Ronald C. Lasky is a Professor of Engineering at Dartmouth College's Thayer School of Engineering and a Senior Technologist at the Indium Corporation. His academic roles include teaching courses such as ENGM 187: Technology Innovation and Entrepreneurship and ENGS 155: Intermediate Thermodynamics . Dr. Lasky holds a BS in Engineering Physics from Cornell University (1970), an MS in Applied Mathematics from Binghamton University (1974), and a PhD in Materials Science from Cornell University (1986). His research focuses on process optimization, electronic assembly, materials science, and environmental compliance. Notable contributions include work on lead-free solder assembly and Lean Six Sigma methodologies. He received the Member of Technical Distinction Award from the SMTA in 2021. Dr. Lasky actively engages with industry, exemplified by his collaboration with Galanz in China, exploring modern manufacturing practices and infrastructure. His insights on global supply chains and technological advancements highlight his dual academic and professional expertise.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۱-۱۵ از ۸٬۵۶۵ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Joseph A. November is an Associate Professor in the Department of History at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. His research focuses on the history of biomedical computing, distributed computing, and the intersection of technology and medicine. He holds a Ph.D. from Princeton University (2006), an M.A. from the University of Chicago (2002), and a B.A. from Hamilton College (1997). His work includes the award-winning book Biomedical Computing: Digitizing Life in the United States (2012), which explores the co-development of biomedicine and computing technologies. Current projects include Revolutions@home , examining distributed computing in protein folding research, and a biography of computing pioneer Robert S. Ledley. He has received grants from the NSF, NIH, and the Charles Babbage Institute. Teaching interests span the history of science and technology, including courses on the history of medicine, digital humanities, and the role of games in historical education. He actively contributes to professional organizations like SHOT and the History of Science Society. Awards include the Computer History Museum Prize (2013) and the National Institutes of Health DeWitt Stetten Fellowship (2007-2008). His research bridges historical analysis with contemporary issues in technology and biomedical ethics.
- Mathematical Finance
- Financial Engineering
- FinTech
- +۷ مورد دیگر
Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.
David Williamson Shaffer is the Sears Bascom Professor of Learning Analytics and Vilas Distinguished Achievement Professor of Learning Sciences at the University of Wisconsin-Madison's Department of Educational Psychology. He is also a Data Philosopher at the Wisconsin Center for Education Research. His work focuses on merging statistical and qualitative methods to model complex human collaboration. Shaffer holds an AB in History and East Asian Studies from Harvard University (1987), and MS/PhD in Media Arts and Sciences from MIT (1996/1998). Before academia, he was a teacher, curriculum developer, and game designer. His research emphasizes Quantitative Ethnography —a methodology combining big data analysis with cultural interpretation. Key contributions include Epistemic Network Analysis (ENA) and over 250 publications, including influential books like How Computer Games Help Children Learn (2006) and Quantitative Ethnography (2017). Awardees of the EU Marie Curie Fellowship (2008) and Spencer Foundation Fellowship (2003), Shaffer's work has been presented globally at conferences like Learning Analytics & Knowledge and Computer Supported Collaborative Learning. His interdisciplinary approach bridges education, data science, and humanities, addressing challenges in authentic STEM experiences, collaborative learning analytics, and culturally responsive pedagogy.
Avi Turetskyمشاهده پروفایل
استاد مدعو
Avi Turetsky is an Adjunct Professor at the Weatherhead School of Management (Case Western Reserve University) and a Research Fellow in private equity. Currently a Partner and Co-Head of the Quantitative Research Group (QRG) at Ares Management, he oversees original research, quantitative software development, and tools production teams. Previously served as Chief Operating Officer for The Riverside Company's Europe Fund and holds advisory roles at EDHEC and INSEAD . Research Focus: Turetsky’s work bridges private equity practice and quantitative finance, examining distributional patterns in investment outcomes, competency frameworks for financial professionals, and mathematical models for performance evaluation. His recent publications explore robust statistical methods in portfolio construction, direct alpha calculations for skill assessment, and sector-based value creation strategies in private equity-owned companies. Key Article Trends: His research spans 2016–2023, emphasizing quantitative methodologies in private equity, including skew analysis , value creation metrics , and competency clustering . Topics range from alliance changes in intercollegiate athletics to mathematical modeling of investment professional performance. Leadership & Teams: At Ares Management, Turetsky co-leads the QRG, managing cross-functional teams in Original Research , Quantitative Software Development , and Tools Production . He collaborates with investment teams to integrate quantitative strategies into decision-making processes.
Nancy Margaret Reid is a University Professor of Statistical Sciences at the University of Toronto, holding the Canada Research Chair in Statistical Theory and Applications. She has served as Scientific Director of the Canadian Statistical Sciences Institute (2015–2019) and led the Department of Statistical Sciences as Chair (1997–2002). Her research focuses on theoretical statistics, particularly likelihood inference and foundational aspects of statistical methodology. Reid earned her PhD from Stanford University (1979) under Rupert G. Miller, with Brad Efron and Vernon Johns on her committee. Reid's accolades include Fellowships from the Royal Society, Royal Society of Canada, and National Academy of Sciences, as well as the Guy Medal in Gold (2022) and David R. Cox Award (2023). She has authored influential books like *Theory of the Design of Experiments* and contributed to courses on mathematical statistics and likelihood inference. Active in academic service, she teaches graduate-level courses and has advised numerous students and postdocs in theoretical and applied statistical research.
Professor Po-Ling Loh is a faculty member at the University of Cambridge, affiliated with the Statistical Laboratory within the Faculty of Mathematics. Her research focuses on statistical theory and methodology, with applications in machine learning, robust statistics, and medical imaging. She holds a professorship position and contributes to advancing computational and theoretical frameworks for high-dimensional data analysis. Loh’s work addresses challenges such as robust regression, differential privacy, and efficient algorithms for complex models. Her educational background includes studies at Cambridge and further academic pursuits, though specific degree details are not provided here. Research interests span statistical learning, adversarial machine learning, and the mathematical foundations of robust algorithms. She actively publishes in top-tier journals and conferences, addressing topics like neural network regularization, privacy-preserving synthetic data, and network analysis. Notably, Loh collaborates on projects involving medical image analysis (e.g., bone age estimation via BAE-ViT) and has contributed to methodological advancements in hypothesis testing and privacy-constrained inference. Her research often bridges theory and practice, emphasizing computational efficiency and statistical rigor. While no specific grants or awards are listed, her prolific publication record reflects sustained academic impact in statistical and machine learning domains. Loh is associated with the Statistical Laboratory, contributing to its research initiatives and possibly advising students in high-dimensional statistics and related fields. Her work frequently intersects with interdisciplinary applications, such as medical imaging and network science, underscoring the practical relevance of her theoretical contributions.
Moe Z. Win is the Robert R. Taylor Professor at the Massachusetts Institute of Technology (MIT), specializing in wireless communications, optical communications, and space communications systems. His research bridges theoretical and applied domains, including quantum sensing, network localization, and signal processing. B.S.E.E., Texas A&M (1987) M.S.E.E. & Ph.D., University of Southern California (1989, 1998) Recent work focuses on quantum-enhanced positioning, machine learning for localization, and next-generation (xG) non-terrestrial networks. He leads research at the Quantum neXus Laboratory (QX Lab), Wireless Information & Network Sciences Lab, and Laboratory for Information and Decision Systems. His career spans the Jet Propulsion Laboratory (1987-1995) and AT&T Research Laboratories (1998-2002). Key methodologies include soft information fusion, variational quantum sensing, and robust beam tracking for terahertz communications.
- Statistical machine learning theory
- High-dimensional statistical inference
- Optimal transport
- +۶ مورد دیگر
Florentina Bunea is a Professor in the Department of Statistics and Data Science at Cornell University’s Bowers College of Computing and Information Science, and an active member of the Graduate Fields of Statistics, Applied Mathematics, and Computer Science. She also serves on the Diversity and Inclusion Council of her college, championing workforce diversity in data-science disciplines. Education & Institutional Roles Professor, Department of Statistics and Data Science, Cornell University Member, Graduate Fields of Statistics, Applied Mathematics, Computer Science Member, Diversity and Inclusion Council, Bowers College of Computing and Information Science Research Interests Professor Bunea’s research lies at the intersection of statistical machine-learning theory and high-dimensional inference. She develops rigorous methodology supported by sharp theoretical guarantees to tackle core problems in modern data science. Recent themes include: Soft-max mixtures for understanding large-language-model/AI algorithms Optimal transport for high-dimensional mixture distributions Wasserstein-distance inference for sparse mixing measures in topic models Latent-space clustering and cluster-based inference in high dimensions Network modeling and hidden-structure inference Applications spanning genetics, systems immunology, neuroscience, sociology, and economics Research Funding & Awards Her work is supported by grants from the National Science Foundation (NSF-DMS). She is a Fellow of the Institute of Mathematical Statistics and a recipient of the IMS Medallion Award. Editorial & Service Contributions Associate Editor: Annals of Statistics, Bernoulli, JASA, JRSS-B, EJS, Annals of Applied Statistics Co-Editor: Chapman & Hall/CRC Statistics and Applied Probability Monograph Series Advising & Collaboration Professor Bunea has mentored numerous doctoral and post-doctoral researchers, including Xin Bing, Shuyu Liu, Seth Strimas-Mackey, and Yang Ning, among others. Collaborative projects extend across Cornell and external institutions, producing widely-used software packages and high-impact publications. Contact Office: 1184 Comstock Hall, Cornell University Email: fb238@cornell.edu Phone: (607) 255-8449
Professor Richard Samworth is a leading academic at the University of Cambridge , affiliated with the Department of Pure Mathematics and Mathematical Statistics and serving as Director of the Statistical Laboratory . His research focuses on Nonparametric and High-dimensional Statistics , addressing challenges in data analysis, statistical learning, and computational methods. Research Interests : Richard Samworth's work emphasizes Nonparametric Statistics , High-dimensional Data , and Statistical Learning . His research spans topics such as Missing Data , Changepoint Detection , Log-concave Density Estimation , and Subgroup Analysis , with applications in Machine Learning and Data Science . Key methodologies include Score Matching , Random Projections , and Minimax Estimation . Recent publications highlight advancements in Semi-Supervised Learning , Robust Statistical Testing , and High-dimensional PCA with heterogeneous missingness. His work bridges theoretical rigor with practical applications, particularly in Statistical Algorithms and Optimization .
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Gloria Milena Monsalve Bravoمشاهده پروفایل
پژوهشگر ارشد
Dr. Gloria Milena Monsalve Bravo is an Advanced Queensland Industry Research Fellow and lecturer at The University of Queensland's School of Chemical Engineering, where she develops novel multiscale simulation techniques combining molecular simulations with macroscopic physics-based modeling to solve complex energy and environmental problems. Her interdisciplinary work bridges applied mathematics and engineering to improve understanding of phenomena in complex systems across chemical, biomedical, and ecological applications. Her research focuses on: Multiscale simulation techniques for complex systems Molecular simulations coupled with macroscopic modeling Gas permeation and separation in mixed-matrix membranes Uncertainty and sensitivity analysis in mathematical models Applied mathematics for engineering problems Dr. Monsalve Bravo's publication record demonstrates a strong trajectory in membrane technology and computational modeling. Her recent work has advanced understanding of gas transport in novel membrane materials, particularly mixed-matrix membranes, with applications in carbon capture and hydrogen storage. She has made significant contributions to theoretical frameworks for modeling permeation in finite-sized composite systems and developed Bayesian approaches for analyzing parameter uncertainty in sorption predictions. Her research bridges fundamental science with practical applications in energy and environmental engineering. Her scientific contributions have been recognized through research funding including: ARC Research Hub for Value-Added Processing of Underutilised Carbon Wastes (2024-2029) Tailor-made composite membranes for greenhouse gas capture (2023-2026) through Advance Queensland Industry Research Fellowships Dr. Monsalve Bravo actively mentors PhD students on cutting-edge projects related to membrane technology, catalyst development, and waste conversion. She collaborates extensively across disciplines, as evidenced by her diverse publication record spanning chemical engineering, materials science, and environmental applications.
Shane G. Henderson is the Charles W. Lake, Jr. Professor in Productivity at Cornell University's School of Operations Research and Information Engineering (ORIE). His research focuses on the intersection of optimization and simulation, with applications to complex systems such as bike-sharing networks, medical scheduling, and ambulance deployment. He emphasizes practical societal relevance, addressing challenges in healthcare, transportation, and parallel computing environments. Education: B.Sc. (Mathematics), University of Auckland (1992) M.S. (Statistics), Stanford University (1995) Ph.D. (Operations Research), Stanford University (1997) Research Interests: Structured simulation optimization, parallel computing applications, applied probability, and case-driven problem-solving. Recent work includes MRI scheduling, radiation treatment planning, and land/air ambulance systems. He advocates for methodologies that leverage convexity or quasi-convexity to enhance algorithmic efficiency and robustness. Awards and Recognition: 2016 Tau Beta Pi Professor of the Year 2014 Sonny Yau '72 Excellence in Teaching Award 2011 Hess Research & Education Award NSF CAREER Award (1999) Teaching & Service: Courses in probability, simulation, and mathematical modeling using case-based learning. Served on award committees for INFORMS Nicholson Prize, Pierskalla Prize, and Impact Prize. Organized major conferences including the 2009 INFORMS Applied Probability Society conference and multiple Winter Simulation Conferences. Currently co-edits simulation-optimization testbeds for experimental comparisons. Labs & Collaborations: Active in Cornell's collaborative research environment, focusing on interdisciplinary projects with industry partners like Motivate (bike-sharing systems). Engaged in advancing methodologies for complex systems through data-driven approaches.
- Syntax
- Language Variation
- Historical Linguistics
- +۳ مورد دیگر
John B. Whitman is a Professor of Linguistics at Cornell University's College of Arts and Sciences. His research focuses on language variation, syntax, historical linguistics, and East Asian languages, particularly Japanese and Korean. He explores typological universals, syntactic variation, and diachronic developments, contributing to debates on configurationality and structural universals. Whitman has published extensively on topics ranging from Japanese historical phonology to comparative syntax across East Asian languages. His work often integrates cross-linguistic analysis with neuroscientific insights, as seen in studies on semantic number processing. He has received grants for projects including language revitalization initiatives and syntactic research, and he teaches courses on Japanese language history and language endangerment. Whitman collaborates with interdisciplinary teams and actively engages in linguistic fieldwork and historical text analysis, maintaining affiliations with the East Asia Program and research labs at Cornell. Research interests include syntactic variation, historical syntax, and the interaction between language structure and diachronic change. His recent work examines the evolution of verbal conjugations in Japanese/Korean and the typological implications of syntactic reanalysis. Whitman's publications also address methodological questions in glossing practices and comparative lexicography, bridging historical linguistics with modern computational analysis. Grants and projects include funding from the New Frontier Grants for collaborative research on language documentation and historical syntax. He advises graduate students in the linguistics program and contributes to Cornell's language revitalization efforts, such as Cayuga language preservation initiatives. Whitman's work frequently intersects with cognitive science, as seen in neuroimaging studies of linguistic structures.
- Welfare Economics
- Normative Ethics
- Legal Theory
- +۷ مورد دیگر
Matthew D. Adler is the Richard A. Horvitz Distinguished Professor of Law and Professor of Economics, Philosophy, and Public Policy at Duke University. He is the founding director of the Duke Center for Law, Economics and Public Policy and holds a 3-year research fellowship at the London School of Economics. Education: B.A., Yale University (1984) J.D., Yale University (1991) M.Litt., University of Oxford (1987) - Marshall Scholar Research Interests: Adler specializes in prioritarianism, a theory that refines utilitarianism by prioritizing the worse-off. He integrates welfare economics, normative ethics, and legal theory to develop policy analysis tools like social welfare functions with distributional weights. His work spans climate change , risk regulation , health policy , and inequality measurement . Publications and Contributions: Adler has authored monographs including New Foundations of Cost-Benefit Analysis and Well-Being and Fair Distribution , and co-edited the Oxford Handbook of Well-Being and Public Policy . His recent work explores implementing prioritarianism in policy domains such as the social cost of carbon , health economics , and intergenerational equity . Scientific Awards: Marshall Scholar Ludwig M. Lachmann Professorial Research Fellow Professional Affiliations: Adler was an editor of Legal Theory until 2017 and is now an editor of Economics and Philosophy . He has held visiting professorships at Columbia, Chicago, and Virginia Law Schools.
فقط ۷۵۰ نتیجه اول نمایش داده میشود. برای رسیدن به نتایج دقیقتر، جستوجو را با فیلترها محدودتر کنید.