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Mostafijur RahmanView profile

Assistant Professor

• Environmental Epidemiology
• Air Pollution
• Climate Change and Health
• +2 more

Mostafijur Rahman is an Assistant Professor of Environmental Health Sciences at Tulane University's School of Public Health and Tropical Medicine. His research leverages large environmental health datasets to study climate impacts on neurodevelopment, mental health, and cardiopulmonary outcomes. Research focuses on: Air pollution impacts on autism spectrum disorders Health effects of extreme heat and air pollution co-exposures Environmental justice and health disparities Climate vulnerability in urban populations Recent publications analyze prenatal exposures to particulate matter, mortality risks from extreme temperatures, and adaptation strategies in vulnerable communities. Methodological strengths include advanced statistical modeling of chemical mixtures and mixed-methods approaches. Awards and honors: Emerging Investigator designation by AJRCCM (2022) Best Poster Award, NYC Epidemiology Forum (2019) Dr. Morton Lippmann Scholarship, AIHA (2018)

Alireza NazarianView profile

Professor

• Cross-Cultural Studies
• Leadership Studies
• Organisational Effectiveness
• +3 more

Dr. Alireza Nazarian is a Reader at Westminster Business School, University of Westminster, specializing in Management and Marketing. He completed his PhD in Management Studies at Brunel University (UK), focusing on cultural dynamics and organizational effectiveness. He has held academic roles since 2013, including Senior Lecturer positions at the University of West London and University of Roehampton before joining Westminster in 2020. His research emphasizes cross-cultural studies, leadership, organizational effectiveness, and tourism hospitality management. He has published widely on topics such as servant leadership in hospitality, AI transparency in healthcare, and disaster resilience in technology-based businesses. Nazarian serves as Editor-in-Chief of the Journal of Organisational Studies and Innovation and holds editorial roles in several ABS-ranked journals. He has supervised 5 completed PhDs and is currently mentoring 3 students. His professional memberships include the Chartered Management Institute, Academy of International Business, and he is a Fellow of the Higher Education Academy (FHEA). He founded the Management & Business Academy and chairs the MBAcademy International Business Conference.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +4 more

Dr. Milos Hauskrecht is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from MIT (1997) and an M.Sc. from Slovak Technical University (1988). His research focuses on AI, machine learning, and data mining, with applications in medicine and finance. He leads projects in real-time clinical monitoring, anomaly detection, and time-series analysis of EHR data. He has advised numerous PhD and MS students, including notable alumni now at Amazon, DeepMind, and Microsoft. Research interests include reasoning under uncertainty, optimization, and AI-driven medical decision support. Current grants include NIH funding for AI in renal therapy and clinical monitoring. He has published widely in top venues like ICML, NeurIPS, and journals such as Artificial Intelligence in Medicine. His work on conditional outlier detection earned the Homer Warner Award (AMIA 2010). He teaches machine learning and advises on interdisciplinary AI projects.

Gaofeng JiaView profile

Associate Professor

• Structural Engineering
• Risk Assessment
• Infrastructure Resilience
• +4 more

Gaofeng Jia is Associate Professor in Civil and Environmental Engineering at Colorado State University's Walter Scott, Jr. College of Engineering. He specializes in natural hazard risk assessment, infrastructure resilience, and uncertainty quantification. Research integrates simulation-based approaches with high-performance computing for assessing complex engineering systems. Core areas include infrastructure deterioration modeling, wave energy converter optimization, tsunami evacuation planning, and surrogate modeling techniques. Recent work emphasizes physics-informed machine learning for engineering applications. Publications demonstrate consistent methodological innovation in uncertainty quantification across domains including seismic engineering, coastal hazards, renewable energy systems, and structural reliability. Articles employ advanced computational statistics, Bayesian methods, and multi-fidelity modeling. Young Researcher Best Paper Award Best Student Paper Award

Rebecca DziedzicView profile

Assistant Professor

• Asset Management
• Water System Sustainability
• Infrastructure Resilience
• +5 more

Rebecca Dziedzic is an Assistant Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University. Her research focuses on asset management, water system sustainability, and infrastructure resilience. She holds a PhD in Civil and Environmental Engineering from the University of Toronto. Dr. Dziedzic's work integrates machine learning, data science, and policy analysis to address challenges in urban infrastructure systems. Her research explores topics such as water distribution network optimization, climate change adaptation in infrastructure, and circular economy strategies for construction. Recent projects include predicting water main breaks using multivariate models, developing frameworks for energy-efficient pump operation, and assessing carbon footprints in industrial facilities. Dr. Dziedzic supervises graduate students in Civil Engineering (MASc/PhD) and maintains an active research group through the UrbanLinks initiative. Her work has been published in over 30 peer-reviewed articles, with a strong focus on smart city technologies, disaster risk reduction, and sustainable infrastructure design.

Di ZhuView profile

Assistant Professor

• Geographic Information Science
• Geospatial Artificial Intelligence (GeoAI)
• Spatial Networks
• +6 more

Di Zhu is an Assistant Professor of Geographic Information Science at the University of Minnesota's Department of Geography, Environment and Society. He directs the Geospatial Data Intelligence (GeoDI) Lab, focusing on GeoAI and social sensing to analyze human-environment interactions in urban systems, public health, and socioeconomic dynamics. His educational background includes a PhD in Cartology and GIScience from Peking University, complemented by a BSc in GIS and a BA in Economics from the same institution. Key research interests include spatial regression models, human mobility patterns, and GeoAI applications. He has collaborated on projects funded by NIH, NSF, and other agencies, exploring topics like spatiotemporal data imputation, urban flow analysis, and pandemic spatial dynamics. His work bridges traditional GIScience with modern machine learning techniques, emphasizing actionable insights from big geospatial data. Teaching focuses on advanced GIS, numerical spatial analysis, and urban sensing. He actively mentors students through the University of Minnesota's Master of GIS program and serves on academic boards including CPGIS. Current projects include analyzing Twin Cities mobility networks and developing intelligent spatial prediction frameworks.

Sudhakar GantiView profile

Associate Professor

• Cloud Computing Resource Management
• Software Defined Networking (SDN)
• Traffic Management and Quality-of-Service
• +3 more

Sudhakar Ganti is an Associate Professor in the Department of Computer Science at the University of Victoria, part of the Faculty of Engineering and Computer Science. He holds a PhD from the University of Ottawa. His research focuses on cloud computing resource management, software-defined networking (SDN), traffic management, quality-of-service optimization, and performance evaluation through queueing theory. His work bridges theoretical frameworks with practical applications in network efficiency and distributed systems. Dr. Ganti’s expertise includes optimizing resource allocation in fog-cloud systems, enhancing telehealth IoT energy efficiency, and developing dynamic defense frameworks for SDN security. His contributions span network traffic prediction, large file transport protocols, and formal verification of networking systems. He has published extensively in top-tier conferences and journals, addressing challenges in distributed computing, cyber security, and edge computing. His research trends emphasize leveraging reinforcement learning for fog-cloud resource allocation, multi-objective optimization in IoT, and SDN-driven network security. Earlier work includes foundational studies on optical router bypass, cloud workload characterization, and conversational agents for smart environments. Despite his prolific output, no academic awards or grants are explicitly mentioned in his profile.

Shaun M. DoughertyView profile

Professor

• Education Policy Analysis
• Career and Technical Education (CTE)
• Causal Program Evaluation
• +7 more

Shaun M. Dougherty is a Professor and Department Chair of Measurement, Evaluation, Statistics, and Assessment (MESA) at Boston College's Lynch School of Education & Human Development. He directs the Catholic Education Research Initiative and serves as a Strategic Data Project (SDP) Faculty Adviser at Harvard University's Center for Education Policy Research. His work bridges academia and policy, focusing on equity and effectiveness in career and technical education (CTE), educational accountability systems, and regression discontinuity methodologies. Dougherty holds an Ed.D. in Quantitative Policy Analysis from Harvard University. Education: Ed.D., Harvard University, Quantitative Policy Analysis Research Interests: Dougherty's research emphasizes education policy analysis , causal program evaluation , and CTE program impacts . He examines how CTE can address human capital development while addressing systemic inequities related to race, class, gender, and disability. His work integrates advanced statistical methods (e.g., regression discontinuity designs) with policy implementation studies in K-12 systems and postsecondary transitions. Grants & Awards: PI: $647,499 grant from Institute for Education Sciences (2020–2025) Co-PI: $1.7 million grant from Institute for Education Science (2022–2026) Co-PI: $250,000 grant from Institute for Education Sciences (2019–2021) His awards include the Outstanding Reviewer (2017, 2020) and Outstanding Research Paper (2020) from leading journals. Labs & Teams: Leads the MESA department's research initiatives and collaborates with the Catholic Education Research Initiative and Harvard's SDP program. His work engages with states and large districts on applied policy analysis, including Massachusetts, Michigan, and Connecticut.

• Economic Theory
• Game Theory
• Mechanism Design
• +4 more

Seungjin Han is a Professor in the Department of Economics at McMaster University, specializing in economic theory with applications in game theory, mechanism design, and public policy. His work bridges theoretical rigor with practical economic questions in taxation, matching markets, and corporate finance. His academic foundation includes a Bachelor of Economics from Korea University, Seoul; Master of Arts in Economics from McGill University, Montreal; and PhD in Economics from the University of Toronto. Research interests center on competing mechanism games, signaling theory, and market design. Key contributions address equilibrium analysis in strategic environments, tax incidence in differentiated markets, and incentive structures in matching frameworks. His theoretical work frequently appears in top journals like the Journal of Economic Theory and Journal of Mathematical Economics. Recent publications (2021-2025) reveal consistent focus on competing mechanisms and signaling dynamics, with growing emphasis on policy-relevant applications in public finance and labor markets. Over half his work involves collaborative research networks spanning international institutions. Teaching encompasses graduate-level Microeconomic Theory (ECON 721), Mathematical Methods (ECON 765), and Advanced Economic Theory (ECON 4T03) since 2017, reflecting deep engagement with core economic methodology.

Chiara SabattiView profile

Professor

• Statistical Genetics
• Computational Biology
• Bayesian Statistics
• +2 more

Chiara Sabatti is a Professor of Biomedical Data Science and Statistics at Stanford University, with affiliations to the Stanford Center for Computational, Evolutionary and Human Genomics (CEHG), Bio-X, and the Stanford Cancer Institute. She serves as Associate Director for Stanford Data Science and has led the development of the Data Science Major curriculum since 2012. Research Focus: Statistical models for high-throughput genomics data, causal inference in genetic studies, false discovery rate control, and knockoff methods for variable selection. Key Leadership: Associate Chair for Education and Training (2020-present), Vice Chair of Biomedical Data Science (2018-2019). Her work bridges statistical genetics with data science education, emphasizing robustness and interpretability in scientific findings. Recent publications highlight innovations in genome-wide association studies (GWAS), causal variant localization, and cost-effective sequencing techniques for underrepresented populations. Current projects include developing knockoff-based methods to address population structure and multi-resolution hypothesis testing. Scientific Awards: Institute of Mathematical Statistics (IMS) Fellow (2022) NSF CAREER Award (2003-2008) She mentors doctoral and graduate students in Biomedical Data Science, collaborates with the Data Studio on interdisciplinary projects, and actively recruits curious researchers to her lab. Her outreach efforts focus on expanding data science education and increasing research participation from underrepresented groups.

Robert (Rob) J. Franzese, Jr.View profile

Professor

• Comparative Political Economy
• International Political Economy
• Quantitative Research Methods
• +5 more

Robert (Rob) J. Franzese, Jr. is a Professor and Associate Chair in the Department of Political Science at the University of Michigan. He also serves as the Edie N. Goldenberg Director of the Michigan in Washington Program and Research Professor at the Center for Political Studies (CPS), Institute for Social Research. His research focuses on comparative and international political economy (C&IPE) of developed democracies, emphasizing the interplay between political and economic institutions, structures, and events on macroeconomic policymaking. Franzese is renowned for his work on spatial-econometric models of interdependence and empirical methodologies in political science. Education: PhD in Government from Harvard University (1996), A.M. in Economics and Government from Harvard (1995 and 1992), and B.A. in Economics and Government from Cornell University (1990, Phi Beta Kappa). He teaches courses on quantitative methods, comparative politics, and political economy. Key affiliations include the ICPSR Summer Program in Quantitative Methods and the Center for Political Studies. His research spans topics such as central bank independence, wage bargaining institutions, and spatial econometrics. He has authored/co-authored numerous books and articles on empirical methodology, policy analysis, and interdependence modeling. His methodological contributions include spatial econometric models, multilevel analysis, and model-based estimation for social science theory (EITM). He has advised on policy issues related to globalization, fiscal policy, and labor markets, with a focus on developed democracies. Fransese has directed the ICPSR Summer Program, which trains researchers in advanced quantitative methods. His work bridges political science and economics, emphasizing interdisciplinary approaches to understanding policymaking dynamics and spatial interdependencies.

Shan YuView profile

Assistant Professor

• Statistical Methodology
• Machine Learning
• Neuroimaging Analysis
• +7 more

Shan Yu is an Assistant Professor in the Department of Statistics at the University of Virginia. His research focuses on developing statistical and machine learning methods for large-scale, complex data, with applications in neuroimaging, genomics, spatial epidemiology, and health disparities. He employs advanced techniques including non/semi-parametric regression, functional data analysis, and distributed learning while emphasizing data privacy. Yu received his Ph.D. in Statistics from Iowa State University (2020), advised by Professors Lily Wang and Dan Nettleton, following a B.S. from the University of Science and Technology of China. His work bridges statistical methodology and real-world problems, addressing challenges in environmental science (e.g., nitrogen dioxide inequalities), public health (e.g., pandemic forecasting), and computational biology (e.g., genotype-environment interactions). He collaborates on tools like the GgAM R package for generalized geoadditive models and contributes to open-source projects such as fFLM for functional linear regression. Key research trends include spatially varying coefficient models, fusion learning for heterogeneous data, and integration of satellite data with environmental health studies. His publications span journals in statistics, epidemiology, and environmental science, reflecting interdisciplinary impact.

João Santos SilvaView profile

Professor

• Econometrics
• Applied Economics
• Quantile Regression
• +4 more

Professor João Santos Silva is a Professor in Economics at the University of Surrey, Department of Economics. His academic journey includes a PhD in Economics from the University of Bristol (1992), a Master's in Applied Mathematics from the Technical University of Lisbon (1988), and a Bachelor's in Economics from the same institution (1985). Prior to Surrey, he taught at the Technical University of Lisbon and the University of Essex. His research focuses on theoretical and applied microeconometrics, with notable contributions in quantile regression, count data models, gravity equations in trade, and mode regression. He co-authored the seminal paper "The Log of Gravity" , recognized as a Classic in Economics. Silva has developed several Stata modules, including fqreg and ppml , advancing econometric software tools. He teaches courses such as ECOM042: Econometrics I and ECOM066: Advanced Econometrics 2. His work has been published in top journals like the Review of Economics and Statistics and Journal of Econometrics . Notable contributions include addressing endogeneity in count data models, machine learning applications in trade research, and analysis of price stickiness in firms. Silva’s research emphasizes methodological rigor and practical relevance in economic analysis.

Alan KaptanogluView profile

Professor

• Applied Mathematics
• Scientific Machine Learning
• Nuclear Fusion
• +3 more

Alan Kaptanoglu is a Professor leading the Plasma Physics Group at New York University's Courant Institute. His research focuses on the intersection of applied mathematics, scientific machine learning, and nuclear fusion. He develops theoretical and computational tools for plasma modeling, control, and optimization in complex dynamical systems. Key areas include stellarator design optimization, machine learning-driven MHD simulations, and reactor-scale plasma confinement solutions. His work advances fusion energy research through innovations in magnetic field shaping, coil optimization, and data-driven modeling of plasma behavior. Research interests span plasma turbulence analysis, magnetohydrodynamics, and the application of physics-informed neural networks (PINNs). He explores how magnetic geometry influences plasma stability and turbulence using machine learning techniques. His team addresses challenges in fusion reactor design, such as minimizing Lorentz forces in electromagnetic coils and optimizing permanent magnet configurations for stellarators. Recent work emphasizes data-driven methods for discovering interpretable models in fluids and plasmas, including reduced-order modeling and sparse regression approaches. He collaborates on projects like the DIII-D tokamak and ITER, advancing fusion energy solutions through interdisciplinary computational and experimental efforts. His group's contributions bridge plasma physics with advanced mathematics and machine learning to tackle grand challenges in energy and astrophysical systems. Advising and grants are not explicitly detailed in the provided texts, but his leadership role suggests active mentorship in graduate research. The NYU Plasma Physics Group serves as a hub for cutting-edge research, hosting internships, summer schools, and collaborations with industry/academic partners.

Yahya FathiView profile

Professor

• Linear Programming
• Nonlinear Programming
• Integer Programming
• +4 more

Professor Yahya Fathi specializes in optimization and operations research at North Carolina State University. His research includes mathematical programming, production systems, and quality engineering, with applications in manufacturing and data analytics. Awarded multiple teaching excellence honors.