جستجوی استاد راهنمای دانشگاهی

Konstantin Zuevمشاهده پروفایل

استاد آموزشی

• Statistical Data Analysis
• Network Data Analysis
• Machine Learning
• +۷ مورد دیگر

Konstantin Zuev is a Teaching Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. He holds dual Ph.D.s in Mathematics (Lomonosov Moscow State University) and Civil Engineering (Hong Kong University of Science & Technology). Zuev is also the Undergraduate Option Representative for Information and Data Sciences and Graduate Minor Advisor for the same field. His research focuses on statistical data analysis, network science, machine learning, and computational methods for complex systems. His teaching includes courses like Applied Linear Algebra, Probability Models, and Statistical Inference. Zuev has received multiple prestigious awards, including the 2023 ASCIT and GSC Teaching Awards, the 2021 Humboldt Research Fellowship, and the 2019 Northrop Grumman Prize for Excellence in Teaching. His work spans interdisciplinary topics such as financial market dynamics, epidemiological modeling, and academic curriculum analysis through network theory. Zuev’s research has been published in journals like *Proceedings of the National Academy of Sciences*, *Scientific Reports*, and *Physical Review E*. He is actively involved in consulting projects with organizations like Virtualitics, Inc., focusing on applications in healthcare and network analytics. Beyond academia, he advises student groups like the Caltech Chess Club and Karate Club, reflecting his passion for fostering community engagement and mentorship.

Venugopal V. Veeravalliمشاهده پروفایل

استاد

• Statistical Inference
• Machine Learning
• Information Theory
• +۹ مورد دیگر

Venugopal V. Veeravalli is the Henry Magnuski Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering, Coordinated Science Laboratory (CSL), Department of Statistics, and Discovery Partners Institute (DPI). He holds a Ph.D. from UIUC (1992) and has been on the faculty since 2000, previously at Cornell University. His research spans statistical inference, machine learning, information theory, and their applications in wireless communications, sensor networks, and cyberphysical systems. He has authored/co-authored influential books like Statistical Inference for Engineers and Data Scientists and Interference Management in Wireless Networks , and serves as Editor-in-Chief of the IEEE Transactions on Information Theory. Notable awards include the IEEE Fellow (2006), PECASE (1998), and twice the Abraham Wald Prize in Sequential Analysis (2016, 2023). He advises students on topics like robust learning, distributed algorithms, and adaptive sensing. Current research focuses on sequential change detection, non-stationary environments, and IoT resilience. Education: B.Tech. (IIT Bombay, 1985), M.S. (Carnegie Mellon, 1987), Ph.D. (UIUC, 1992) Professional Roles: Editor-in-Chief (IEEE Transactions on Information Theory), NSF Program Director (2003–2005), Fulbright-Nokia Chair (2023) Research Interests: His work bridges theoretical foundations and practical applications, including quickest change detection, robust machine learning, and distributed systems. Recent projects involve anomaly detection in power grids, pandemic monitoring via wastewater data, and adaptive algorithms for wireless networks. Grants & Collaborations: Active in NSF-funded projects on robust learning and IoT resilience. Collaborates internationally, including with Aalto University (Finland) under the Fulbright-Nokia Chair. Teaching: Teaches courses on probability, optimization, communication theory, and data science. Recognized with the Michael Tien Excellence in Teaching Award (1999).

• Systems and Signal Processing
• Control Theory
• Ill-Conditioned Inverse Problems
• +۷ مورد دیگر

Professor Victor Solo serves as Director of Research with the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW). With an extensive academic career spanning over four decades since earning his PhD from the Australian National University in 1979, he has established himself as a leading expert in multiple interdisciplinary fields. University: University of New South Wales School: School of Electrical Engineering and Telecommunications Department: Electrical Engineering and Telecommunications Position: Professor and Director of Research Professor Solo received his BSc from the University of Queensland, followed by a BSc (first class honors) and BE (first class honors) from UNSW, culminating in a PhD from ANU in 1979. His educational background provided the foundation for his diverse research career spanning engineering, mathematics, and biomedical applications. His research interests encompass a wide range of theoretical and applied topics, with particular emphasis on Systems and Signal Processing, Control Theory, and Ill-Conditioned Inverse Problems. He has made significant contributions to Econometrics and Time Series Analysis, developing innovative approaches to System Identification. His work extends into biomedical domains through research in Medical Imaging and Computer Vision, as well as Neuroengineering through studies of Neural Coding and Point Processes. Professor Solo's interdisciplinary approach bridges theoretical mathematics with practical applications across engineering and medical fields. Analysis of Professor Solo's recent publications reveals a strong focus on advanced statistical modeling techniques, particularly in time series analysis and point process modeling. His work consistently addresses stability and identifiability challenges in complex models, with recent publications exploring Vector Autoregressive models, Hawkes processes, and stochastic differential equations on manifolds. The research demonstrates a progression from foundational theoretical work to increasingly sophisticated applications in network modeling and biomedical signal processing. Professor Solo has maintained an exceptionally productive research career with publications spanning from 1981 to the present, demonstrating remarkable longevity and adaptability in his research focus. His work shows consistent contributions across multiple high-impact journals including IEEE Transactions on Signal Processing, Automatica, and Neural Computation. While specific awards are not listed in the available information, his sustained publication record in top-tier journals indicates significant recognition within his fields of expertise. As Director of Research, Professor Solo likely oversees research strategy and development within the School of Electrical Engineering and Telecommunications. His extensive publication record suggests active supervision of graduate students and postdoctoral researchers, though specific names of advisees are not provided in the available information. His research has likely attracted substantial grant funding given the scope and duration of his work across multiple domains.

• Corporate Law
• Corporate Finance
• Securities Law
• +۴ مورد دیگر

Jonathan R. Macey is the Sam Harris Professor of Corporate Law, Corporate Finance, and Securities Law at Yale Law School and a Research Member of the European Corporate Governance Institute (ECGI). His research spans corporate law, securities regulation, banking law, and the law-and-economics of financial institutions, with more than 150 scholarly papers and multiple books to his credit. Education & Affiliations Yale Law School – Sam Harris Professor (endowed chair) European Corporate Governance Institute (ECGI) – Research Member Regular visiting or adjunct affiliations with Columbia, NYU, Cornell, Chicago, and other leading institutions (evidenced by co-authorship networks) Research Interests Macey investigates the intersection of law, finance, and governance. Central themes include: Corporate governance mechanisms – board structure, fiduciary duties, shareholder rights, and the role of controlling shareholders. Securities regulation and market structure – disclosure regimes, insider trading, market manipulation, and the impact of technological change on trading venues. Banking and systemic risk – governance of banks and bank holding companies, money-market mutual funds, resolution regimes, and the regulation of systemically important financial institutions. Law-and-economics methodology – applying economic analysis to assess legal rules, with particular attention to regulatory efficiency and distributive consequences. Publication Trends Between 2019 and 2025 Macey has published at an accelerating pace on post-crisis financial regulation, ESG investing, digital-asset markets, and Delaware corporate law. His recent work critiques the shareholder-primacy narrative, explores extended liability regimes for mega-banks, and interrogates the legal foundations of retail trading platforms such as Robinhood. Scientific Awards & Honors Sam Harris Professor of Corporate Law, Corporate Finance, and Securities Law (endowed chair, Yale Law School) Top-1,524 SSRN download rank among all scholars worldwide Over 39,000 cumulative SSRN downloads; 6,982nd most-cited author on SSRN Frequent keynote speaker at ALEA, ABF, NBER Corporate Finance, and SFS Cavalcade Advising & Grants Macey has served as principal investigator or co-investigator on grants from the John M. Olin Foundation, the Ewing Marion Kauffman Foundation, and the George Mason Law & Economics Center. He has advised the SEC, FDIC, Federal Reserve Board, and numerous international regulators on corporate governance and systemic-risk issues, and is regularly retained as an expert witness in high-profile securities and appraisal litigation. Laboratories & Research Teams While not running a traditional laboratory, Macey collaborates through the Yale Law & Economics Research Group and the ECGI Law Working Paper series, mentoring PhD students, post-docs, and JD researchers working at the intersection of law, finance, and public policy.

Lang Tongمشاهده پروفایل

استاد

• Energy Systems
• Smart Power Grid
• Data Analytics
• +۸ مورد دیگر

Lang Tong is the Irwin and Joan Jacobs Professor of Engineering at Cornell University's School of Electrical and Computer Engineering. He earned his B.E. from Tsinghua University (1985) and Ph.D. in Electrical Engineering from the University of Notre Dame (1991), with postdoctoral work at Stanford's Information Systems Laboratory. Research Focus: Energy systems, smart power grids, data analytics, market design for renewables, cyber-physical security, and machine learning. Projects: NSF grants on equitable energy access, ARPA-e work on EV charging infrastructure, PSERC collaborations on DER aggregation. Publication Trends: Recent work bridges AI and power systems for grid monitoring, renewable integration, and market optimization, emphasizing probabilistic forecasting, storage bidding, and voltage instability detection. Awards: IEEE Fellow, 2018 Fulbright Distinguished Chair in Alternative Energy, multiple IEEE Best Paper Awards (2004–2019), and best paper recognitions at PES General Meeting. Labs: Leads the Digital Energy and Power Systems (DEPS) group, focusing on AI-driven grid resilience and decentralized energy management.

Lei Sunمشاهده پروفایل

استاد

• Statistical Genetics
• Statistical Genomics
• Applied Statistics
• +۷ مورد دیگر

Lei Sun is a Professor in the Department of Statistical Sciences at the Faculty of Arts and Science, University of Toronto. She holds a cross-appointment in the Division of Biostatistics, Dalla Lana School of Public Health. Her research lies at the intersection of statistics and genetics, with a focus on developing robust and scalable methods for analyzing complex human traits. Her educational background includes a B.Sc. in Mathematics from Fudan University and a Ph.D. in Statistics from the University of Chicago (2001). Since then, she has remained at the University of Toronto, contributing significantly to statistical genetics methodology. Lei Sun's research interests include statistical genetics and genomics, multiple hypothesis testing, selective inference, robust association methods, and multivariate analysis. Her work emphasizes open-source implementation and reproducible research, with tools and materials shared via GitHub. She actively develops statistical methodologies to address challenges in genetic studies, particularly in high-dimensional and correlated data settings. The trend in her recent scholarly outputs shows a strong emphasis on education, method dissemination, and open science. Her publications and materials focus on teaching statistical genetics, guiding GWAS practices, and promoting polygenic risk score applications, reflecting both methodological innovation and a commitment to training the next generation of researchers. CRM-SSC Prize in Statistics (2017) NSERC Discovery Accelerator Supplement (2018) Lei Sun has been continuously funded by major Canadian agencies including NSERC and CIHR, supporting her research program in statistical genetics. She mentors students and early-career researchers through direct supervision and leadership in the CANSSI-Ontario STAGE training program. Her grants support the development of novel statistical tools for genetic epidemiology and genomic data analysis. She leads the Statistical Methods for Genetics and Genomics (SMG) seminar series and is involved in the CANSSI-Ontario Strategic Training for Advanced Genetic Epidemiology (STAGE) program, fostering collaborative research and training in statistical genetics across Ontario.

Ewa Tomczak-Łukaszewskaمشاهده پروفایل

مدرس ارشد

• Psycholinguistics
• Bilingualism
• Eye-tracking in Translation Studies
• +۱۴ مورد دیگر

Ewa Tomczak-Łukaszewska is a Senior Lecturer at the Faculty of English, Adam Mickiewicz University in Poznań, Poland. Her research integrates psycholinguistics, bilingualism, translation studies, and cognitive science, employing empirical methods such as eye-tracking, key-logging, and statistical modeling. She is actively involved in major research projects funded by the National Science Centre and collaborates with leading scholars in cognitive translation studies. B.A. in English, Poznań, 2008 M.A. in English, Poznań, 2010 M.A. in Psychology (Clinical Psychology), Poznań, 2018 Postgraduate degree in Management and Organisational Psychology, Poznań, 2018 Her research focuses on psycholinguistics, bilingualism, eye-tracking in reading and translation, figurative language processing, visual perception in sports, and applied statistics . She explores how cognitive processes shape language use, translation decisions, and perception in both academic and athletic contexts. Her methodological expertise includes R, Python, and advanced statistical analyses. Her recent publications reveal a strong trend in cognitive translation studies , particularly the impact of translation direction on lexical selection and information behavior. She also investigates visual perception strategies in fencing , comparing experts and novices, and left- vs right-handed opponents. Another key area is language teaching innovation , such as using songs to teach multi-word units. Her interdisciplinary approach bridges linguistics, psychology, and sport science. Her scientific awards include: Scholarship from the Minister of Science and Higher Education for outstanding young researchers (2023–2026) Prize of the Rector of Adam Mickiewicz University for Excellence in Teaching (2023) Award for best M.A. thesis in linguistics (2010) Multiple travel grants and scholarships for conference participation Ewa Tomczak-Łukaszewska has extensive teaching experience in TEFL, academic English, and statistics. She has served as a statistical analyst and co-investigator in major research projects and has presented her work at numerous international conferences. She is a member of professional organizations including the European Society for Translation Studies (EST), EUROSLA, and EMRA. She has received specialized training in eye-tracking, R, Python, and Bayesian statistics, and has contributed to editorial work as a guest editor for Poznań Studies in Contemporary Linguistics . She is affiliated with the Psycholinguistics Reading Group and has organized international events such as EUROSLA 22 and RaAM Seminar 4. Her lab work involves the EYE-LANG – Eye-tracking Laboratory for Research in Language , where she conducts experiments on language and cognition.

• Probability Theory
• Mathematical Statistics
• Characterizations of Distributions
• +۷ مورد دیگر

Prof. Dr. hab. Jacek Wesołowski is a faculty member at Warsaw University of Technology, affiliated with the Faculty of Mathematics and Information Science, where he serves as Head of the Department of Probability and Mathematical Statistics. He is actively involved in research and teaching, with a focus on probability theory and mathematical statistics. His research interests include probability theory, mathematical statistics, characterizations of distributions, stochastic processes, survey sampling, multivariate analysis, free probability, and quadratic harnesses . These areas are evident from his extensive publication record in top-tier journals such as the Annals of Probability, Journal of Multivariate Analysis, and Stochastic Processes and their Applications. The trends in his recent publications reveal a strong focus on distributional characterizations (e.g., gamma, Kummer, Wishart), regression properties of order and record statistics , quadratic harnesses and martingale structures , and methodological contributions to survey sampling and imputation . His work often bridges theoretical probability with statistical applications. No scientific awards were mentioned in the provided text. He advises graduate students, as indicated by his organization of probabilistic workshops for PhD and master’s students. He has collaborated extensively with researchers such as W. Bryc, G. Letac, H. Massam, and G. Rempała. While no specific grants are listed, his sustained research output suggests active funding. He teaches core courses such as Probability Calculus I/II and Survey Sampling. There is no mention of specific labs or research teams, but his departmental leadership and collaborative publications suggest an active research group environment.

Michael Mumaمشاهده پروفایل

استاد

• Robust Data Science
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Luca RIGOBIANCOمشاهده پروفایل

پژوهشگر

• Historical Linguistics
• Indo-European Linguistics
• Digital Epigraphy
• +۳ مورد دیگر

Luca Rigobianco is a researcher in Glottology and Linguistics at Ca' Foscari University of Venice, affiliated with the Department of Humanities. He serves as Department Delegate for Internal Communication. His qualifications include a PhD in Linguistics from the University of Padua (2011) and extensive postdoctoral research experience across multiple institutions. His primary research focuses on historical linguistics, Indo-European studies, and digital epigraphy with an emphasis on ancient Italian languages like Etruscan and Latin. Education: Bachelor's Degree in Literature (Ca' Foscari University) Master's Degree in Linguistics (University of Padua) PhD in Linguistics (University of Padua) Research Interests: Rigobianco explores linguistic reconstruction of fragmentary languages, ancient writing systems, and computational methods in epigraphy. Key areas include: Language contact phenomena in ancient Italy Morphosyntactic analysis of Etruscan and Sabellic languages Digital tools for epigraphic data management Grants & Projects: Principal Investigator for Languages and Cultures of Ancient Italy (PRIN 2020) Lead on SaInAT-Ve (Sacred Inscriptions of Venetia) European Commission-funded project (Seal of Excellence 2017) Awards: Recipient of the European Commission's H2020-MSCA-IF-2016 Seal of Excellence (2017). Professional Activities: Member of editorial boards for journals like Ariodante and Cultural Anthropology . Serves on scientific committees for major research initiatives. Active in international conferences as keynote speaker. Teaching: Current courses include Historical Linguistics and Ethnolinguistics at the master's level, alongside foundational linguistics modules for undergraduate programs.

Heman Shakeriمشاهده پروفایل

استادیار

• Machine Learning
• Learning-Based Control
• Network Science
• +۱۵ مورد دیگر

Heman Shakeri is an Assistant Professor at the School of Data Science, University of Virginia (UVA), where he leads the DYNAMO Lab. He is also a member of the UVA Center of Diabetes Technology and the UVA Cancer Center, reflecting his interdisciplinary research in biomedical applications of machine learning and control systems. Ph.D. in Electrical and Computer Engineering, Kansas State University M.S. in Mechanical Engineering, Amirkabir University of Technology – Tehran Polytechnic B.S. in Mechanical Engineering, Amirkabir University of Technology Dr. Shakeri's research focuses on algorithm development in machine learning , learning-based control , and network science , with applications in diabetes management, single-cell signaling, glaucoma risk assessment, and complex dynamical systems. His work bridges artificial intelligence, control theory, and data science to solve real-world biomedical challenges. The 15 most recent publications highlight a strong trend in dynamical systems modeling , stochastic flow matching , Koopman operator theory , and reinforcement learning for biomedical control . His papers frequently appear in top-tier venues such as ICML, NeurIPS, ICLR, and IFAC, demonstrating consistent innovation in both methodological and applied domains. Dr. Shakeri has received numerous scientific awards and grants, including: NIH R01 Grant ($1.6M) for optimizing renal cancer treatment Capital One Data Design Fellowship ($55k) LaunchPad Grant ($200k) for artificial pancreas research UVA Research Communications Fellow (2024) Multiple pilot awards from UVA Cancer Center, Brain Institute, and Ivy Foundation He actively mentors graduate students and postdoctoral researchers in the DYNAMO Lab, supporting translational research from algorithm design to clinical applications. His lab has secured funding from NIH, NCI, Oracle, and industry partners, indicating strong external validation of his research vision. The lab collaborates extensively with clinical and computational teams across UVA, particularly in diabetes and cancer research. The DYNAMO Lab is a hub for advancing machine intelligence for dynamical systems , with current projects including smart artificial pancreas systems, explainable AI for glaucoma, operator-theoretic methods, and complex network analysis. The lab emphasizes interdisciplinary collaboration, real-world impact, and methodological rigor.

• Theoretical Computer Science
• Statistics
• High-Dimensional Estimation
• +۷ مورد دیگر

Tselil Schramm is an Assistant Professor in the Department of Statistics at Stanford University, with courtesy appointments in Computer Science and Mathematics. She is actively engaged in research and teaching in theoretical computer science and statistics. Department: Department of Statistics School: School of Humanities and Sciences University: Stanford University Office: CoDa E254 Email: tselil@stanford.edu She earned her PhD from UC Berkeley under Prasad Raghavendra and Satish Rao, followed by postdoctoral work at Harvard and MIT with Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo. Her research lies at the intersection of theoretical computer science and statistics, focusing on high-dimensional estimation, information-computation tradeoffs, sum-of-squares algorithms, and random graph theory. She develops algorithms for statistical problems and investigates the boundaries between what is statistically possible and what is computationally feasible. Her recent publications span topics including the overlap-gap property, discrepancy algorithms, robust message passing, semidefinite programming, spectral clustering, and random geometric graphs, appearing in top venues such as STOC, FOCS, COLT, NeurIPS, and The Annals of Statistics. She teaches a range of courses, including Introduction to Statistics (STATS 60), Theory of Statistics II (STATS 300B), and Machine Learning Theory (STATS 214 / CS 228M), reflecting her expertise in both foundational and advanced statistical theory. Runner-up for Best Paper at COLT 2021 Invited to STOC 2022 special issue of SICOMP Invited to SODA 2016 special issue of ACM Transactions on Algorithms Invited to CCC 2019 special issue of Theory of Computing Tselil Schramm advises and collaborates with numerous students and researchers, including Shuangping Li, Misha Ivkov, and Siqi Liu. She has been involved in multiple research grants and projects, particularly in the areas of high-dimensional inference and algorithmic robustness. Her work often bridges theoretical guarantees with practical algorithmic design. She is affiliated with Stanford’s theoretical computer science and statistics research groups, contributing to a vibrant academic environment. Her future work is expected to further explore the limits of efficient computation in statistical settings, with potential applications in machine learning, signal processing, and network analysis.

Yury Polyanskiyمشاهده پروفایل

استاد

• Information Theory
• Machine Learning
• Statistics
• +۳ مورد دیگر

Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS), the Institute for Data, Systems, and Society (IDSS), and the MIT Statistics and Data Science Center. He holds a Ph.D. from Princeton University (2010) and an M.S. from the Moscow Institute of Physics and Technology (2005). His research focuses on information theory, machine learning, statistical inference, error-correcting codes, and wireless communication. He has contributed to fundamental limits of communication systems, finite-blocklength analysis, and applications of information theory to learning and signal processing. Notable awards include the 2020 IEEE Information Theory Society James Massey Award, the 2013 NSF CAREER Award, and the 2011 IEEE Information Theory Society Paper Award. His work spans theoretical advancements and practical applications, including the development of the SPECTRE toolbox for short-packet communication. He is also co-authoring a textbook on information theory. Recent research highlights include studies on quantization techniques for machine learning (e.g., NestQuant), transformer-based empirical Bayes methods, and novel approaches to massive random access in wireless networks (e.g., unsourced multiple access). His contributions bridge information theory and modern data science, addressing challenges in high-dimensional data representation, neural network dynamics, and efficient communication architectures.

John E. Kolassaمشاهده پروفایل

استاد

• Asymptotics
• Biostatistics
• Nonparametric Statistics
• +۳ مورد دیگر

John E. Kolassa is a Professor of Statistics at Rutgers, the State University of New Jersey. He is affiliated with the Department of Statistics, where he conducts research and teaching in asymptotics and biostatistics. His academic credentials include a Ph.D. from the University of Chicago, and he maintains an active research profile with numerous publications and contributions to statistical methodology. Ph.D., University of Chicago Dr. Kolassa's research is centered on asymptotic theory, nonparametric statistics, and biostatistical methods. His work includes the development and analysis of saddlepoint approximations, Edgeworth expansions, and inference techniques for complex data. He has a strong focus on theoretical statistics, with applications in medical and biological contexts. His expertise spans categorical data analysis, life data analysis, and regression models, as reflected in his teaching of graduate courses such as 960:555 (Nonparametric Statistics) and 960:583 (Methods of Inference). The 15 most recent publications, spanning from 2021 to 2013, demonstrate a consistent focus on statistical theory and methodology. Key themes include the refinement of approximation techniques (e.g., Edgeworth and saddlepoint), inference in complex models (e.g., posterior densities, penalized likelihood), and nonparametric methods. His work often addresses foundational issues in statistical inference, such as the validity of expansions, the reliability of p-values, and the handling of zero-event studies in meta-analysis. The research bridges theoretical development with practical application in biostatistics and health sciences. Fellow of the American Statistical Association (ASA) Fellow of the Institute of Mathematical Statistics (IMS) Elected member of the International Statistical Institute (ISI) Editor, Stat Dr. Kolassa is an active advisor and researcher, contributing to the academic community through his editorial role for the journal Stat . He has received significant recognition through his fellowships in the ASA and IMS, highlighting his impact on the field. His work has been supported through academic appointments and professional activities, though specific grant details are not provided in the source material. He is also involved in the development of statistical software, having created R packages for nonparametric methods and infinite estimates. Dr. Kolassa leads a research team focused on theoretical and applied statistics, with a particular emphasis on developing and validating statistical methodologies. He has mentored students and collaborated on interdisciplinary research, particularly in biostatistics and health outcomes. His laboratory or research group is centered on computational and theoretical statistics, utilizing tools like R for simulation and analysis.

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).