. Key collaborations involve institutions across Canada and China, addressing global health issues such as osteoporosis management in pediatric populations and environmental health interventions.

Louis DurlofskyView profile

Professor

• Subsurface Flow Modeling
• Reservoir Engineering
• Carbon Sequestration
• +6 more

Louis Durlofsky is the Otto N. Miller Professor of Energy Science & Engineering at Stanford University's School of Earth, Energy & Environmental Sciences. He specializes in subsurface flow modeling, reservoir simulation, and carbon sequestration. His research bridges computational methods with practical applications in energy systems and environmental science. Education: PhD in Chemical Engineering from MIT (1986), with earlier degrees from MIT and Penn State. He has held academic and industry roles, including Chevron (1987–1999) and Stanford since 1998, where he chaired the Energy Resources Engineering department (2006–2012). Research Interests: Focus on advanced reservoir engineering, data assimilation, machine learning for subsurface modeling, and closed-loop reservoir management. His work integrates deep learning surrogates, reduced-order models, and geological parameterization to address challenges in CO2 storage and energy systems optimization. Awards: Member of the National Academy of Engineering, SPE Distinguished Member, and multiple best-paper awards in geosciences and reservoir engineering. He leads the Stanford Smart Fields Consortium and Reservoir Simulation Research programs. Teaching: Courses on subsurface flow simulation, reservoir engineering, and energy-environment interactions. Advises over 20 PhD/Master’s students and postdocs, contributing to next-generation energy research. Labs & Collaborations: Directs industrial affiliates programs and collaborates globally on initiatives like the Stanford Center for Carbon Storage. Active in editorial roles for Computational Geosciences and Mathematical Geosciences .

• Big Data Analysis
• Bayesian Statistics
• Genomics Data Analysis
• +3 more

Madhuchhanda Bhattacharjee is a Professor of Statistics and Data Science with expertise in statistical methodology for large-scale data analysis. Her research focuses on Bayesian integrated modeling of omics data, spatio-temporal processes, and stochastic processes. She holds advanced degrees including a Ph.D. in Statistics from the University of Pune (2000), an M.Phil. in Statistics from the University of Pune (1995), and a Master of Statistics from the Indian Statistical Institute (1993). She is a Fellow of the Royal Statistical Society and an Elected Member of the International Statistical Institute, among other prestigious memberships. Her work addresses critical areas such as microbiome analysis, spatial epidemiology of diseases like cancer, and modeling climate variability using random matrix theory. She has contributed to the development of novel statistical methods for assessing dependence structures in spatio-temporal data and has published extensively on Bayesian approaches to integrate multi-omics datasets. Key research themes include: Inference on large-scale data using random matrices and parallel processing Bayesian modeling of heterogeneous biomedical data Spatio-temporal modeling of public health crises like the 2020 pandemic Her recent work explores statistical methods for predicting preterm birth via maternal blood multi-omics integration and spatial association measures for time series. She co-leads the Statistical Advisory Unit (SAU), providing advanced statistical methodologies for interdisciplinary research projects. Major awards include Fellow of the Royal Statistical Society (2005) and Elected Membership in the International Statistical Institute (2005). She has advised on several grants focusing on omics data integration and disease marker identification. Her contributions span academic leadership roles, including memberships in global statistical societies like the International Indian Statistical Association and the Caucus for Women in Statistics. She has pioneered statistical methodologies applied to environmental and healthcare challenges, emphasizing reproducibility in data-intensive research.

Tailen HsingView profile

Professor

• Functional Data Analysis
• Spatial Data Analysis
• Extreme Value Theory
• +2 more

Tailen Hsing is the Michael B. Woodroofe Collegiate Professor of Statistics at the University of Michigan's Department of Statistics, part of the College of Literature, Science, and the Arts (LSA). His research focuses on functional data analysis, spatial statistics, and extreme value theory. He has held academic leadership roles, including serving as Chair of the Department of Statistics from 2010 to 2015. Education: Tailen Hsing earned his Ph.D. in Statistics from the University of North Carolina in 1984, following an M.S. in Statistics from the same institution in 1983, and a B.S. in Mathematics from National Taiwan University in 1978. His academic career includes prior appointments at Ohio State University, the National University of Singapore, and Texas A&M University. Research Interests: Hsing’s work centers on extreme value problems, functional data analysis, long-range dependence, and space-time models. He has made significant contributions to theoretical foundations of functional data, including a notable book published in 2015. His current research explores high-dimensional parameters in functional data and inference for locally stationary spatial processes. Publications: His recent work includes advancements in spectral density estimation for spatial processes, functional data segmentation, and multivariate spatial models. These contributions highlight his expertise in bridging theoretical and applied statistical methods. Editorial Roles: He serves as an Associate Editor of Statistical Science and the Editorial Board of SpringerBriefs in Probability and Mathematical Statistics, among other editorial positions. Previously, he co-edited the Annals of Statistics and contributed to editorial roles for leading journals like Bernoulli and Statistica Sinica . Advising and Grants: While specific grants and student advisees are not detailed here, his extensive publication record reflects collaborative work with students and colleagues. His research has been supported through various academic collaborations and institutional resources. Labs and Teams: Though no specific labs are mentioned, his collaborations with researchers such as Stoev and Yarger indicate active participation in interdisciplinary teams focused on spatial and functional data analysis.

Ryan TurnerView profile

Associate Professor

• Water Quality
• Pollution Science
• Sediment Transport
• +2 more

Dr. Ryan Turner is Associate Professor at the University of Queensland and Director of the Reef Catchments Science Partnership. His research addresses water quality challenges in Great Barrier Reef catchments through advanced monitoring and risk assessment frameworks. Turner develops novel methods for assessing pesticide risks and sediment transport using sensor networks, machine learning, and statistical modeling. His work informs policy through the Pesticide Decision Support Tool and guides catchment management strategies. Recent innovations include temporal response surface methods for cumulative risk assessment and unsupervised anomaly detection for water quality sensor data. Turner leads multidisciplinary teams integrating chemistry, hydrology, and data science for environmental protection.

• Machine Learning
• Natural Language Processing
• Computational Linguistics
• +3 more

Prof. Ryan Cotterell is an Assistant Professor in the Department of Computer Science at ETH Zürich. His research focuses on machine learning and natural language processing, particularly in transformer models, syntactic control of language systems, and computational linguistics. He teaches courses such as Neural Networks and Computational Complexity, Natural Language Processing, and Advanced Topics in Machine Learning. His work bridges theoretical foundations and practical applications, exploring topics like language model expressivity, information locality, and formal language theory. Recent publications highlight innovations in controlled generation, model evaluation, and multimodal systems. While no specific awards are listed, his contributions to the field are evident through his extensive publication record and academic roles. Advising and grant details are not explicitly mentioned in the provided texts, but his involvement in teaching and research indicates active mentorship and project leadership. No lab or team affiliations specific to Ryan Cotterell are detailed here.

• Environmental Engineering
• Climate Change Adaptation
• Flood Resilience
• +5 more

Professor Lindsay Beevers is the Chair of Environmental Engineering and Head of Research Institute at the University of Edinburgh's School of Engineering. She holds a PhD in Civil Engineering from the University of Glasgow and a PGCert in Academic Practice from Heriot-Watt University. Her career spans over 20 years in industry (e.g., Jacobs) and academia, with extensive experience in climate change adaptation, hydrological modeling, and water resource management. Her research focuses on flood resilience, urban systems, and ecosystem services. Notable projects include EPSRC LWEC Challenge Fellowship work on Water Resilient Cities and studies on climate impacts in Africa, Asia, and the UK. She has authored over 50 peer-reviewed papers and contributed to UNESCO-IHE initiatives in river systems. Publications emphasize probabilistic assessments of hydro-hazards, urban resilience frameworks, and the integration of social media data in flood management. Her work bridges engineering, ecology, and policy, addressing systemic challenges in climate adaptation and sustainable development.

Cynthia RiginosView profile

Professor

• Ecological and evolutionary genomics
• Genetic markers
• Dispersal and gene flow
• +7 more

Professor Cynthia Riginos is a leading academic in Ecological and Evolutionary Genomics at the University of Queensland , affiliated with the School of the Environment and Centre for Marine Science . Her research focuses on genetic markers to study dispersal, gene flow, and conservation implications in marine species like fishes, mussels, and corals. Research themes: Connectivity across land and seascapes, Stochasticity in planktonic dispersal, Biological invasions Current grants: Empowering community-led coral restoration (2024-2027), Invasive species adaptation (2022-2026), Crown-of-Thorns Starfish control (2020-2025) Her educational background includes a Doctor of Philosophy and a Masters (Coursework) of Science from the University of Arizona . Her recent publications highlight coral climate resilience, cryptic species dynamics, and genomic patterns in marine organisms. She has supervised numerous PhD and Master’s students on topics like genomic adaptation in corals, invasive mussel genetics, and reef connectivity. Her lab collaborates with institutions such as the Australian Institute of Marine Science and Centre for Biodiversity and Conservation Science .

• Predictive Analytics
• Recommendation Systems
• Graph Learning
• +4 more

Professor Hongzhi Yin is a leading academic at The University of Queensland, Australia, serving as a Professor and director of the Responsible Big Data Intelligence Lab (RBDI). His research focuses on predictive analytics, recommendation systems, graph learning, and decentralized intelligence. He holds an ARC Future Fellowship and has received numerous awards, including the 2023 Young Tall Poppy Science Award and multiple best paper awards. His work spans machine learning, data mining, and social media analysis, with over 350 publications and an H-index of 83. He advises PhD students and leads collaborative projects in trustworthy AI, federated learning, and privacy-preserving systems. Education: Completed his PhD at Peking University, receiving recognition for his thesis. Research contributions include foundational work on graph condensation, federated recommendation systems, and generative AI trustworthiness. He serves as an editor for top journals like IEEE Transactions on Knowledge and Data Engineering and ACM Transactions on Information Systems . Awards and Honors: ARC Future Fellowship, Young Tall Poppy Science Award (2023), AI 2000 Most Influential Scholar Honorable Mention (2022-2024), and eight international best paper awards. He is also a recognized leader in data mining and computer science, ranked #52 in Australia's Best Scientists 2025. Research Interests: Trustworthy recommendation systems, graph neural networks, federated learning, and privacy-preserving AI. His lab develops methods for robust graph condensation, secure decentralized systems, and ethical AI applications. Recent work includes surveys on point-of-interest recommendation and graph condensation techniques. Labs/Teams: Leads the Responsible Big Data Intelligence Lab (RBDI), fostering innovation in data science and AI ethics. Collaborates globally with institutions like MIT, Stanford, and Carnegie Mellon on foundational AI research.

Adam SzpiroView profile

Professor

• Environmental Biostatistics
• Spatial Statistics
• Epidemiological Methods
• +3 more

Adam Szpiro is a Professor of Biostatistics at the University of Washington, serving as Chair of the Department since 2009. He earned his PhD and ScM in Applied Mathematics from Brown University (1999 and 1994) and a BA in Mathematics from UC San Diego (1993). His research focuses on environmental and spatial statistics, particularly in exposure modeling and epidemiological methods. He leads major projects such as the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) and the Environmental Influences on Child Health Outcomes (ECHO) consortium. Key research interests include spatio-temporal exposure modeling, machine learning for environmental data, and statistical methods for analyzing mixtures of pollutants. He has pioneered techniques to address measurement error and unmeasured confounding in environmental health studies. Notable collaborations include randomized trials like the Gates Foundation-funded DO ART trial (improving HIV treatment delivery) and NIH-funded Deliver Health and Lotto-to-Link trials. He also contributed to the Duwamish Air Improvement Study for Youth (DAISY) and studies on greenness effects in urban environments. Awards include recognition for methodological work, including the 2018 Rothman Epidemiology Prize runner-up. Prior to academia, he worked at MIT Lincoln Laboratory on national security-related modeling (1999–2006).