Paul Gessler is Professor of Remote Sensing and Geospatial Ecology at the University of Idaho's College of Natural Resources, Department of Forest, Rangeland and Fire Sciences. He holds a Ph.D. in Resource Management and Environmental Science from Australian National University, an M.S. in Environmental Engineering from University of Wisconsin-Madison, and a B.S. in Natural Resources from University of Wisconsin-Madison. His research integrates remote sensing, geospatial analysis, and environmental modeling to study forest ecosystems, soil-landscape relationships, and cyberinfrastructure development. Current projects focus on agricultural insurance modeling under climate variability, streamflow permanence prediction, Lassa virus ecology, and open science frameworks for soil carbon modeling. Gessler's technical expertise encompasses terrain analysis, geographic information systems (GIS), digital image processing, airborne mapping, and global positioning systems. His work demonstrates strong commitment to open science principles and cyberinfrastructure development for earth science data management.
Professor J.P.G. Jones is a leading academic at Bangor University's School of Natural Sciences, specializing in conservation biology and environmental management. His roles include teaching modules such as Environmental Management and Conservation, and directing interdisciplinary research projects. He has been Principal Investigator (PI) on major initiatives like the Darwin Initiative-funded 'Conservation Agreements in the Comores' and the UKAid-backed Forest4Climate&People project. His work emphasizes evidence-based conservation strategies, payment for ecosystem services, and biodiversity offsetting. Jones has supervised 15 PhD students, focusing on topics like REDD+ implementation and conservation impact evaluation. He advocates for rigorous impact assessment methodologies, including randomized control trials (RCTs), and critiques voluntary carbon markets for inadequacies in ensuring genuine environmental benefits. His research spans tropical conservation, community forest management, and the mitigation hierarchy in development projects. Education: While specific qualifications aren't detailed, his career trajectory suggests advanced training in ecology, conservation science, and environmental policy. Key Projects: Led projects on conservation agreements, forest carbon programs, and invasive species impacts in Madagascar and the Comores. Grants: Secured funding from NERC, Darwin Initiative, Leverhulme Trust, and international partnerships. Awards: Though not explicitly listed, his leadership in high-impact projects indicates recognition in the field. Research interests include conservation effectiveness, causal inference in environmental interventions, and the intersection of business and biodiversity. He emphasizes the need for transparency and rigorous evaluation in conservation strategies to ensure both ecological and socio-economic benefits.
Dr. Dexin Shi is an Associate Professor in the Department of Psychology at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. He holds expertise in quantitative psychology, focusing on statistical methodology development and application. Dr. Shi earned his Ph.D. in Quantitative Psychology from the University of Oklahoma (2016) and completed a postdoctoral fellowship at the University of South Carolina. His research emphasizes psychometrics, causal inference, missing data analysis, and model selection, with applications in educational, clinical, and health psychology. He has published over 50 peer-reviewed articles in top journals like Psychological Methods and Structural Equation Modeling . Notable awards include the 2021 Rising Star Award from the Association for Psychological Science and the McCausland Faculty Fellowship (2021–2024). Dr. Shi teaches courses such as PSYC 220 (Psychological Statistics) and advanced quantitative methods like PSYC 815 (Causal Inference). He has received grants for innovative pedagogy and open educational resources. His research trends highlight advancements in statistical techniques, including Bayesian methods, machine learning applications in equating, and causal pathway identification. He actively explores methodological challenges in psychological assessment and data analysis.
Chris Hand is a Professor of Marketing at the Department of Strategy, Marketing and Innovation, Kingston University London, within the Faculty of Business and Social Sciences. He leads the Customer Insights Research Hub and has held academic leadership roles including Course Director for BSc International Business and BBA programs. His academic background includes a PhD in Economics from the University of Portsmouth (2001), with prior research in Television Audience Statistics at Bournemouth Media School and Royal Holloway. Education BA(Hons) Economics MSc Business Economics MA Strategic Marketing Management PhD Economics (Thesis: Empirical Studies of the Demand for Cinema in the UK) PgC Learning and Teaching in Higher Education Research Interests Professor Hand’s work bridges economics and marketing, focusing on quantitative analysis of consumer behavior, entrepreneurship, and multichannel retailing. His research applies econometric methods to study self-employment impacts, digital media effects, and arts consumption patterns. Recent studies explore cyber aggression, random forest accuracy in modeling, and spatial influences on well-being. Publications Trends His 2020s research emphasizes entrepreneurship dynamics (self-employment well-being links), digital consumer behavior (social media compulsion effects), and spatial economics (regional convergence theories). Earlier work includes foundational studies on cinema demand modeling and arts audience behavior. Awards Fellow of the Higher Education Academy Certified Member of the Market Research Society Leadership & Impact He has directed Kingston’s MRes Business and Management doctoral training program and contributed to curriculum development in strategic marketing. His research actively informs policy discussions on entrepreneurship and cultural economics through applied quantitative frameworks. Research Hub As head of the Customer Insights Research Hub, he fosters interdisciplinary work on consumer decision-making and data-driven marketing strategies.
Frank Schaarschmidt is a researcher affiliated with the Department of Biostatistics and the Institute of Cell Biology and Biophysics at Leibniz University Hannover within the Faculty of Natural Sciences. His work focuses on developing and applying advanced statistical methodologies across diverse fields such as biomedicine, ecology, agriculture, and molecular biology. Key research interests include statistical inference techniques, prediction modeling for overdispersed data, and the design of experiments. He has contributed extensively to methodologies for simultaneous confidence intervals, multiple testing adjustments, and applications in clinical trials and environmental risk assessment. Collaborations span disciplines from cell biology to soil science, reflecting a strong interdisciplinary approach. Publications highlight his expertise in statistical methods for medical monitoring, ecological modeling, and molecular biology. Notable contributions include work on hydrogel platforms for stem cell research and studies on pyrimidine metabolism in plants. His research often bridges theoretical statistical developments with practical applications in health sciences and environmental sustainability. Frank Schaarschmidt holds the title of PD Dr. (Privatdozent) and has been actively involved in academic management within the Department of Biostatistics.
Wei Long is an Associate Professor and Director of Undergraduate Studies in the Department of Economics at Tulane University, within the School of Liberal Arts. He holds a MA in Statistics from Columbia University (2010) and a PhD in Economics from Texas A&M University (2015). His research focuses on applied economics with concentrations in the economics of crime and financial econometrics, emphasizing econometric methodologies such as quantile regression, copula models, and panel data analysis. His work has been published in top-tier journals like the Journal of Econometrics and Journal of the American Statistical Association. Key research areas include evaluating police effectiveness, crime deterrence strategies, financial market predictability, and the impact of policy reforms. He has contributed to understanding how oversight mechanisms influence policing outcomes and how socioeconomic factors shape criminal behavior. His recent studies explore machine learning applications in panel data models and privacy-preserving quantile regression techniques for large datasets. Despite not listing explicit scientific awards, his prolific publication record and academic roles reflect significant scholarly contributions. His advising and grants are not detailed here, but his research spans interdisciplinary topics such as income inequality in Latin America, stock market bubbles, and the effects of highly publicized police incidents on community policing strategies.
Timothy Havens is a Professor at Michigan Technological University in the Department of Computer Science within the College of Computing. He holds the William and Gloria Jackson Professorship and serves as Executive Director of both the Great Lakes Research Center and the Institute of Computing and Cybersystems. Dr. Havens also directs the PRIME Lab and has been recognized for both teaching and research excellence, including the 2014-15 Professor of the Year award from IEEE Eta Kappa Nu and Best Paper Awards at FUZZ-IEEE 2012 and IEEE SMC 2011. Dr. Havens received his Ph.D. in Electrical and Computer Engineering from the University of Missouri, Columbia in 2010. Prior to joining Michigan Tech, he was an NSF/CRA Computing Innovation Postdoctoral Fellow at Michigan State University under Dr. Anil Jain. Before his Ph.D. work, he was an Associate Technical Staff member at MIT Lincoln Laboratory. His educational background includes an M.S. in Electrical Engineering (2000) and a B.S. in Electrical Engineering (1999), both from Michigan Technological University. His research focuses on pattern recognition and machine learning, signal processing, and sensor fusion, with specific expertise in fuzzy integrals, Choquet integration, community detection in networks, and heterogeneous data mining. Dr. Havens has made significant contributions to explainable AI, particularly through the application of fuzzy integrals to deep learning systems. His recent publications demonstrate strong activity in developing novel regularization techniques for fuzzy Choquet integrals, efficient algorithms for community detection, and similarity measures for interval data. Dr. Havens' research has been consistently funded by prestigious organizations including DARPA, NSF, US Navy, Office of Naval Research, National Geospatial-Intelligence Agency, Ford Motor Company, MIT Lincoln Laboratory, and numerous other government and industry partners. His current projects include significant grants for radar systems in the Great Lakes, generative modeling of satellite imagery, and algorithms for autonomous robot systems. Scientific Awards: 2014-15 Professor of the Year by IEEE Eta Kappa Nu, Beta Gamma Chapter Best Paper Award at FUZZ-IEEE 2012 Best Paper Award at IEEE SMC 2011 Best Student Paper Award Finalist (2018) IEEE Franklin V. Taylor Memorial Best Paper Award (2011) Dr. Havens has successfully mentored numerous graduate students, many of whom appear as co-authors on his publications. His research grants demonstrate strong leadership in coordinating multi-institutional collaborations with substantial funding. Beyond his academic work, Dr. Havens is also an active musician, playing bass in several bands including MUFJAC, FLOTUS, Defenestra, Skills of Ortega, Psylocubik, Triptych, Wheels of Fire, and Odibil libidO.
Dr. Hodjat Shiri is an Associate Professor in Civil Engineering at Memorial University of Newfoundland, holding the Wood Group Chair in Arctic and Harsh Environments. His research addresses Arctic offshore challenges, including ice-seabed interactions, subsea pipeline design, and reliability assessment. Shiri integrates machine learning with geotechnical modeling to predict iceberg impacts and pipeline performance. Recent work explores trenching techniques, layered seabed responses, and fatigue in riser systems. Publications emphasize practical solutions for energy infrastructure in climate-sensitive regions, combining numerical simulations with field data validation.
Dr. V.B. S. Prasath is a Professor in the Department of Biomedical Informatics at the University of Cincinnati and leads the Prasath Lab at Cincinnati Children's Hospital Medical Center (CCHMC). His research focuses on AI/ML-driven biomedical informatics, integrating genomics, imaging, and clinical data to address pediatric healthcare challenges. The lab collaborates with multiple institutions, including the Center for Pediatric Genomics (CPG) and the National Institutes of Health (NIH). Key research areas include: Medical imaging analysis (histopathology, MRI, ultrasound) Single-cell genomics (ATAC-seq, scRNA-seq, spatial transcriptomics) Multimodal data integration (scTriangulate framework) Antimicrobial resistance prediction (MGS2AMR) Recent work includes developing tools like maxATAC for transcription factor binding prediction and DeepImmuno for immunogenic peptide analysis. The lab has secured grants from NIH, Cystic Fibrosis Foundation, and Crohn's & Colitis Foundation.
Zhen Liu is an Adjunct Associate Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University. His research integrates multiphysics modeling, AI, and geotechnical engineering for intelligent infrastructure and system resilience. Teaching includes soil mechanics, foundation engineering, AI applications, and numerical simulations. Recent publications focus on machine learning in geosystems, pavement management, and computational methods.
Krzysztof Pytka is an Assistant Professor of Quantitative Macroeconomics at the University of Mannheim's Department of Economics since 2018. He holds a Ph.D. in Economics from the European University Institute (2017). His research focuses on household consumption dynamics, search theory, and computational economics, leveraging large microeconomic datasets and machine learning techniques. Current projects examine retail market frictions, post-job displacement earnings losses, and the macroeconomic implications of consumer behavior. Education: Ph.D. in Economics (2017) - European University Institute, Florence; Earlier degrees not specified. Research Areas: Macroeconomic modeling of heterogeneous agents Consumption patterns using scanner data Machine learning applications in structural econometrics Price dispersion mechanisms Papers emphasize empirical validation of theoretical models using novel datasets. Recent work on Covid-19 labor market impacts has been published in CEPR's policy-oriented journal. Teaching includes Ph.D.-level courses on quantitative macroeconomics and machine learning applications. Advising/Grants: No formal advisees listed in current materials. Research collaborations with Daniel Runge and Andreas Gulyas.
Sastry Pamidi is the Chair Professor of Electrical & Computer Engineering at Florida State University (FSU), serving as Associate Director of the Center for Advanced Power Systems (CAPS). He holds an MBA in Financial Management from FSU and a Ph.D. in Materials Chemistry from the University of Bombay. His research focuses on superconducting power systems, applied cryogenics, and advanced electrical insulation systems. Pamidi leads a multidisciplinary team at CAPS, collaborating with industry partners to develop superconducting devices and cryogenic technologies. His academic career spans over three decades, including roles at the National High Magnetic Field Laboratory and the University of Aberdeen. Pamidi is a Senior Member of IEEE and holds a Project Management Professional (PMP) certification. He has published 150 peer-reviewed papers and contributed to book chapters on superconducting power grid applications. Key projects include gaseous helium-cooled superconducting cables, fault current limiters, and cryogenically insulated power systems for electric aircraft and ships. Research interests emphasize AC loss measurements, novel insulation materials, and cryogenic thermal management. Pamidi’s work addresses challenges in electric transportation, energy storage, and high-power systems. His team explores hybrid cryogenic systems, additive manufacturing for insulation components, and fault detection using machine learning. Honors include sustained leadership in applied superconductivity through editorial roles and professional development initiatives. Current projects aim to enhance superconducting cable resilience, optimize cryostat designs, and develop HTS-based solutions for all-electric naval vessels and aviation systems.
Vincent Botta is a researcher at the University of Liège, Belgium, specializing in bioinformatics and machine learning. His work bridges computational methods with genetic studies, particularly focusing on Genome-Wide Association Studies (GWAS) using Random Forest algorithms. He holds a PhD in Computer Science (2008-2013), a Master of Advanced Studies in Computer Science (2006-2008), and a Master in Computer Science (2002-2006) from the same institution. Education : PhD in Computer Science, Master of Advanced Studies, and Master in Computer Science from the University of Liège. Experience : Data Scientist at Kensu.io (2016-present), Cytomine.be (2014-present), University of Liège (2006-present), and former roles at Diagenode (2015-2016) and Antibody-Adviser.org (2011-present). His research applies machine learning to genetic data, with publications analyzing SNP correlations, haplotype blocks, and supervised learning for disease risk prediction. Later works extend to pharmacological applications in asthma models. His technical skills include Python, C++, and data mining, while personal interests span web technologies, photography, and visual communication. Vincent contributes to open-source projects like Cytomine.be and co-founded Antibody-Adviser.org. His publications highlight interdisciplinary work between computer science and biomedical research, though no scientific awards are explicitly mentioned.
Russ Schumacher is a Professor in the Department of Atmospheric Science at Colorado State University (CSU) and serves as Colorado State Climatologist and director of the Colorado Climate Center. He holds a B.S. in Meteorology and Humanities from Valparaiso University (2001), and M.S. (2003) and Ph.D. (2008) in Atmospheric Science from CSU. His career includes a postdoctoral fellowship at NCAR (2008–09), an Assistant Professorship at Texas A&M University (2009–11), and promotion to Professor at CSU in July 2022. He currently edits the Monthly Weather Review and leads research on mesoscale meteorology, precipitation extremes, and societal impacts of weather. Research focuses on mesoscale convective systems, flash floods, and climate change in Colorado. His work is funded by NSF, NASA, NOAA, and COMET. Awards include the NSF CAREER Award (2010), AMS Clarence Leroy Meisinger Award (2020), and the 2012 Outstanding Professor of the Year. He advises numerous students, including Ph.D. recipients Allie Mazurek (2024), Eric James (2023), and Casey Zoellick (2025), and leads the Precipitation Systems Research Group. His team develops tools like the CSU-Machine Learning Probabilities (MLP) forecasting system, highlighted for operational weather prediction advancements. As State Climatologist, Schumacher oversees climate data for Colorado, including record-keeping and public outreach. His lab collaborations span international field campaigns (e.g., RELAMPAGO in Argentina) and interdisciplinary projects on wildfire meteorology (WE-CAN). Teaching interests include mesoscale meteorology, severe weather, and numerical forecasting.
Dr. Georg Mayr is an Associate Professor in the Department of Atmospheric and Cryospheric Sciences (ACINN) at the University of Innsbruck. His research focuses on atmospheric dynamics, lightning prediction, foehn wind patterns, and climate modeling. He leads projects such as LightningPredict and Profcast , addressing environmental risks like upward lightning at wind turbines and atmospheric deserts' impact on extreme weather events. Mayr's work integrates machine learning and statistical methods for probabilistic forecasting, including lightning processes and thunderstorm environments. He has co-authored over 50 peer-reviewed articles since 2016, emphasizing interdisciplinary approaches to climate and meteorological challenges. His team collaborates on tools like foehnix for scalable diagnostics and Cholesky-based regression models for multivariate data analysis. Key contributions include long-term foehn wind reconstructions, spatial lightning climatologies, and risk assessments for tall structures. Mayr's research supports practical applications in renewable energy safety and airport low-visibility forecasts. His lab, ACINN, fosters collaboration across atmospheric science and cryospheric studies.
