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Thomas de GraaffView profile

Associate Professor

• Spatial Economics
• Urban Economics
• Regional Economics
• +8 more

Thomas de Graaff is an Associate Professor in the Department of Spatial Economics at Vrije Universiteit Amsterdam, affiliated with the School of Business and Economics and Tinbergen Institute. He holds a Master's and PhD in econometrics and spatial economics from VU Amsterdam. His research focuses on empirical spatial economics, particularly socio-economic behavior related to migration, tourism, commuting, and agglomeration economies. He emphasizes reproducibility, Bayesian multilevel models, and gravity models. Key research areas include meta-analyses of agglomeration externalities, crime social interactions, and urban overhead effects. He has published widely on topics such as ethnic diversity valuation, telework substitution effects, and housing market dynamics. His work contributes to UN Sustainable Development Goals related to urban sustainability and economic growth. Teaching includes courses on spatial econometrics, network analysis, and research design. He has supervised three PhD theses and contributed datasets to studies on agglomeration economies. Collaborations span global institutions, with recent projects in New Zealand and Europe.

Jian GaoView profile

Research Fellow

• Machine Learning
• Computer Vision
• Transfer Learning
• +3 more

Jian Gao is a Research Fellow at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science. His work focuses on transferability and generalization in machine learning, particularly applied to computer vision and remote sensing. He contributes to UN Sustainable Development Goals through research on green space analysis and environmental applications. His ORCID is 0000-0004-3302-9018 , and he can be reached at j.gao@qub.ac.uk . Dr. Gao's research interests emphasize cross-domain learning, regularization techniques, and domain adaptation. His recent work explores applications in image segmentation for urban environments, limited-sample learning using normalizing flows, and unsupervised domain adaptation. He has published widely on topics like feature alignment, uncertainty reduction, and video object detection. No scientific awards are listed. His publications include collaborations on environmental computer vision and foundational machine learning challenges. No advising or grant information is provided, and no specific laboratories or teams are mentioned in the profile.

Brian BushView profile

Researcher

• Nuclear Physics
• Statistical Physics
• System Dynamics Modeling
• +4 more

Brian Bush is a Researcher at the National Renewable Energy Laboratory (NREL) and a Haskell software engineer at Input Output (IOHK). He holds a Ph.D. in theoretical physics from Yale University (1990) and a B.S. in physics from Caltech (1985). His academic roles include Principal Scientist at NREL, Technical Staff Member at Los Alamos National Laboratory (LANL), and visiting scientist at the National Center for Atmospheric Research (NCAR). His research spans nuclear physics, statistical mechanics, quantum computing, and infrastructure systems modeling. Bush’s work focuses on energy systems, including biomass-to-bioenergy supply chains, renewable energy infrastructure, and quantum information science. He developed the BioLUC land-use change model and contributed to projects like TRANSIMS (traffic simulation) and EPISIMS (epidemic modeling). His software expertise includes C++, Haskell, and system dynamics tools like the Biomass Scenario Model (BSM). His research interests include formal methods for domain-specific languages (e.g., Marlowe for smart contracts), data mining, and visualization. Bush has led interdisciplinary teams in critical infrastructure protection, energy policy analysis, and pandemic simulation. His technical contributions include patents, open-source software, and peer-reviewed publications in physics, environmental science, and computational modeling.

James E. FauldsView profile

Research Professor

• Geologic mapping
• Structural geology
• Tectonics
• +5 more

James E. Faulds is a Research Professor at the University of Nevada, Reno (UNR), affiliated with the Mackay School of Earth Sciences and Engineering and the Nevada Bureau of Mines and Geology (NBMG), where he has served as Director and State Geologist since 2012. His roles include leading geothermal research, structural geology studies, and geological mapping across Nevada and western Turkey. Faulds holds a Ph.D. (University of New Mexico, 1989), M.S. (University of Arizona, 1986), and B.S. (University of Montana, 1981). His research focuses on structural geology, tectonics, and geothermal systems, with notable projects including the Neogene evolution of the Walker Lane transform boundary and geothermal activity in the Great Basin. He has led mapping initiatives in regions like the Colorado River extensional corridor and the northern Walker Lane, emphasizing structural controls on geothermal reservoirs and tectonic evolution. Key contributions include geologic maps of Nevada’s fault zones, analyses of magmatism-extension linkages, and studies on the paleogeography of the Colorado River. His work integrates field data, geophysical modeling, and paleomagnetic analysis to address continental deformation and resource exploration. Faulds has authored/co-authored over 100 publications, including seminal papers on transform plate boundaries and geothermal system exploration. His research has been supported by NSF, DOE, and industry grants, with a focus on applied geoscience for energy and hazard mitigation.

Xun HuanView profile

Associate Professor

• Uncertainty Quantification
• Data-Driven Modeling
• Numerical Optimization
• +3 more

Xun Huan is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor. He holds a PhD in Computational Science and Engineering from MIT (2015), an SM in Aeronautics and Astronautics from MIT (2010), and a B.A.Sc. in Engineering Science (Aerospace) from the University of Toronto (2008). His research focuses on uncertainty quantification, data-driven modeling, numerical optimization, Bayesian analysis, and machine learning applications across engineering systems. Key research areas include optimal experimental design, stochastic modeling for manufacturing processes, and interdisciplinary applications in aerospace, energy systems, and biomedical engineering. He leads a research group conducting weekly meetings, emphasizing collaborative problem-solving and student independence. His work has been supported by grants such as the Keck Foundation award for cancer cell research (2022). He actively mentors PhD students, emphasizing continuous literature engagement, flexible work hours, and strategic internship planning. Education : Ph.D., MIT (2015) S.M., MIT (2010) B.A.Sc., University of Toronto (2008) Awards/Grants : Keck Foundation Collaborative Research Award (2022) Regents-approved promotion to Associate Professor (2024) Advising Philosophy : Weekly one-on-one meetings transitioning from hands-on to independent guidance Encourages academic career exploration through teaching opportunities Supports strategic internships, particularly in later PhD stages Current research emphasizes uncertainty-aware AI for digital twins, Bayesian methods in complex systems, and optimal experimental design frameworks. His group collaborates on projects ranging from material flow analysis to space weather prediction, leveraging interdisciplinary methods to address real-world challenges.

Laurence TALINIView profile

Researcher

• Soft Matter Physics
• Fluid Dynamics
• Interfacial Phenomena
• +3 more

Laurence Talini is a Researcher affiliated with CNRS and the Department of Physics at École Normale Supérieure - PSL (ENS-PSL). Her work focuses on soft matter physics, particularly the dynamics of liquid films, foaming, and interfacial phenomena in complex fluids. She leads research on drying processes of polymer solutions, film stability in oil mixtures, and intermolecular effects on ultra-thin films. Affiliations: CNRS Researcher Saint-Gobain Research Collaborator Adjunct faculty at ENS-PSL for Soft Matter and Biological Physics programs Research Highlights: Developed methods to control drying front propagation in polymer films Investigated foam stability and surfactant-like effects in liquid mixtures Advanced thermal fluctuation techniques to probe interfacial dynamics Teaching Contributions: M2 Soft Matter and Biological Physics courses Tutoring in Polymer Physics at ESPCI Paris Key Collaborations: Joint work with Saint-Gobain Research International collaborations on interfacial rheology and emulsion dynamics Labs & Teams: Formerly part of SIMM and FAST labs, now leading studies at ENS-PSL on out-of-equilibrium soft matter systems.

• Programming Languages
• Type Theory
• Category Theory
• +4 more

Vikraman Choudhury is a Marie Skłodowska-Curie Fellow at the Department of Computer Science and Engineering, University of Bologna, and affiliated with the INRIA OLAS Team. He holds a Chancellor's Fellowship in Mathematically Structured Programming at the University of Strathclyde. Previously, he was a Research Associate at the University of Glasgow and a Visiting Research Fellow at the University of Cambridge under a Paul Purdom Fellowship. His research focuses on foundational aspects of computation through algebraic lenses, encompassing programming languages, type theory, category theory, logic, and formalization. He holds a PhD in Computer Science from Indiana University (2022), where his thesis explored mathematical models of resource-conscious computation. His prior work includes contributions to distributed issue tracking systems using patch theory and formal methods in concurrency. Vikraman has taught courses in semantics, logic, and programming languages at the University of Cambridge and Indiana University. Research Interests : Foundations of Programming Languages Type Theory and Homotopy Type Theory Category Theory in Computer Science Reversible and Quantum Programming Formal Methods and Verification Algebraic Semantics and Constructive Mathematics Grants & Projects : Marie Skłodowska-Curie Actions Grant (€172k) for ReGraDe-CS project Chancellor's Fellowship at University of Strathclyde Paul Purdom Fellowship at University of Cambridge Awards : Marie Skłodowska-Curie Fellowship Chancellor's Fellow in Mathematically Structured Programming His research includes pioneering work on reversible debugging of concurrent systems (ReGraDe-CS project), quantum session types, and semantic foundations of directional logic programming. He actively contributes to open-source initiatives including GenoCoRe and formalization efforts in Cubical Agda.

Jan AdamowskiView profile

Professor

• Sustainable Agriculture
• Water Resources Management
• Environmental Engineering
• +4 more

Jan Adamowski is a Full Professor at McGill University's Department of Bioresource Engineering, affiliated with the Brace Water Centre and the Faculty of Agricultural and Environmental Sciences. His research focuses on sustainable agriculture, water resources management, and climate change adaptation. He holds the William Dawson Scholar and Liliane and David M. Stewart Scholar titles in Water Resources. Key research areas include food security, renewable energy integration in agriculture, and hydrological modeling. His work often involves participatory systems modeling and machine learning to address complex environmental challenges. Notable projects include studies on Quebec pesticide management, flood susceptibility in Italy's Basento River, and drought assessment in India's Ganga Basin. Dr. Adamowski has contributed to over 150 publications since 2021, with recent trends emphasizing generative AI for climate-adaptive agriculture, coupled human-water system modeling, and equity in low-carbon infrastructure planning. His methodologies bridge environmental science, engineering, and social systems analysis. While no formal awards or grants are explicitly listed, his work demonstrates significant interdisciplinary impact. He collaborates with global institutions, focusing on data-poor regions like Guatemala and the Qinghai-Tibetan Plateau. His teaching and research emphasize participatory approaches to sustainability challenges.

Meng QiangView profile

Professor

• Transportation Network Modeling and Optimization
• Urban Mobility Systems
• Shipping and Intermodal Freight Transportation
• +5 more

Professor Meng Qiang serves as Professor in the Department of Civil and Environmental Engineering (CEE) at the National University of Singapore (NUS), where he directs both the Centre for Transport Research (CTR) and the NUS Guangzhou Research Translation and Innovation Institute (NUS-GRTII). His academic leadership extends to editorial roles including Co-Editor-in-Chief of Multimodal Transportation and Advisory Editor for Transportation Research Part E . His educational background spans Bachelor of Science from East China Normal University (1984), Master of Science from Chinese Academy of Sciences (1989), and PhD in Civil Engineering from Hong Kong University of Science and Technology (2000). Prior to joining NUS, he held academic positions at Shandong Normal University where he became Professor in the Information and Management School after completing his PhD. Professor Meng's research expertise focuses on three interconnected domains: urban mobility modelling and optimization (including AI-based traffic prediction, autonomous vehicle operations, and EV infrastructure planning); shipping and intermodal freight transportation (covering liner network optimization, container logistics, and maritime cybersecurity); and quantitative risk assessment of transport operations (particularly for urban tunnels and maritime safety). His work integrates advanced computational methods with real-world transportation challenges, as evidenced by over 260 high-impact publications. His scholarly impact is demonstrated through consistent recognition as a Top 2% Scientist in logistics and transportation (Stanford University, 2019-2022) and numerous prestigious awards including the Omega Best Paper Award (2023), TSL Freight Transportation Award (2020), and Singapore MOT Minister's Innovation Award (2009). His publications reveal evolving research trajectories toward autonomous vehicle integration, maritime decarbonization, and AI-driven transportation solutions. Best Paper Award of Omega 2023 Member, Academy of Engineering Singapore (2022) TSL Best Paper Award in Freight Transportation and Logistics (2020) OCDI Takeuchi Yoshio Logistics Award (2019) Chang Jiang Scholar Chair Professor (2017) WCTR Society Prize for Best Paper (2013) Singapore MOT Minister's Innovation Award (2009) Professor Meng has supervised 17 PhD students and mentored 20 postdoctoral fellows, with many graduates achieving prominent academic positions internationally including Distinguished Young Scholars in China. His industry contributions include the QRAFT risk assessment software for road tunnels and an AI-based traffic prediction model developed with ST Engineering. He leads the Centre for Transport Research which conducts government and industry-funded projects addressing Singapore's transportation challenges through advanced modeling and optimization techniques.

Thomas MagnantiView profile

Professor

• Operations Research
• Optimization
• Network Design
• +7 more

Thomas Magnanti is an Institute Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Operations Research Center. His research focuses on optimization, scheduling algorithms, network design, and mathematical programming. He has contributed extensively to the fields of operations research, discrete optimization, and algorithm development, with notable work on network flows, combinatorial optimization, and stochastic modeling. His research bridges theoretical foundations with practical applications in transportation systems, telecommunication networks, and educational methodologies. Magnanti's work emphasizes solving complex systems through rigorous mathematical frameworks and algorithmic innovations. His research interests span a wide range of topics, including scheduling heterogeneous jobs, network routing optimization, maintenance scheduling for modular systems, and stochastic user equilibrium models. He has also explored interdisciplinary applications, such as optimizing student allocation in capstone projects and improving active learning strategies in calculus education. Magnanti’s contributions to the field are reflected in foundational textbooks like Network Flows: Theory, Algorithms, and Applications , which remains a key resource in operations research. Throughout his career, Magnanti has addressed challenges in both theoretical and applied domains, such as designing robust algorithms for telecommunication networks and developing methodologies to handle uncertainty in transportation systems. His work consistently highlights the interplay between computational efficiency and real-world problem-solving.

Cong CaoView profile

Lecturer

• Machine Learning
• Environmental Economics
• Health Economics
• +3 more

Cong Cao is a Postdoctoral Instructor in Science, Society, and Public Policy at the California Institute of Technology (Caltech). His primary affiliation is within the Division of Humanities and Social Sciences. He holds a Ph.D. from the Norwegian University of Science and Technology (NTNU), an M.A. from the University of International Business and Economics, and a B.S. from Hunan Normal University. Dr. Cao's research integrates machine learning and artificial intelligence into environmental and health economics, focusing on predictive modeling, feature selection, and causal inference for complex healthcare systems. Key areas include air pollution impacts, climate policy analysis, and public health economics. He has designed courses like 'Introduction to Public Health Economics and Policy' at Caltech, emphasizing interactive teaching methods and real-world case studies. His work spans interdisciplinary collaborations, including studies on urban air quality prediction, the health effects of traffic-related pollution, and the application of physics-based models to environmental challenges. Notable publications include analyses of Norway’s air quality data, plastic surgery risk factors using NSQIP data, and the intersection of climate change and HIV-related aging. Dr. Cao has co-supervised research initiatives such as a Cambridge Master’s thesis on sustainable building performance and a Caltech undergraduate project on environmental aging effects. He actively reviews for journals like Scientific Reports and conferences like the Association of Environmental and Resource Economists (AERE). His contributions to GitHub repositories reflect advanced machine learning techniques applied to environmental and health datasets.

• Applied Mathematics
• Fluid Mechanics
• Mathematical Modeling
• +5 more

Bertrand Maury is a Professor of Mathematics at Mines Paris – PSL and holds an Associate Professor position at the École Normale Supérieure - PSL (ENS-PSL). He leads the Mathematics teaching unit for the Civil Engineering Cycle at Mines Paris. His career includes roles at Université Pierre et Marie Curie and Université Paris-Saclay. He was elected to the French Academy of Sciences in 2024 for his interdisciplinary contributions in mathematical modeling across industrial processes, life sciences, and social sciences. Education: École Polytechnique (1988), PhD and habilitation from Université Paris-Saclay. Research Focus: Fluid dynamics, blood/respiratory flow modeling, crowd movement analysis, and social network dynamics. Co-founder of startup Signactif for movement tracking solutions. Achievements: Recognized for bridging mathematics with applications in medicine, engineering, and geography. His work emphasizes open, cross-disciplinary approaches, recently extending into geography and emergency evacuation modeling.

Lucas DRUMETZView profile

Associate Professor

• Remote Sensing
• Signal Processing
• Machine Learning
• +5 more

Lucas DRUMETZ is an Associate Professor at the Signal & Communications Department of IMT Atlantique, affiliated with the TOMS team at UMR CNRS 6285 Lab-STICC since 2017. His research focuses on satellite/airborne imagery, inverse problems in remote sensing, signal processing, optimization, and machine learning/AI. He holds a PhD in image/signal processing from Université Grenoble Alpes (2016), awarded the university's PhD honor in 2017. He also held visiting roles at UCLA (2017) and the University of Tokyo (2017). Education: PhD in Image and Signal Processing, Université Grenoble Alpes (2016) Research interests span environmental monitoring through satellite data analysis, geospatial modeling, and machine learning applications in ecological systems. He develops methods for biodiversity tracking, climate-related variables estimation, and spatio-temporal data interpolation. His recent work emphasizes frameworks for coral reef/seagrass ecosystems, super-resolution techniques for land surface temperature, and neural approaches to long-term time series forecasting. He contributes to projects like SCO-BioEOS for coastal biodiversity mapping. Awards: 2017 PhD Award, Université Grenoble Alpes Teaching involves courses on signal processing, machine learning, and mathematics at IMT Atlantique. Collaborates on interdisciplinary projects with marine science and climate research teams. Labs/Teams: Member of TOMS team (Lab-STICC) focusing on signal processing and statistical methods.

• Machine Learning
• Computational Statistics
• Causal Inference
• +4 more

Dr. Rob Cornish is a Florence Nightingale Bicentenary Research Fellow at the University of Oxford's Department of Statistics. His research focuses on robustness in complex systems, emphasizing valid methodologies under minimal assumptions for real-world applications. He holds a DPhil from the AIMS CDT program under Arnaud Doucet and George Deligiannidis, and conducted postdoctoral work with Chris Holmes and Arnaud Doucet on causal inference and conformal prediction. Key research areas include uncertainty quantification, causal inference, and machine learning. His work spans Bayesian computational methods, Monte Carlo techniques, and generative models. Recent publications highlight advancements in neural network symmetries, equivariant diffusions, and causal falsification of digital twins. He is affiliated with the Statistical Theory and Methodology and Computational Statistics and Machine Learning research groups. Publications reflect a trend toward integrating theoretical rigor with applied challenges, such as scalable Bayesian inference and robust predictive models. His contributions bridge foundational statistical theory and modern machine learning applications, particularly in safety-critical system reliability.

• Granular Materials Mechanics
• Porous Media Transport
• Energy Systems
• +5 more

Associate Professor Yixiang Gan is an academic at The University of Sydney, holding positions as Associate Head of the School of Civil Engineering and Deputy Director of The University of Sydney Nano Institute. He holds a Dr.-Ing degree from Karlsruhe Institute of Technology (Germany) and BE/BBA/ME degrees from Xi'an Jiaotong University (China). His research focuses on granular and porous media mechanics, energy systems, and multiphase flow, with applications in carbon geo-sequestration and thermal energy storage. Education: Dr.-Ing (summa cum laude), Karlsruhe Institute of Technology, Germany BE/BBA and ME, Xi'an Jiaotong University, China Research interests emphasize the mechanics of heterogeneous materials and their role in energy systems, including lithium-ion batteries, fuel cells, and thermal storage. Collaborations span institutions globally, such as Tsinghua University (China), Ecole des Ponts ParisTech (France), and UCLA (USA). Awards include the Endeavour Executive Leadership Award (2019), Sydney Accelerator Fellowship (2017), and DECRA (2013). He supervises PhD students in topics like granular transport and phase change materials. Teaching includes courses on Energy Geotechnics, Statics, and Capstone Projects. His lab explores multiscale mechanics and interfaces, with projects on unsaturated soils and contact mechanics.