جست‌وجوی استاد و پژوهشگر

Simon Friederichمشاهده پروفایل

دانشیار

• Philosophy of Science
• Foundations of Physics
• Philosophy of Mathematics
• +۳ مورد دیگر

Dr. Simon Friederich is an Associate Professor of Philosophy of Science at University College Groningen , part of the University of Groningen. He serves as co-editor-in-chief of the Journal for General Philosophy of Science and associate editor of Foundations of Physics . His work bridges philosophy, physics, and technology, with a focus on foundational questions in quantum mechanics, multiverse theories, and ethical implications of AI. Education: PhD in Theoretical Physics (Heidelberg, 2011) and PhD in Philosophy (Bonn, 2014) Research Themes: Quantum foundations, epistemology of multiverse theories, philosophy of mathematics, and existential risks His research explores how quantum theory can be reconciled with classical concepts, particularly through retrocausality and non-locality. He investigates the philosophical implications of fine-tuning in cosmology and has developed a novel argument for the multiverse immune to the inverse gambler's fallacy. In ethics, he addresses AI's societal impact and sustainability, collaborating on critiques of nuclear energy and energy justice. Recent work includes the NWO Vidi project "Saving Reality with Exotic Causality" (2016-2021), which reinterprets quantum mechanics as akin to classical statistical mechanics. His publications span journals like Foundations of Physics , British Journal for the Philosophy of Science , and Philosophical Studies , with a monograph Multiverse Theories: A Philosophical Perspective (Cambridge University Press, 2021). Scientific Awards: NWO Vidi Grant (2016-2021) NWO Veni Grant (2016-2019) Co-editor roles in leading journals He teaches courses on logic, philosophy of science, and existential risks , and co-founded the German ecomodernist society WePlanet DACH. His interdisciplinary approach combines physics, philosophy, and ethics, with ongoing collaborations on AI, cosmology, and energy policy.

Herbie Leeمشاهده پروفایل

استاد

• Bayesian statistics
• Computer models
• Spatial inverse problems
• +۷ مورد دیگر

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

Carsten Dormannمشاهده پروفایل

استاد

• Statistical ecology
• Species distribution modeling
• Plant-pollinator interactions
• +۷ مورد دیگر

Carsten Dormann is a Full Professor at the University of Freiburg since 2011, working in the Department of Biometry and Environmental System Analysis within the Faculty of Biology. His work bridges statistical methodology with ecological applications, focusing on improving analytical approaches in environmental science. He leads research on statistical ecology, species distribution modeling, and plant-pollinator interactions, with a strong emphasis on methodological rigor and evidence-based environmental science. Professor Dormann completed his Diploma (equivalent to an MSc) in Biology at the University of Kiel (1996), followed by a PhD in Plant Ecology from the University of Aberdeen (2001) under Dr. Sarah Woodin and Prof. Steve Albon. He earned his Habilitation at the University of Göttingen (2008), and worked as a PostDoc and Senior Research Scientist at the Helmholtz Center for Environmental Research-UFZ (2002-2011) before joining Freiburg. Dr. Dormann's research focuses on comparing, challenging and improving the toolbox of statistical ecology . He investigates how ecological datasets, often small but complex, can be properly analyzed when common statistical approaches may fail. His work emphasizes formal statistical integration of ecological models and data , advocating for rigorous representation of ecological understanding through quantitative predictions. He champions an evidence focus in environmental science , drawing parallels with evidence-based medicine to promote transparent evaluation of causal mechanisms. Specific areas include spatial autocorrelation, null models, collinearity, species distribution modeling, and plant-pollinator interactions. His recent publications reveal a strong focus on ecological network analysis, species distribution modeling under climate change, and methodological improvements in ecological statistics. The research spans theoretical developments in network topology and practical applications in conservation, with increasing integration of machine learning approaches while maintaining ecological interpretability. A notable trend is the emphasis on temporal dynamics in ecological systems and developing more robust methods for predicting ecological responses to environmental change. Professor Dormann currently supervises twelve PhD students across various ecological and statistical topics, with an extensive record of past supervision spanning over thirty doctoral candidates. His teaching contributions include authoring the textbook Environmental Data Analysis: An Introduction with Examples in R (2017) and developing statistics courses for environmental sciences. He maintains an active scholarly blog discussing methodological challenges in ecology, with recent posts addressing species richness metrics, bias-variance trade-offs, and the relationship between ecological science and policy. His work bridges theoretical statistical development with practical ecological applications, emphasizing scientific credibility and methodological rigor throughout.

Chu-An Liuمشاهده پروفایل

پژوهشگر ارشد

• Econometrics
• Model Averaging
• Forecast Combination
• +۳ مورد دیگر

Chu-An Liu is a Research Fellow at the Institute of Economics, Academia Sinica (Taipei, Taiwan). He received a PhD in Economics from the University of Wisconsin-Madison in 2012. His teaching experience includes graduate-level and PhD econometrics courses at National Chengchi University (Spring 2022-present) and National University of Singapore (2013-2015). Research Interests: His work focuses on econometrics , model averaging , and forecast combination , with significant contributions to nonparametric methods, causal inference, and statistical modeling for high-dimensional economic data. Key areas include bounds estimation for continuous treatments, spectral analysis in time series, and model uncertainty frameworks. Publications: He has published extensively in top journals like Journal of Econometrics , Econometric Theory , and Journal of Business & Economic Statistics . Recent trends emphasize kernel regressions, autoregressive models, and integration of machine learning with traditional econometric techniques. Academic Collaborations: He has collaborated with scholars such as Xinyu Zhang, Ying-Ying Lee, and Biing-Shen Kuo on topics spanning model selection, causal inference, and nonstationary data analysis.

Zachary Liptonمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +۵ مورد دیگر

Zachary Lipton is an Assistant Professor at Carnegie Mellon University (CMU) jointly appointed in the Tepper School of Business and the Machine Learning Department. He holds courtesy affiliations with the Heinz School of Public Policy and Societal Computing. His research bridges core ML methods, healthcare applications, natural language processing, and critical analysis of AI's societal impacts. Tepper School of Business Machine Learning Department Heinz School of Public Policy (courtesy) Societal Computing (courtesy) Dr. Lipton leads the Approximately Correct Machine Intelligence (ACMI) Lab, focusing on robust ML systems, causal representation learning, and ethical AI development for clinical medicine. He co-founded Abridge, a healthcare AI company, and authored the interactive textbook Dive into Deep Learning . His work emphasizes clear scientific communication through expository efforts like literature reviews and the Approximately Correct blog. Recent publications highlight ACMI Lab's contributions to synthetic data quality, causal fairness analysis, diffusion model hallucinations, and medical LLM adaptation. Key research themes include distribution shift, human-AI alignment, and empirical evaluation of AI's societal impacts. Contact: zlipton@cmu.edu

• Machine Learning
• Explainable AI
• Fairness in AI
• +۳ مورد دیگر

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.

• Computational Intelligence
• Evolutionary Multi-objective Optimization
• Evolutionary Machine Learning
• +۵ مورد دیگر

Professor Hisao Ishibuchi is Chair Professor of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China, a role he has held since April 2017. Previously, he spent nearly three decades at Osaka Prefecture University, progressing from Research Associate (1987-1993) to Assistant Professor (1993), Associate Professor (1994-1999), and full Professor (1999-2017). He is an IEEE Fellow , served as Vice-President of the IEEE Computational Intelligence Society (2010-2013) , and is currently President of the Japan Society for Evolutionary Computation (2016-2018) . He is Editor-in-Chief of IEEE Computational Intelligence Magazine (2014-2019) and the Journal of the Japan EC Society (2014-2018). Education: Ph.D. in Engineering, Osaka Prefecture University, 1992 M.S. in Engineering, Kyoto University, 1987 B.S. in Engineering, Kyoto University, 1985 Research Focus: Professor Ishibuchi is internationally recognised as a pioneer of computational intelligence , with seminal contributions to evolutionary multi-objective optimisation , evolutionary machine learning , fuzzy systems , neural networks , and hybrid intelligent systems . He introduced the first multi-objective memetic algorithm and early methods for multi-objective fuzzy rule-based classifier design that balance accuracy and interpretability. Publications & Impact: With over 100 journal papers in top-tier venues such as IEEE Transactions on Evolutionary Computation and nearly 500 conference papers, his work has attracted more than 24 000 Google-Scholar citations and an h-index of 68. His recent articles concentrate on many-objective optimisation, fuzzy machine learning, and transfer learning techniques. Honours & Awards: IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award 2019 IEEE Fellow 2014 JSPS Prize 2007 (Japan’s most prestigious mid-career award) Multiple Best Paper Awards from GECCO, FUZZ-IEEE, SCIS & ISIS, WAC, ACIIDS, HIS-NCEI, and others Teaching & Mentoring: At SUSTech he teaches Advanced Algorithms and Advanced Optimization Algorithms , covering greedy algorithms, hyper-heuristics, memetic algorithms, multi-objective optimisation, and performance assessment. His research group actively recruits post-doctoral fellows and research assistants in evolutionary computation, fuzzy systems, and neural networks. Labs & Teams: He leads the Computational Intelligence Research Group at SUSTech, maintaining active collaboration networks across Asia, Europe, and North America, and supervising several post-doctoral researchers and graduate students working on next-generation intelligent systems.

Veera Baladandayuthapaniمشاهده پروفایل

استاد

• High-dimensional data modeling
• Bayesian inference
• Functional data analysis
• +۱۱ مورد دیگر

Dr. Veera Baladandayuthapani is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as the Director of the Cancer Data Science Shared Resource and previously held positions as Professor and Institute Faculty Scholar at the University of Texas MD Anderson Cancer Center. His research focuses on high-dimensional data modeling and Bayesian inference, with applications in genomics, epigenomics, transcriptomics, proteomics, and cancer imaging. PhD, Statistics, Texas AandM University, 2005 MA, Statistics, University of Rochester, 2000 BS, Mathematics, Indian Institute of Technology (IIT) Kharagpur, 1999 His research develops Bayesian probabilistic models and machine learning methods for biomedical big data, emphasizing integrative models combining multi-omics and imaging data for biomarker discovery and clinical prediction. Current projects include Bayesian hierarchical models, graphical models, and scalable software tools for precision medicine. Recent publications span Bayesian graphical regression, proteogenomics, and spatial cellular interaction modeling. He holds multiple NIH and NSF grants for advancing biostatistical methods in oncology. Fellow of the American Statistical Association Elected member of the International Statistical Institute He has advised numerous PhD students and postdoctoral researchers, with alumni positions at institutions like Emory University, University of Warwick, and Pfizer. His BayesRx research group actively recruits post-doctoral fellows.

Jean Morrisonمشاهده پروفایل

استادیار

• Statistical Genetics
• Genomics
• Causal Inference
• +۴ مورد دیگر

Jean Morrison serves as the John G Searle Assistant Professor in the Department of Biostatistics at the University of Michigan School of Public Health, a position she assumed in 2020 following postdoctoral work at the University of Chicago and doctoral studies at the University of Washington. Her academic trajectory reflects deep specialization in statistical methodologies for complex biological systems. Her educational foundation includes: PhD in Biostatistics, University of Washington (2016) BA in Mathematics, University of Chicago (2009) Dr. Morrison's research centers on statistical genetics and genomics , with pioneering work in high-dimensional phenotype analysis (e.g., brain imaging, proteomics, and clinical trait clusters). She develops advanced causal inference frameworks, notably for Mendelian randomization with robust pleiotropy handling, and integrates empirical Bayes methodologies with deep learning for genomic applications. Her approach emphasizes biologically interpretable low-rank representations of genetic associations. Recent publications (2022-2025) reveal escalating focus on multi-omics integration (genome, transcriptome, proteome), probabilistic fine-mapping of causal variants, and methodological refinements for Mendelian randomization. Her work increasingly addresses genetic pleiotropy complexity through factor analysis and develops computational tools like GWASBrewer for realistic simulation of genetic data. No scientific awards are documented in available sources. Similarly, public records contain no details regarding student advising, research grants, or laboratory leadership.

Xiaoquan Wenمشاهده پروفایل

استاد

• Bayesian Statistics
• Computational Statistics
• Genetics
• +۵ مورد دیگر

Xiaoquan Wen is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health, where he joined the faculty in 2011 after earning his PhD in Statistics from the University of Chicago. His research focuses on developing advanced Bayesian and computational statistical methods for genetics and genomics applications. His educational background includes a PhD in Statistics (2011) from the University of Chicago, an MS in Computer Science (2002), and an MS in Mathematics (2001), both from the University of Illinois. His research spans Bayesian model comparison, multiple hypothesis testing, probabilistic graphical models, and molecular QTL analysis, with applications in genetics and functional genomics. Wen's publication trends reveal consistent contributions to statistical genetics methodology, particularly in false discovery rate control, eQTL discovery, and integrative genomic analyses. His work increasingly emphasizes multi-omics integration and causal inference for complex traits, as evidenced by recent publications on metabolome-wide Mendelian randomization and multi-resolution genomic clustering. He is an active participant in the NIH GTEx project and affiliated with the Center of Statistical Genetics at the University of Michigan. His software contributions include BLIMP for imputation, SLAT for gene-level testing, and STRUCTURE for population genetics analysis. Wen teaches advanced courses including BIOS 699 (Design and Analysis of Biostatistical Investigations), BIOS 680/MATH 627 (Applications of Stochastic Processes), and BIOS 830 (Methods and Applications of Statistical Learning), demonstrating his commitment to statistical education.

Sarah Peskoeمشاهده پروفایل

استادیار

• Biomarker Evaluation
• Electronic Health Records-based Research
• Risk Prediction
• +۳ مورد دیگر

Sarah Peskoe serves as an Assistant Professor in the Department of Biostatistics & Bioinformatics at Duke University, where she is affiliated with the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core. She maintains active collaborations with researchers in the Center for the Study of Aging and Human Development, Department of Surgery, and Department of Medicine. Her educational credentials include: Sc.D. in Biostatistics from Brown University (2011) Ph.D. in Biostatistics from Harvard University (2017) Dr. Peskoe's research program centers on methodological innovations to correct biases from measurement error and selection in epidemiologic studies. Her core expertise spans biomarker evaluation , electronic health records-based research , risk prediction modeling , and advanced survival analysis techniques. She develops statistical frameworks for handling complex data structures while maintaining rigorous causal inference standards. Analysis of her 2023-2025 publications reveals strong thematic convergence around health equity applications in transplantation and aging research. Her work consistently bridges methodological biostatistics with clinical implementation, particularly in kidney/heart/lung transplantation outcomes, geriatric care interventions, and disparities reduction. The publications demonstrate increasing focus on real-world data from electronic health records and pragmatic clinical trials. Dr. Peskoe currently leads 11 major research initiatives funded by NIH, NIA, and PCORI: Impact of hypertension and aging on postoperative delirium (NIH, 2025-2030) Integration of Geriatric Care into Dialysis Clinics (NIA, 2023-2028) Evaluation of Unilateral vs Bilateral Hearing Aids (PCORI, 2021-2026) Health System Outreach to Eliminate Kidney Transplant Disparities (NIH, 2020-2023) Through the Quantitative Team Science (QuanTS) Program, she actively builds infrastructure for statistical collaboration units while mentoring junior quantitative scientists across Duke's clinical research enterprise.

• Machine Learning
• Medical Imaging
• Natural Language Processing
• +۶ مورد دیگر

Jonathan Bates is a Lecturer at the Yale School of Public Health, affiliated with the Yale Center for Medical Informatics. His work focuses on applying machine learning and data science to public health challenges, particularly in HIV care, falls prevention, and medical device safety. His educational background includes: PhD in Biomathematics from Florida State University (2013) Postdoctoral Fellowship in Medical Informatics at VA Connecticut/Yale (2015) Dr. Bates' research spans machine learning and health informatics , with expertise in natural language processing , predictive modeling , and causal inference . His work targets vulnerable populations including older adults with HIV and veterans, addressing critical gaps in fall prevention, fracture risk assessment, and healthcare delivery optimization through advanced analytics. Analysis of his 15 most recent publications (2015-2023) reveals three dominant research thrusts: predictive modeling for geriatric falls and fractures in HIV populations, machine learning applications in medical device surveillance , and social network analysis for care coordination . These studies consistently leverage electronic health records and focus on high-impact public health problems affecting marginalized groups. Dr. Bates maintains active collaborations across Yale University, the Veterans Health Administration, and clinical departments including Cardiology and Emergency Medicine, demonstrating strong interdisciplinary integration of methodological innovation with real-world health challenges.