جست‌وجوی استاد و پژوهشگر

Elham Kazemiمشاهده پروفایل

استاد

• Mathematics Education
• Teacher Education
• Educational Equity
• +۶ مورد دیگر

Elham Kazemi is a Professor of Mathematics Education at the University of Washington’s College of Education, holding the Geda and Phil Condit Endowed Chair. She specializes in elementary teacher education, instructional leadership, and equity-focused mathematics pedagogy. Her research emphasizes professional learning communities, student-centered classroom practices, and disrupting inequities in education. Education: PhD in Mathematics Education, UCLA (1999) MA in Educational Psychology, UCLA (1997) BS in Psychology (Elementary Teaching Certificate), Duke University (1992) Research Interests: Kazemi’s work focuses on equitable mathematics instruction, teacher learning, and leadership development. She designs school-wide professional development models (e.g., Math Labs) and studies classroom discourse, student disciplinary identities, and organizational learning. Key projects include the Labs Project (funded by JSMF) and RMLL (NSF-funded leader learning). Publications & Grants: Over 40 peer-reviewed articles, including work in American Educational Research Journal and Journal of Teacher Education . Co-authored influential books like Choral Counting and Counting Collections and Intentional Talk . Recipient of $5.4M+ in grants from NSF, McDonnell Foundation, and others for projects on teacher leadership and equitable instruction. Awards: 2022 AERA Open Article of the Year 2019 Outstanding Faculty Award for Educational Equity 2015 Geda & Phil Condit Professorship Advising & Leadership: Kazemi co-leads initiatives like the Leading Towards Racially Just Mathematics Instruction partnership. She has supervised numerous graduate students and collaborates with schools to implement equity-driven practices. Labs & Teams: Her work includes the Labs Project (designing Learning Labs for mathematics/literacy discussions) and the Mathematics Education Project (MEP), fostering statewide collaboration among educators.

Byung S. Leeمشاهده پروفایل

استاد

• Database
• Data Mining
• Data Science
• +۵ مورد دیگر

Professor Byung S. Lee is a distinguished faculty member in the Department of Computer Science at the University of Vermont's College of Engineering and Mathematical Sciences. He joined UVM in 1999 and continues to be actively engaged in teaching, research, and service. His office is located in Innovation Hall at the Burlington campus, where he maintains regular office hours and oversees his research lab. Professor Lee holds a Ph.D. from Stanford University, an MS from Korea Advanced Institute of Science and Technology, and a BS from Seoul National University. His educational background provided the foundation for his extensive career in computer science research and education. Professor Lee's research spans multiple domains within computer science, with a particular focus on database systems, data mining, and data science. His work increasingly integrates machine learning techniques with traditional database approaches, especially in the analysis of time series data. He has made significant contributions to graph theory applications, anomaly detection methods, and environmental data analysis. His research often bridges computer science with practical applications in healthcare, environmental science, transportation, and astrophysics through interdisciplinary collaborations. An analysis of his recent publications reveals a strong trend toward time series analysis and anomaly detection, particularly applied to environmental monitoring and healthcare data. His work demonstrates a consistent evolution from foundational database research to more applied machine learning approaches, with increasing emphasis on real-world problem solving across multiple scientific domains. Professor Lee has served as primary advisor for numerous graduate students across multiple cohorts, including PhD candidates, Master's students, and postdoctoral researchers. His advising portfolio reflects the breadth of his research interests, with students working on topics ranging from graph neural networks to medical informatics applications. He has also been actively involved in professional service, serving on program committees for major conferences including SAC, PAKDD, DASFAA, and CIKM. Professor Lee leads a vibrant research laboratory that focuses on cutting-edge data science methodologies and their applications. His team collaborates extensively with researchers in environmental science, hydrology, and healthcare, demonstrating the interdisciplinary nature of modern data science research. The lab maintains active projects in time series analysis, graph analytics, and environmental monitoring systems, often working with large-scale datasets from real-world applications.

Keming Yuمشاهده پروفایل

استاد

• Quantile Regression
• Bayesian Statistics
• Survival Analysis
• +۱۲ مورد دیگر

Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.

George Skiadopoulosمشاهده پروفایل

استاد

• Asset Pricing
• Commodities
• Financial Derivatives
• +۳ مورد دیگر

George Skiadopoulos is a Professor of Finance at the University of Piraeus (Department of Banking and Financial Management) and Queen Mary University of London (School of Economics and Finance). He serves as Director of the Institute of Finance and Financial Regulation (IFFR) and holds an Honorary Senior Visiting Fellowship at Bayes Business School, City University of London. His research focuses on asset pricing, commodities, financial derivatives, climate finance, and ESG integration. He has published in prestigious journals like Management Science and Journal of Financial and Quantitative Analysis, and his work influences policy at institutions like the European Securities Markets Authority (ESMA). Education: PhD in Finance from the University of Warwick, M.Sc. in Mathematical Economics from LSE, and a Ptychion in Economics from Athens University of Economics and Business. He has advised financial institutions globally and received grants from the Chicago Mercantile Exchange and others. His notable award is the 2018 German Finance Association best paper prize for work on transaction costs and stock returns. He has also contributed to executive training and policy discussions on climate-related financial risks.

Arpit Guptaمشاهده پروفایل

دانشیار

• Household Finance
• Real Estate
• Urban Economics
• +۸ مورد دیگر

Arpit Gupta is an Associate Professor of Finance at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since 2016. His research lies at the intersection of real estate, household finance, and urban economics, with a strong empirical focus on using large datasets to analyze financial behavior and market dynamics. Ph.D. in Finance and Economics, Columbia Business School B.S. in Mathematics and Economics, University of Chicago His research interests include household finance, real estate markets, mortgage default, bankruptcies, urban economics, and the financial impacts of health shocks and pandemics. He explores how financial constraints affect minority borrowers, how remote work reshapes urban real estate, and how public infrastructure influences property values. His recent publications span top journals such as the Journal of Finance , American Economic Review , Journal of Financial Economics , and PNAS . Themes across his work include the economic consequences of the COVID-19 pandemic, the valuation of private equity, and the spillover effects of foreclosures. His research often combines innovative data sources with rigorous econometric methods. Notable scientific awards include the Brattle Prize First Place (2019) and the 2016 Top Finance Graduate Award from Copenhagen Business School. He has advised on policy-relevant topics such as office-to-residential conversions, housing equity, and bail reform, and has received media attention for his insights on urban development and real estate trends. Professor Gupta collaborates with a wide network of researchers and maintains active engagement through a Substack newsletter and public data repositories. He advises students and contributes to academic discourse through conference presentations and policy discussions. He is affiliated with research labs and teams focused on urban economics and financial innovation, often collaborating with scholars at Columbia, NYU, and other institutions. His ongoing projects continue to explore the evolving dynamics of housing, finance, and urban resilience in the post-pandemic era.

• Sociology of Education
• Critical Pedagogies
• Culturally Responsive Schooling
• +۵ مورد دیگر

Associate Professor Samantha Schulz is a sociologist of education at The University of Adelaide's School of Education, within the Faculty of Arts, Business, Law and Economics. She specializes in educational justice, focusing on First Nations education, race critical theory, affect studies, and gender equity. Her work spans international contexts including Kenya, India, China, and Australia's APY Lands. She is co-convenor of the Pedagogies for Justice Research Group, affiliated with CRESI and the Fay Gale Centre for Research on Gender. As Program Director for the Master of Teaching program, she emphasizes socially engaged pedagogies. Recent research includes the ARC-funded 'Culturally Responsive Schooling' project (2022-24) and studies on gender-based violence in educational settings. Her publications explore decolonial methodologies, critical race theory, and the intersection of affect and pedagogy. Her grants include leadership roles in projects addressing Indigenous sovereignty in education, culturally responsive curriculum design, and restorative practices. Notable collaborations involve UniSA and Open University of Cyprus. She co-edits the Routledge 'Local Global Issues in Education' series and actively publishes on topics ranging from toxic masculinity in academia to anti-racist policy frameworks. Her advising focuses on fostering critical scholarship in Masters and PhD candidates, with research supervision emphasizing justice-oriented educational practices. She leads initiatives like 'Teaching in an era of digital influence' and has contributed to policy discussions on preventing violent extremism in schools. Her work bridges theoretical rigor with practical advocacy for marginalized communities in education systems globally.

Victor M. Yakovenkoمشاهده پروفایل

استاد

• Econophysics
• Statistical Mechanics
• Condensed Matter Physics
• +۳ مورد دیگر

Victor M. Yakovenko is a Professor of Physics at the University of Maryland, College Park, and a Fellow of the American Physical Society. His research spans econophysics, condensed matter theory, and theoretical physics, with a focus on income/wealth distribution, entropy in economics, and quantum materials. He holds a Ph.D. in Theoretical Physics from the Landau Institute for Theoretical Physics (1987) and joined UMD in 1993, becoming a Full Professor in 2004. Notable awards include the Packard Fellowship and Sloan Research Fellowship. Research interests include applications of statistical physics to economic systems, dynamics of population growth, CO₂ emissions, and optical control of magnetism. He leads the Condensed Matter Theory group and is affiliated with the Joint Quantum Institute (JQI). Recent talks address econophysics, population trends, and interdisciplinary collaborations. Publications span econophysics models, energy inequality analysis, and topological materials. Grants include an Institute for New Economic Thinking (INET) project on income distribution. His work bridges physics and socioeconomic systems, with media coverage in New Scientist , Science , and public policy discussions.

• Climate Change Adaptation in Agriculture
• Land Use Modeling
• Biodiversity Conservation
• +۳ مورد دیگر

Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.

Barak Weissمشاهده پروفایل

استاد

• Homogeneous Dynamics
• Ergodic Theory
• Diophantine Approximation
• +۴ مورد دیگر

Professor Barak Weiss is a distinguished faculty member in the School of Mathematical Sciences at Tel Aviv University's Faculty of Exact Sciences. His research focuses on the intersection of dynamical systems, number theory, and geometry, particularly in the areas of homogeneous dynamics, ergodic theory, and Diophantine approximation. Professor Weiss has made significant contributions to the understanding of translation surfaces, lattice orbits, and the dynamics of flows on homogeneous spaces. His work often bridges pure mathematics with applications in number theory and geometry, revealing profound connections between seemingly disparate fields. His research on horocycle dynamics, measure rigidity for fractal carpets, and the classification of cut-and-project sets has advanced our understanding of geometric structures and their dynamical properties. His recent publications (2023-2025) demonstrate a strong focus on equidistribution phenomena, statistical properties of dynamical systems, and the application of homogeneous dynamics to problems in geometric number theory. A notable trend in his work is the interplay between geometric structures and their arithmetic properties, particularly in the context of Diophantine approximation. Professor Weiss actively organizes the "Homogeneous Dynamics and Applications" seminar at Tel Aviv University, which has been running continuously since at least 2014 with detailed schedules available through 2025. This seminar serves as a hub for cutting-edge research discussions, featuring both local and international speakers working on dynamical systems and related areas. He teaches advanced courses in analysis and supervises graduate students, with recent teaching assignments including Real Analysis for summer semester 2025. His office is located in Schreiber building, room 329, and his regular office hours are Tuesdays from 15:00-16:00.

Jun Liمشاهده پروفایل

استاد

• Applied Statistics
• Bioinformatics
• Biostatistics
• +۴ مورد دیگر

Jun Li is a Full Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame's College of Science. He specializes in developing statistical and computational methods for big data, with a focus on interdisciplinary applications in bioinformatics, machine learning, and data mining. His career includes tenure as an Assistant Professor (2012–2017) and promotion to Associate Professor (2017) before becoming Full Professor (2020). Dr. Li holds a Ph.D. in Statistics from Stanford University (2012), supervised by Robert Tibshirani, and earlier degrees from Tsinghua University: a B.E. in Automation (2004) and an M.S. in Pattern Recognition and Intelligent Systems (2007). Research Interests : Dr. Li’s work centers on advancing computational frameworks for handling large-scale datasets, integrating statistical rigor with algorithmic innovation. Recent themes include AI-driven code improvement, ethical LLM applications in HCI, and GUI automation. His methodologies emphasize human-AI collaboration and transparency in algorithmic systems. Publications : His 2025 work explores LLM vulnerabilities in GUI agents, AI-assisted education tools like GLITTER, and ethical challenges in HCI research. Earlier studies (2024–2023) address topics such as natural language database queries, privacy-preserving app promotion analysis, and multimodal task learning. Lab/Teams : Affiliated with Notre Dame’s computational statistics research groups, focusing on interdisciplinary projects bridging statistics, computer science, and applied mathematics. His work often involves collaborations with industry and academic partners to translate theoretical advancements into practical applications.

Andrea Groverمشاهده پروفایل

دانشیار

• Citizen Science
• Information Systems
• Open Collaboration Systems
• +۳ مورد دیگر

Dr. Andrea Grover is an Associate Professor in the Department of Information Systems and Quantitative Analysis at the University of Nebraska Omaha’s College of Information Science & Technology. Her research focuses on citizen science, technology design, and collaboration systems in data-intensive environments. She holds a Ph.D. in Information Science & Technology from Syracuse University, an MS in Information from the University of Michigan, and a BA in Mathematics from Alma College. Her research interests include organizational impacts of technology, open collaboration frameworks, and ethics in information systems. She teaches courses on management and IT ethics, employing ungrading techniques to enhance student engagement. Notable contributions include work on citizen science data quality, cyber-security tools leveraging crowdsourcing, and barriers in STEM education equity. Dr. Grover has been recognized with the 2022 Rising Star Distinguished Ecologist award and serves on editorial boards (e.g., Frontiers in Ecology) and conference committees (e.g., ACM CSCW DEI Co-chair). She has secured grants such as the NSF-funded “Streamlining Embedded Assessment to Understand Citizen Scientists' Skill Gains” (2017–2019) and contributed to media discussions on citizen science via National Public Radio. Her recent publications explore hybrid human-AI tools, agile citizen science methodologies, and systemic barriers in education. She actively participates in interdisciplinary initiatives, bridging technology, ecology, and social sciences to advance participatory research practices.

Ulrich Bergerمشاهده پروفایل

استاد

• Game Theory
• Behavioral Economics
• Evolutionary Game Theory
• +۳ مورد دیگر

Ulrich Berger is a Professor of Economics at the Department of Economics of WU Vienna University of Economics and Business (WU Vienna). He serves as Editor-in-Chief of the journal Games and is actively involved in promoting science through the Vienna Skeptics Society. His research focuses on game theory, including non-cooperative, evolutionary, behavioral, and experimental variants. He has been recognized with multiple awards, including the WU Best Paper Award and an Outstanding Reviewer Award. His work explores dynamics of cooperation, reputation systems, and strategic behavior in economic contexts. Key research areas include evolutionary stability in reputation games, indirect reciprocity, and cognitive hierarchies in strategic interactions. His publications span peer-reviewed journals like PLoS ONE and Scientific Reports , as well as popular science articles in outlets like derStandard.at . Berger has led research projects on topics such as cognitive hierarchies in minimizer games and has contributed to policy discussions on access pricing in telecommunications. His academic contributions reflect a blend of theoretical rigor and practical engagement, with recent work emphasizing evolutionary mechanisms of deterrence and experimental validation of equilibrium concepts.

Mark Battyمشاهده پروفایل

استاد

• Concurrent Systems
• Formal Verification
• Programming Languages
• +۳ مورد دیگر

Mark Batty is a Professor in the School of Computing at the University of Kent, specializing in formal methods for concurrent systems. His work bridges hardware-software interfaces, focusing on memory models for C/C++, OpenCL, and architectures including x86, ARM, POWER, and GPUs. As a member of the Programming Languages and Systems Research Group, he develops mathematical specifications and verification tools for real-world concurrency challenges. His research centers on empirical testing of hardware/compiler behavior, formal modeling of system components, and verification of fine-grained concurrent algorithms. Key contributions address relaxed memory semantics, transactional memory, and compositional reasoning for concurrent data structures. His work combines theoretical rigor with practical tool development to ensure correctness in complex concurrent environments. Analysis of his 2015-2025 publications reveals consistent focus on memory consistency models, formal verification of weak memory concurrency, and compiler optimizations. Dominant themes include C/C++11 standards, GPU concurrency semantics, and mechanized verification techniques. His research demonstrates strong industry relevance through collaborations with hardware vendors and contributions to language standards. Mark Batty has received significant recognition: John C. Reynolds Doctoral Dissertation Award (2015) from ACM SIGPLAN CPHC and BCS Distinguished Dissertation Award (2015) Lloyds Register Foundation and Royal Academy of Engineering Research Fellowship (2016) He actively leads major research initiatives and mentors next-generation researchers: Current Funding: EPSRC Standard Grant 'Verifiably Correct transactional memory' (2018), VeTTS Grant 'Specification and verification of C++ data structure libraries' (2018), EPSRC First Grant 'Compositional, dependency-aware C++ concurrency' (2018) PhD Recruitment: Actively seeking candidates for UKRI-funded studentship in Verified Trustworthy Software Systems Batty drives community engagement through Kent Concurrency Workshop (2016) and South of England Programming Language Seminars, fostering national collaboration in programming languages research. His leadership in organizing Royal Society discussions underscores his influence in trustworthy systems verification.

• Algorithms
• Algorithmics
• Big Data
• +۷ مورد دیگر

Stephen Alstrup is a Professor in the Algorithms and Complexity section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His research bridges theoretical computer science with practical applications in modern computational challenges. His primary research interests include: Algorithm design and analysis Graph algorithms and data structures Big Data processing techniques Streaming algorithms and Internet distribution Theoretical foundations with practical implementations Alstrup's work demonstrates how theoretical algorithm research can lead to real-world applications, as evidenced by his development of Octoshape technology for large-scale Internet streaming. His research spans from fundamental theoretical problems to applications in Big Data, cloud computing, and information retrieval systems. He has published extensively with 93 research outputs including journal articles, conference proceedings, and books. His recent work focuses on graph spanners, semantic hashing, recommendation systems, and universal graph structures, showing continued productivity in theoretical computer science. Alstrup actively engages with industry and media, contributing to discussions about Big Data applications, technology innovation, and how businesses can collaborate with universities to access cutting-edge knowledge and funding opportunities. His work has been featured in 10 media contributions discussing practical applications of algorithms in education, municipal IT projects, and business innovation.

Paul Raysonمشاهده پروفایل

استاد

• Natural Language Processing
• Corpus Linguistics
• Semantic Multilingual NLP
• +۷ مورد دیگر

Professor Paul Rayson is a Professor of Natural Language Processing in the School of Computing & Communications at Lancaster University, UK. He serves as Director of the UCREL (University Centre for Computer Corpus Research on Language) interdisciplinary research centre and is affiliated with multiple research institutes including Security Lancaster, the Lancaster Centre for Digital Humanities, and the Data Science Institute. Education: PhD in Computer Science, Lancaster University (2003) BSc (Hons) Computer Science and Mathematics, Lancaster University (1990) Professor Rayson's research focuses on semantic multilingual Natural Language Processing (NLP) in challenging linguistic environments with noisy language data, including historical texts, learner language, speech, email, and other computer-mediated communication. His work spans applications in dementia detection, mental health analysis, online child protection, cyber security, learner dictionaries, and text mining of biomedical literature, historical corpora, and financial narratives. He has developed semantic tagging tools like USAS (UCREL Semantic Analysis System) and Wmatrix for corpus analysis. Major Awards and Honors: FHEA (Fellow of the Higher Education Academy) MBCS (Member of the British Computer Society) Professor Rayson has supervised numerous PhD students in NLP and corpus linguistics, with eight current students and seven completed doctorates. He has led or co-investigated multiple major research projects including the £3.5m ESRC-funded Centre for Corpus Approaches to Social Science (CASS), the National Corpus of Contemporary Welsh, and projects related to mental health forums, financial narrative analysis, and cyber security. His research has been supported by ESRC, EPSRC, and other funding bodies. As Director of UCREL, he oversees research in corpus linguistics and NLP. He is also active in the Cyber Security Research Centre, Digital Health Group, and multiple Data Science Institute initiatives. His lab has developed several widely-used NLP tools including CLAWS for English POS tagging, USAS semantic analysis system, Wmatrix corpus analysis tool, and the Variant Detector (VARD) for historical texts.