جست‌وجوی استاد و پژوهشگر

Matthias Felleisenمشاهده پروفایل

استاد

• Programming
• Programming Languages
• Type Systems
• +۵ مورد دیگر

Matthias Felleisen is a Professor in the College of Computer Science at Northeastern University. He is renowned for founding the PLT research group and pioneering the TeachScheme! educational initiative, which evolved into the Bootstrap project. His work spans programming languages, type systems, and educational tools. Affiliation: Northeastern University Academic Rank: Professor Research Interests: Programming Languages, Type Systems, Domain-Specific Languages, Concurrency Models His recent research includes type tailoring, effectful software contracts, and trace contracts. He has contributed extensively to programming language design and implementation, with a focus on gradual typing, language workbenches, and educational frameworks. Conference Contributions (2012-2025): ECOOP 2015-2025: Papers on gradual typing, contract systems ICFP 2016-2024: Research on Typed Racket, blame evaluation, concurrency SPLASH 2012-2023: OOPSLA papers on type migration, macros, visual syntax Mentoring Activities: Keynote speaker at PLMW 2016 ICFP 2020 mentoring sessions

Fang Xuمشاهده پروفایل

مدرس ارشد

• Time Series Econometrics
• Empirical Macroeconomics
• Empirical Finance
• +۱ مورد دیگر

Fang Xu is a Senior Lecturer in Economics at Brunel University London, Department of Economics and Finance, since 2018. Previously, she served as a Lecturer at the University of Reading (2011-2017) and was a Max Weber Fellow at the European University Institute (2008-2010). Her research spans three core areas: Time Series Econometrics: Specializes in bounded stationarity tests, functional coefficient models, and multivariate GARCH models. Empirical Macroeconomics: Focuses on current account imbalances, monetary policy impacts, and economic forecasting. Empirical Finance: Investigates stock market volatility, investor attention metrics, and asset pricing anomalies. She has secured grants from the British Academy (2022-2024) for analyzing news intensity impacts on financial markets, and from the Fritz-Thyssen Foundation (2008-2011) for studying current account sustainability. Her recent work explores: Time-varying monetary policy effects on stock markets News coverage's role in economic uncertainty Multi-lingual news analysis across countries High-frequency market risk assessment Price-to-dividend ratio determinants Scientific contributions appear in top journals including Journal of Econometrics , European Financial Management , and Journal of Money, Credit and Banking . She teaches: Quantitative Methods for Business Macro and Financial Econometrics Corporate Investment Research Methods in Economics and Finance

Volker Schomerusمشاهده پروفایل

استاد

• String Theory
• Quantum Field Theory
• Mathematical Physics
• +۲ مورد دیگر

Volker Schomerus is a Professor of Mathematical Physics at the University of Hamburg and a Lead Scientist at DESY (Deutsches Elektronen-Synchrotron). His research focuses on the unification of string theory, quantum field theory, and mathematical physics, particularly through geometric quantization of spacetime and solving quantum theory challenges via geometric techniques. He coordinates the European RTN GATIS initiative and previously led the DESY Theory Group (2007–2010). Research Interests : Schomerus explores string theory's role in fundamental physics, including quantum geometry, holographic dualities, and integrable models. His work bridges particle physics, conformal field theory, and statistical mechanics, with emphasis on bootstrap methods, thermal correlators, and defect CFTs. Publications : Recent articles (2020–2025) demonstrate advances in multipoint conformal bootstrap, holographic interfaces, and thermal field theory. Key themes include integrability in CFT, lightcone limits, and applications of Gaudin models to higher-dimensional quantum systems. Academic Leadership : Heads research groups at DESY and Universität Hamburg, fostering collaborations in theoretical high-energy physics. No awards or students are explicitly listed in the sources.

Zulal Denauxمشاهده پروفایل

استاد

• Open-Economy Macroeconomics
• Applied Economic Growth and Development
• International Economics
• +۷ مورد دیگر

Zulal Denaux is a Professor of Economics at the Harley Langdale Jr. College of Business Administration, Valdosta State University , where she has served since 2002. She coordinates study abroad programs (e.g., Business in Italy) and contributes to departments including Economics, Finance & Health Care Administration. Her academic journey includes a Ph.D. in economics (minor in statistics) from North Carolina State University , an MBA with International Business Certificate from Southern New Hampshire University , and a B.A. in economics and political science from the University of Ankara . Research Interests: Open-economy macroeconomics, international trade dynamics (exchange rate volatility, capital inflows), environmental economics (agricultural efficiency, pollution control), fiscal policy analysis, and data envelopment analysis (DEA). Article Trends: Focus on Turkey's macroeconomic responses to exchange rate shocks, energy-GDP causality in OECD/US, financial-development growth links in South Korea, and educational efficiency disparities in Georgia public schools. Awards: Recipient of 15+ grants (e.g., Steele Summer Research Grants, Georgia Gulf Sulfur Corporation funding) and 6 major teaching/service awards (e.g., 2018 Outstanding Teacher Award, 2013 Service Award).

Veronika Rockovaمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• High-dimensional Data Analysis
• +۳ مورد دیگر

Veronika Rockova is the Bruce Lindsay Professor of Econometrics and Statistics in the Wallman Society of Fellows at the University of Chicago Booth School of Business. She joined Booth after postdoctoral training at the Wharton School and has been internationally recognized for her work at the intersection of statistics and machine learning. Her research focuses on developing decision-centric statistical tools for large datasets, specializing in Bayesian computation Variable selection High-dimensional decision theory Hierarchical modeling Uncertainty quantification for generative AI Recent publications highlight trends in Bayesian CART mixing rates Generative posterior sampling Deep learning integration with Bayesian frameworks Tree-based bandit approaches for ABC Quantile methods for credible sets Scientific recognition includes COPSS President's Award (2024) COPSS Emerging Leader Award (2023) NSF CAREER Award (2020) She currently serves on editorial boards for Annals of Statistics Journal of the American Statistical Association Journal of the Royal Statistical Society (Series B) and mentors PhD students in econometrics and statistics.

Lance J. Dixonمشاهده پروفایل

استاد

• Scattering Amplitudes
• Precision Higgs Physics
• Standard Model
• +۵ مورد دیگر

Lance J. Dixon is a Professor at SLAC National Accelerator Laboratory , operated by Stanford University. His research focuses on theoretical elementary particle physics , particularly higher-order calculations in perturbative QCD, Standard Model Higgs physics, and gauge-gravity dualities. Current affiliation: SLAC National Accelerator Laboratory, Stanford University Email: lance@slac.stanford.edu Phone: (650) 926-2627 Research Interests include: Scattering amplitudes in quantum field theory Precision Higgs and Standard Model calculations Theoretical methods for collider physics Interconnections between gauge theory and gravity Early universe cosmology applications Publications span multi-loop amplitudes in N=4 SYM, Higgs boson phenomenology, and quantum gravity relations. Key contributions include: High-order QCD corrections for collider observables Hexagon function formalism for amplitude bootstrapping Antipodal duality in eight-loop amplitudes Multi-Regge limit analyses Software Tools developed: Vrap : NNLO Drell-Yan rapidity distributions Hexagon function libraries for amplitude calculations

Erhan Öztopمشاهده پروفایل

استاد

• Computational Neuroscience
• Robotics
• Machine Learning
• +۴ مورد دیگر

Erhan Öztop is a Professor at Özyeğin University's Computer Science Department and Co-Director of the Ozyegin University Robotics Laboratory. He holds a Specially Appointed Professor position at Osaka University's Symbiotic Intelligent Systems Research Center. Previously, he worked at Advanced Telecommunications Research Institute International (ATR) in Japan from 2002-2011, leading as Vice Head of the Communication and Cognitive Cybernetics Department. He earned a Ph.D. in Computer Engineering (2002) from the University of Southern California, M.S. in Computer Engineering (1996) from METU, and B.S. in Computer Engineering + Mathematics (1993) from METU. His research focuses on computational modeling of intelligent behavior, human-robot adaptation, cognitive neuroscience, and machine learning. His work explores visuomotor learning , mirror neuron systems , grasp affordance learning , and human-in-the-loop robot control . Key projects include: Dexterous manipulation via human-robot body schema integration Mental state inference using visual control parameters Motor interference as a metric for human-like robot perception Sign representation of Boolean functions Recent publications emphasize human-robot co-adaptation , emotion modeling , and affordance-based architectures for collaborative systems. His work bridges robotics , cognitive neuroscience , and machine learning to develop biologically inspired intelligent systems.

Salvador Naya Fernándezمشاهده پروفایل

استاد

• Statistics
• Operational Research
• Thermal Analysis
• +۴ مورد دیگر

Salvador Naya Fernández is a Full Professor in the Department of Statistics and Operational Research at the Higher Polytechnic School of the University of A Coruña (Spain). He leads statistical research projects in materials science, thermal analysis, and industrial quality control, with a focus on functional data analysis and reliability engineering. Affiliations: MODES research group, CITIC research center Education: Not explicitly stated in provided texts Research Interests span Statistics , Operational Research , and Materials Science , with specific applications to: Thermal degradation modeling of polymers and composites Functional data analysis for interlaboratory studies Statistical quality control in industrial processes Reliability engineering for mechanical systems Image analysis for defect detection in manufacturing Publication Trends show expertise in applying statistical learning to materials engineering , particularly through thermal analysis (TGA, DSC, DMA) and functional data methods . Collaborative work extends to biomedical scaffolds , energy efficiency , and maritime engineering . Teaching Contributions include advising 25+ graduate theses (2010-2024) in statistical applications to: Biopolymer degradation Credit risk modeling Industrial anomaly detection Maritime traffic analysis Energy efficiency Scientific Awards : No specific awards mentioned in provided texts. Labs & Collaborations : Works with the MODES research group and CITIC at University of A Coruña. Collaborates with universities in Ecuador, France, and Portugal, and participates in international conferences like ISI World Statistics Congress and NATAS .

• Health Psychology
• Clinical Psychology
• Medical Education
• +۴ مورد دیگر

Changiz Mohiyeddini serves as Professor of Foundational Medical Studies at Oakland University William Beaumont School of Medicine, where he teaches core courses including "Brain and Behavior", "Psychopathology", "Clinical-Health Psychology", "Personality Psychology", and "Advanced Quantitative Methods". He holds significant editorial leadership as Assistant Field Chief Editor for Frontiers in Psychology, Specialty Chief Editor for Health Psychology, and Associate Editor for Psychology for Clinical Settings, with 266 edited publications and 6 edited research topics documented. His research centers on critical intersections of health psychology and medical education, particularly addressing moral injury frameworks, resilience mechanisms in healthcare contexts, and cross-cultural competency development. Recent work investigates AI-driven assessment tools, pandemic-responsive curriculum adaptations, and statistical methodologies for improving medical examination quality, reflecting urgent real-world applications in clinical training environments. Analysis of his 2023-2024 publications reveals concentrated innovation in educational technology (including ChatGPT applications for pharmacology questions), trauma psychology, and globalized healthcare training. This body of work consistently bridges theoretical psychology with practical medical education challenges, emphasizing student feedback integration, cultural competence, and assessment reliability during systemic disruptions like the COVID-19 pandemic. Scientific Awards: No awards documented in available materials Advising and Grants: While editorial leadership demonstrates scholarly influence, specific information regarding student mentorship, research grants, or funded projects was not provided in the source materials. His longitudinal study on student feedback integration suggests active engagement with educational research methodologies. Labs and Teams: No dedicated research laboratories, collaborative teams, or institutional research centers were mentioned in the available documentation, though his editorial roles indicate broad cross-institutional academic networks.

Stanislav Volgushevمشاهده پروفایل

دانشیار

• Data Science
• Probability
• Theoretical Statistics
• +۷ مورد دیگر

Stanislav Volgushev is an Associate Professor in the Department of Statistical Sciences at the University of Toronto, with a cross-appointment in the Department of Mathematical & Computational Sciences at the University of Toronto Mississauga (UTM). His work bridges theoretical statistics with practical applications in big data analysis, with a focus on developing robust statistical methods for complex data structures. His educational background includes: Ph.D. in Mathematics from Ruhr University Bochum (2010), advised by Prof. Holger Dette Diploma in Mathematics from Ruhr University Bochum (2007) Volgushev's research spans multiple areas of statistical theory and methodology. His primary interests include quantile regression, empirical process theory, extreme value theory, time series analysis, copulas, and bootstrap methods. He is particularly focused on addressing statistical and computational challenges presented by big data, developing methods that maintain theoretical rigor while being practically applicable to modern data analysis problems. His work often involves the development of nonparametric and semiparametric methods that can handle complex dependence structures. Analysis of his recent publications reveals a strong focus on high-dimensional statistics, with particular attention to change-point detection, extremal graphical models, and quantile-based methods. His work bridges theoretical statistics with machine learning, showing increasing interest in optimization methods for statistical learning and causal inference. There's a clear trajectory toward addressing computational challenges in modern statistics, with many papers developing methods specifically designed for big data contexts. Volgushev serves as an associate editor for several prestigious journals: Electronic Journal of Statistics (since 2022) Canadian Journal of Statistics (since 2022) Scandinavian Journal of Statistics (since 2021) Statistical Inference for Stochastic Processes (since 2014) Previously served at Computational Statistics and Data Analysis (2018-2023) and Bernoulli (2016-2018) He has taught various courses including STA107 (Introduction to Probability and Modelling), STA2211 (Probability Theory II), and STA314 (Introduction to Statistical Learning) at both the St. George and Mississauga campuses. His teaching reflects his research expertise, covering both theoretical foundations and modern applications of statistical methods. Administratively, he serves as Associate Chair, Graduate Studies in his department, demonstrating leadership in academic governance.

Carlos Lamarcheمشاهده پروفایل

استاد

• Econometrics
• Quantile Regression
• Longitudinal Data Analysis
• +۳ مورد دیگر

Carlos Lamarche is a University Research Professor and Gatton Endowed Professor of Economics at the University of Kentucky , with a joint appointment as Professor of Statistics. His work focuses on developing advanced econometric methods for robust empirical analysis. Education: Ph.D. and M.S. from University of Illinois at Urbana-Champaign Research Interests: He pioneers innovations in quantile regression for longitudinal and panel data, addressing challenges in heterogeneous preferences, censored data, and interactive effects. His methodologies are applied to topics like welfare reform, child development, and strategic bidding. Publications: His influential work appears in top journals such as Journal of Political Economy , Journal of Econometrics , and International Economic Review , reflecting interdisciplinary impacts across economics, statistics, and data science. Contact: Email clamarche@uky.edu for inquiries.

Johann Peter Gogartenمشاهده پروفایل

استاد

• Molecular evolution
• Origin and early evolution of cellular life
• Evolution of the eukaryotic endomembrane system
• +۶ مورد دیگر

Johann Peter Gogarten is a Board of Trustees Distinguished Professor in the Department of Molecular and Cell Biology and Microbiology at the University of Connecticut. With a distinguished career spanning over three decades, he has established himself as a leading authority in molecular evolution and microbial genomics. His research bridges computational biology with experimental microbiology to address fundamental questions about the origin and evolution of cellular life. He maintains an active laboratory and collaborates with an international network of researchers advancing our understanding of evolutionary mechanisms. Dr. Gogarten's educational background includes: Ph.D. from the University of Giessen, Federal Republic of Germany Postdoctoral study at the University of California, Santa Cruz Professor Gogarten's research program centers on molecular evolutionary mechanisms, with particular emphasis on horizontal gene transfer as a major force shaping microbial evolution. His work challenges traditional tree-based evolutionary models by demonstrating how genetic exchange between distantly related organisms fundamentally alters evolutionary trajectories. He investigates the origin of cellular life, the development of the eukaryotic endomembrane system, and the evolutionary dynamics of molecular parasites like inteins. His laboratory employs both computational approaches and experimental validation to study these complex evolutionary processes across multiple levels of biological organization, from individual genes to entire genomes. Analysis of Professor Gogarten's recent publications (2017-2024) reveals a consistent research trajectory focused on horizontal gene transfer mechanisms and their evolutionary implications across diverse microbial systems. His work spans multiple domains of life with particular emphasis on archaea, examining genomic islands in haloarchaea, intein interference mechanisms, and bacterial cooperation through genetic exchange. His research increasingly incorporates advanced computational methods for phylogenomic analysis and genome comparison, reflecting the growing importance of bioinformatics in evolutionary studies. The interdisciplinary nature of his work connects molecular biology, computational science, and evolutionary theory to address fundamental questions about life's history. Among Professor Gogarten's notable recognitions is his appointment as a Board of Trustees Distinguished Professor at the University of Connecticut, representing one of the highest honors the university bestows on its faculty. This distinction recognizes his exceptional contributions to research, teaching, and service to the scientific community. His extensive publication record in top-tier journals including Nature Microbiology, PNAS, and Genome Biology and Evolution demonstrates the impact and significance of his work in the field of molecular evolution. Professor Gogarten has mentored numerous graduate students and postdoctoral researchers who have gone on to successful careers in academia and industry. His laboratory has received continuous funding from the National Science Foundation and other agencies to support research on molecular evolution and horizontal gene transfer. He has organized multiple international conferences and workshops that have brought together researchers from diverse fields to address fundamental questions in evolutionary biology. His collaborative network spans multiple continents, reflecting the global impact of his research program and his leadership in the field. The Gogarten laboratory at the University of Connecticut maintains a vibrant research program focused on molecular evolution and microbial genomics. His team combines computational approaches with experimental validation to investigate fundamental questions about evolutionary processes. The laboratory has developed innovative methods for detecting horizontal gene transfer and analyzing evolutionary relationships, which have been widely adopted by the research community. Current work focuses on inteins, genomic islands, and the evolutionary dynamics of archaeal and bacterial systems, continuing Professor Gogarten's long-standing commitment to understanding the complex mechanisms that have shaped life on Earth.

Patti Frazer Lockمشاهده پروفایل

استاد

• Graph Theory
• Mathematics Education
• Statistics Education
• +۳ مورد دیگر

Patti Frazer Lock is a Professor of Mathematics in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University . She has co-authored multiple influential textbooks including Statistics: Unlocking the Power of Data and works with the Calculus Consortium for Higher Education. Her academic contributions span mathematics education, graph theory, and curriculum development. Education: Ph.D. in Mathematics, University of Massachusetts (1981) M.A. in Mathematics, University of Massachusetts (1978) B.A. in Mathematics, Colgate University (1975) Her research focuses on graph theory and mathematics education , with particular emphasis on statistical pedagogy and calculus reform. Her publications include foundational work on uniquely Hamiltonian-connected graphs and innovative approaches to teaching introductory statistics through data-driven methods. Recent publications highlight her contributions to statistics education (2021), multivariable calculus (2021), and applied calculus (2022) textbooks. Earlier works explore quantum logics (1984), graph connectivity (1991), and interdisciplinary applications in biology (2008). Lock actively participates in professional service through roles on the AMS-ASA-MAA-SIAM Joint Data Committee , ASA-MAA Joint Committee on Undergraduate Statistics , and editorial boards for PRIMUS and Math Horizons journals. She has led workshops nationally on undergraduate mathematics and statistics education. Contact: plock@stlawu.edu | Office: Valentine Hall 219 | Sabbatical: Spring 2025

Henry Cohnمشاهده پروفایل

استاد

• Discrete Mathematics
• Sphere Packing
• Conformal Field Theory
• +۸ مورد دیگر

Henry Cohn is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), specializing in discrete mathematics, sphere packing, conformal field theory, and fast matrix multiplication. His work bridges pure and applied mathematics, emphasizing symmetry in exceptional structures and applications to physics, cryptography, and information theory. He supervises graduate students and research interns, particularly at Microsoft Research and MIT. Education: Ph.D., Mathematics, Harvard University (2000) B.S., Mathematics, MIT (1995) Professional Background: Postdoc to long-term member, Microsoft Research Redmond (2000–2007) Head, Cryptography Group, Microsoft Research (2007–2008) Founding member, Microsoft Research New England (2008–2024) Professor, MIT Department of Mathematics (2025–present) Research Interests: Cohn explores symmetry in mathematics and physics, focusing on sphere packing, potential energy minimization, and applications of harmonic analysis and representation theory. His work spans discrete geometry, coding theory, cryptography, and theoretical computer science. Publications & Grants: He has published extensively on sphere packings, error-correcting codes, and matrix multiplication, often leveraging modular forms and semidefinite programming. His research is supported by institutions like the Simons Foundation. Teaching & Mentorship: Cohn teaches courses in number theory, discrete geometry, and mathematical logic at MIT and programs like PROMYS. He has advised numerous Ph.D. students and research interns, including Yichi Zhang, James Hirst, and Alex Cohen.

Stefan Sperlichمشاهده پروفایل

استاد

• Nonparametric Statistics
• Semiparametric Methods
• Small Area Statistics
• +۷ مورد دیگر

Stefan Sperlich is a Full Professor and Director of the Research Institute for Statistics and Information Science at the University of Geneva's Geneva School of Economics and Management. He holds dual appointments in the Department of Econometrics and Statistics, with affiliations in both the Research Institute for Statistics and Information Science and the Institute of Economics and Econometrics. Dr. Sperlich earned his diploma in mathematics from the University of Göttingen and completed his PhD in economics at Humboldt University of Berlin. His academic career includes professorships at University Carlos III de Madrid (1998-2006) and the University of Göttingen (2006-2010), before joining the University of Geneva in 2010. Professor Sperlich's research spans nonparametric and semiparametric statistics , small area estimation , and impact evaluation methods . His work bridges theoretical econometrics with practical applications in development economics, policy evaluation, and poverty measurement. He has made significant contributions to specification testing, causal inference methodologies, and the development of robust statistical techniques for small area estimation. His research often addresses real-world problems through collaborations with international institutions and development programs. His recent publications reveal a strong focus on advancing methodological frameworks for small area statistics, causal inference, and nonparametric estimation. Key themes include developing robust inference techniques for linear mixed models, improving bandwidth selection methods, and creating model-free approaches to difference-in-differences estimation. His work increasingly integrates computational statistics with traditional econometric methods to address challenges in big data analysis and distributed data environments. Professor Sperlich has received numerous accolades including: Tjalling C. Koopmans Econometric Theory Prize (2000-2002) Augusto Gonzalez Linares award (2014) for attracting international talent Elected member of the International Statistical Institute (since 2025) Special rewards from the Economics Department at University Carlos III de Madrid (2004-2005) Grants from the Institute Flores de Lemus (2001-2003) As an advisor and researcher, Professor Sperlich has supervised numerous graduate students and led significant research initiatives. He co-founded the research center 'Poverty, Equity and Growth in Developing Countries' at the University of Göttingen and serves as a research fellow at the Center for Evaluation and Development in Mannheim, Germany. His consultancy work spans regional, national, and international institutions, with participation in development programs like EUROSOCIAL and UN assessment reports. He has secured multiple research grants supporting his work in statistical methodology and economic applications. Professor Sperlich leads research teams focused on nonparametric methods, small area statistics, and impact evaluation. His research group develops innovative statistical approaches for poverty mapping, causal inference, and composite indicator construction. The team maintains strong connections with statistical offices and international organizations, ensuring their methodological advances have practical applications in policy development and evaluation.