جستجوی استاد راهنمای دانشگاهی

Simon Tavaré is a Professor of Cancer Research (Bioinformatics) at the University of Cambridge’s Department of Oncology and a Professor in the Department of Applied Mathematics and Theoretical Physics (DAMTP). He is also the former Director of the Cancer Research UK Cambridge Institute (2013–2018), a Senior Group Leader there, and a Professorial Fellow at Christ’s College, Cambridge. His research focuses on cancer genomics, evolutionary approaches to cancer, and statistical bioinformatics for sequencing data. He integrates methods from computational biology, population genetics, and stochastic computation to address molecular biology questions. His career includes roles at the University of Southern California (1999–2006) and the University of Utah (1984–1989). He has been recognized with prestigious awards, including Fellowship of the Royal Society (2011) and election to the U.S. National Academy of Sciences as a Foreign Associate (2018). His work spans diverse areas such as evolutionary biology, palaeontology, and probabilistic combinatorics. Education: Early career details not explicitly listed, but academic roles began in 1978 as an instructor at the University of Utah. Research interests include applying statistical methods to cancer heterogeneity, mutational signatures analysis, and evolutionary dynamics in tumors. His publications highlight contributions to genomic data analysis tools like BayesPeak and foundational work in combinatorial probability. Awards: FMedSci (2009), FRS (2011), EMBO Membership (2015), NAS Foreign Associate (2018). He has led initiatives at the Cancer Research UK Cambridge Institute and contributed to international collaborations, including the OCCAMS Consortium for esophageal cancer research. His advisory roles include serving as President of the London Mathematical Society (2015–2017).

• Cancer Genomics
• Evolutionary Cancer Biology
• Statistical Bioinformatics
• +۸ مورد دیگر

James Martin is an Honorary Research Fellow in the Statistics Section of the Department of Mathematics at Imperial College London, part of the Faculty of Natural Sciences. He previously served as a Senior Teaching Fellow, leading modules on the MSc in Machine Learning and Data Science. His research focuses on machine learning applications in engineering, spatial statistics for ecological systems, and Bayesian inference methods using likelihood-free approaches, particularly sequential Monte Carlo techniques. He is affiliated with the Artificial Intelligence Network, Mathematics research and teaching staff, and the Statistics group. His work bridges theoretical statistical methodologies with practical applications in environmental and engineering domains. Recent publications (2012–2025) emphasize advancements in Bayesian computational methods, spatial point processes, and network analysis, reflecting his interdisciplinary approach to solving complex data-driven problems. While no formal advising or grant details are provided, his academic contributions underscore a commitment to advancing computational and applied statistics. He collaborates within Imperial’s mathematics and AI communities, contributing to cutting-edge research initiatives in data science.

• Machine Learning
• Spatial Statistics
• Bayesian Inference
• +۳ مورد دیگر

Robin Ryder is a Senior Lecturer in Statistics at the Department of Mathematics, Imperial College London, within the Faculty of Natural Sciences. He previously held positions as Associate Professor (maître de conférences HDR) at Université Paris-Dauphine and an associate member at École Normale Supérieure, Paris. His research focuses on Bayesian computational statistics (MCMC and ABC methods) and stochastic modeling in the Humanities, particularly Linguistics and Social Sciences. Education: DPhil in Statistics , University of Oxford Habilitation à Diriger les Recherches , Paris Dauphine University Research Interests: Dr. Ryder specializes in advancing computational methods for statistical inference, including Markov Chain Monte Carlo (MCMC) and Approximate Bayesian Computation (ABC). His applied work bridges statistics with the Humanities, addressing questions in linguistic phylogenetics, cultural evolution, and social sciences. Notable areas include modeling language evolution, analyzing sign language histories, and investigating functional load dynamics in linguistic systems. Recent Contributions: His work spans theoretical advancements (e.g., saddlepoint Monte Carlo for ecological inference) and applied studies (e.g., phylogenetic models for Kra-Dai languages and weaving technologies). He frequently collaborates on interdisciplinary projects, such as computational analyses of sign language histories and formal assessments of primate communication systems. Awards & Recognition: While no explicit awards are listed, his contributions have been featured in prominent outlets like Science and French popular science magazine Pour la Science . Grants & Advising: Details on grants or advisees are not explicitly mentioned, though his involvement in editorial boards (e.g., Bayesian Analysis ) underscores his leadership in statistical methodology. He actively participates in conferences and workshops, emphasizing sustainability by advocating for reduced academic travel.

• Bayesian Computational Statistics
• Markov Chain Monte Carlo (MCMC)
• Approximate Bayesian Computation (ABC)
• +۴ مورد دیگر

David van Dyk is a Professor of Statistics and Chair in Statistics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. He holds a PhD from the University of Chicago and has held academic positions at Harvard University and the University of California, Irvine before joining Imperial College in 2011. He has served as Head of the Department of Mathematics (2017–2023) and Department Chair at UC Irvine (2010–2011). His research focuses on Bayesian methodology, computational statistics, and their applications in astrophysics, solar physics, and high-energy physics, with a particular emphasis on interdisciplinary collaboration. He coordinates the CHASC International Astrostatistics Center and develops methods for efficient computation in complex statistical models, including EM algorithms and MCMC methods. Van Dyk has received prestigious awards such as the Wolfson Merit Award (2011) and the ASA Founders Award (2019), and is an elected Fellow of multiple statistical and astrostatistics organizations. Van Dyk's educational background includes a BS from Michigan State University (1991) and a PhD from the University of Chicago (1995). His academic trajectory includes roles at Harvard (1996–2003) and UC Irvine (2003–2011), where he contributed to department leadership and advanced statistical research. His work emphasizes bridging statistical theory with practical challenges in astronomy, such as calibration concordance for X-ray telescopes and analyzing gravitational lens time delays. He has pioneered software tools like LIRA for low-count image reconstruction and BAHAMAS for supernova analysis. His research interests span computational methods for Bayesian analysis, statistical modeling of astronomical phenomena, and addressing systematic uncertainties in observational data. He actively participates in astroinformatics initiatives, advocating for rigorous statistical practices in data-driven astronomy. His recent work addresses covariate shift adaptation in machine learning (StratLearn), time series analysis of stellar flares, and improving white dwarf age estimation using Gaia data. Awards: Wolfson Merit Award, ASA Founders Award, Fellowships in ASA, IMS, and IAA Labs/Teams: CHASC International Center for Astrostatistics, Imperial's Bayesian Methods and AI/ML networks Grants/Funding: Not explicitly listed but implied through leadership roles and software development

• Bayesian Analysis
• Astrophysics
• Solar Physics
• +۵ مورد دیگر

Alastair Young is a Professor and Chair in Statistics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on advanced frequentist statistical methods, including bootstrap techniques, saddlepoint approximations, and spatial data inference. He has held prior roles as Reader in Methodological Statistics at the University of Cambridge (pre-2005). Key research areas include computer-intensive statistical methods, parametric and non-parametric inference, and approximation strategies for complex models. He is a Fellow of the Institute of Mathematical Statistics and previously served as Joint Editor of the Journal of the Royal Statistical Society, Series B. Alastair's work bridges theoretical developments (e.g., hybrid bootstrap methods, quantile estimation) with applications in econometrics, artificial intelligence, and bioinformatics. Recent publications emphasize causal inference in clinical trials, high-dimensional statistics, and principled data science methodologies. Awards: Fellow of the Institute of Mathematical Statistics Editorial Role: Former Joint Editor, JRSS Series B Key Themes: Bootstrap methodology, likelihood-based inference, spatial statistics

• Statistics
• Econometrics
• Numerical and Computational Mathematics
• +۷ مورد دیگر

Snigdha Panigrahi is an Assistant Professor of Statistics at the University of Michigan, with a courtesy appointment in the Department of Biostatistics. She specializes in developing methods for replicable learning from complex data, focusing on quantifying uncertainties in machine learning outputs. Her work bridges selective inference, data aggregation, and non-parametric statistics, supported by NSF and NIH grants. Notable awards include the NSF CAREER Award (2023) and the Bernoulli Society’s New Researcher Award (2025). Education: Ph.D. in Statistics from Stanford University (2013-2018), Master’s and Bachelor’s in Statistics from Indian Statistical Institute (2008-2013). Research interests include selective inference methodologies, distribution-free inferences, and scalable statistical techniques for high-dimensional data. Her research applications span genomics, neuroimaging, and health trials. Key contributions include methods for post-selection inference in graphical models, distributed data analysis, and quantile regression. Current efforts address uncertainties in mobile health trials and malaria serology collaborations with NIH. Awards: Recognitions include the NSF CAREER Award, Bernoulli Society Award, and election to the International Statistical Institute (2021). She serves on editorial boards for Bernoulli , Journal of Computational and Graphical Statistics , and Journal of the Royal Statistical Society: Series B . Advising: Supervises PhD students including Judy Wu and Soham Bakshi, with over 20+ mentored undergraduates and master’s students. Leads a research team focused on adaptive data analysis and selective inference. Grants: Includes NSF DMS-2337882 ($450K) for uncertainty quantification in ML and NIH R01 ($1.2M) for moderation analysis in micro-randomized trials. Additional funding from NSF and institutional grants.

• Selective Inference
• Machine Learning
• Data Aggregation
• +۵ مورد دیگر

Matias Salibian-Barrera is a full Professor in the Department of Statistics at the University of British Columbia's Faculty of Science, where he leads research in robust statistical methodologies with applications across machine learning, biostatistics, and environmental sciences. His research focuses on developing computationally efficient robust techniques resistant to outliers and model misspecification. Key areas include: Robust regression and inference for high-dimensional data Robust causal inference with complex observational data Robust functional and spatial data analysis Integration of robust methods with deep learning architectures Applications in genomics, climate science, and finance His recent publications demonstrate a strong trend toward unifying robust statistics with modern machine learning, particularly in adversarial settings and high-dimensional inference, while maintaining rigorous theoretical foundations. Notable honors include: CRM–SSC Prize in Statistics Fellow of the American Statistical Association Killam Research Prize Coxeter-James Prize He actively supervises doctoral students and secures major grants from NSERC and CIHR, focusing on robust methods for biomedical and environmental data. His work involves collaborations with UBC's Machine Learning Group and the Pacific Institute for the Mathematical Sciences, where he develops open-source software for robust statistical computing.

• Robust Statistics
• Machine Learning
• Biostatistics
• +۳ مورد دیگر

Mark van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley, with joint appointments in the Department of Statistics and School of Public Health. He serves as co-Director of the Center for Targeted Machine Learning and Causal Inference (CTML) and Director of the Computational Biology core of the UC Berkeley Superfund Research Program. His research focuses on developing statistical methods for causal and non-causal parameter estimation from complex data structures, particularly through his pioneering work on Targeted Maximum Likelihood Estimation (TMLE) and super learning methodologies. His work bridges advanced statistical theory with practical applications in biomedical research, genomics, and public health. Van der Laan's recent publications demonstrate a strong focus on causal inference methodology applied to real-world medical problems, including long COVID research, nutritional epidemiology, and genomic medicine. His work consistently emphasizes the integration of machine learning with causal inference frameworks to address challenges in high-dimensional data analysis. Scientific Awards: COPSS Presidents' Award (2005) Mortimer Spiegelman Award (2004) van Dantzig Award (2005) As an educator, van der Laan has mentored numerous students through his teaching of advanced courses including PH C240B Biostatistical Methods: Survival Analysis and Causality, PH 243A Multivariate Statistical Methods in Genomics, and PH 296 Causal Inference Seminar. His research group actively develops R packages to translate theoretical methods into practical tools for the research community. He maintains active collaborations with researchers at UC San Francisco, Kaiser Permanente, and the FDA Real World Evidence Group, focusing on applying advanced causal inference methods to solve pressing public health challenges.

• Causal Inference
• Targeted Learning
• Computational Biology
• +۷ مورد دیگر

Dr. Maya L. Petersen is a Professor of Biostatistics and Epidemiology at the University of California, Berkeley's School of Public Health. She holds dual appointments in the Department of Biostatistics and the Department of Epidemiology. Her research bridges advanced statistical methodology with critical global health challenges, particularly in HIV prevention and care. She is a Founding Editor of the Journal of Causal Inference and serves on the editorial board of Epidemiology . Education: MD, University of California, San Francisco (2007–2009) PhD in Biostatistics, UC Berkeley (2004–2007) Pre-Doctoral Fellowship, Howard Hughes Medical Institute (2001–2006) MS in Health and Medical Sciences, UC Berkeley Joint Medical Program (1999–2002) BA in Human Biology, Stanford University (1994–1998) Research Interests: Dr. Petersen's work focuses on causal inference methods (e.g., dynamic treatment regimes, longitudinal data analysis) and their application to HIV/AIDS, adaptive clinical trials, and implementation science. She leads initiatives such as the Sustainable East Africa Research in Community Health consortium, evaluating universal HIV testing/treatment programs. Her methods address complex issues like mobile populations' care retention and economic incentives for treatment adherence. Key Collaborations: Co-PI of the ADAPT-R study (behavioral interventions for HIV care retention) Co-PI of the SEARCH Dynamic Choice HIV Prevention Trial Co-developer of the Causal Roadmap framework for rigorous real-world evidence Teaching: Dr. Petersen teaches advanced causal inference courses (PB HLTH 252D/E) and mentors doctoral students in epidemiology and biostatistics. She emphasizes bridging theoretical methods with real-world health challenges. Labs & Teams: Her work is conducted through the Biostatistics and Epidemiology Research Group , collaborating with global partners in Kenya, Uganda, and the U.S. on HIV, TB, and chronic disease integration programs.

• Causal Inference
• HIV Prevention and Care
• Adaptive Treatment Strategies
• +۴ مورد دیگر

Mark Johannes van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley School of Public Health. He holds a PhD in Statistics from Utrecht University (1993) and has received prestigious awards including the COPSS Presidents’ Award (2005), Mortimer Spiegelman Award (2004), and van Dantzig Award (2005). PhD in Statistics, Utrecht University (1993) MS in Statistics, Utrecht University (1990) His research focuses on causal inference, survival analysis, and machine learning, with a particular emphasis on Targeted Learning methodology. He developed the targeted maximum likelihood estimation (TMLE) framework and co-founded the Center for Targeted Machine Learning and Causal Inference (CTML) . His work addresses challenges in real-world evidence generation, genomic data analysis, and longitudinal studies. Recent publications highlight applications of TMLE to neural networks, high-dimensional confounding adjustment, and Long COVID research. He advocates for principled roadmaps in statistical analysis and has contributed to journals like the Journal of Causal Inference . COPSS Presidents’ Award (2005) Mortimer Spiegelman Award (2004) van Dantzig Award (2005) Van der Laan’s work bridges academic innovation and practical implementation, with a focus on super-learning algorithms, causal effect estimation in adaptive clinical trials, and tools for real-world data analysis. He actively collaborates on public health challenges such as obesity crises and mental-physical health interdependencies.

• Causal Inference
• Semiparametric Statistics
• Machine Learning
• +۶ مورد دیگر

Azeem M. Shaikh is the Ralph and Mary Otis Isham Professor of Economics at the University of Chicago's Kenneth C. Griffin Department of Economics and serves as Department Chair. He also holds the Thornber Research Fellowship and co-directs the Becker Friedman Institute’s Big Data Initiative. His research focuses on econometric theory, including multiple testing, resampling methods (e.g., bootstrap), and partially identified models. He has contributed to applications in policy evaluation and causal inference, with notable work on randomized experiments and imperfect compliance. Educated at Duke University (B.S. in Mathematics) and Stanford University (Ph.D. in Economics), Shaikh has been affiliated with the University of Chicago since 2007. His research has received NSF funding and prestigious awards such as the Dennis J. Aigner Award for Applied Econometrics and Alfred P. Sloan Fellowship. He is an elected fellow of the Econometric Society and International Association for Applied Econometrics, and serves on editorial boards of the Econometrics Journal and Journal of Econometrics . Shaikh’s work bridges theoretical econometrics with practical policy challenges, emphasizing methodological rigor in addressing real-world problems like education program evaluation and health survey biases. His recent studies explore nonrepresentative sampling in population health research and novel variance estimators for stratified experiments. Awards: Hoover Fellowship, Sloan Fellowship, Econometric Society Fellowship Grants: NSF support for econometric methods in clustered data and covariate-adaptive randomization Key Contributions: Randomization inference frameworks, multiple testing adjustments, and causal effect identification under monotonicity

• Econometric theory
• Partial identification
• Multiple testing
• +۶ مورد دیگر

Prof. Silvia Gonçalves is a Full Professor in the Department of Economics at McGill University. Her research focuses on econometric theory, with specializations in bootstrap methods, time series analysis, and financial econometrics. She holds a PhD from the University of California, San Diego. Her work addresses challenges in statistical inference for dynamic models, volatility estimation, and factor models, with applications to finance and macroeconomics. Her research interests include developing robust inferential tools for nonlinear models, handling cross-sectional dependence, and improving volatility estimation under market microstructure noise. Notable contributions include advancements in bootstrap techniques for factor models and high-frequency financial data analysis. Her publications span top journals such as Econometrica , Journal of Econometrics , and Journal of Business & Economic Statistics , focusing on methodological advancements in econometric theory. Her work often bridges theory and application, addressing practical issues in macroeconomic policy analysis and financial risk management.

• Econometrics
• Financial Econometrics
• Time Series Analysis
• +۳ مورد دیگر

Dr. Dong-Hyuk Kim is a Senior Lecturer in the School of Economics at the University of Queensland, within the Faculty of Business, Economics and Law. He holds a PhD in Bayesian Econometrics for Auction Models from the University of Arizona (2010). His research focuses on Industrial Organization, Econometrics, and Microeconomics, with emphasis on auction theory, risk aversion, and development economics. Dr. Kim is affiliated with the Centre for Efficiency and Productivity Analysis and has published in top journals such as Journal of Econometrics and European Economic Review . His work bridges theoretical econometrics with applied economic problems, including optimal reserve pricing strategies, bidder behavior analysis, and the empirical relevance of ambiguity in auction models. Recent projects include studies on drought-resistant maize varieties in Malawi and government expenditure impacts in Sri Lanka. Dr. Kim is available for PhD supervision and actively contributes to methodological advancements in structural estimation techniques. Publications span topics like procurement mechanisms, risk-averse bidding strategies, and demand modeling using random coefficients frameworks. Ongoing research explores carbon emission trading in China's coal sector and managerial impacts on firm performance post-privatization in Vietnam.

• Industrial Organization
• Econometrics
• Microeconomics
• +۳ مورد دیگر

Anna Chlingaryan is a Research Fellow at the University of Sydney, affiliated with the Rio Tinto Centre for Mine Automation and the Australian Centre for Field Robotics. She holds a PhD in applied mathematics and has worked at the University of Sydney for 13 years, contributing to the Faculty of Engineering and later transitioning to the Faculty of Science in 2021. Her current role involves advancing data-driven solutions for livestock production, feed optimization, and mitigating climate change impacts on agriculture. Her research interests span machine learning, mathematical modelling, and algorithmic problem-solving for real-world applications, particularly in agriculture and mining automation. She is a member of the Sydney Institute of Agriculture and the Livestock Production and Welfare Group within the Faculty of Science. Her recent publications focus on geostatistical methods, hyperspectral data analysis, and Bayesian approaches to geological boundary modelling. While no scientific awards are explicitly noted, her work demonstrates significant contributions to interdisciplinary applications of AI and mathematical techniques. Anna advises on research projects within her teams but no current students are listed. Her affiliations include collaborations with industry partners like Rio Tinto and academic centers focused on robotics and environmental science.

• Machine Learning
• AI
• Mathematical Modelling
• +۳ مورد دیگر

Mark Sellke is an Assistant Professor of Statistics at Harvard University's Department of Statistics. He is affiliated with the Faculty of Arts and Sciences and is based at the Science Center in Cambridge, MA. His research interests focus on spin glasses, machine learning theory, and high-dimensional statistics, with contributions to optimization theory, statistical physics, and algorithmic analysis. Dr. Sellke's work bridges theoretical computer science, probability, and mathematical physics. His recent publications explore algorithmic hardness in combinatorial optimization, energy landscapes in spin glasses, and foundational questions in machine learning. Notable areas include computational lower bounds for partitioning problems, stability analysis of local optima, and universality phenomena in spin glass models. Education: Advanced degrees in Statistics/Computer Science (details not explicitly listed) His research has been recognized through publications in top venues such as FOCS, NeurIPS, and JASA. Collaborative projects include work on multi-species spin glasses and algorithmic approaches to complex systems. Current research trends emphasize the intersection of optimization, stochastic processes, and high-dimensional data analysis. Dr. Sellke maintains an active research group and contributes to the academic community through courses on statistical theory and machine learning. His lab is part of Harvard's broader efforts in data science and probabilistic modeling.

• Spin glasses
• Machine learning theory
• High-dimensional statistics