جست‌وجوی استاد و پژوهشگر

Chris Oatesمشاهده پروفایل

استاد

• Computational Statistics
• Probabilistic Machine Learning
• Bayesian Inference
• +۳ مورد دیگر

Chris Oates leads a research team in Computational Statistics and Probabilistic Machine Learning at Newcastle University's School of Mathematics, Statistics and Physics. He serves on the Programme Committee of the ICMS (2025–2028) and as an Associate Editor of the SIAM/ASA Journal of Uncertainty Quantification (2025–2027). His work focuses on advancing Bayesian computation, Monte Carlo methods, and uncertainty quantification, supported by grants such as a £0.8M EPSRC award (2022–2026) for Stein Discrepancy methodologies. Research interests include robust Bayesian inference for intractable likelihoods, optimal thinning of MCMC outputs, and reinforcement learning for adaptive sampling. His team develops software like Stein Thinning (stein-thinning.org) and has published breakthroughs in probabilistic Richardson extrapolation and prediction-centric uncertainty quantification using MMD. Awarded the Guy Medal in Bronze (2024) and a Leverhulme Prize (2024), he has advised over a dozen PhD students and postdocs, including co-supervised projects at King’s College London and Imperial College. Current funding includes a Heilbronn Institute grant for next-generation extrapolation methods and a Turing Fellowship (2024–2026). Labs/Teams: Active in the Alan Turing Institute collaboration network, leading a multidisciplinary group spanning computational statistics, machine learning, and interdisciplinary applications in geospatial systems and cardiovascular data science.

Rosario Baroneمشاهده پروفایل

استادیار

• Statistics
• Economics
• Finance
• +۲ مورد دیگر

Rosario Barone is an Assistant Professor of Statistics at the University of Rome Tor Vergata. His research focuses on methodological development of statistical tools with applications in economics, finance, social sciences, and medicine. He specializes in Bayesian inference, Markov processes, and copula models, with recent work addressing complex dependencies and stochastic systems. His research interests include Bayesian nonparametrics, time-interaction processes, and semi-Markov models. Applications span healthcare (e.g., preterm birth analysis) and econometrics. Barone's publications highlight advancements in MCMC methods for continuous-time models and high-dimensional data analysis. He has contributed to interdisciplinary fields, merging statistical theory with practical applications in diverse domains.

Gersende Fortمشاهده پروفایل

پژوهشگر

• Stochastic Approximation
• Bayesian Statistics
• Machine Learning
• +۳ مورد دیگر

Gersende Fort is a CNRS Senior Researcher affiliated with the Institut de Mathématiques de Toulouse (IMT) at the University of Toulouse. Her research focuses on stochastic approximation methods, Bayesian statistics, optimization algorithms, and computational statistics. She has presented at major conferences such as ICASSP 2025 (Suzhou & Hyderabad) and the French-German-Spanish conference on Optimization (Gijon, 2024), often collaborating with researchers like Eric Moulines and Hoi To Wai. Recent work includes developing sampling techniques for nonsmooth log-concave densities, hierarchical Bayesian models for epidemiological analysis (e.g., COVID-19 reproduction number estimation), and federated learning algorithms. She leads the MAD project funded by the French National Research Agency (ANR), advancing scalable optimization methods. Her publications span technical reports on stochastic proximal-gradient algorithms, fluid-limit-based MCMC tuning, and PLS classification in microarray data analysis. Fort actively engages in academic outreach, including the 'AI and Society' Summit in Paris (2025) and a workshop on mathematics of machine learning. She also contributed to educational initiatives like the Women and Mathematics event in Lavelanet (2024). Her research bridges theoretical foundations with applications in health, optimization, and machine learning.

• Bayesian Statistics
• Markov Chain Monte Carlo
• Probability Theory
• +۳ مورد دیگر

Georgios Vasdekis is a Research Fellow at the Department of Statistics, University of Warwick. He completed his PhD in 2020 under the supervision of Prof. Gareth Roberts and Dr. Jon Warren, focusing on Markov Chain Monte Carlo (MCMC) techniques and their Bayesian applications. His work includes advancing Piecewise Deterministic Markov Processes (PDMPs) for Big Data sampling, notably the Zig-Zag process. He holds a B.Sc. in Mathematics from the University of Athens (2015) and a MASt from Homerton College, University of Cambridge (2016). Education: B.Sc. Mathematics, University of Athens, 2015 MASt, University of Cambridge, 2016 Research Interests: Vasdekis specializes in theoretical and applied probability, particularly in MCMC methods and PDMPs. His current projects explore the Zig-Zag process for efficient posterior sampling in high-dimensional Bayesian models. He also investigates stochastic integrable systems and broader applications of probability theory. Talks/Posters: Key presentations include discussions on the Zig-Zag process’s geometric ergodicity (IPSA Colloquium 2019), its generalization (Warwick Seminar 2019), and applications in Bayesian tools at the Greek Stochastics Meeting (2018). He actively participates in Probability and Simulation reading groups at Warwick. Awards: EPSRC-funded PhD studentship (2016–2020) Onassis Foundation Scholarship (2015–2016) Teaching: From 2017–2019, Vasdekis taught Probability, Analysis, and Mathematical Statistics courses as a Teaching Assistant. He supervised first-year non-mathematics students in multiple terms, fostering foundational understanding in quantitative disciplines. Events & Collaborations: He attended key events like the LMS Summer School in Computational Statistics (2018), the Greek Stochastics Meeting (2018), and the MASDOC retreat. His work aligns with interdisciplinary teams at Warwick, including the Probability group and MASDOC Centre.

Johannes U. Langeمشاهده پروفایل

پژوهشگر

• Galaxy Formation
• Cosmology
• Dark Matter Halos
• +۳ مورد دیگر

Johannes U. Lange is an astronomer specializing in galaxies, gravity, and cosmology, currently affiliated with American University. Previously, he completed his Ph.D. at Yale University under Prof. Frank van den Bosch, focusing on empirically modeling the galaxy-halo connection. He holds a B.Sc. from the Free University of Berlin, an M.Sc. from Heidelberg University, and participated in exchange programs at UC Santa Barbara and Chinese University of Hong Kong. His research explores questions like the validity of Lambda-CDM model, dynamics of galaxies under general relativity, and relationships between galaxies and dark matter halos. He has contributed to open-source projects like halotools and TabCorr. In 2017, he was a Graduate Fellow at KITP. Key research areas include galaxy clustering, galaxy-galaxy lensing, and assembly bias. His work emphasizes developing efficient statistical methods for analyzing large-scale structure surveys.

Mark Newmanمشاهده پروفایل

استاد

• Network Science
• Statistical Physics
• Complex Systems
• +۳ مورد دیگر

Mark Newman is the Anatol Rapoport Distinguished University Professor of Physics at the University of Michigan, affiliated with the Department of Physics within the College of Literature, Science, and the Arts. His research focuses on network science, statistical physics, and complex systems, with notable contributions to community detection algorithms, network topology analysis, and applications in epidemiology, social dynamics, and computational modeling. Newman's work has been widely recognized, including his election to the Royal Society (2022) and the 2024 Leo P. Kadanoff Prize. His research spans theoretical frameworks and practical applications, such as developing methods for inferring network structures from noisy data, analyzing core-periphery hierarchies in social systems, and exploring the interplay between luck, skill, and competition in games. He is also renowned for his cartogram mapping techniques, featured in the Washington Post for visualizing election data through network-based spatial distortions. Newman's academic contributions include foundational textbooks like *Networks: An Introduction* (2010), which remain pivotal in teaching and research. His recent work addresses challenges in drug-disease network analysis, Bayesian mixture models, and improving mutual information measures for classification tasks. Awards and honors include the Royal Society Fellowship and the Kadanoff Prize, underscoring his impact on both theoretical and applied physics.

Gurprit Singhمشاهده پروفایل

پژوهشگر

• Monte Carlo Integration
• Markov Chain Monte Carlo Sampling
• Generative AI
• +۳ مورد دیگر

Gurprit Singh is a Researcher at the Max Planck Institute for Informatics, Saarbrücken, Germany. His work focuses on advancing Monte Carlo integration techniques and their applications in generative AI, physically based rendering, and optimization. He has contributed to conferences such as SIGGRAPH, Eurographics (EG), and Pacific Graphics (PG), serving in roles like Technical Program Committee member and co-chair for Doctoral Consortium programs. Roles: Associate Senior Researcher, Conference Co-chair (EGSR 2021), and active in academic service. Research Interests: Monte Carlo methods, MCMC sampling, gradient-based optimization, and generative models. His research bridges rendering, optimization, and AI, with notable work on noise optimization in diffusion models and perceptual error minimization. He has received the Best Student Paper Award at ICPRAM 2025.

Yan Gaoمشاهده پروفایل

استادیار

• Biostatistics
• Clinical Trials
• Bayesian Methods
• +۷ مورد دیگر

Dr. Yan Gao is an Assistant Professor of Biostatistics at the Medical College of Wisconsin (MCW), affiliated with the Cancer Center and Department of Medicine. Her research focuses on Bayesian survival analysis, experimental design in clinical trials, and accelerating MCMC algorithms. She holds a PhD in Biostatistics from the University of Illinois at Chicago (2021) and prior master's degrees in Biostatistics, Social Medicine, and Public Health. PhD: Biostatistics, UIC (2021) MSc: Biostatistics, UIC (2016) MSc: Social Medicine, Fudan University (2009) BMedSc: Public Health, Harbin Medical University (2006) Research interests include immunology, oncology, numerical integration, and algorithm design. She collaborates with MCW's Department of Medicine and Cancer Center, contributing statistical expertise to NIH grants and protocol reviews. Key methodological work involves joint models, multiple testing procedures, and clinical trial design.

Ruobin Gongمشاهده پروفایل

دانشیار

• Bayesian Methods
• Imprecise Probability
• Differential Privacy
• +۳ مورد دیگر

Ruobin Gong is an Associate Professor of Statistics at Rutgers University, with a status-only appointment at the University of Toronto's Department of Statistical Sciences. She holds a PhD from Harvard University and focuses on foundational statistical theory and privacy-aware methodologies. Her research integrates Bayesian and imprecise probability frameworks with differential privacy challenges, emphasizing ethical data science practices. Gong's work includes developing the dapper R package for private posterior estimation and organizing events like the Annual Symposium on Applications of Contextual Integrity and NBER workshops on privacy in applied research. Her research interests span theoretical foundations of uncertainty reasoning (including Bayesian methodology, random sets, and Dempster-Shafer theory) and practical applications in privacy-preserving statistical inference. She serves as an associate editor for Harvard Data Science Review, JASA/TAS Reviews, and Statistics and Public Policy, while also contributing a monthly column, 'Sound the Gong,' to the IMS Bulletin. Gong's recent work addresses challenges in privacy-aware computation, invariant-constrained data sanitization, and the philosophical implications of credence dynamics. Her contributions include pioneering subspace differential privacy frameworks and formal privacy analyses of historical data swapping methods. Gong actively engages in interdisciplinary collaborations, bridging statistical theory with real-world policy applications through workshops and publications on topics like algorithmic fairness and disclosure avoidance systems.

Elizabeth Stojanovskiمشاهده پروفایل

مدرس ارشد

• Biostatistics
• Longitudinal Modelling
• Multivariate Statistics
• +۶ مورد دیگر

Dr. Elizabeth Stojanovski is a Senior Lecturer at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Priority Research Centre for Computer-Assisted Research Mathematics and its Applications (CARMA) . She leads the Mathematics Education Research Group and manages the statistical consulting unit, serving industry, academics, and postgraduate students. With a PhD in Statistics from the University of Newcastle, she specializes in Biostatistics , Longitudinal Modelling , and Multivariate Statistics . Education : PhD (University of Newcastle), Bachelor of Mathematics (Honours), Bachelor of Mathematics Professional Roles : Senior Lecturer (2007–present), Research Academic (2004–2006) Memberships : Member of the Australian Statistics Society Her research spans health and education , focusing on longitudinal relationships between life events and physical/mental health. She has developed Bayesian frameworks , longitudinal models , and meta-analytic techniques for applications in tobacco control, cardiovascular disease, cancer treatment, and mathematics education. Her work includes collaborations with Eric Beh on Nobel Prize data analysis, Manohar Garg on lipid-lowering studies, and Suzanne Snodgrass on physiotherapy biomechanics. Recent publications highlight her expertise in correspondence analysis for educational and scientific data, multivariate assessment of diabetes risk , and reinforcement learning for optimization. She has supervised 15 PhD/Masters students and peer-reviewed for journals and grants. Her grants total $496,207 , including projects on statistical literacy and phytosterol efficacy. Scientific Awards : 2001 "Temporal association between life events and health" Elizabeth's teaching spans theoretical and applied statistics, with a focus on psychology, health, and business applications. She has also contributed to forensic science through cut mark analysis on bone and policy research on tobacco investments in pension funds. Her leadership in CARMA and statistical consulting underscores her interdisciplinary impact.

• Machine Learning
• Statistics
• Computational Statistics
• +۳ مورد دیگر

Yuansi Chen is an Assistant Professor at Duke University. He completed his Ph.D. in Statistics at the University of California, Berkeley, between 2015 and 2019, under the supervision of Bin Yu. His doctoral research focused on developing fast MCMC algorithms, stability analysis, and applications in DeepTune. Research interests include computational statistics, Bayesian methodologies, and machine learning, with a focus on algorithmic efficiency and theoretical stability in high-dimensional settings. No specific grants, awards, or affiliated labs/teams are explicitly mentioned in the provided text.

ZHANG Xinyuمشاهده پروفایل

پژوهشگر ارشد

• Chronic diseases modelling
• Bayesian modelling
• Crossed mixed effects models
• +۱ مورد دیگر

ZHANG Xinyu is a Research Fellow at the Saw Swee Hock School of Public Health, National University of Singapore. Her research focuses on chronic diseases modelling using Bayesian methods and stochastic gradient MCMC algorithms, with current emphasis on frailty and cancers. She holds a BEc in Insurance from Fudan University, an MSc in Statistics from NUS, and a PhD in Statistics from NUS. Education: PhD in Statistics, National University of Singapore MSc in Statistics, National University of Singapore BEc in Insurance, Fudan University Her research interests include advanced statistical methodologies applied to public health challenges, such as developing large crossed mixed effects models. She contributes to initiatives like the Singapore Breast Cancer Cohort (SGBCC) and collaborates on strategic programs like Breast Cancer Prevention and Climate, Environment and Health. Her work bridges biostatistics, epidemiology, and health systems research. ZHANG Xinyu is involved in teaching within NUS Medicine and participates in interdisciplinary research teams, including those focused on public health genomics and health intervention policies. She actively engages in the School’s efforts to address global health priorities through innovative modelling approaches.

Nhung Nghiemمشاهده پروفایل

دانشیار

• Economic Evaluation in Clinical Trials
• Health Economic Modelling
• Machine Learning in Healthcare
• +۳ مورد دیگر

Nhung Nghiem is an Associate Professor of Health Economics in the Department of Genome Sciences at the John Curtin School of Medical Research (JCSMR), Australian National University (ANU). She is affiliated with the Clinical Hub for Interventional Research (CHOIR) as a Senior Fellow. Her interdisciplinary research focuses on economic evaluation, health economic modeling, and machine learning applications in healthcare, with notable contributions to public health policy, including the world's first tobacco endgame law in New Zealand. Dr. Nghiem has secured over $21 million in research funding, including a $5 million NHMRC grant, and serves on global advisory boards for the WHO and World Bank. Research Interests: Economic Evaluation in Clinical Trials, Health Economic Modelling, Machine Learning in Healthcare, Health Equity, Causal Inference Methods. Publications: Over 60 peer-reviewed articles in high-impact journals like The Lancet Public Health and PLOS Medicine, with a focus on infectious disease epidemiology, cardiovascular health, and policy modeling. Recent work includes forecasting respiratory disease trends using machine learning and assessing the impact of food taxes on health equity. Awards: Ranked in the top 2% of global researchers by citation impact (Stanford, 2022–present). Active roles in health economics committees, including the Arrow Award Committee and WHO Technical Advisory Groups. Grants & Funding: Led a prestigious Marsden Fast Start project and secured major grants from NHMRC and international bodies. Contributions to policy include modeling tobacco control and climate-resilient diets. Lab/Team: Member of the Polizzotto Group at CHOIR, advancing interventional research in healthcare and public health.

Anna Scampicchioمشاهده پروفایل

پژوهشگر

• Intelligent Control Systems
• Model Predictive Control
• Machine Learning
• +۳ مورد دیگر

Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.

Eli Upfalمشاهده پروفایل

استاد

• Algorithms
• Randomized Algorithms
• Probabilistic Analysis
• +۵ مورد دیگر

Eli Upfal is the Rush C. Hawkins Professor of Computer Science at Brown University, where he has been a faculty member since 1998 and served as department chair from 2002 to 2007. Prior to Brown, he held positions as a researcher and project manager at IBM Almaden Research Center and a professor at the Weizmann Institute of Science. He earned his undergraduate degree in mathematics and statistics and a doctorate in computer science from the Hebrew University in Jerusalem, Israel. Education: B.Sc./M.Sc. in Mathematics & Statistics, Ph.D. in Computer Science Previous Institutions: IBM Almaden Research Center, Weizmann Institute Upfal's research focuses on the design and analysis of algorithms, particularly randomized algorithms and probabilistic analysis. His work spans combinatorial and stochastic optimization, routing and communication networks, computational biology, and computational finance. He has also contributed to applying Rademacher Averages and VC-dimension to sampling-based algorithms for graph analysis and pattern mining. Recent publications emphasize algorithmic applications in graph theory, machine learning, and handling distribution drift in data analysis. His work includes practical implementations of theoretical concepts, such as Abra for betweenness centrality approximation and frameworks for secure data exploration like VizCertify and VizRec . Scientific Awards IEEE Fellow ACM Fellow IBM Outstanding Innovation Award IBM Research Division Award Upfal has advised PhD student Matteo Riondato and received funding from the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Office of Naval Research (ONR), and National Institute of Health (NIH). He co-authored the textbook Probability and Computing: Randomized Algorithms and Probabilistic Analysis with M. Mitzenmacher.