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Tenley ConwayView profile

Professor

• Urban Forest Governance
• Human-Environment Interactions
• Geospatial Analysis
• +5 more

Professor Tenley Conway is a faculty member at the University of Toronto Mississauga within the Department of Geography, Geomatics and Environment. Her research focuses on urban forest governance, human-environment interactions, and geospatial analysis of urban ecosystems, with specific interests in environmental justice, green infrastructure, and socio-ecological systems. PhD in Geography from Rutgers University MSc in Geography from Rutgers University BSc in Natural Resources from Cornell University Conway's work integrates environmental geography, urban ecology, and landscape ecology to examine urban forest governance structures, resident engagement with green infrastructure, and the ecological impacts of policy decisions. She employs diverse methodologies including GIS, remote sensing, statistical analyses, and community surveys to address questions about urban tree distribution equity, invasive species management, and the effects of urban greening initiatives. Recent publications demonstrate her focus on quantifying urban forest diversity using national datasets, developing cost-effective canopy monitoring techniques through Landsat imagery, and analyzing the ethical frameworks of arboricultural practices. She has contributed to understanding how invasive pests reshape distributional justice in urban forests and the relationship between tree proximity and subjective wellbeing across Canadian and Australian cities. As associate editor of Urban Forestry and Urban Greening and board member of LEAF and EcoSpark, she actively participates in urban forestry governance discussions. Her HOUSE Laboratory examines human-environment interactions across urban-suburban-exurban gradients, emphasizing community engagement in sustainable urban forest management.

• Structural Mechanics
• Fracture Mechanics
• Fatigue of Materials
• +9 more

Andrea Carpinteri is a Full Professor of Structural Mechanics in the Department of Engineering and Architecture at the University of Parma, Italy. He has been a leading figure in the fields of fracture mechanics, fatigue of materials, and structural integrity for over three decades. He previously served as an Associate Professor at the University of Parma (1994–2000) and the University of Padua (1988–1994). He earned his degree in Civil Engineering from the University of Bologna in 1980 with top honors (110/110 cum laude). His academic journey reflects a strong foundation in structural engineering, which evolved into a research career focused on material failure mechanisms. His research interests include fracture mechanics , multiaxial fatigue , size effects in structures , fatigue crack propagation , and constitutive modeling of traditional and advanced materials . He has developed influential fatigue criteria, such as the Carpinteri-Spagnoli (C-S) criterion, and applied fractal theories to model fatigue behavior. His work bridges theoretical modeling, numerical simulation, and experimental validation. The 15 most recent publications highlight a consistent focus on multiaxial fatigue , fretting fatigue , crack path modeling , and energy-based life assessment . His research spans metallic alloys (e.g., Inconel 718, Al 7075), composites, and natural fiber-reinforced materials, often using critical plane and damage mechanics approaches. Recent works emphasize random loading, spectral analysis, and innovative modeling of crack morphology. ESIS Fellow (2012) IGF Honorary Member (2017) Publons Reviewer Award (Top 1% in Engineering, 2018) Multiple 'Most Active Reviewer Awards' (2013–2018) Winner of the BANDO OPEN-UP Prize (2018) International Prize on Renewable Energy Projects (2011) He has supervised numerous PhD students and collaborated with researchers globally. He has been the Principal Investigator or Local Coordinator of multiple national and EU-funded research projects, including H2020 and MIUR grants. His editorial leadership includes serving as Guest Editor for 26 special issues and as a board member of 10 international journals. He chairs TC3 (Fatigue) of ESIS and has organized over a dozen international conferences on fatigue and fracture. He leads research in structural integrity, particularly through his involvement in the Laboratory of Materials and Structures Testing at the University of Parma. His team focuses on both theoretical advancements and practical applications in civil, mechanical, and aerospace engineering.

Ruben ValbuenaView profile

Professor

• Remote Sensing
• LiDAR
• Forest Structure
• +7 more

Ruben Valbuena is a Professor at Bangor University, affiliated with the School of Natural Sciences and the Department of Forest Sciences. He is also recognized as an Honorary Professor, reflecting his significant contributions to forest science and remote sensing. His research leverages big data, particularly LiDAR and remote sensing, to analyze ecosystem structure, carbon stocks, and habitat suitability at landscape and community levels. His research interests lie at the intersection of ecology, remote sensing, and forest dynamics. He has pioneered the adaptation of Lorenz curves to quantify forest structural complexity, a methodological innovation recognized with the IUFRO 2019 Outstanding Doctoral Research Award. His work spans tropical and boreal forests, focusing on aboveground biomass estimation, forest disturbance detection, and ecosystem service modeling. He actively integrates airborne and satellite data, including NASA's GEDI mission, to develop scalable ecological models. The recent publications (2017–2023) reflect a consistent focus on LiDAR applications, forest structure, biomass modeling, and ecological theory. Key themes include the fusion of remote sensing technologies, the development of structural complexity indices, and the application of machine learning in forest inventory. His research increasingly addresses global challenges such as carbon monitoring, urban greening, and sustainable forest management. His scientific honors include: International Union of Forest Researchers (IUFRO) Outstanding Doctoral Research Award (2019) Early-Stage Researcher Prize “José L. Labrandero” (2017) Valbuena leads multiple research projects, including the European Forest Information Network (EFINET), AMAZECO (focused on Amazon ecosystem structure), and a KESS II PhD project with Forest Research. He has supervised at least one research student and maintains active collaborations across Europe and the Americas. His work is supported by grants from national and international funding bodies, enabling high-impact research in forest ecology and remote sensing. He is associated with key research initiatives and labs focused on remote sensing and forest ecosystem dynamics, contributing to large-scale projects that integrate field data with satellite and airborne observations.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

Christopher JanjigianView profile

Assistant Professor

• Mathematical Physics
• Probability Theory
• Random Walks in Random Environments
• +3 more

Christopher Janjigian is an Assistant Professor in the Department of Mathematics at Purdue University, specializing in probability theory with a focus on random walks in random environments, KPZ universality class phenomena, and stochastic partial differential equations. He holds a Ph.D. from the University of Wisconsin-Madison (2016) and has held postdoctoral positions at Université Paris Diderot (2016–2017) and the University of Utah (2017–2020). His research explores the infinite volume structure of random walks in random potentials and connections to models like the Kardar-Parisi-Zhang equation. He organizes the Purdue University Probability Seminar and has taught advanced courses including Stochastic Processes, Probability Theory, and Stochastic Calculus. His work bridges theoretical probability with applications in mathematical physics, emphasizing geometric and dynamic properties of stochastic systems. Recent research focuses on geodesic structures in percolation models, Busemann functions, and ergodic properties of directed polymer models. His articles investigate topics like exit point bounds in last-passage percolation and synchronization in the KPZ equation, contributing to the understanding of universal behaviors in stochastic systems.

Eyal LubetzkyView profile

Professor

• Probability Theory
• Combinatorics
• Graph Theory
• +3 more

Eyal Lubetzky is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University, and an affiliate Associate Professor at the University of Washington's Mathematics Department since 2008. He holds a PhD from Tel-Aviv University (2007), supervised by Noga Alon, and completed postdoctoral research at Microsoft Research. His research focuses on Probability Theory, Combinatorics, and their applications to statistical physics and theoretical computer science. Key areas of research include phase transitions in statistical mechanics, mixing times of Markov chains, and probabilistic combinatorics. He has received prestigious awards such as the AMS Centennial Fellowship (2016–2017), Rollo Davidson Prize (2013), and fellowships from the American Mathematical Society and Institute of Mathematical Statistics. Lubetzky has mentored numerous interns and serves on editorial boards of journals like Probability and Mathematical Physics and Communications in Pure and Applied Mathematics . His teaching includes advanced courses on probability theory, stochastic processes, and spin glass dynamics. Collaborative work spans interdisciplinary topics, including percolation, random graphs, and the interplay between combinatorial structures and probabilistic models. His research outputs address foundational questions in statistical mechanics, such as cutoff phenomena in Glauber dynamics, interface fluctuations in Ising/Potts models, and the geometry of SOS surfaces. He has also contributed to understanding phase transitions in random media and the dynamics of complex systems.

Daniel W. ApleyView profile

Professor

• Industrial and engineering statistics
• Statistical learning and data mining
• Predictive analytics
• +7 more

Daniel W. Apley is Professor of Industrial Engineering and Management Sciences at the McCormick School of Engineering and Applied Science, Northwestern University, where he has served since 2003. He is Editor-in-Chief-Elect of Technometrics and previously Editor-in-Chief of the Journal of Quality Technology . He is also affiliated with Northwestern’s Master of Science in Machine Learning and Data Science Program. Education PhD Mechanical Engineering, University of Michigan, Ann Arbor MS Electrical Engineering, University of Michigan, Ann Arbor MS Mechanical Engineering, University of Michigan, Ann Arbor BS Mechanical Engineering, University of Michigan, Ann Arbor Research Interests Professor Apley is an industrial statistician whose work sits at the intersection of engineering modeling, statistical analysis, and predictive analytics. His major thrusts include statistical modeling of complex engineering and enterprise systems, machine learning for manufacturing data, quality engineering and Six Sigma methodologies, and computer-experiment–based design optimization under uncertainty. Recent applications span healthcare risk modeling, credit-risk analytics, materials microstructure prediction, and autonomous process control. Scientific Awards NSF CAREER Award IIE Transactions Best Paper Award (Quality & Reliability) Wilcoxon Prize for best practical application paper in Technometrics Teaching & Advising At Northwestern he teaches undergraduate courses in Statistical Methods for Quality Improvement, Introductory Statistics, and Statistical Tools for Data Mining, as well as graduate courses in Predictive Analytics, Engineering Applications of Data Mining, and Intermediate Statistics. His research has been supported by numerous industrial partners and federal agencies, underscoring a strong record of funded graduate and post-doctoral advising. Leadership & Service Beyond editorial roles, Professor Apley has chaired the Quality, Statistics & Reliability Section of INFORMS and served as Director of the Manufacturing and Design Engineering Program at Northwestern, shaping interdisciplinary curriculum and research initiatives.

Denka KutzarovaView profile

Professor

• Functional Analysis
• Geometry of Banach Spaces
• Approximation Theory
• +3 more

Denka Kutzarova is a Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign. She holds an office in Altgeld Hall and specializes in functional analysis, particularly focusing on the geometry of Banach spaces and approximation theory. Her work includes studies on subsymmetric sequences, norms in Banach spaces, and greedy algorithms. Education: 1983 Ph.D. in Mathematics, University of Sofia, Bulgaria Research Interests: Dr. Kutzarova’s research explores advanced topics in functional analysis, including the geometric properties of Banach spaces, renorming theory, and the structure of infinite-dimensional spaces. She investigates properties like uniform convexity, uniform smoothness, and the interplay between these properties and operator theory. Her work also delves into approximation theory, greedy algorithms, and the construction of explicit matrices with restricted isometry properties for compressed sensing applications. Publications Overview: Her recent work focuses on subsymmetric sequences, 2-rotund norms, and metric embeddings. Key themes include the structural analysis of Banach spaces, properties of sequence spaces, and applications to transportation cost spaces. Her research bridges theoretical functional analysis with practical applications in approximation and matrix theory. Additional Contributions: Beyond academic work, she authored a series of novels titled Journey of the Guinea Pig , exploring themes of academia and cultural identity. She also contributes poetry, including works published in both Bulgarian and Russian.

Tom PalmerView profile

Senior Lecturer

• Biostatistics
• Genetic Epidemiology
• Public Health
• +3 more

Dr. Tom Palmer is a Senior Lecturer in Biostatistics Applied to Genetics at the University of Bristol's Bristol Medical School. He holds advanced degrees including a PhD and specializes in biostatistical methodologies for genetic and epidemiological research. His work integrates Mendelian randomization techniques and large-scale observational studies, particularly focusing on cardiovascular diseases, diabetes, and the impacts of the SARS-CoV-2 pandemic. Palmer's research emphasizes causal inference and methodological innovations in public health, leveraging electronic health record systems like OpenSAFELY to analyze vaccine efficacy, post-infection complications, and chronic disease outcomes. His studies often involve large cohorts (e.g., 16 million people in England) and address critical questions about the long-term health effects of infectious diseases and vaccination strategies. Education: PG Cert, PhD, MSc, BSc (specific degree institutions not explicitly stated) Affiliations: Bristol Medical School (PHS), OpenSAFELY initiative His research interests include: Mendelian randomization and causal inference methods Genetic epidemiology of obesity, cardiovascular disease, and diabetes Evaluation of vaccine effectiveness and safety Public health responses to pandemics Palmer collaborates closely with leading epidemiologists and statisticians at the University of Bristol, including Professors George Davey Smith, Kate Tilling, and Jonathan Sterne. His work has significant implications for healthcare policy and clinical decision-making in post-pandemic and chronic disease management contexts.

Glen McGeeView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Environmental Health
• +3 more

Glen McGee is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a PhD in Biostatistics from Harvard University and a BScH in Mathematics from Queen's University. His research focuses on developing statistical tools for epidemiology, environmental health, and health policy, with a particular emphasis on environmental mixture analysis, cluster-correlated data modeling, and outcome-dependent sampling methodologies. Education : PhD in Biostatistics, Harvard University BScH in Mathematics, Queen's University Research Interests : McGee advances methodologies for analyzing complex environmental mixtures and their health impacts, including incorporation of biological knowledge into statistical frameworks. His work addresses challenges in multigenerational studies, informative cluster sizes, and measurement error correction in case-crossover designs. Key application areas include hospital profiling, exposure misclassification, and longitudinal health data analysis. Research Trends : His publications emphasize Bayesian methods for mixture modeling, innovative sampling strategies for clustered data, and causal inference techniques. Notable contributions include frameworks for integrating biological pathways into environmental health analyses and developing efficient sampling approaches for healthcare performance evaluation. Grants & Labs : McGee collaborates on projects involving CMS data applications and maintains GitHub repositories like hospODS for hospital profiling methodology implementation. His work bridges statistical theory with practical public health applications, particularly in environmental epidemiology and healthcare analytics.

Ping ZhongView profile

Associate Professor

• Free Probability
• Random Matrix Theory
• Operator Algebras
• +2 more

Ping Zhong is an Associate Professor in the Department of Mathematics at the University of Houston. He holds a Ph.D. from Indiana University Bloomington and joined UH in 2024 after serving as Assistant Professor at the University of Wyoming (2018-2024) and completing postdoctoral work at the University of Waterloo. His research explores free probability theory, operator algebras, and applications of random matrix theory to high-dimensional statistics. Educational background includes: Ph.D. Mathematics, Indiana University (2014) M.S. Mathematics, Peking University (2008) B.S. Applied Mathematics, Huazhong University of Science and Technology (2005) Research focuses on fundamental mathematical structures in probability with emerging applications in quantum physics and statistical learning. Current investigations bridge theoretical frameworks with computational approaches for high-dimensional data analysis. Recent publications demonstrate consistent contributions to spectral analysis of random operators and convergence properties in non-commutative probability spaces. Works frequently appear in premier mathematics journals including Transactions of the AMS and Journal of the European Mathematical Society.

Gary W. RitterView profile

Professor

• Education Policy
• Teacher Quality and Training
• School Discipline Practices
• +3 more

Gary W. Ritter is the Dean and Professor of Education at Saint Louis University's School of Education. He holds a Ph.D. in Education Policy and Leadership from the University of Pennsylvania, an M.A. in Public Policy and Management from the Wharton School, and additional degrees from the University of Manchester and John Carroll University. His research focuses on education policy, including teacher quality, school discipline disparities, and the impacts of charter schools and college access programs like the El Dorado Promise Scholarship. Ritter has conducted randomized experiments evaluating interventions such as college campus visits and has authored influential studies on disciplinary policies and equity in Arkansas schools. Education: Ph.D., Education Policy and Leadership, University of Pennsylvania M.A., Public Policy and Management, University of Pennsylvania (Wharton) M.A., Social Policy, University of Manchester B.A., Finance, John Carroll University Key Research Areas: Access to postsecondary education for low-income students Teacher pay, training, and retention strategies School discipline reform and racial disparities Charter school policies and integration outcomes Publications: Over 50 peer-reviewed articles and book chapters on education policy topics Lead author of studies on the El Dorado Promise Scholarship and KIPP schools Grants & Collaborations: Extensive work with educational institutions and policymakers Focus on translating research into actionable policy recommendations

• Combinatorial group theory
• Geometric semigroup theory
• Algorithmic problems in algebra
• +6 more

Robert D. Gray is a Professor of Mathematics at the School of Mathematics, University of East Anglia. His research focuses on combinatorial and geometric group and semigroup theory, algorithmic problems in algebra, decidability, homological finiteness properties, and group actions on graphs and topological spaces. EPSRC Research Fellow Editorial Board: International Journal of Algebra and Computation Available for PhD supervision in semigroup and inverse monoid theory His recent work explores: Topological finiteness properties of monoids Undecidability in one-relator inverse monoids Algorithmic properties of inverse monoids Maximal subgroups in special inverse monoids Key article trends include: Geometric and algorithmic aspects of inverse monoids Homological properties of semigroups Connections between group theory and semigroup theory Applications to graph theory and automata Scientific Awards: EPSRC Fellowship EP/V032003/1 EPSRC grant EP/N033353/1 EPSRC Postdoctoral Fellowship EP/E043194/1 Contact: Room S1.29, School of Mathematics, University of East Anglia, Norwich NR4 7TJ. Email: Robert.D.Gray@uea.ac.uk . Phone: +44 1603 591443.

Jonah GasterView profile

Assistant Professor

• Topology
• Geometric Group Theory
• Combinatorial Topology
• +1 more

Jonah Gaster is an Assistant Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee, where he also serves as the Program Coordinator for the Topology Research Group and Colloquium Chair. His research focuses on mathematical topology, geometric group theory, and combinatorial structures in low-dimensional topology. His research explores the interplay between combinatorial methods and geometric structures, with specific interests in curve systems on surfaces, harmonic maps between manifolds, and the computational aspects of topological invariants. Recent work investigates combinatorial representations of geometric objects and their applications in low-dimensional topology. Analysis of Dr. Gaster's recent publications reveals a consistent focus on topological combinatorics, surface embeddings, and geometric group theory. His work bridges discrete mathematics with continuous geometric structures, particularly in the contexts of hyperbolic geometry and curve complexes. He leads the Topology Research Group at UWM, fostering collaborative investigations into fundamental questions in geometric topology and discrete mathematics.

Vladimir PodolskiiView profile

Associate Professor

• Computational complexity
• logical foundations of computer science
• tropical geometry

Vladimir Podolskii serves as an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering and holds a concurrent appointment in the Department of Mathematics within the School of Arts and Sciences. His academic work bridges theoretical computer science and mathematical foundations, with a focus on computational boundaries and algebraic structures. He completed his doctoral studies at Lomonosov Moscow State University in Moscow, Russia, receiving his PhD in 2009. Podolskii's research program centers on computational complexity theory, examining the inherent difficulty of computational problems through decision trees and threshold functions. His investigations extend to the logical underpinnings of computation and the application of tropical geometry in optimization contexts. This interdisciplinary approach connects discrete mathematics with theoretical computer science frameworks. His 2022 publications reveal concentrated exploration of decision tree efficiency for threshold functions and classification systems for ontology-mediated queries, demonstrating methodological rigor in analyzing computational limits and knowledge representation structures. Podolskii actively mentors graduate researchers through Dissertation Research courses while teaching core curriculum including Algorithms and specialized seminars on computational complexity toolkits, shaping the next generation of theoretical computer scientists.