Amina Schartup is Associate Professor at Scripps Institution of Oceanography, UC San Diego, affiliated with the Geosciences Research Division. Her research examines mercury cycling in marine environments, climate change impacts on contaminant distribution, and public health implications. Research focuses on: Mercury biogeochemical cycling Trace metal interactions Marine food web contamination Climate-contaminant interactions Environmental toxicology Leads the Schartup Lab investigating trace metal effects on planetary and human health. Current projects include mercury speciation time series at Scripps Pier and studies of mercury dynamics in coastal systems.
Dr. Diane Srivastava is a Professor in the Department of Zoology at the University of British Columbia's Faculty of Science, and Director of the Canadian Institute of Ecology and Evolution (CIEE). Her research focuses on community ecology, particularly biodiversity's role in ecosystem functioning and responses to environmental changes. Her work addresses how trophic diversity influences ecosystem processes, the regional species pool's impact on local diversity, habitat effects on species richness, and human-driven changes. She leads the Living Data Project, preserving legacy environmental datasets through collaborations with early-career researchers. Key research areas include tropical ecosystem dynamics, climate change impacts, and functional trait-based community assembly. Recent studies highlight cross-ecosystem interactions (e.g., bromeliad microcosms), predator-prey dynamics under climate variability, and urban biodiversity patterns. Her work spans field experiments and collaborative data science initiatives. Dr. Srivastava’s projects emphasize global ecological challenges, including biodiversity loss mitigation and climate resilience strategies. She actively promotes equitable academic practices, such as triple-blind peer review and inclusive working groups. She directs the CIEE, fostering collaborative research across ecology and evolutionary biology. Her work is supported by UBC’s Biodiversity Research Centre and involves international field sites in tropical regions like Costa Rica and Trinidad.
John W. Galbraith is a Professor of Economics at McGill University in Montreal, specializing in econometrics, macroeconomics, and financial economics. His research focuses on high-frequency data analysis, economic forecasting, consumer behavior, and the impact of crises on economic activity. He holds a D.Phil. from the University of Oxford and has contributed extensively to the use of transaction data for real-time economic insights, including studies on consumer mobility, retail dynamics, and pandemic effects. Key research areas include nowcasting GDP using electronic payments data, analyzing asymmetries in unemployment forecasts, and evaluating the robustness of critical infrastructure networks. His work spans both theoretical econometric methods and applied studies in art valuation, international trade, and policy evaluation. Recent publications emphasize the role of digital commerce and transaction data in understanding geographic economic activity and crisis responses.
Simone Ferrari is a Fixed-term Assistant Professor in the Department of Energy (DENERG) at Politecnico di Torino. He is a member of the Interdepartmental Center PEIC (Power Electronics Innovation Center) and affiliated with the College of Electrical and Energy Engineering and College of Mechanical, Aerospace and Automotive Engineering as an invited member. His research focuses on electric machines, finite element analysis, and open-source software development for electromagnetic systems. He has supervised multiple PhD students in electrical engineering and has contributed to commercial consulting projects such as the design of electric motors for lifting applications and supervision of high-power motor development. Teaching responsibilities include courses on Propulsion of Hybrid and Electric Vehicles, Applied Electromagnetism, and Electrical Machines across multiple academic years. His work emphasizes multiphysics simulation, data-driven modeling, and the development of efficient traction motors for electric vehicles. Key research outputs include advancements in flux-map-based modeling, fault-tolerant motor designs, and thermal/structural scaling techniques for synchronous machines. Dr. Ferrari holds a patent for a method to identify spatial harmonic flux and torque maps without torque transducers. His recent publications (2024-2025) address challenges in electric motor efficiency, fault performance, and NVH mitigation, reflecting his commitment to advancing sustainable power electronics and clean energy technologies aligned with SDGs 7 and 9.
Ivan Jeliazkov is an Associate Professor of Economics and Statistics at the Department of Economics, University of California, Irvine. His research focuses on Bayesian econometrics and simulation-based inference, emphasizing methodologies like Markov chain Monte Carlo and econometric modeling. He holds a Ph.D. in Economics from Washington University in St. Louis and a BA in Economics and Business Administration from Coe College. His key research areas include Bayesian Econometrics, advanced simulation techniques, and causal inference applications. Notable work addresses heteroskedasticity in causal studies, quantile analysis of rental markets, and simultaneous equation models for discrete data. Recent advising includes 2024 Ph.D. graduates Robert MacDonald, Parush Arora, and Jieyu Gao. His articles span topics like dynamic factor models, regression discontinuity designs, and model comparison techniques. He has contributed to interdisciplinary research in marketing, finance, and historical economic analysis. His methodological innovations emphasize practical applications of Bayesian methods to address econometric challenges such as uncertainty quantification and model specification. Current work continues advancing computational tools for complex econometric problems.
Alexander Sahn is an Assistant Professor of Political Science and the Thomas J. Pearsall Fellow at the University of North Carolina at Chapel Hill. He specializes in political economy and representation, focusing on subnational U.S. governments, housing policies, and urban governance. His research examines racial backlash in exclusionary zoning, public comment influence on housing approvals, and municipal civil service reforms. Education: PhD in Political Science from the University of California, Berkeley (2020); Postdoctoral Fellow at Princeton University’s Center for the Study of Democratic Politics (2020–2023). Research Interests: Political economy of housing and urban development Institutional design of local governments Racial dynamics in policy implementation Public participation mechanisms Awards: Recognized with the Susan Clarke Young Scholar Award (APSA Urban Politics) and Emerging Scholar Award (APSA American Political Economy Section). Advising/Grants: Currently mentoring students in urban politics and political methodology. No active grants explicitly mentioned, but his research often addresses applied policy questions. Labs/Teams: Active in UNC’s political science department, collaborating on projects about neighborhood representation and climate communication.
Xinge Jessie Jeng is an Associate Professor of Statistics at North Carolina State University (NC State), Department of Statistics. She serves as Director of Masters Programs and holds a Ph.D. in Statistics from Purdue University (2009). Her research focuses on high-dimensional inference, machine learning, weak signal discovery, and statistical genomics, with applications in genetics and bioinformatics. Her work develops methodologies for analyzing large-scale genomic data, including false negative control in high-dimensional settings, transfer learning for polygenic risk prediction, and eQTL mapping. She has contributed R packages such as AFNC and FastLORS for statistical analysis. Her GitHub repository demonstrates active software development in statistical genomics and machine learning. Recent research emphasizes adaptive methods for weak signal detection under dependence, spatially adaptive variable screening in neuroimaging, and efficient SNP ranking in genetic studies. She collaborates across disciplines to bridge statistical theory with real-world genomic and biomedical applications.
Arnab Maity is an Associate Professor in the Department of Statistics at North Carolina State University (NC State). His research focuses on functional data analysis, kernel machine regression, and semiparametric methods with applications in environmental epidemiology and epigenetics. He holds a Ph.D. in Statistics from Texas A&M University (2008), an M.S. from the same institution (2005), and a B.Stat. (Honors) from the Indian Statistical Institute (2003). Education: Ph.D. in Statistics, Texas A&M University, 2008 M.S. in Statistics, Texas A&M University, 2005 B.Stat. (Honors), Indian Statistical Institute, 2003 Research interests span functional data analysis, kernel methods, semiparametric inference, and their applications in genomic studies, environmental health, and epigenetics. He has contributed to statistical tools for analyzing high-dimensional gene-environment interactions and longitudinal data. Recent work includes advancements in robust kernel association testing and functional concurrent models. His publications reflect interdisciplinary collaboration, addressing challenges in genetic epidemiology, biostatistics, and environmental health. Notable grants include NIH-funded projects on imprint regulatory regions in childhood obesity and functional data integration in genomics. Awards include the ASA Noether Young Researcher Award (2014) and the Cavell Brownie Mentoring Award (2020). He has advised十余名博士生 and served on numerous editorial and review boards. Active in academic service, he chairs committees and organizes seminars on functional data analysis and statistical genetics.
Dr. Emma Towlson is an Assistant Professor in the Department of Computer Science at the University of Calgary's Faculty of Science, serving as Assistant Head of Undergraduate Curriculum. She holds a Full Member position at the Hotchkiss Brain Institute and is a Child Health & Wellness Researcher at the Alberta Children's Hospital Research Institute. Her research focuses on network neuroscience, exploring how brain networks' structure-function relationships underpin health and disease. Key areas include network control theory, mental illness vulnerability, and brain connectivity analysis across species. Dr. Towlson's work bridges computer science, physics, neuroscience, and biology. She investigates structural brain networks' topological features (e.g., rich clubs, modules) and their spatial constraints, applying these insights to disorders like schizophrenia, depression, and Alzheimer's. Her lab uses network control principles to identify therapeutic targets for brain stimulation therapies and personalized medicine approaches. Recent grants include a $250,000 New Frontiers in Research Fund (NFRF) Explorations grant (2022) to study preterm birth's impact on brain networks and mental illness risk. She teaches courses on social network analysis (CPSC 572/672) and data visualization (DATA 601). Collaborative efforts include partnerships with Harvard Medical School and Monash University's Turner Institute. Her team’s interdisciplinary projects include analyzing mouse brain connectomes, simulating connectome perturbations, and developing open-source network neuroscience tools. Ongoing work emphasizes translating network science insights into clinical applications for mental health interventions.
Jeremy W. Fox is a Professor in the Department of Biological Sciences at the University of Calgary. He holds a PhD in Ecology and Evolutionary Biology from Rutgers University (2000) and a BA from Williams College (1995). His work focuses on community assembly processes, combining experimental and theoretical approaches with microbial systems. Key interests include dispersal effects, ecosystem function, and the dynamics of competitive interactions. Research highlights include challenging the Intermediate Disturbance Hypothesis (2013), quantifying biodiversity's role in ecosystem stability (2013), and analyzing meta-analytic approaches in ecology (2022). His lab explores how environmental fluctuations and species traits influence community structure and stability. Fox has been recognized with awards including the British Ecological Society Early Career Award (2007) and Alberta Ingenuity grants (2005-2007). Teaching includes courses on quantitative biology (ECOL 425, BIOL 315). His work emphasizes experimental rigor, theoretical frameworks, and open science practices. The Fox Lab's research spans ecological restoration, food web dynamics, and evolutionary ecology, with a focus on long-term community processes and their applied implications.
Marc Adams is an Assistant Dean and Professor at the School of Technology for Public Health, Arizona State University. His research focuses on physical activity promotion, built environment influences on health, and the design of behavioral interventions targeting obesity, nutrition, and cardiovascular health. He leads studies on mobile health technologies (e.g., smartphone apps) and community-based initiatives to improve health equity through environmental modifications. Key research areas include the efficacy of financial incentives in physical activity interventions, the role of neighborhood walkability in promoting health behaviors, and the intersection of crime perception and recreational walking. He collaborates internationally on projects analyzing urban design impacts on public health outcomes, such as the IPEN Adult Study and WalkIT Arizona trials. Adams has developed culturally tailored interventions like the Smart Walk program for African American women and contributed to evidence-based guidelines for school nutrition policies. His work emphasizes interdisciplinary approaches, integrating data science, environmental engineering, and behavioral economics to address complex health challenges.
Robert Balling is a Professor at Arizona State University (ASU) within the School of Geographical Sciences and Urban Planning. He holds affiliations with the Urban Climate Research Center, Water Institute, and Global Drylands Center. His research focuses on climatology, global climate change, and GIS applications, with a particular emphasis on blending numerical climate models with observational data. Balling has authored four books on climate change and served as a science consultant to the United Nations. He directed the Master of Advanced Study in Geographic Information Systems (MAS-GIS) program and received the 2011 ASU Professor of the Year award from the Order of Omega Greek Honorary Society. Education: Ph.D. Geography, University of Oklahoma (1979); M.A. Geography, Bowling Green State University (1975); A.B. Geography, Wittenberg University (1974). Research Interests: Climatology, global climate change, GIS applications, precipitation patterns, urban climate dynamics, and water resource management. His work often involves analyzing climate model outputs, satellite-based observations, and historical climate data to assess climate variability and human impacts. Grants & Service: Balling has led numerous grants, including projects on climate adaptation, drought forecasting, and urban CO2 domes. He served as a Senior Fellow at the Goldwater Institute and contributed to the UN’s Intergovernmental Panel on Climate Change (IPCC). His presentations and lectures address climate change skepticism and policy implications. Awards: Recognized for his teaching excellence and contributions to climate science, including the 2011 ASU Professor of the Year award.
Jin Zhu is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE), working with Prof. Chengchun Shi on reinforcement learning and machine learning. His research focuses on developing algorithms with statistical and computational guarantees, alongside statistical software design to enhance algorithmic applications. Prior to LSE, he earned his PhD in Statistics at Sun Yat-Sen University under Dr. Xueqin Wang and Dr. Na You. Key expertise includes reinforcement learning, machine learning, and computational statistics. His work addresses challenges in off-policy evaluation, robustness in RL, and sparsity-constrained optimization. He has contributed to open-source tools like skscope and abess for efficient statistical computation. Research interests also span causal inference, high-dimensional data analysis, and algorithmic design for complex systems. Notable contributions include methodologies for genetic factor identification, spatial experimental design, and nonparametric statistical inference. Jin’s research bridges theoretical advancements with practical software implementations to address real-world computational and statistical challenges.
Benjamin Adric Dunn is an Associate Professor in (Neural) Data Science at the Department of Mathematical Sciences, NTNU. His research focuses on computational neuroscience, systems neuroscience, and topological data analysis. He holds a PhD and postdoctoral experience from NTNU's Kavli Institute for Systems Neuroscience, an MS in Computational Engineering from Purdue University, and a BS in Applied Mathematics from the University of Connecticut. Previously, he worked as a Computational Methods Engineer at Pratt & Whitney, UTC. His work explores neural mechanisms underlying spatial navigation, grid cell dynamics, and neural coding in rodents. Key contributions include studies on toroidal representations in grid cells and cortical coding of 3D posture. Dunn has published extensively in journals like Nature , Science , and Neuron , with a focus on interdisciplinary methods combining topology, statistics, and neuroscience. He has collaborated on projects involving neural ensemble activity analysis and hidden variable modeling in biological data. Dunn’s research also addresses computational challenges in neuroscience, such as removing experimental variability in functional data.
Bin Peng is a Professor in the Department of Econometrics and Business Statistics at Monash University. His research focuses on developing novel econometric models and methods, particularly in panel data analysis, time series econometrics, and climate data modeling. He holds a PhD in Econometrics from Monash University (2013) under Professors Giovanni Forchini and Don Poskitt, preceded by a BSc in Mathematics from Nanjing University (2007). His work addresses structural changes in factor models, time-varying parameters in vector error-correction frameworks, and productivity convergence in manufacturing sectors. Key contributions include nonparametric panel models for climate data and methodologies for handling interactive effects in panel data with general factors. Peng has received multiple Dean’s Awards, including the 2021 Early Career Research Excellence Award, 2023 Commendation for Excellence, and 2024 Researcher of the Year. He leads a 2021–2025 project on modeling time trends in panel data, funded by Monash University. His recent articles (2021–2025) emphasize methodological advancements in econometric theory, applied to climate science, economic growth, and macroeconomic policy.
