James Rosindell is a Professor of Biodiversity Theory at Imperial College London's Department of Life Sciences (Silwood Park), part of the Faculty of Natural Sciences. He leads the Computational Methods in Ecology and Evolution (CMEE) program and co-founded the OneZoom charity to visualize the tree of life. His research focuses on biodiversity theory, conservation prioritization, and computational ecology. Research interests include: Neutral theory applications in biodiversity Phylogenetic diversity metrics Conservation biology and prioritization Ecological modeling His publications highlight innovations like the OneZoom tree visualization tool and frameworks for quantifying extinction risks. Key contributions address reptilian phylogenetic diversity conservation and microbial community dynamics under pollution.
Daniel Katz is an Assistant Professor in the School of Integrative Plant Science at Cornell University, specializing in plant ecology and aerobiology. His work focuses on linking ecological processes to public health outcomes, particularly through modeling airborne pollen concentrations and urban tree impacts. He holds a Ph.D. from the University of Michigan (2015) and a B.A. from Bard College (2007). His research integrates field studies, remote sensing, and Bayesian statistics to address ecosystem services and disservices. Key projects include assessing allergenic pollen in Detroit, modeling Ashe juniper pollen in Texas, and developing national-scale airborne pollen frameworks. Recent work explores urban greening’s role in heat mitigation and health equity. Publications span interdisciplinary topics like pollen-asthma correlations, urban tree classification, and climate change effects on plant phenology. Media highlights include 2024 findings on pollen-driven asthma spikes and 2023 profiles of his faculty role at Cornell. Based in Bradfield Hall, his lab (Plants, Pollen and People) emphasizes collaboration with communities and stakeholders to translate ecological insights into actionable health strategies.
Brian Stone, Jr. is a Professor in the School of City & Regional Planning at Georgia Institute of Technology's College of Design. He directs the Urban Climate Lab, focusing on urban environmental planning, climate change adaptation, and heat island mitigation. His work integrates environmental science, urban design, and public health to develop strategies for climate-resilient cities. Stone's research is supported by the NSF, CDC, and EPA, with recent studies emphasizing extreme heat risk assessments, urban infrastructure resilience during compound climate events, and the health impacts of heat exposure. Education: Ph.D. and degrees in environmental management/planning from Duke University and Georgia Tech. Specialization includes urban form-climate interactions and climate justice. His book Radical Adaptation (2024) proposes transformative urban strategies for climate changed worlds, building on his earlier work honored with the 2012 Choice Award. Media coverage includes features in New York Times , Washington Post , and NPR. Key contributions include defining 'urban heat management,' modeling heat-wave/blackout compounding risks, and evaluating tree planting/cool roofing strategies. Current projects address climate viability thresholds in cities, spatial equity in heat exposure, and municipal adaptation frameworks. Grants support work on heat tolerance indices, indoor thermal resilience, and regional climate modeling. Awards include the 2012 Choice Award for The City and the Coming Climate . His lab's collaborations span academic, governmental, and NGO sectors. Stone advises on local and national climate policy, emphasizing actionable science for urban planners.
Elena De Angelis is a Tenured Associate Professor at the Polytechnic University of Turin, holding a position in the Interuniversity Department of Territorial Sciences, Project and Policies (DIST). She serves as Deputy Coordinator of the Planning and Design College and is a member of the Interdepartmental Centre R3C (Responsible Risk Resilience Centre). Additionally, she acts as Scientific Advisor to the European Association EPIC (European Photonics Industry Consortium). Her research focuses on applied mathematics, mathematical modeling in life sciences, and urban resilience. Key projects include the TReE initiative (2023-2025) for ecological economy transitions in Italian cities and prior EU-funded research such as SAFECITI (2014-2016) and EVACUATE (2013-2017). She teaches courses in mathematical analysis and territorial planning, contributing to doctoral programs in Civil and Environmental Engineering. Her publications emphasize system dynamics models, resilience assessment frameworks, and agent-based modeling for urban regeneration and environmental systems. Notable works include applications to winemaking regions (Douro Valley) and green gentrification dynamics. She advises PhD students on urban development models and collaborates with institutions like the R3C Centre and EPIC.
Dr. Anthony Onoja is a Research Fellow at the University of Surrey's School of Health Sciences, affiliated with the Centre of Excellence on Ageing (CEA). He holds a PhD in Data Science from Scuola Normale Superiore (Italy), an MSc in Mathematical Statistics from Pan African University/Kenyatta University (Kenya), and a First Class BSc in Statistics from the University of Jos (Nigeria). His work focuses on applying AI, data science, and statistical methods to health challenges, particularly in biomarker identification, patient stratification, and interpretable machine learning for chronic diseases. He currently contributes to the CEA's transdisciplinary research on healthy ageing, collaborating with national and global networks. Research interests include: Interpretable/explainable AI for medical decision-making Multimorbidity analysis using multi-omics data Biomarker validation reproducibility Genetic interactions in disease severity (e.g., COVID-19) Notable contributions include developing explainable models for myocardial infarction patient profiling, lipidomics biomarker reproducibility frameworks, and genetic severity prediction systems for infectious diseases. His work emphasizes translational research with clinical and public health relevance. Current research projects include bioinformatics analysis of the NURTuRE-CKD cohort and applications of domain knowledge integration in biomedical AI systems.
Georgia Peterson is an Adjunct Assistant Professor in the Department of Forestry at Michigan State University , affiliated with the College of Agriculture & Natural Resources and MSU Extension . Her work focuses on social forestry, community engagement, and applied forest management. She provides expertise in invasive species control, forest health, and citizen science initiatives. Education background not explicitly stated in provided texts. Active in developing educational resources like the Identifying Trees of Michigan guide and facilitating workshops on leadership and forest stewardship. Collaborates with conservation districts and leads innovative projects adapting citizen science to pandemic challenges. Key areas of engagement include: Invasive species management strategies Urban and community forestry practices Public education through workshops and digital tools Forest health diagnostics and long-term planning Her recent work emphasizes practical solutions for landowners and educators, combining scientific rigor with actionable advice for sustainable forest management.
Thijs van Ommen is an Assistant Professor in the Department of Information and Computing Sciences at Utrecht University. His research centers on causal inference, machine learning, and statistical methodologies for data science applications. He teaches courses in Advanced Machine Learning and core Machine Learning principles, emphasizing algorithmic foundations and real-world implementations. Research explores causal entropy, graphical models, and robust decision-making under uncertainty. Recent publications address causal bandits, information bottlenecks, and efficient algorithms for structural equation models. Van Ommen actively presents at conferences like SIAM Applied Algebraic Geometry and co-chairs sessions at INFORMS. His work bridges theoretical machine learning with practical challenges in causal discovery and adaptive systems.
Dr. AJ (Ad) Feelders is an Associate Professor at Utrecht University, affiliated with the Department of Information and Computing Sciences within the Faculty of Science. His primary research focuses on Data Mining, Machine Learning, and Applied Data Science, with a particular emphasis on algorithmic data analysis and fraud detection. He has organized major conferences like the Symposium on Intelligent Data Analysis (IDA 2020) and contributed to editorial roles for journals such as Intelligent Systems in Accounting, Finance & Management . Roles: Associate Professor, Department of Information and Computing Sciences Affiliations: Utrecht University, Faculty of Science His research spans diverse applications, including renewable energy analysis, Bayesian networks, and time series forecasting. Notable projects include developing methods for detecting critical events in energy production and creating graph embeddings for historical data analysis. He has also worked on fraud detection in international shipping and real-time outlier detection in water sensor data. Feelders has authored or co-authored over 80 publications, including influential works on exceptional model mining, isotonic regression, and monotonic classification. His contributions bridge theoretical advancements with practical applications in domains like environmental science, healthcare, and digital humanities.
Mahdi Roozbahani is a Lecturer at Georgia Tech's School of Computational Science and Engineering and School of Computing Instruction. He earned his Ph.D. in Computational Science and Engineering from Georgia Tech (2019), along with three master's degrees in Civil and Environmental Engineering (Georgia Tech), Geotechnical Engineering (University Putra Malaysia), and a bachelor's from Iran University of Science and Technology. His research focuses on modeling/simulation, network analysis, and machine learning. Roozbahani founded Filio, a cloud-based photo management platform through Create-X incubator. He has published over 20 peer-reviewed papers and teaches courses like Machine Learning and Data Analytics. Awards: Jean-Lou Chameau Research Excellence Award (twice) NSF IRES Fellowship (Ecole des Ponts, Paris) Top Five Featured Paper (Materials Journal, 2017) 2021 Class of 1934 CIOS Honor Roll for Teaching Excellence He advises multiple students in OMSCS/OMSA programs and leads research in computational methods for disaster assessment, educational technology, and granular material analysis. His work bridges academia and industry via Filio's tech innovations.
Gabriela Nunez-Mir is an Assistant Professor in Biological Sciences at the University of Illinois Chicago's College of Liberal Arts and Sciences. Her research leverages large-scale datasets to investigate patterns of biological invasions and ecosystem change. A 2024 Walder Foundation Biota Award recipient, she develops computational methods for ecological literature synthesis and invasion forecasting. Research Focus: Macroscale analysis of biological invasions using machine learning, spatial modeling, and meta-research approaches to address knowledge gaps in invasion ecology and conservation. Education: PhD Forestry and Natural Resources, Purdue University (2018) BS Biology/Biotechnology & BA Environmental Studies, Worcester Polytechnic Institute (2013) Methodologies: Combines machine learning, Bayesian statistics, and geospatial analysis to study invasive species dynamics and ecosystem impacts across spatial scales.
Dr. Christie Godsmark is a Lecturer in the School of Public Health at University College Cork (UCC) and Academic Director of the MSc in Occupational Health (online) program. She is affiliated with UCC's Environmental Research Institute. Her research focuses on climate change impacts on human health, particularly in vulnerable populations such as informal settlement residents in Dar es Salaam and temperate climate regions like Ireland. She coordinates the CATCH project (Communication and Action through Tree-planting for Climate-Health), funded by the Irish Research Council. Her teaching includes developing a 5-credit Environmental Health module for the MSc program. Education: Doctorate in Human Thermoregulation (University of Portsmouth, 2016) MSc in Medicine (Cardiovascular Physiology) – University of Cape Town (2012) BSc Honours in Human Kinetics/Ergonomics – Rhodes University (2009) Bachelor of Arts in Psychology – Rhodes University (2008) Research Interests: Climate change and health co-benefits, heat-health vulnerability, environmental health advocacy, and Sustainable Development Goals integration. Her work emphasizes actionable solutions that align climate action with health equity and environmental sustainability. Grants & Projects: CATCH project: €4,808 from Irish Research Council (2020–2021) Development of climate adaptation frameworks for South African provinces and Irish temperate regions Awards: Honorary Lecturer at University of Cape Town (2018–2021). Advising & Grants: Leads UCC's MSc Occupational Health program and contributes to policy briefs for governments in Tanzania and South Africa. Active in the Irish Climate and Health Alliance, UCC's Athena SWAN committee, and Green Campus Forum. Labs/Teams: Collaborates with the Environmental Research Institute and international teams on climate health projects.
Hoang Nguyen is an Associate Professor in Cyber Security at Swansea University with research focus on automotive and autonomous systems security. Research Focus: Specializes in model-based verification methods and simulation techniques for security validation of cyber-physical systems. Current projects include VRBMAS, ACID, Ditto, QRNG for CAV, Secure CAV, and AutoCHERI funded by EPSRC, RSSB, InnovateUK, and Digital Catapult. Publication Trends: Recent work centers on automotive cybersecurity frameworks, threat modeling for connected vehicles, and formal verification techniques. Contributions include simulation-based risk assessment, attack-defense tree generation, and secure communication protocols for vehicular platoons.
William Rogers is a Professor and Associate Department Head for Graduate and Undergraduate Programs at Texas A&M University's Department of Rangeland Ecology and Management, within the College of Agriculture & Life Sciences. He holds a B.A. in Biology and Chemistry from Gustavus Adolphus College, a Ph.D. in Biology from Kansas State University, and postdoctoral training at Rice University. His research focuses on exotic plant invasions , conservation of rare plants , fire ecology , ecosystem resilience , and disturbance regimes . He has pioneered studies on Chinese Tallow Tree invasions and their evolutionary adaptations, as well as grassland restoration strategies involving fire and grazing management. Dr. Rogers has taught courses on Ecological Restoration and Fire Management. His work integrates field experiments with modeling approaches to address critical issues like invasive species control, drought impacts on ecosystems, and social-ecological system resilience. Current projects include endangered orchid conservation, fire's role in combating woody encroachment, and mitigating feral hog impacts. His research emphasizes interdisciplinary approaches, linking ecological processes with management practices. Notable contributions include the SheFire soil heating model for fire effects prediction and studies on prescribed fire efficacy under climate change scenarios. He collaborates with institutions like Rice University and South African research teams to develop globally applicable ecological solutions.
Sebastian Engelke is an Associate Professor at the Research Center for Statistics at the University of Geneva. His research focuses on extreme value theory, spatial statistics, graphical models, and machine learning applications in climate science and risk assessment. He holds an SNSF Eccellenza Grant and has contributed to foundational work on extremal graphical models and causal inference in heavy-tailed systems. Affiliations: University of Geneva, Research Center for Statistics Grants: SNSF Eccellenza Grant (2020–2025), Ambizione Fellowship (2015–2018) Education: PhD in Mathematics (2013, Georg-August-University of Göttingen), studies at UC Berkeley and University of Göttingen. Research Interests Engelke’s work bridges extreme value theory with modern machine learning, addressing challenges in climate extremes, risk quantification, and graphical model structures. Key areas include: Statistical modeling of multivariate extremes Causal discovery in heavy-tailed distributions Applications in environmental science and climate informatics Key Contributions Recent work includes advancements in extremal graphical models (via matrix completions), neural network approaches for extreme quantile regression, and validation methods for deep learning weather models. His research is published in top journals like Annals of Statistics and Journal of the American Statistical Association . Awards & Recognition Lambert Award (2021) Gumbel Lecture (2023) Fields Research Fellowship (2019) Teaching & Supervision Engelke teaches courses on machine learning, probability, and advanced topics in statistics at the University of Geneva. He supervises PhD students and postdocs in machine learning and extreme value theory. His students include Edoardo Vignotto (extremal random forests) and Nicola Gnecco (causal discovery in heavy-tailed models).
Piotr Zwiernik is a Professor Agregat in the Department of Economics and Business at Universitat Pompeu Fabra (UPF), Barcelona, and a member of the BSE Data Science Center. He is currently on leave from the Department of Statistical Sciences and the Department of Mathematics at the University of Toronto. His research bridges statistics, algebraic geometry, and machine learning, focusing on graphical models, covariance estimation, tensors, and algebraic methods in statistics. Research Interests: Graphical Models Covariance Matrix Estimation Convex Analysis Tensors Algebraic and Combinatorial Methods in Statistics Statistical Learning Theory His recent publications span high-impact journals such as the Annals of Statistics , Biometrika , and Journal of the Royal Statistical Society . The work demonstrates a consistent trend in developing mathematically rigorous frameworks for understanding dependence structures, latent variable models, and high-dimensional inference, often leveraging tools from algebraic geometry and convex optimization. Key themes include total positivity, tensor methods, and the geometry of statistical models. Scientific Recognition: Editorial Board Member, Journal of the Royal Statistical Society Series B (JRSS-B) Editorial Board Member, Biometrika Editorial Board Member, Scandinavian Journal of Statistics Editorial Board Member, Algebraic Statistics Zwiernik actively mentors students and is seeking PhD candidates with strong mathematical backgrounds at UPF and the Institute of Mathematics of UPC. He has led the development of several open-source R packages, including golazo , MTP2binary , and StructuralEM , facilitating research in graphical models and latent structures. His work is supported by a network of collaborations with leading statisticians and has significant theoretical and applied implications in data science. Laboratories and Research Groups: Statistics@UPF BSE Data Science Center
