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Liang HuaView profile

Professor

• Partially Linear Models
• High-Dimensional Semi-parametric Modeling
• Model Averaging and Model Selection
• +4 more

Roles and Affiliations: Liang Hua is a Professor of Statistics and Director of Doctoral Programs at the Department of Statistics, The George Washington University. He previously held positions at the University of Rochester Medical Center, St. Jude Children's Research Hospital, and Humboldt University as an Alexander von Humboldt Fellow. Education: Hua holds two Ph.D.s: one in Statistics from Texas A&M University (2001) under Raymond J. Carroll, and another in Mathematical Statistics from the Chinese Academy of Sciences (1992) under Ping Cheng. Professional Service: He serves as an Associate Editor for multiple journals including JASA, Journal of Nonparametric Statistics, and Biostatistics. His expertise spans partially linear models, high-dimensional modeling, and HIV/AIDS clinical trial design. Research Interests: Hua focuses on statistical methodology development, including model averaging, longitudinal data analysis, and measurement error models. His work bridges theoretical advancements and practical applications in biostatistics and public health. Awards and Recognition: Hua is a Fellow of the ASA, IMS, and Royal Statistical Society, and an elected member of the International Statistical Institute. He also received the prestigious H. O. Hartley Award from Texas A&M University. Grants and Advising: While specific grants are not detailed, his extensive editorial roles and high-impact publications reflect significant research leadership. He has advised numerous students through doctoral programs at GWU.

• Bayesian Statistics
• Biostatistics
• Mixture Models
• +6 more

Mahlet G. Tadesse is a Professor and Chair of the Department of Mathematics & Statistics at Georgetown University. She previously held an Assistant Professor position at the University of Pennsylvania. She earned her Ph.D. and M.Sc. in Biostatistics from Harvard University and a B.S. in Mathematics from the University of the District of Columbia. Her research focuses on developing statistical and computational tools for analyzing genomic data, with emphasis on Bayesian methods, variable selection, and integrative approaches to genomic data analysis. Her expertise spans Bayesian statistics, biostatistics, mixture models, and omics-based bioinformatics. She investigates high-dimensional datasets to identify biological markers, predict clinical outcomes, and integrate biological knowledge into genomic studies. Current projects include biomarker discovery for hepatocellular carcinoma, analysis of maternal-fetal genetic interactions, and modeling species dynamics in biodiversity-rich ecosystems. Dr. Tadesse has published extensively on topics such as Bayesian variable selection, integrative genomics, and computational methods in proteomics and metabolomics. Her work bridges statistical theory and application, addressing challenges in precision medicine, cancer biology, and ecological dynamics. Scientific Awards: Elected Member of the International Statistical Institute (2006), Fellow of the American Statistical Association (2013) Her contributions include advancing alignment methods for LC-MS data, developing hierarchical models for multi-omics integration, and exploring racial disparities in cancer outcomes. She leads Georgetown’s Mathematics & Statistics Department, fostering interdisciplinary research and education in data science.

• Trustworthy Machine Learning
• Automated Machine Learning
• Data-Driven Software Engineering
• +3 more

Dr. Huong Ha is a Senior Lecturer in Computer Science at RMIT University's School of Computing Technologies, located at the City Campus in Australia. Her research focuses on trustworthy machine learning, automated machine learning, and data-driven software engineering. She holds an ORCID ID (0000-0003-2463-7770) and is actively involved in supervising Masters and PhD students. Prior to her academic role, she worked as a Data Scientist at Freelancer International Pty Limited in Sydney from 2017 to 2018. Her research interests emphasize practical applications of machine learning in software systems optimization, including root cause analysis for microservices, Bayesian optimization techniques, and performance prediction for configurable software. She has published extensively in top conferences such as ICSE, AAAI, and NeurIPS, addressing challenges in high-dimensional optimization, system monitoring, and algorithmic efficiency. Current supervision projects include topics like Explainable AI in Human-Agent Planning, Fraud Detection on Blockchain networks, and High-dimensional Bayesian Optimization via Evolutionary Computation. Her work bridges theoretical advancements with real-world software engineering problems, contributing to robust and scalable solutions in AI-driven systems.

Joseph BafumiView profile

Associate Professor

• Electoral behavior
• Campaigns and Elections
• Ideology
• +9 more

Joseph Bafumi is an Associate Professor in the Department of Government at Dartmouth College. His research focuses on electoral behavior, public opinion, campaigns, and elections, with expertise in statistical methods like Bayesian inference and multilevel modeling. He holds a B.A. and M.A. from the University of Connecticut and an M.Phil. and Ph.D. from Columbia University. B.A., University of Connecticut M.A., University of Connecticut M.Phil., Columbia University Ph.D., Columbia University His research interests span electoral processes, ideological representation, Supreme Court dynamics, and fiscal policy analysis. Bafumi has published works on voter confidence in Miami-Dade, House election forecasting, and the unusual 2010 South Carolina Senate primary. He teaches courses on the Federal Budget and Campaigns & Elections.

Svante EriksenView profile

Associate Professor

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

Svante Eriksen is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His research spans statistics, forensic genetics, and computational modeling, with a focus on Bayesian networks, graphical models, and statistical methods for forensic DNA analysis. He is actively involved in interdisciplinary research and software development for probabilistic genotyping and large-scale inference. Research Interests: Bayesian Networks and Graphical Models Statistical Methods in Forensic Genetics SNP and Y-STR Genotyping Data Mining and Knowledge Discovery Model Selection and Context-Specific Independence Software Development for Statistical Inference Recent Publication Trends (2024–2025): His recent work focuses on improving SNP genotyping accuracy using logistic regression models, developing efficient software (jti and sparta) for Bayesian network inference, and advancing forensic DNA analysis through dynamic SNP selection and probabilistic modeling of Y-STR databases. These contributions reflect a strong integration of statistical theory, computational efficiency, and real-world forensic applications. Scientific Contributions: Principal contributor to software packages for Bayesian network prediction. Developer of statistical models for forensic DNA data interpretation. Collaborator on projects involving digital learning analytics and student retention. Advising and Grants: While specific student names are not listed, the profile indicates involvement in PhD supervision (4 cases). He has participated in multiple externally funded research projects, including those supported by Novo Nordisk and Danish research councils, focusing on forensic DNA analysis, graphical models, and educational data mining. Research Groups and Collaborations: He is part of a strong research network in forensic genetics and statistical modeling at Aalborg University, collaborating with leading researchers such as N. Morling, M. M. Andersen, and T. Tvedebrink. His work is closely tied to the development and application of statistical software in both forensic and educational domains.

Jaron Skovsted GundersenView profile

Academic

• Privacy-Preserving Computation
• Secure Multi-Party Computation
• Federated Learning
• +15 more

Jaron Skovsted Gundersen is a Research Assistant at the Department of Electronic Systems, within The Technical Faculty of IT and Design at Aalborg University, Denmark. He is actively involved in the Automation & Control group and the Learning and Decisions Lab, focusing on privacy-preserving distributed systems, quantum coding, and decentralized control for infrastructure resilience. His research centers on advanced topics in secure computation and machine learning, including privacy-preserving distributed consensus , secure multi-party computation using Shamir secret sharing , federated learning , and quantum stabilizer codes . His work integrates theoretical foundations with practical applications in critical systems such as water and power distribution networks. The trend in his publications shows a strong emphasis on data privacy in distributed machine learning , leveraging techniques like subspace perturbation and differential quantization. His recent articles span high-impact journals such as IEEE Transactions on Information Forensics and Security and IEEE Journal on Selected Areas in Information Theory, reflecting contributions to both theoretical and applied aspects of information security and control systems. He has been a project participant in the SWIFT research initiative (2019–2024), which investigates decentralized control solutions for electric and water distribution systems. His activities include multiple conference presentations, participation in academic workshops, and public engagement through events like the PDJF Grundfos Prize 'The Stars of Tomorrow' EXPO. He also delivered a lecture on technological solutions in water technology at a national climate meeting in 2022. PhD graduate (March 2021) Active researcher in privacy-preserving machine learning and quantum coding Contributor to resilient infrastructure control systems Regular participant in international conferences and workshops Gundersen is affiliated with the Learning and Decisions Lab at Aalborg University, where he collaborates on cutting-edge research in distributed intelligence, secure computation, and adaptive control systems. The lab fosters interdisciplinary work combining control theory, information theory, and machine learning for real-world applications.

• Ecology
• Statistics
• Species Distribution Modeling
• +3 more

Robert Brian O'Hara is a Professor in the Department of Mathematical Sciences at NTNU. His research focuses on the intersection of ecology and statistics, particularly developing models to analyze species distributions and dynamics. He leads a research group addressing challenges in biodiversity monitoring, including citizen science data integration and statistical tool development. Current projects include the GreenPlan initiative for land-use impact modeling and the Transforming Citizen Science for Biodiversity project. His work emphasizes integrating diverse data sources (e.g., observational, experimental, citizen science) to improve model accuracy. Notable contributions include the PointedSDMs R package for species distribution modeling and collaborations on projects like the gllvm package for model-based ordination. He supervises PhD students Kwaku Peprah Adjei, Philip Stanley Mostert, and Ron Tuganov, whose research spans data integration, statistical tools, and ecological modeling. Key themes in his publications include niche overlap prediction, climate-driven ecosystem shifts, and methodological advancements in ecological statistics. His research aims to bridge gaps between statistical rigor and ecological complexity to inform conservation and policy decisions.

• Hydrological Modeling
• Environmental Engineering
• Bayesian Inference
• +3 more

Professor Dmitri Kavetski is a faculty member in the School of Architecture and Civil Engineering at the University of Adelaide, specializing in Environmental Engineering. His work focuses on developing advanced hydrological models and Bayesian methods for uncertainty analysis, including the Bayesian Total Error Analysis (BATEA) framework. He collaborates internationally with institutions like the Swiss Federal Institute for Aquatic Science and Technology (EAWAG) and the US National Center for Atmospheric Research (NCAR). His research emphasizes probabilistic predictions, numerical algorithms, and improving water resource management through robust modeling techniques. Research interests include Bayesian inference, hydrological modeling, numerical analysis, and climate impacts. His contributions to frameworks like BATEA have been applied in operational forecasting systems, such as the Australian Bureau of Meteorology’s seasonal predictions. He is actively involved in supervising postgraduate students and has secured significant research funding. Publications highlight advancements in probabilistic hydrology, subseasonal forecasting, and model adequacy testing. His work bridges theoretical developments with practical applications, addressing challenges in ungauged basins and ephemeral catchments. Collaborations span global institutions, reflecting his commitment to advancing environmental engineering through interdisciplinary approaches.

Johan PensarView profile

Associate Professor

• Statistical Machine Learning
• Probabilistic Graphical Models
• Causal Learning
• +3 more

Johan Pensar is an Associate Professor of Statistics and Data Science at the University of Oslo's Department of Mathematics. He holds a PhD from Åbo Akademi University (2016) and was a postdoc at the University of Helsinki (2016–2020). His research focuses on statistical machine learning, probabilistic graphical models, causal inference, and applications in genomics. He has supervised multiple PhD students and co-supervised others in interdisciplinary projects, including causal modeling in healthcare and machine learning for microbiology. Education: PhD in Statistics, Åbo Akademi University, 2016 Postdoctoral Researcher, University of Helsinki, 2016–2020 Research Interests: Pensar's work integrates statistical theory with practical applications. Key areas include developing methods for causal discovery, probabilistic graphical models (e.g., Bayesian networks), and their use in genomics and healthcare. He emphasizes interpretable machine learning and robust statistical frameworks for complex data. Publications: Recent work spans causal inference, microbial genome analysis, and housing market prediction. His methods address challenges like confounding bias, generalization in ML, and uncertainty quantification in valuation models. Awards: Finnish Statistical Society Doctoral Thesis Award (2013–2016) Teaching & Advising: Pensar teaches advanced courses in statistical learning and probabilistic graphical models. He advises PhD students on causal modeling, ML in healthcare, and data science applications. He collaborates with industry partners like Integreat and Eiendomsverdi AS. Lab/Teams: He is affiliated with the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) and leads research on Bayesian methods in ML.

• Statistics
• Spatio-temporal Modeling
• Epidemiological Modeling
• +5 more

Álvaro Briz-Redón is an Assistant Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. He is affiliated with the PROMEDyA research group, focusing on prediction and optimization under uncertainty using dynamic stochastic models. Research Interests: Statistics and Operations Research Spatio-temporal modeling of public health data Mechanistic modeling of infectious diseases, particularly COVID-19 Bayesian inference and stochastic processes His recent work demonstrates a strong focus on applying advanced statistical methods to model the spatio-temporal dynamics of epidemics. The trend in his publications centers on integrating mechanistic models with real-world epidemiological data to improve forecasting and policy evaluation. Scientific Contributions: Published in journals such as Biometrical Journal , Stat , and Statistica Neerlandica Active collaborator with researchers in spatial statistics and epidemiology He received his PhD from the University of Valencia in 2020 with a thesis on spatio-temporal analysis of crime and traffic safety data. He has no listed scientific awards or advisees in the provided information. His work is supported by his affiliation with the University of Valencia and the PROMEDyA research group.

Pekka ParviainenView profile

Associate Professor

• Machine Learning
• Bayesian Networks
• Graphical Models
• +7 more

Pekka Parviainen is an Associate Professor in the Department of Informatics at the University of Bergen, within the Faculty of Mathematics and Natural Sciences. His research spans machine learning, probabilistic modeling, and AI theory, with a focus on Bayesian and Markov networks, adversarial robustness, fairness, and energy forecasting. He is affiliated with the Center for Data Science (CEDAS), an active research center at the university. His research interests include: Structure learning in graphical models Probabilistic forecasting using graph neural networks Adversarial robustness and defense mechanisms Fairness in clustering and machine learning Optimization and approximation in learning algorithms Applications in renewable energy and quantum sensing His recent publications (2020–2025) reflect a strong theoretical grounding combined with real-world applications, particularly in energy systems and AI safety. The works trend toward scalable and interpretable models, with increasing focus on fairness and robustness. Key themes include Bayesian network learning, metric learning, and causal graph modeling. Scientific contributions include: Development of novel adversaries (e.g., Voronoi-epsilon) for measuring robustness Scalable algorithms for learning large DAGs and Bayesian networks Integration of continuous optimization with combinatorial heuristics Applications in electricity demand forecasting and gas sensing Parviainen advises PhD students, including Hyeongji Kim (2023 thesis on distance in machine learning), and collaborates extensively with researchers in Norway and internationally. He has received computational support via Sigma2 (NN9884K) and is part of the CEDAS project, which fosters interdisciplinary data science research. While no specific grants are detailed, his involvement in funded projects and high-impact publications indicates active grant engagement. He is associated with the Center for Data Science (CEDAS), where he contributes to advancing data-driven methodologies across domains. The team emphasizes scalable, robust, and fair AI systems, aligning with national and international research priorities in trustworthy machine learning.

Saptarshi ChakrabortyView profile

Assistant Professor

• Statistical Computing
• Bayesian Modeling
• Machine Learning
• +8 more

Saptarshi Chakraborty is an Assistant Professor in the Department of Biostatistics at the School of Public Health and Health Professions, State University of New York at Buffalo. He serves as Director of the Statistical Consulting Lab at the Biostatistics, Epidemiology and Research Design (BERD) Core of CTSI. His research focuses on statistical computing, Bayesian modeling, cancer genomics, and high-dimensional data analysis. Chakraborty holds a PhD from the University of Florida (2018), an MS from the Indian Statistical Institute (2013), and a BSc from Presidency College, Kolkata (2011). He completed a postdoctoral fellowship in statistical genomics at Memorial Sloan Kettering Cancer Center (2018–2020). His work bridges theoretical statistics and applied biomedical research, with contributions to machine learning, computational biology, and drug safety assessment. Notable areas include developing Bayesian frameworks for envelope models, analyzing somatic mutations in cancer, and optimizing nanoparticle drug delivery systems. He is also involved in mentoring through his roles and serves as IBS Biometric Bulletin Correspondent for ENAR. Publications highlight interdisciplinary collaboration, with recent work on photoacoustic imaging, nuclear morphology in cancer, and prenatal exposure effects. Chakraborty is affiliated with the American Statistical Association and International Indian Statistical Association, emphasizing his commitment to advancing statistical methodologies in health sciences.

Peter GrünwaldView profile

Professor

• Machine Learning
• Statistics
• Safe Learning
• +8 more

Peter Grünwald is a Full Professor of Statistical Learning at Leiden University's Mathematical Institute and heads the Machine Learning group at CWI (Centrum Wiskunde & Informatica) in Amsterdam. He serves as President of the Association for Computational Learning and has held editorial roles at Foundations and Trends in Machine Learning. His research bridges machine learning theory and statistical methodology. Research Interests Safe Learning: Ensuring robustness in statistical inference and machine learning Safe Probability: Developing probability frameworks for partial domain modeling Replicability Crisis Mitigation: Addressing statistical misinterpretations in applied sciences Learning Bounds: Quantifying data requirements for reliable conclusions MDL Principle: Advancing Minimum Description Length methodology PAC-Bayesian Methods: Bridging frequentist and Bayesian paradigms Scientific Awards Van Dantzig Prize (2010) - Highest Dutch award in statistics IMS Fellow - Recognition from the Institute of Mathematical Statistics Grants NWO TOP-1 Grant (2016) NWO VICI Grant (2010) NWO VIDI Grant (2005)

• Machine Learning
• Natural Language Processing (NLP)
• Political Science
• +4 more

Victor Kristof is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the INDY2 laboratory. His work spans interdisciplinary domains, combining Natural Language Processing (NLP) , Machine Learning , and Social Process Modeling to analyze legislative dynamics, vote prediction, and environmental perception. Research Focus: Kristof develops interpretable models for democratic transparency, including aligning interest group positions with parliamentary speeches. He pioneered methods for predicting legislative edit acceptance using matrix factorization and NLP. His work on Swiss referendum prediction integrates historical data with real-time analysis via the Predikon platform . Methodological Contributions: He applies Bayesian statistics , time-dynamic pairwise comparison models , and active learning algorithms to diverse problems, from carbon footprint perception to sports analytics. His War of Words framework reveals ideological patterns in EU law-making, while his Player Kernel model improves football match prediction. Labs & Collaborations: Based at EPFL's Laboratory of Dynamic Information and Networks (INDY2) , he collaborates with researchers like Matthias Grossglauser and Patrick Thiran. His datasets on legislative edits and carbon perception have advanced transparency studies.

Wesley TanseyView profile

Assistant Professor

• Statistical Machine Learning
• Computational Oncology
• Bayesian Statistics
• +3 more

Wesley Tansey serves as Assistant Professor in the Computational Oncology group within the Department of Epidemiology and Biostatistics at Memorial Sloan Kettering Cancer Center (MSKCC). His research bridges statistical machine learning with cancer biology, focusing on developing novel computational frameworks for oncology applications. Dr. Tansey's research program centers on Bayesian statistical methods for biological data analysis, with particular emphasis on spatial transcriptomics (evidenced by his BayesTME framework), drug response modeling , and multi-omics integration . His lab develops scalable algorithms for high-dimensional biological data, including UnitedMet for metabolite imputation and MultiTME for spatial profiling analysis. Current projects address combinatorial drug screening optimization, tumor microenvironment characterization, and predictive oncology platforms for rare cancers. His recent publications (2023-2025) demonstrate strong focus areas: Bayesian active learning for drug screening (6+ publications) Spatial biology methods (BayesTME, MultiTME) Metabolomics-transcriptomics integration (UnitedMet) Causal inference in biological systems Scientific recognition includes serving as Area Chair for AISTATS 2022 and frequent invited talks at major conferences including SIAM's Mathematics of Data Science meeting. Dr. Tansey actively mentors lab members including Sophie Jaro (Spotlight presenter at ICML Workshop), Haoran Zhang (contributed talk presenter), Christopher Tosh (Associate Research Scientist), and Jeff Quinn (Bioinformatics Software Engineer). His lab receives research funding supporting development of computational oncology platforms with clinical translation potential. The VIVO Lab maintains active GitHub repositories for core methodologies including BayesTME, reflecting strong software engineering practices in computational biology.