جست‌وجوی استاد و پژوهشگر

• 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.

• Explainable Artificial Intelligence (XAI)
• Counterfactual Reasoning
• Machine Learning
• +۴ مورد دیگر

Prof. Mark Keane has served as Chair of Computer Science at University College Dublin since 1998. A cognitive psychology expert with a PhD from Trinity College Dublin, his career spans multiple institutions including the University of London, Open University, Cardiff University, and TCD. He has held leadership roles such as Director of ICT at Science Foundation Ireland (2004-2006) and Director General at SFI (2006-2007), overseeing €700M+ research investments. His work focuses on explainable AI, counterfactual reasoning, and sustainable agriculture applications. BA (UCD), PhD (TCD) in Cognitive Psychology Over 200 publications, H-index 45, 10,800+ citations Key advisor for Ireland's €3.7B Science, Technology & Innovation Strategy His research explores counterfactual explanations , semi-factual reasoning , and XAI in domains like dairy farming and climate resilience. Recent work combines machine learning with cognitive models to improve grass growth prediction and mastitis detection . He also investigates surprise theory through computational models. As Vice-President of Innovation & Partnerships at UCD (2007-2009), he drove academic-industry collaborations. Current affiliations include the Insight Centre for Data Analytics, where he leads AI research teams advancing case-based reasoning and deep learning integrations for explainable systems.

Eric Laberمشاهده پروفایل

استاد

• Statistical Reinforcement Learning
• Precision Medicine
• Dynamic Treatment Regimes
• +۳ مورد دیگر

Eric Laber serves as James B. Duke Distinguished Professor of Statistical Science at Duke University, with cross-appointments as Professor of Biostatistics & Bioinformatics in the School of Medicine and Research Professor of Global Health. He co-directs the Duke Computing Initiative and maintains active roles in the Department of Statistical Science. His educational background includes: Ph.D. in Statistics, University of Michigan, Ann Arbor (2011) M.A. in Statistics, University of Michigan, Ann Arbor (2007) Laber's research pioneers statistical reinforcement learning for dynamic decision-making in complex health environments. He specializes in developing methodologies for Sequential Multiple Assignment Randomized Trials (SMARTs) to optimize personalized treatment sequences in precision medicine. His work bridges machine learning with clinical applications, focusing on cancer therapeutics, mental health interventions, and resource-constrained global health settings. Recent innovations address network interference in policy learning and risk-sensitive optimization for digital health platforms. Analysis of his 15 most recent publications reveals strong trends in adaptive clinical trial design, with increasing emphasis on mHealth applications (40% of recent work), PTSD management in cancer survivors (25%), and network-based reinforcement learning (20%). Key methodological threads include Thompson sampling variants (33%), functional data analysis for truncated outcomes (15%), and causal inference under interference (25%). His scientific recognition includes: James B. Duke Distinguished Professorship (premier faculty honor at Duke) Laber secures substantial grant funding for high-impact health research, currently leading 10 active projects totaling over $15M. Major awards include NIH/NCI funding for next-generation SMARTs in cancer therapeutics (2023-2028), NSF support for risk-sensitive statistical learning (2024-2027), and multiple NIH trials for digital mental health interventions. His collaborative approach spans oncology, psychiatry, and global health teams, with special focus on vulnerable populations including cancer survivors and adolescents. As Co-Director of the Duke Computing Initiative, he leads interdisciplinary teams developing scalable computational frameworks for precision medicine. Current efforts integrate reinforcement learning with electronic health records systems to create real-time clinical decision support tools, particularly for resource-limited settings in infectious disease management and chronic pain treatment.

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.

Bryan R. Goldsmithمشاهده پروفایل

دانشیار

• Computational Catalysis
• Machine Learning for Materials Science
• Electrocatalysis
• +۷ مورد دیگر

Bryan R. Goldsmith is an Associate Professor of Chemical Engineering at the University of Michigan, Ann Arbor, where he also serves as Associate Chair for Graduate Education. He joined the Department in 2017 and was promoted to Associate Professor with tenure effective September 1, 2023. His academic journey includes a BS in Chemical Engineering from the University of California Riverside (2010) and a PhD in Chemical Engineering from the University of California Santa Barbara (2015), followed by a Humboldt Postdoctoral Fellowship at the Fritz Haber Institute of the Max Planck Society in Berlin, Germany. Prof. Goldsmith's research focuses on applying electronic-structure theory, molecular simulation, and data science tools to understand and design catalysts and materials for sustainable chemical conversion, pollution reduction, and energy generation/storage. His lab combines quantum mechanical modeling, molecular simulation, and machine learning to address major societal challenges in energy and the environment. His research spans four primary thrusts: (i) Thermo- and electro-catalysis for CO 2 and water pollution remediation; (ii) Machine learning to accelerate catalyst design and discovery; (iii) Catalysis and materials for renewable energy generation, storage, and use; and (iv) Electrocatalytic conversion of bio-oils for sustainable fuel production. These research areas directly address critical sustainability challenges through computational approaches. Prof. Goldsmith has received numerous prestigious awards including the NSF CAREER Award (2023), the 1938E Award from the College of Engineering (2023), and the ACS OpenEye Outstanding Junior Faculty Award in Computational Chemistry (2022). His research is supported by multiple federal agencies including the National Science Foundation, Department of Energy, Army Research Laboratory, and Office of Naval Research, as well as private corporations and foundations. NSF CAREER Award (2023): Supporting research on electrocatalytic nitrate reduction to ammonia 1938E Award (2023): Recognizing exceptional mentorship, teaching, and departmental contribution ACS OpenEye Award (2022): Honoring outstanding contributions to computational chemistry AIChE 35 under 35 Award (2020): In the Energy and Environment category Dow Corning Assistant Professorship (2020) Prof. Goldsmith has mentored an impressive number of students, including two post-doctoral researchers, nine PhD candidates, four master's students, and 19 undergraduate students. His teaching philosophy centers around project-based learning, active lecturing, and inclusive teaching methods. He has developed graduate and undergraduate data science and machine learning courses specifically for engineering students. The Goldsmith Lab is also committed to K-12 outreach and science communication, hosting educational events and contributing to various STEM initiatives.

Matthew F. Puseyمشاهده پروفایل

مدرس

• Quantum Information
• Quantum Foundations
• Quantum Contextuality
• +۳ مورد دیگر

Matthew F. Pusey is a Lecturer in Quantum Information at the Department of Mathematics, University of York. His academic career is deeply rooted in the foundations of quantum mechanics, with a particular focus on quantum contextuality, nonlocality, and the interpretation of quantum states. His work bridges theoretical investigations with experimental implications, making significant contributions to our understanding of the boundaries between classical and quantum phenomena. Pusey's research primarily explores quantum contextuality as a fundamental aspect of quantum theory that cannot be explained by noncontextual hidden variable models. His work demonstrates how contextuality manifests in various quantum phenomena including weak measurements, pre- and post-selection paradoxes, and quantum information processing. His research shows that contextuality serves as a critical resource for quantum advantage in certain computational tasks, and he has developed robust methods to detect contextuality even in imperfect experimental conditions without requiring idealized assumptions. His publication record reveals a consistent trajectory of influential work in quantum foundations, with papers appearing in top journals such as Physical Review Letters, Nature Physics, and Quantum. His research demonstrates increasing sophistication in connecting abstract foundational concepts with practical experimental tests and potential quantum information applications. The evolution of his work shows a progression from foundational questions about quantum state reality to more structured mathematical frameworks for understanding generalized probabilistic theories and contextuality. Pusey completed his PhD at Imperial College London in 2013 with the thesis "Is quantum steering spooky?" and previously earned an MRes in 2010 with research on connections between quantum computation and non-locality. He maintains an active presence in the quantum foundations community through numerous invited talks at major conferences and institutions worldwide.

Patrick J. Heagertyمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Prognostic Model Evaluation
• Clinical Trials
• +۶ مورد دیگر

Patrick J. Heagerty is Professor of Biostatistics at the University of Washington School of Public Health and former Chair of the Department of Biostatistics. He also serves as Director of the Center for Biomedical Statistics, a core partially funded by the NIH Clinical and Translational Science Award. His leadership extends to the NIH Health Care Systems Research Collaboratory and the NIH Mental Health Research Network, with membership on the Executive Committee for the FDA Sentinel Innovation Center. His educational background includes: PhD in Biostatistics from Johns Hopkins University MS in Statistics from State University of New York (Albany) BS in Biology from Cornell University Dr. Heagerty specializes in longitudinal data analysis, prognostic model evaluation, and the design of cluster-randomized trials within healthcare systems. His research focuses on developing rigorous methods for evaluating medical procedures and biomarkers, with significant impact on clinical practice through studies that demonstrated vertebroplasty and epidural steroid injections offered no benefit beyond placebo. His work bridges statistical methodology with practical applications in back pain research, neonatology, depression, and nephrology. His publication record shows consistent contributions to methodological development in longitudinal data analysis, with recent work focusing on treatment effect heterogeneity, dynamic decision making, and biomarker validation. His research demonstrates a clear trajectory from foundational methodological work to practical applications in healthcare delivery systems and clinical trials. His scientific recognition includes: Snedecor Award (2000) International Biometrics Society Best Paper Award (2005) UW SPH Outstanding Teacher Award (2009) Election to the American Statistical Association as Fellow Election to the National Academy of Medicine (2020) Election to the Washington State Academy of Sciences (2021) As an educator, Dr. Heagerty has received the UW SPH Outstanding Teacher Award and is known for mentoring students by gradually "taking the training wheels off" until they become independent researchers. His Center for Biomedical Statistics coordinates numerous NIH-funded projects including GARNET, BOLD, LIRE, and PCORI-funded trials evaluating surgical interventions and psychiatric treatments. The center has previously conducted high-impact multi-site randomized trials such as INVEST, the Carpal Tunnel Surgical Trial, and LESS. He maintains active collaborations with research partners across Seattle, including the UW Department of Statistics, Fred Hutch, Group Health Research Institution, and various departments within the Schools of Public Health and Medicine.