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Robert AgansView profile

Associate Professor

• Survey Methodology
• Public Health Research Methods
• Biostatistics
• +3 more

Dr. Robert Agans is an Associate Professor in the Department of Biostatistics at the UNC Gillings School of Global Public Health. He specializes in complex survey research, including sample design, questionnaire validation, and analysis of both quantitative and qualitative data. Dr. Agans has directed over 50 projects since joining UNC in 2000, focusing on population-based surveys at local, state, and national levels. His research emphasizes innovative methodologies for hard-to-reach populations and cross-cultural survey validation. Education: PhD in Social Psychology from Texas A&M University (1992), MA in Clinical Psychology from West Chester University (1989), and BA in Psychology from Saginaw Valley State University (1984). Research interests include multi-mode data collection, questionnaire standardization, and translation methodologies. His recent work addresses challenges in enumerating marginalized groups, such as homeless populations, through advanced sampling techniques. Dr. Agans has served as Chair of the SAPOR Prothro Student Paper Competition since 2005 and has reviewed for journals like American Journal of Public Health and Personality and Social Psychology Bulletin. Publications highlight his contributions to survey design innovation, including hybrid sampling methods (RDD+RDS) and translation validation frameworks. His work bridges statistical rigor with real-world public health applications, particularly in underrepresented communities.

• Theoretical Ecology
• Tropical Forest Ecology
• Evolutionary Biology
• +3 more

Dr. Edwin Pos is an Associate Professor at Utrecht University's Faculty of Science, Department of Environmental Biology. His research focuses on biodiversity organization, origins, and conservation through theoretical and empirical approaches, integrating disciplines like information theory and physics. He leads the Amazonian Tree Diversity Network and the Nutrient Network, studying tropical forests and global grasslands. Teaching responsibilities include coordinating the Evolution and Biodiversity track and advising the Biology department's educational board. His work emphasizes mathematical modeling, maximum entropy principles, and chaos theory to understand ecological patterns. Research interests span theoretical ecology, tropical forest dynamics, and evolutionary processes. Collaborations include large-scale data networks addressing climate change impacts and biodiversity loss. Teaching methodologies combine classical and digital tools to foster student curiosity and engagement across diverse class sizes. Ongoing projects explore biodiversity's role in climate resilience and Amazonian species distribution.

Boualem DjehicheView profile

Professor

• Stochastic Analysis
• Stochastic Control
• Insurance Mathematics
• +8 more

Boualem Djehiche is a Professor of Mathematical Statistics at the Department of Mathematics, KTH Royal Institute of Technology. He is affiliated with the Digital Futures Faculty and the SCI School at KTH. His research focuses on Stochastic Analysis, including Stochastic Control, Insurance Mathematics, Mathematical Finance, and Game Theory. Djehiche holds editorial roles in journals such as Scandinavian Actuarial Journal and Finance and Stochastics . Education details are not explicitly stated in the provided texts. His teaching responsibilities include courses like Game Theory, Probability Theory, and Financial Mathematics. He advises students in these areas, though explicit student names are not listed. His research explores advanced topics such as mean-field games, time-inconsistent optimal control, and applications in finance and economics. Recent publications address topics like zero-sum Dynkin games, generative AI outcomes as Nash equilibria, and commodity futures pricing with regime switching. Research Interests: Stochastic Control, Insurance Mathematics, Mathematical Finance, Mean-Field Games, System Identification. Editorial Duties: Editor-in-Chief of Scandinavian Actuarial Journal , Associate Editor of Finance and Stochastics , and roles in multiple other journals. Grants & Collaborations: Collaborations include work on disability insurance modeling, credit scoring, and energy market dynamics via mean-field-type games. Labs/Teams: Involved in cross-disciplinary initiatives like the Digital Futures research center, focusing on digital technologies and societal challenges.

• Aquatic Conservation
• Fisheries Management
• Ecohydrology
• +3 more

Mark Poesch, PhD, is an Associate Professor in the Department of Renewable Resources at the University of Alberta, Faculty of Agricultural, Life and Environmental Sciences. His research focuses on aquatic conservation, fish and fisheries management, ecohydrology, and ecosystem modeling. He specializes in freshwater systems, addressing species imperilment through studies on anthropogenic disturbances such as land-use change, hydrologic alteration, climate change, and invasive species. Dr. Poesch’s work spans Canada, including British Columbia, Alberta, Saskatchewan, the Northwest Territories, and Ontario. He develops innovative assessment methods, including functional diversity metrics and habitat connectivity models, and contributes to restoration strategies like habitat offsets. His research integrates field studies, laboratory analyses, and broad-scale data to address conservation challenges. Key Roles: Associate Editor of the Canadian Journal of Fisheries and Aquatic Sciences, member of COSEWIC Freshwater Fishes Subcommittee, and leader in recovery teams for endangered species like the Mountain Sucker and Redside Dace. Education: PhD (specific institution not explicitly stated). Teaching: Teaches courses on fisheries management (REN R 376), directed research (REN R 402A/B), and advanced management strategies (REN R 476/576/771). Community Engagement: Leads initiatives like Tracking Change (indigenous youth engagement) and U School, introducing students to fish biology. His research emphasizes interdisciplinary approaches, combining ecological science with policy and traditional knowledge to address freshwater biodiversity threats. Current projects include assessing Prussian carp invasions, climate change impacts, and habitat mitigation strategies.

• Theoretical Physics
• Nuclear Physics
• Particle Physics
• +7 more

Christian Forssén is a Professor in theoretical physics at Chalmers University of Technology, affiliated with the Department of Physics and the Division of Subatomic, High Energy and Plasma Physics. His research focuses on theoretical nuclear and particle physics, employing advanced computational tools like Bayesian methods and machine learning to study low-energy effective field theories, nuclear forces, and astrophysical connections. He is an elected Fellow of the American Physical Society and a member of The Royal Society of Arts and Sciences in Gothenburg. Key research interests include nuclear many-body systems, symmetry energy in neutron-rich matter, and precision calculations of nuclear observables. His work bridges fundamental symmetries and astrophysical phenomena, such as neutron star properties and dark matter interactions. Recent studies emphasize Bayesian parameter estimation in chiral effective field theory and emulation techniques for large-scale ab initio calculations. Publications highlight breakthroughs in neutron skin predictions for 208Pb, first observations of exotic nuclei like 28O, and rigorous constraints on three-nucleon forces. Awards include recognition for contributions to nuclear theory and computational methods. Ongoing projects involve collaborations on dark matter detection and precision measurements of neutron halo nuclei.

Paulo RauberView profile

Lecturer

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +4 more

Dr. Paulo Rauber is a Lecturer in Artificial Intelligence at the School of Electronic Engineering and Computer Science, Queen Mary University of London. He holds a PhD from the joint program between University of Campinas (Brazil) and University of Groningen (Netherlands), and previously conducted postdoctoral research at the Swiss AI Lab (IDSIA) under Jürgen Schmidhuber. Education: BSc in Computer Science, Federal University of Santa Catarina (2011) PhD in Computer Science, University of Campinas & University of Groningen (2017) Research: Focuses on Bayesian reinforcement learning methods addressing exploration, planning, and generalization. His work spans scalable algorithms for non-stationary environments and deep reinforcement learning applications. Teaching: Teaches postgraduate courses such as Neural Networks and Deep Learning, Artificial Intelligence in Games, and Data Mining. Grants & Projects: Secured a Swiss National Science Foundation grant (NEUSYM, ~$700,000) in 2019, and leads projects like Colosseum and Hindsight Policy Gradients. Affiliations: Member of the Centre for Multimodal AI and actively supervises PhD students in reinforcement learning and Bayesian methods.

Nan ChenView profile

Professor

• Financial Engineering
• FinTech
• Quantitative Finance
• +7 more

Prof. Nan Chen is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He holds a PhD from Columbia University (2006) and directs the Bachelor of Engineering in Financial Technology (FinTech) and the Master of Science in Financial Engineering at CUHK-Shenzhen. His research focuses on quantitative finance, systemic risk, Monte Carlo simulation, and reinforcement learning. He has authored over 40 papers in top journals like Operations Research and Review of Financial Studies . Education : BSc and MSc in Probability & Statistics, Peking University (1998–2001) MPhil/PhD in Operations Research, Columbia University (2005–2006) Research Interests : FinTech and quantitative finance Systemic risk modeling and network analysis Reinforcement learning and stochastic control Monte Carlo methods and computational finance Awards & Grants : Best Student Paper Award, INFORMS (2006) Exemplary Teaching Awards (2009, 2017) Over HK$7M in GRF grants (2008–2023) Second Prize, Jiangsu Province Social Science Award (2023) Public Service : Member of Hong Kong's Payment Systems Tribunal and Insurance Appeals Tribunal, and reviewer for major journals like Mathematical Finance and Operations Research . Key Contributions : Pioneered Ito-Taylor expansions for diffusion models, dual methods for stochastic control, and liquidity risk frameworks. Led development of Hong Kong's first FinTech undergraduate program.

Haiyan ZhengView profile

Associate Professor

• Clinical Trials
• Precision Medicine
• Bayesian Statistics
• +3 more

Haiyan Zheng is a Reader in the Department of Mathematical Sciences at the University of Bath, affiliated with the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) and the Institute for Mathematical Innovation (IMI). She holds visiting roles at the MRC Biostatistics Unit and the Institute of Cancer Research. Her research focuses on advancing clinical trial methodologies, particularly in precision medicine, with an emphasis on Bayesian approaches, adaptive designs, and multi-agent trial optimizations. Key areas include improving trial efficiency through data borrowing, mid-trial adaptations, and simultaneous treatment/subgroup evaluations. Her work bridges statistical innovation with practical clinical applications, addressing challenges in oncology trials, biomarker utilization, and rare disease research. She leads projects funded by Cancer Research UK and the National Institute for Health Research, focusing on statistically efficient trial designs and seamless protocol implementations. Haiyan is actively involved in training doctoral students through SAMBa, requiring a strong quantitative background. Recent research trends include developing Bayesian hierarchical models for basket trials, integrating preclinical data into early-phase oncology studies, and optimizing sample size determination through robust priors. Her contributions emphasize reproducibility, safety monitoring, and cross-subgroup analysis in adaptive trial frameworks. Notable grants include the £5M STEEP project (2024–2030) for precision medicine trials and the IDEAS initiative (2023–2024) enhancing oncology trial design. She collaborates internationally to advance methodologies for late-onset toxicity handling and biomarker-guided trial interpretation.

Marcia SchafgansView profile

Associate Professor

• Microeconometrics
• Semi-Nonparametric Econometrics
• Spatial Econometrics
• +3 more

Marcia Schafgans is an Associate Professor of Economics at the London School of Economics and Political Science (LSE), where she also serves as Programme Director for the 2-year MSc Economics and the LSE Summer School. She holds a PhD in Economics from Yale University (1996) and has been a faculty member at LSE since 1995, previously serving as Senior Lecturer (2005–2013) and Lecturer (1995–2005). Her research focuses on Microeconometrics, Semi- and Nonparametric Econometric techniques, and Spatial Econometrics. She has contributed to studies on gender and ethnic wage gaps, household decision-making in developing economies, and panel data methodologies. Her teaching portfolio includes courses such as EC220 (Introduction to Econometrics), EC443 (Econometrics for MRes Students), and Summer School modules on Advanced Econometrics. She has also served as an external examiner for the MPhil in Economics at Cambridge University and contributed to curriculum reviews and academic governance at LSE. Her research is affiliated with the STICERD Econometrics Programme. Key publications include groundbreaking work on panel data analysis, semiparametric estimation techniques, and applications in development economics. Her recent work addresses dynamic panels and cross-sectional dependence, reflecting her focus on advancing econometric methodologies for complex data structures.

• Machine Learning
• Stochastic Optimization
• Networked Systems
• +4 more

Juan Bazerque Giusto is a Visiting Assistant Professor at the Department of Electrical and Computer Engineering, University of Pittsburgh, within the Swanson School of Engineering. He holds a B.Sc. in Electrical Engineering from Universidad de la República (Uruguay), and M.Sc. and Ph.D. degrees from the University of Minnesota. His research focuses on machine learning, stochastic optimization, and networked systems, with emphasis on reinforcement learning, swarm robotics, and power systems optimization. Education: B.Sc., Electrical Engineering, Universidad de la República, 2003 M.Sc., Electrical and Computer Engineering, University of Minnesota, 2010 Ph.D., Electrical and Computer Engineering, University of Minnesota, 2013 His work bridges theoretical advancements in optimization and signal processing with practical applications in robotics, energy systems, and wireless networks. Notable contributions include multiagent systems for mobile infrastructure, safe reinforcement learning algorithms, and sparse kernel-based methods for signal recovery. Publications: Over 15 peer-reviewed articles in IEEE Transactions and top conferences, emphasizing interdisciplinary research in reinforcement learning, distributed optimization, and cognitive networks. Recent work explores networked robotics and energy-efficient datacenter management. Awards: University of Minnesota Master Thesis Award (2009-2010) Best Paper Award at ICCRON 2007 Professional Experience: Previously served as Assistant Professor at Universidad de la República (Uruguay) before relocating to the U.S. in 2022.

Hai ShuView profile

Assistant Professor

• High-dimensional statistics
• Machine learning
• Deep learning
• +5 more

Dr. Hai Shu is an Assistant Professor in the Department of Biostatistics at NYU's School of Global Public Health. He earned his Ph.D. in Biostatistics from the University of Michigan and B.S. from Harbin Institute of Technology. Previously, he was a Postdoctoral Fellow at MD Anderson Cancer Center. Education: Ph.D. in Biostatistics - University of Michigan B.S. in Information and Computational Science - Harbin Institute of Technology His research focuses on high-dimensional data analysis, machine/deep learning, and medical image applications in neurodegenerative diseases and oncology. He develops statistical methods for analyzing complex biomedical data from neuroimaging and genomics. His publications demonstrate consistent focus on developing novel statistical methods for medical imaging data, with increasing emphasis on deep learning approaches and multi-modal data integration in recent years. Awards: NYU GPH Goddard Award (2023) He mentors graduate students and serves as associate editor for Statistica Sinica and The American Statistician. His NIH-funded research includes studies on neuroimaging analysis and AI applications in healthcare. Leads research in medical image analysis and statistical learning, collaborating with neuroscience and oncology teams to develop computational tools for disease diagnosis and progression tracking.

Graham CormodeView profile

Professor

• Data Sketching
• Streaming Algorithms
• Differential Privacy
• +7 more

Graham Cormode is a Professor in the Department of Computer Science at the University of Warwick. He has held research positions at Bell Laboratories and AT&T Labs-Research and leads major research projects in algorithms for big data. He is affiliated with the Centre for Discrete Mathematics and its Applications (DIMAP), the Warwick Institute for the Science of Cities (WISC), and the Alan Turing Institute (ATI). His research focuses on the data lifecycle , particularly data sketching , streaming algorithms , and privacy-preserving data analysis . He develops efficient summaries for massive datasets, enabling scalable analytics under constraints of space, time, and privacy. His work spans theoretical algorithm design and practical deployment, especially in differential privacy and distributed systems. His recent publications show a strong trend in private data release , quantile estimation , and interactive proofs for outsourced computation. He frequently publishes in top venues like PODS, VLDB, and STOC, demonstrating sustained impact in database theory and algorithms. Adams Prize, University of Cambridge (2017–2022) Royal Society Wolfson Research Merit Award - Small Summaries for Big Data (2014–2019) Cormode supervises PhD students and postdoctoral researchers, including Pavel Veselý and Michael Shekelyan. He has secured substantial funding from EPSRC, ERC, GCHQ, and industry partners like Microsoft and AT&T. His projects include Small Summaries for Big Data (ERC), FAIR (EPSRC), and ComPaTrIoTs . He is actively involved in organizing workshops and giving keynote talks, such as at PODC and DISC. He leads a research group focused on algorithms for big data , particularly within the ERC-funded “Small Summaries for Big Data” project. The team works on sketching, streaming, and privacy-preserving techniques, often in collaboration with DIMAP, WISC, and the Alan Turing Institute.

Gregory W. ThiemannView profile

Associate Professor

• Foraging Ecology
• Behavioural Ecology
• Wildlife Conservation
• +7 more

Gregory W. Thiemann is an Associate Professor in the Department of Biology, Faculty of Science at York University. He is actively involved in research and graduate supervision, focusing on Arctic carnivore ecology and conservation. His work bridges scientific analysis with environmental monitoring in northern ecosystems. His research interests center on foraging ecology, behavioural ecology, and wildlife conservation , particularly in the context of climate change. He employs advanced techniques such as fatty acid signature analysis to investigate the diets of top predators like polar bears and ringed seals. His work helps define food web structures and track ecosystem changes in the Arctic, contributing to both scientific understanding and conservation policy. The recent publications show a strong focus on Arctic marine mammals, predator-prey dynamics, climate impacts on sea ice habitats, and the integration of Traditional Ecological Knowledge. His research spans methodological development, physiological ecology, and applied conservation, with a consistent emphasis on the Canadian Arctic. Multiple studies involve York University graduate students, indicating active mentorship. While no formal scientific awards are listed in the provided text, his publications appear in high-impact journals such as Global Change Biology , Polar Biology , and PLoS ONE , reflecting strong scholarly recognition. He collaborates with researchers from various institutions across Canada and the United States. Dr. Thiemann advises several graduate students, including T.R. Ross, M.P. Galicia, K. Florko, L. Sciullo, and B.J. Laforest, many of whom are co-authors on his recent publications. Although specific grants are not mentioned, his sustained research output suggests active funding. His work often involves fieldwork in remote Arctic regions and collaboration with northern Indigenous communities. While no specific lab or research team name is provided, his research group can be inferred from his publication co-authors, particularly his graduate students and collaborators at Fisheries and Oceans Canada and other Canadian institutions. His work is highly interdisciplinary, combining ecology, physiology, biogeochemistry, and social-ecological systems.

Michael SørensenView profile

Professor

• Statistical Inference for Stochastic Processes
• Stochastic Differential Equations
• Financial Mathematics
• +3 more

Michael Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. His primary research focuses on statistical inference for stochastic processes, particularly stochastic differential equations and jump processes, with applications in finance, physics (e.g., wind-blown sand dynamics), and biology. He has authored/co-authored influential books such as Exponential Families of Stochastic Processes and edited volumes on empirical process techniques and statistical methods for stochastic differential equations. His work bridges theoretical statistics with applied problems in natural sciences and finance. Research interests include modeling turbulence, sand transport dynamics, and protein structure evolution. Collaborations with earth scientists like Keld Rømer Rasmussen have advanced understanding of aeolian processes. His methodologies emphasize likelihood-based inference and estimating functions, with contributions to high-frequency data analysis and diffusion bridge simulations. A comprehensive CV and full publication list are available on his profile. Key contributions span stochastic modeling in physics (e.g., sand dune dynamics), financial econometrics, and computational statistics. He has pioneered techniques for analyzing multi-modal diffusions and developed frameworks for mixed-effects stochastic differential equations. His work is widely cited in both theoretical and applied statistical literature.

Esther UlitzschView profile

Associate Professor

• Psychometric Model Development
• Bayesian Latent Variable Models
• Small-Sample Estimation Techniques
• +3 more

Esther Ulitzsch is an Associate Professor at the University of Oslo 's Centre for Educational Measurement (CEMO) . Her research focuses on advancing psychometric models, particularly Bayesian latent variable techniques for small-sample conditions, and analyzing digital interaction data from simulated learning environments. She holds a PhD from Freie Universität Berlin and previously worked as a Research Associate at the IPN – Leibniz Institute for Science and Mathematics Education in Kiel, Germany. Education: PhD in Educational Measurement (Freie Universität Berlin) Research Associate at IPN Kiel Research Interests: IRT model development for aberrant response detection Efficient estimation in small samples Clickstream analysis for student behavior Test-taking engagement dynamics Publications: Over 20 peer-reviewed articles since 2017, including work on mixture models for careless responding, neural networks for IRT estimation, and Bayesian factor modeling. Recent contributions address response time analysis, cross-country measurement invariance, and sequential process mining in interactive tasks. Affiliations: Active in the CREATE (Research on Equality in Education) and FREMO (Frontier Research in Educational Measurement) groups at CEMO.