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Alan EdelmanView profile

Professor

• Julia Programming
• High Performance Computing
• Linear Algebra
• +3 more

Alan Edelman is a Professor at the Massachusetts Institute of Technology (MIT), renowned for his work in high-performance computing, linear algebra, and random matrix theory. He is a co-creator of the Julia programming language, which emphasizes efficiency and versatility for scientific computing. His research integrates mathematics and computer science, focusing on numerical methods, parallel computing, and applications in fields like quantum control and climate modeling. Edelman leads projects such as Oceananigans.jl for geophysical fluid dynamics and Circuitscape for connectivity analysis in conservation biology. His academic contributions span theoretical advancements in matrix theory and practical implementations of computational tools. Edelman collaborates across disciplines, bridging scientific computing with machine learning and quantum physics. His work on differentiable programming frameworks and GPU acceleration has impacted both academic research and industry applications. Key Projects: Julia programming language, Oceananigans.jl, Circuitscape Research Themes: High-performance computing, numerical linear algebra, random matrices, scientific machine learning Affiliations: MIT’s Theory of Computation Community of Research, PI of multiple NSF-funded projects Edelman’s recent work emphasizes interdisciplinary applications, including climate policy modeling and automated materials discovery. His publications reflect a blend of foundational mathematics and cutting-edge computational techniques.

• Geographic Information Systems (GIS)
• Map Generalization
• Spatial Data Mining
• +3 more

Jan-Henrik Haunert is a Professor at the University of Bonn, affiliated with the Institute of Cartography and Geoinformatics within the Faculty of Civil Engineering and Geodetic Science. His research focuses on map generalization, geographic information systems (GIS), and optimization techniques in cartography. He has contributed to projects funded by the German Research Foundation (DFG), particularly in deriving scale-dependent representations of geographic data. His work emphasizes logical consistency, semantic accuracy, and quality assessment in geospatial data processing. Key research areas include land cover map generalization, spatial data integration (e.g., volunteered geographic information), and rule-based incremental generalization. He has developed methodologies leveraging mixed-integer programming and straight skeleton algorithms for cartographic automation. His publications span peer-reviewed journals like GeoInformatica and Photogrammetrie - Fernerkundung - Geoinformation , as well as conference proceedings such as AGILE and ACM-GIS. Haunert’s contributions address challenges in geospatial data quality, including polygon simplification and river dataset matching. He has also explored applications in vehicle localization and virtual reality systems like the GeoScope for urban planning. His work bridges theoretical GIScience with practical applications in cartography and geoinformatics.

• Machine Learning
• Time Series Analysis
• Space Weather Prediction
• +4 more

Soukaina Filali Boubrahimi serves as an Assistant Professor in the Computer Science Department within the College of Engineering at Utah State University. Her academic appointment is based in the SER 332 building located at 4205 Old Main Hill, Logan, UT 84322-0001. She maintains a research-active position with a focus on computational methods for complex temporal data analysis. Dr. Filali Boubrahimi's research program centers on time series analysis , machine learning , and space weather prediction , with particular emphasis on solar flare forecasting and counterfactual explanation systems. Her work bridges theoretical machine learning advancements with practical applications in heliophysics, hydrology, and social media analysis. The research portfolio demonstrates significant expertise in handling imbalanced temporal datasets, developing novel data augmentation techniques, and creating interpretable AI systems for critical prediction tasks. Analysis of her recent publication trajectory reveals consistent contributions to counterfactual explanation frameworks for time series data (Info-CELS, M-cels, ACTS), space weather prediction systems (solar flare and energetic particle event forecasting), and generative modeling approaches (AVATAR, ChronoGAN). Her work frequently addresses the challenges of severely imbalanced datasets through contrastive learning and sophisticated preprocessing techniques, demonstrating methodological innovation in handling rare but critical space weather events. While no specific awards are documented in the available information, her research program appears substantial based on the volume and quality of recent publications spanning multiple high-impact domains. The research demonstrates strong interdisciplinary connections between computer science, space physics, and environmental science. Her laboratory activities focus on developing machine learning frameworks for temporal data analysis, with particular attention to space weather prediction systems. The research group appears to specialize in creating robust models for rare event prediction, explainable AI systems for time series classification, and novel data augmentation techniques for imbalanced temporal datasets. Current projects likely include the development of multimodal fusion approaches for solar energetic particle prediction and spatio-temporal modeling for hydrological applications.

Brigitte PientkaView profile

Professor

• Type theory
• Programming languages
• Logical frameworks
• +7 more

Brigitte Pientka is a Full Professor in the School of Computer Science at McGill University, where she leads the Computation and Logic group. She received her PhD from Carnegie Mellon University in 2003, and previously studied at the University of Edinburgh and Technical University of Darmstadt. Her educational background includes: PhD from Carnegie Mellon University (2003) Studies at the University of Edinburgh Studies at Technical University of Darmstadt Dr. Pientka's research focuses on developing theoretical and practical foundations for building and reasoning about reliable, safe software systems. She combines theoretical research on logical foundations of computer science in programming languages and verification with system building. Her work spans logics (classical and non-classical), type theory, theorem proving, logic and functional programming, and logical frameworks. She has made significant contributions to the field of contextual types and mechanized metatheory. Her recent publications demonstrate a strong trend toward modal type theory, contextual types, and session types, with applications to functional programming, verification, and meta-programming. She has made significant contributions to the Beluga system, which explores how to combine functional programming with dependently-typed, higher-order data specified in the logical framework LF. Her work often bridges theoretical foundations with practical implementations for mechanized reasoning. Dr. Pientka has received several notable awards: Humboldt Fellowship Test of Time Award @ PPDP'18 for 'Programming with proofs and explicit contexts' Best student paper award at ICLP'03 She is actively involved in mentoring and academic service, having served as PC Chair for ICFP'24, CPP'23 and CPP'24, and as General Chair for POPL'20. She is an Executive Editor of Logical Methods in Computer Science and serves on the editorial boards of the Journal of Functional Programming and ACM Transactions of Computational Logic. She leads the Computation and Logic group at McGill University, which focuses on developing theoretical and practical foundations for reliable software systems through research in logical frameworks, type theory, and programming language design.

Brigitte PientkaView profile

Professor

• Programming Languages
• Verification
• Type Theory
• +6 more

Brigitte Pientka is a Professor at McGill University's School of Computer Science , where she leads the Computation and Logic group . She earned her PhD from Carnegie Mellon University in 2003 and previously studied at the University of Edinburgh and Technical University of Darmstadt. Education PhD in Computer Science, Carnegie Mellon University (2003) University of Edinburgh Technical University of Darmstadt Research Interests Her work focuses on the theoretical and practical foundations for building reliable software systems, combining logic, type theory, and verification with system-building. Key areas include: Type Theory and Dependent Types Logical Frameworks and Mechanized Metatheory Session-Typed Concurrency and Linear Logic Metaprogramming and Contextual Type Systems Theorem Proving and Formal Verification Functional Programming and Language-Based Security Professional Roles She has served as: PC Chair for ICFP'24 and CPP'24 General Chair for POPL'20 Executive Editor of Logical Methods in Computer Science Steering Committee Member for LICS, POPL, and ESOP Scientific Awards Dr. Pientka has received: Test of Time Award at PPDP'18 Humboldt Fellowship for research at MPI-SWS, Germany Labs & Teams She actively develops the Beluga programming language , a tool for mechanizing meta-theory proofs and type-driven program manipulation.

Fei ChiangView profile

Associate Professor

• Data Management
• Data Quality
• Data Privacy
• +6 more

Dr. Fei Chiang is an Associate Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her research focuses on data management , with emphasis on data quality, data privacy, information extraction , and contextual data cleaning . She has collaborated with IBM Global Services and Microsoft Research on improving data quality in enterprise systems. Key research themes include graph databases , temporal data analysis , and privacy-aware data processing Recent publications explore federated learning , SQL understanding in LLMs , and temporal graph constraints Industry collaborations with IBM Toronto Lab and Microsoft Research have led to innovations in data cleaning automation and semantic analysis. Her work bridges database theory with machine learning applications in healthcare inventory optimization and flight reliability prediction.

Nadine KetelView profile

Associate Professor

• Labor Economics
• Educational Economics
• Economics of Crime
• +3 more

Dr. Nadine Ketel is an Associate Professor in Economics at Vrije Universiteit Amsterdam and Research Affiliate at Gothenburg University, IZA, and CEPR's Labour Economics Programme. She holds a PhD in Empirical Studies in Labor and Education Economics from the University of Amsterdam (2016). Her research focuses on labor economics, educational economics, and the economics of crime, primarily using natural and field experiments. Education: PhD in Economics, University of Amsterdam (2011-2015) MSc in Economics, University of Amsterdam (not explicitly dated) Research Interests: Dr. Ketel examines how educational policies, labor market dynamics, and crime victimization interplay. Her work employs experimental methods to evaluate policy impacts, such as admission policies' effects on educational equity and criminal victimization's socioeconomic consequences. Recent studies include spillover effects in educational choices and age-related victimization profiles. Grants & Awards: NWO Veni Grant (2024-2027): €280,000 for education admission policy research Vetenskapsrådet Grant (2018-2020): SEK 4.8M for crime victimization study Joop Hartog Thesis Award (2018) and Best Master's Thesis Award (2009) H. Gregg Lewis Prize (2024) for Journal of Labor Economics paper Teaching & Projects: Teaches Empirical Economics and Introduction to Econometrics. Leads the NWO-VENI project on educational equality and collaborates on NWO-VICI initiatives improving human capital allocation. Active in policy-oriented research, including welfare-to-work experiments and healthcare market analysis. Labs & Teams: Affiliated with Tinbergen Institute and contributes to interdisciplinary collaborations in labor and education economics. Engages with policy institutions like IZA and CEPR to translate research into practical solutions.

Eugenia Nijgh de Sampayo GarridoView profile

Research Fellow

• Marine Ecology
• Coral Reef Studies
• Symbiotic Relationships
• +3 more

Dr. Eugenia Nijgh de Sampayo Garrido is a Research Fellow at the University of Queensland 's School of the Environment within the Faculty of Science . Her research focuses on coral reef ecology , particularly the symbiotic relationships between corals and Symbiodinium algae, and how these associations influence reef resilience under climate change . Education : PhD in Coral Symbiosis (University of Queensland, 2008) Key Research Areas : Symbiotic flexibility in climate-stressed corals Latitudinal range dynamics of coral communities Larval recruitment and settlement ecology Microbial community interactions in reef systems Publication Trends show consistent focus on coral-Symbiodinium interactions (2008-2023), with recent work on marine heatwave impacts (2023), microbiome plasticity (2020), and adaptive mechanisms in marginal reef populations (2017). She collaborates extensively with Prof. John Pandolfi 's lab and contributes to PeerJ consensus studies (2023). Supervision : Available for supervision, with completed roles as associate advisor for three PhD projects (2019-2022) examining coral range dynamics , early life history interactions , and maternal investment trade-offs .

• Evolutionary Biology
• Phylogenetics
• Ecology
• +7 more

Luke J Harmon serves as Professor in the Department of Biological Sciences within the College of Science at the University of Idaho, with additional appointments in the Initiative for Bioinformatics and Evolutionary Studies, Institute for Interdisciplinary Data Sciences, and Office of Research and Economic Development. Education: PhD in Evolution, Ecology, and Population Biology (2005) from Washington University in St. Louis BS in Zoology (1998) from Iowa State University Research Focus: Harmon's work integrates phylogenetic comparative methods with computational biology to investigate adaptive radiations , trait evolution , and biogeographic patterns . His research bridges micro-macroevolutionary scales through innovative statistical modeling, particularly in evolutionary medicine applications and speciation dynamics. As developer of key R packages ( rwty , treeducken , SSARP ), he advances methodological frameworks for analyzing evolutionary rates and character correlations. Publication Trends: Recent work (2022-2025) demonstrates increasing emphasis on software tool development for phylogenetic analysis, with significant contributions to evolutionary oncology, fire-adapted trait evolution in conifers, and niche theory applications in adaptive radiations. His publications consistently address methodological challenges in modeling rate heterogeneity, species delimitation, and cross-scale evolutionary processes. Research Infrastructure: Harmon leads the Harmon Lab, which operates within the University of Idaho's Initiative for Bioinformatics and Evolutionary Studies. The lab focuses on computational approaches to evolutionary questions, leveraging high-performance computing resources and interdisciplinary collaborations through the Institute for Interdisciplinary Data Sciences.

Fan WenfeiView profile

Professor

• Database systems
• Database theory
• Big data
• +3 more

Professor Wenfei Fan is a Chair of Web Data Management at the University of Edinburgh since 2006. He holds adjunct roles at Huawei Edinburgh Research Laboratory and the International Research Center on Big Data at Beihang University. His research focuses on database systems, theory, big data, data quality, and constraint applications. He is a Fellow of the ACM and Royal Society of Edinburgh, and recipient of the ERC Advanced Grant (2015). His work includes foundational contributions to XML constraints, data quality management, and graph databases, with over 160 top-tier publications and 8 patents. Education: BSc and MSc from Peking University (1985, 1988); PhD from the University of Pennsylvania (1999). Professional Roles: Editor for TODS, TCS, VLDBJ, and TBD; PC Chair for PODS, CIKM, APWeb, and others. Key awards include the Roger Needham Award (2008), Yangtze River Scholar (2007), and multiple best paper awards at SIGMOD, PODS, VLDB, and ICDE. He has led over 15 grants totaling €6M and advised students who all received top conference awards. His labs and initiatives drive industry collaboration, with deployed solutions at Huawei for big data query optimization.

• Nanotechnology
• Quantum Physics
• Surface Science
• +3 more

PD Dr. Thilo Glatzel is a Research Associate and Lecturer at the Department of Physics, University of Basel. He is affiliated with the Faculty of Humanities and Natural Sciences and the FG Meyer research group. His roles include course coordination and student advising within the study/teaching division. Research Interests : Glatzel's work focuses on nanoscale phenomena, particularly friction and lubrication at interfaces, quantum behaviors in molecular systems, and the application of advanced microscopy techniques like atomic force microscopy (AFM) and Kelvin probe force microscopy (KPFM). His research explores superlubricity in 2D materials, charge redistribution in molecular assemblies, and quantum spin dynamics on superconducting surfaces. Publications Overview : His recent work highlights studies on heterostructure interfaces (e.g., Au/MoS2), molecular dimerization on catalytic surfaces, and nonlinear dynamics in moiré systems. These studies bridge fundamental physics with applications in nanotechnology and materials science. Teaching & Coordination : Glatzel oversees course coordination and student advising, contributing to both bachelor's and doctoral programs in physics. He is involved in structuring experimental and theoretical modules, including advanced laboratory courses and the Honors Track. Labs & Groups : His research aligns with the Department's strengths in quantum computing (via the NCCR SPIN initiative) and nanoscience (Swiss Nanoscience Institute).

Marjorie B. McElroyView profile

Professor

• Labor Economics
• Financial Economics
• Family Economics
• +7 more

Marjorie B. McElroy is a Professor of Economics at Duke University since 1986, affiliated with the Trinity College of Arts & Sciences and the Duke Population Research Center. Her research focuses on labor economics, family decision-making, marriage markets, and financial economics. She has held leadership roles including Department Chair (1996–2002) and Acting Chair (1995–1996). McElroy earned a Ph.D. in Economics from Northwestern University (1969) and a B.A. from Pennsylvania State University (1965). Her research explores altruism in marriage markets, bargaining theory, and demographic policy impacts, particularly China’s population policies. She has received National Science Foundation grants for studies on family decisions and marriage markets. Notable publications include work on gender dynamics in academia, crime deterrence models, and divorce law effects. McElroy teaches courses in labor and family economics, applied microeconomics, and honors research supervision. McElroy’s academic contributions span theoretical and empirical economics, with a focus on household economics and policy analysis. Her work bridges microeconomic theory with real-world applications in family structure, public policy, and financial markets. She is recognized for advancing methodological approaches in panel data analysis and arbitrage pricing theory.

Enrico VaninoView profile

Senior Lecturer

• Applied Microeconometrics
• International Economics
• Economic Geography
• +5 more

Dr Enrico Vanino is a Senior Lecturer in Economics at the University of Sheffield's School of Economics, appointed in 2019. He holds a PhD in Economics from the University of Birmingham (2016) and has prior affiliations with the London School of Economics (LSE) and the National Institute of Economic and Social Research (NIESR). His roles include organizing the Sheffield Economic Research Paper Series and economics seminars. Enrico specializes in applied microeconometrics, focusing on international economics, economic geography, and regional development. Education: Bachelor's in International Economics, University of Padova (2009) MSc in Economics, University of Birmingham (2011) PhD in Economics, University of Birmingham (2016) Research interests span firms' behavior under globalization, trade policy impacts, and spatial economic disparities. He examines topics like trade defense instruments, infrastructure-driven economic diversification, and environmental adaptation strategies. Enrico collaborates with institutions like LSE's Department of Geography and Environment and has published in journals such as Economica and Journal of International Economics . Teaching: Enrico teaches Research Methods and Introductory Econometrics, emphasizing real-world applications and linking coursework to cutting-edge research. He actively supervises PhD students in microeconometrics, trade, and development economics. Affiliations: In addition to the University of Sheffield, he is affiliated with LSE and collaborates with NIESR on external research projects.

Wenfei FanView profile

Professor

• Database Theory
• Data Science
• Information Visualization
• +9 more

Wenfei Fan holds the Personal Chair in Web Data Management at the School of Informatics, University of Edinburgh . He is affiliated with the Database Group , LFCS (Laboratory for Foundations of Computer Science), Bell Labs , and the International Research Center on Big Data at Beihang University. His research spans database theory, data science, and big data systems. PhD in Computer and Information Science, University of Pennsylvania, USA MS in Computer Science, Peking University, China BS in Computer Science, Peking University, China His research interests focus on database theory and systems , including big data, data quality, data integration, database security, distributed query processing, query languages, social networks, and XML. He has pioneered techniques for graph pattern matching, data dependencies, and scalable algorithms. Recent publications emphasize graph data management , rule-based systems , and ML-data quality integration . Key themes include incremental computation, GPU-accelerated graph cleaning, entity linking, and logic-driven ML. Articles like 'Rock' and 'MiniClean' demonstrate hybrid approaches for real-world data challenges. Fellow of the Royal Academy of Engineering (FREng), 2023 Foreign Member, Chinese Academy of Sciences (ForMemCAS), 2019 Fellow of the Royal Society (FRS), 2018 ERC Advanced Fellowship, 2015 ACM Fellow (FACM), 2012 Roger Needham Award, 2008 He leads the ERC project: Resource-Bounded Graph Query Answering and contributes to systems like GRAPE and GraphScope , which parallelize sequential algorithms and process large graphs. He mentors students and collaborators globally, with a focus on data quality and graph analytics.

• Time Series Analysis
• Energy Analytics
• Statistical Clustering
• +3 more

Dr. Andrés Modesto Alonso is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Energy Analytics research group and Flores de Lemus Institute. His work spans computer science, economics, and statistics through advanced time series analysis and energy forecasting methodologies. Primary affiliation: Department of Statistics, UC3M Research groups: Energy Analytics, Flores de Lemus Institute His research focuses on time series analysis , energy forecasting , and statistical clustering with applications in electricity markets, smart grids, and environmental data. Recent publications emphasize deep learning models for energy prediction, dynamic factor models, and market-based distance metrics. Scientific output trends show 15 recent articles (2018-2024) covering topics like: Electricity market price forecasting Smart grid optimization through clustering Adaptive control charts for industrial processes Precision matrix estimation in high-dimensional statistics Extreme value analysis for environmental monitoring Dr. Alonso has supervised multiple theses on time series modeling and classification techniques, while collaborating on grants related to stochastic optimization and responsible AI applications in economic forecasting.