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Donald RobertsonView profile

Professor

• Econometrics
• Applied Macroeconomics
• Financial Economics
• +3 more

Professor Donald Robertson is a faculty member at the University of Cambridge , holding the position of Professor of Economics and Director of Graduate Studies and PhD Programme within the Faculty of Economics . He is affiliated with Pembroke College and contributes to econometric research and graduate education. Research Interests : His work focuses on Econometrics , Applied Macroeconomics , and Financial Economics , with methodological expertise in Time Series Analysis , Panel Data Analysis , and Predictive Modeling . His publications address topics like cross-sectional dependence, unit root testing, and instrumental variable estimation. Teaching : He instructs modules such as Introduction to Probability and Statistics , Time Series Methods , and MPhil Prep Course - Statistics . Publications : Recent contributions include work on R² bounds for predictive models, factor residuals in panel data, and fiscal fatigue in debt ratios, reflecting his focus on econometric theory and macroeconomic applications. Contact : Email dr10011@cam.ac.uk or phone +44(0)1223 335270. Office hours by email appointment in Room 70.

M. Tolga AkçuraView profile

Professor

• Marketing Strategy
• Brand Valuation
• Consumer Behavior
• +7 more

Professor Tolga Akçura is a distinguished faculty member at Özyeğin University's Faculty of Business, Department of Business Administration. With over 25 years of academic experience, he has developed and taught courses on marketing strategy, marketing analytics, and marketing research at prestigious institutions including Carnegie Mellon University, Purdue University, and Long Island University. At Özyeğin University, he teaches Marketing Strategy, Integrated Marketing Communication Strategies, Innovation, Business Model Development, and Advanced Topics in Marketing for undergraduate, graduate, and executive students. Professor Akçura holds a B.Sc. in Industrial Engineering from Boğaziçi University (1990), an MA in Business Administration from Boğaziçi University (1996), an MBA from Carnegie Mellon University (1998), and a Ph.D. in Quantitative Marketing from Carnegie Mellon University (2000). Before joining academia, he worked for Procter & Gamble across multiple European locations including Brussels, London, Manchester, and Istanbul. His research focuses on the intersection of Information Technology and Marketing, Brand Valuation, Consumer Learning Behavior, Structural Choice Models, Brand Equity dynamics, and Competitive Pricing Strategies. Professor Akçura has made significant contributions to marketing science through his extensive publication record in top-tier journals. His recent scholarly work demonstrates a strong trend toward digital marketing, AI applications in marketing analytics, healthcare marketing, and the strategic implications of data-driven decision making. His publications span from foundational work on brand equity to cutting-edge research on patient-generated health data and deep learning applications in campaign participation prediction. William W. Cooper Award (awarded twice for publications in Management Science and Marketing Science) Professor Akçura has successfully bridged academic research with practical business applications through his role as founder of eBrandValue A.Ş. and as a Y-Combinator alum (YCW15). He is an active member of professional organizations including the Institute for Operations Research and Management Science, American Marketing Association, and Direct Marketing Institute. His industry experience complements his academic work, providing students with valuable real-world insights into marketing strategy and implementation.

• Transportation Engineering
• Road Safety
• Remote Sensing
• +5 more

Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.

Joseph RosoView profile

Assistant Professor

• Sociology of Religion
• Political Sociology
• Technology and Society
• +3 more

Joseph Roso is an Assistant Professor of Sociology at Ambrose University, specializing in the practices and political activities of religious congregations and leaders. His work examines how religious institutions adapt to 21st-century challenges, including technological integration and political polarization. Roso holds a PhD from Duke University, where he also completed his MA, and a BA from Vanderbilt University, all in Sociology. He teaches courses on quantitative methods, multivariate statistics, and sociological principles. His research focuses on congregational adaptation, clergy activism, and the intersection of religion and politics. Education: PhD in Sociology, Duke University MA in Sociology, Duke University BA in Sociology, Vanderbilt University Roso's research interests center on religious organizations' responses to societal changes, including technology adoption (e.g., streaming services pre-pandemic), political polarization impacts, and clergy-lay dynamics. His recent studies highlight tensions between religious leaders and their congregants on political issues, as well as institutional disaffiliation trends in denominations like the United Methodist Church. His articles analyze topics ranging from evangelical rhetoric on immigration to clergy political activism, leveraging data from the National Survey of Religious Leaders. He has contributed to debates on congregational technological readiness and worship practice evolution. Labs/Teams: Collaborates with projects like the National Congregations Study and National Survey of Religious Leaders, emphasizing large-scale survey methodologies to map religious institutional trends.

• Environmental Chemistry
• Analytical Chemistry
• Health Risk Assessment
• +4 more

Jelena Vesković is a Researcher at the Department of Analytical Chemistry and Quality Control, Technical Faculty in Bor, University of Belgrade. Her work focuses on environmental chemistry, health risk assessment, and pollution source apportionment using advanced statistical models. Research Interests: Jelena specializes in analyzing potentially toxic elements in water and soil systems. Her studies integrate Monte Carlo simulations, multivariate analysis, and receptor models to assess contamination sources and health risks. Key areas include groundwater pollution, rare earth elements, and urban sediment toxicology. Recent Publications: Her 2025–2024 articles investigate radiological risks in groundwater, dietary cadmium exposure, PAH contamination in lakes, and soil-to-groundwater heavy metal migration using Monte Carlo and multivariate methods. Laboratory Affiliation: Works in the Department of Analytical Chemistry and Quality Control, contributing to environmental monitoring projects in mining regions and urban environments.

Vikas SinghView profile

Professor

• Image Analysis
• Machine Learning
• Computer Vision
• +3 more

Vikas Singh is a Professor in the Department of Biostatistics at the University of Wisconsin-Madison, with appointments in Computer Sciences and Statistics. He also serves as a part-time Faculty Researcher at Google DeepMind. His research focuses on image analysis, machine learning, and medical imaging applications, particularly in neuroimaging and Alzheimer's disease studies. Singh holds a Ph.D. in Computer Science from SUNY Buffalo and has taught courses such as BMI/CS 767 (Medical Image Analysis) and CS 766 (Computer Vision). Affiliations: UW Computer Vision Group, Wisconsin Alzheimer's Disease Research Center (W-ADRC), Machine Learning@UW. Research: Develops algorithms for medical image analysis, including tools for neuroimaging and longitudinal biomarker studies. Grants: Collaborates on grants related to Alzheimer's progression modeling and imaging techniques. His work emphasizes interdisciplinary applications, bridging statistics, geometry, and optimization to solve real-world problems in healthcare and engineering.

• System Informatics
• Big Data Analytics
• Data Fusion
• +9 more

Dr. Kaibo Liu is the Grainger STAR Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and serves as Associate Director of the UW-Madison IoT Systems Research Center. He earned his B.S. from the Hong Kong University of Science and Technology (2009), and M.S. and Ph.D. from Georgia Tech (2011/2013). His research focuses on system informatics, big data analytics, and data fusion for process modeling, monitoring, and decision-making. He has been funded by NSF, ONR, DOE, and industry partners. Notable awards include the 2024 Hromi Medal (ASQ), 2021 IISE Technical Innovation Award, and multiple early-career recognitions. Recent work emphasizes real-time cyber-physical security, reinforcement learning for data streams, and Bayesian methods for prognosis. He edits IEEE Transactions on Automation Science and Engineering and IISE Transactions on Data Science.

Naisyin WangView profile

Professor

• Longitudinal Data Analysis
• Functional Data Analysis
• Biostatistics
• +5 more

Naisyin Wang is a Professor of Statistics at the University of Michigan, where she has been since 2009. Previously, she served as a faculty member in Statistics and Toxicology at Texas A&M University from 1992. She holds a Ph.D. in Statistics from Cornell University (1992), an M.A. in Statistics from Ohio State University (1987), and a B.S. in Mathematics from National Tsing-Hua University, Taiwan (1986). Her research focuses on longitudinal and functional data analysis, measurement error models, semiparametric methods, and applications in biological and medical fields, particularly genomics and metabolomics. Key contributions include methodologies for handling missing data, mixed effects models, and clustering techniques. Education: Ph.D. in Statistics, Cornell University (1992) M.A. in Statistics, Ohio State University (1987) B.S. in Mathematics, National Tsing-Hua University (1986) Dr. Wang’s honors include the College of Science Distinguished Alumni Award (2012), the Distinguished Achievement Award in Research (2003), and fellowships from the AAAS, ASA, and IMS. She has held leadership roles, including Co-editor of Statistica Sinica (2011–2014) and President of the International Chinese Statistical Association (2010). Her teaching includes courses such as Applied Statistics (STATS 500), Linear Models (STATS 600), and Special Topics in Applied Statistics (STATS 700). She has advised numerous students and contributed to research on cancer genomics, dietary interventions, and statistical methodology.

• Robust Statistics
• Dimension Reduction
• Meta-Analysis
• +3 more

Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.

Mathias DrtonView profile

Professor

• Graphical models
• Algebraic statistics
• Causal inference
• +3 more

Prof. Mathias Drton holds the Chair of Mathematical Statistics at the Technical University of Munich (TUM), within the Department of Mathematics and School of Computation, Information and Technology. His research focuses on graphical models, algebraic statistics, causal inference, and multivariate data analysis. He has authored numerous publications in top-tier journals and conferences, including work on conditional independence, sparse factor analysis, and causal discovery in linear models. Drton has supervised a large number of theses, mentoring students in areas like high-dimensional statistics, graphical models, and causal inference. He is actively involved in teaching advanced courses such as 'Graphical Models in Statistics' and 'Fundamentals of Mathematical Statistics.' His academic contributions span theoretical developments in statistical methodology and computational tools, including R packages like SEMID and symRC . Drton collaborates internationally, contributing to projects like the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. His work bridges algebraic methods with statistical challenges, addressing identifiability in latent variable models and robust graphical modeling under non-Gaussian assumptions. Recent research emphasizes causal structure learning under partial homoscedasticity, distribution-free independence tests, and multi-domain causal representation learning. Drton’s lab actively explores applications in genomics, epidemiology, and machine learning, leveraging both theoretical rigor and practical computational methods.

• Computational Neuroscience
• Bayesian Statistics
• Neural Data Analysis
• +3 more

Robert E. (Rob) Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, holding joint appointments in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. His research spans Bayesian statistics, neural data analysis, and computational neuroscience. Kass earned a B.A. in Mathematics from Antioch College, a Ph.D. in Statistics from the University of Chicago, and has been at CMU since 1981. He has served as Department Head of Statistics (1995–2004) and Interim Co-Director of the CNBC (2015–2018). His work focuses on statistical methods for neuroscience, particularly analyzing spike train data and identifying cross-brain interactions. Notable contributions include co-authoring Analysis of Neural Data and foundational articles on Bayesian inference. Kass has received prestigious awards such as the National Academy of Sciences membership and COPSS Distinguished Achievement Award. Research interests include computational neuroscience, statistical modeling of neural systems, and interdisciplinary education. He has advised numerous students and co-organized major workshops like the Statistical Analysis of Neuronal Data series. Kass’s work emphasizes the interplay between statistical rigor and scientific insight, bridging theoretical and applied domains. Education: B.A. in Mathematics, Antioch College Ph.D. in Statistics, University of Chicago Postdoctoral Fellow, Princeton University Scientific contributions include advancements in spike train analysis, Bayesian model assessment, and statistical methods for brain connectivity. His work on neural synchrony and population coding has influenced both theoretical and applied neuroscience.

Leah K VanWeyView profile

Professor

• Social Demography
• Environmental Social Science
• Land Use Change
• +7 more

Leah K VanWey is Professor of Environment and Society and Sociology at Brown University, where she also serves as Dean of the School of Professional Studies. She holds affiliations with the Department of Sociology, Institute at Brown for Environment and Society, Population Studies and Training Center, Spatial Structures in the Social Sciences, and Department of Portuguese and Brazilian Studies. Previously, she served as Senior Deputy Director of the Institute at Brown for Environment and Society, Associate Director of the Population Studies and Training Center, and Associate Provost for Academic Space. VanWey completed her education at the University of North Carolina, earning a BA (1996), MA (1999), and PhD (2001). Her research focuses on social demography and environmental social science, particularly examining household responses to reforestation programs in Brazil's Atlantic Forest and the social/environmental impacts of frontier settlement in the Amazon. Her research interests include agricultural intensification, land use change, migration patterns, population-environment interactions, household dynamics, intergenerational support systems, and remittance economies, with a geographic focus on Brazil. She employs interdisciplinary approaches, collaborating with anthropologists, geographers, ecologists, and other social scientists. VanWey has secured significant research funding including grants from NSF, NIH, and NASA totaling over $2.5 million. Major projects include studying agricultural expansion in Brazil, climate variability impacts, land use change dynamics, and population-environment linkages. She mentors graduate students and postdoctoral researchers, including Trina Vithayathil. She teaches courses on human-environment interactions and research methods, including: Feeding Ten Billion, Methods for Interdisciplinary Environmental Research, Multivariate Statistical Methods, Fields and Methods of Social Research, and Professional Development seminars.

Rohit KannanView profile

Assistant Professor

• Learning + Optimization
• Optimization Under Uncertainty
• Global Optimization
• +2 more

Rohit Kannan is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He holds a Ph.D. and M.S. in Chemical Engineering from MIT and a B.Tech. from IIT Madras. His research focuses on integrating machine learning with global optimization and optimization under uncertainty, emphasizing energy systems applications. Previous roles include postdoc positions at Los Alamos National Laboratory and the Wisconsin Institute for Discovery. Education: Ph.D., Chemical Engineering, Massachusetts Institute of Technology, 2018 M.S., Chemical Engineering Practice, MIT, 2014 B.Tech., Chemical Engineering, IIT Madras, 2012 Research Interests: Global optimization, optimization under uncertainty, computational optimization, energy systems, and machine learning integration. Recent Highlights: Recipient of the Excellence in Teaching Spotlight Award (2024) Lead researcher in stochastic optimization and energy systems (e.g., hybrid polygeneration systems) Developed algorithms for chance-constrained nonlinear programs and distributionally robust optimization Service & Leadership: Elected Vice-Chair of Global Optimization, INFORMS Optimization Society (2025–2026) Reviewer for top journals like Operations Research and Mathematical Programming Advisor to ISE InclusiveVT and Graduate Admissions Committee Labs & Collaborations: Directs a research group advancing optimization and machine learning for energy and engineering systems. Active in interdisciplinary projects with LANL and UW-Madison.

Ye ZhishengView profile

Associate Professor

• Inventory Control
• Reliability Engineering
• Emergency Response Systems
• +4 more

Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.

• Breast Cancer Prevention
• Spatiotemporal Disease Modelling
• Health Economics
• +3 more

Dr. Jannah Baker is a Research Fellow at the Sydney School of Public Health, University of Sydney. She holds a PhD in Statistics specializing in Bayesian spatiotemporal modelling of chronic diseases, alongside dual postgraduate diplomas in Public Health and Statistics. Her clinical background includes five years as a practicing physician. Her research focuses on cancer prevention (breast, endometrial, melanoma), spatiotemporal disease modeling, health economics, and clinical trial design. She has led projects on sepsis detection systems, diabetes management, and surgical outcomes analysis. Notable contributions include a seminal systematic review on fertility-sparing endometrial cancer treatments referenced in international guidelines. Jannah has attracted $6M+ in collaborative funding and oversees grants such as the 2023 NHMRC-funded ROADMAP trial and a Department of Health-funded study on pediatric sepsis management. Her work spans 50+ peer-reviewed articles across journals like Journal of Medical Internet Research and PLOS One . Professional activities include clinical trials statistics, health economics analysis, and implementation science lectures. She has mentored multiple research teams and maintains an ORCID profile (0000-0002-2208-6584).