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Xinyu JiaView profile

Research Fellow

• Uncertainty Quantification
• Structural Reliability
• Risk Assessment
• +3 more

Xinyu Jia is currently a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich since June 2024, and concurrently serves as Associate Professor in the Department of Mechanical Engineering at Hebei University of Technology, China since October 2022. Her research focuses on advancing uncertainty quantification, structural reliability, and risk assessment methodologies for engineering systems. Her academic background includes: PhD in Mechanical Engineering, University of Thessaly, Greece (2018-2021) Bachelor of Engineering and Master of Science in Mechanical Engineering, Hunan University, China (2011-2018) Dr. Jia specializes in Bayesian learning frameworks for physics-based models, with particular expertise in uncertainty propagation in structural dynamics and industrial robotics applications. Her work develops hierarchical Bayesian approaches that integrate multi-level data to enhance predictive accuracy for complex engineering systems, addressing critical challenges in structural health monitoring and risk-informed decision making. Analysis of her 2022-2023 publications reveals a concentrated research trajectory in applying Bayesian inference to structural dynamics, with emphasis on hierarchical modeling techniques, variational inference schemes, and nonlinear model updating. These contributions predominantly appear in top-tier mechanical engineering journals, demonstrating methodological innovations that bridge theoretical statistics with practical engineering reliability problems. Her scientific recognition includes: Humboldt Research Fellowship (2023) Marie Curie Early Stage Researcher Fellowship (2018) No specific student advisement records are documented, though her Associate Professor role implies teaching responsibilities. Her fellowship awards represent significant research funding supporting her work in uncertainty quantification. As an active member of TUM's Engineering Risk Analysis Group, she contributes to high-impact projects including digital twins for ships, S3UQDyn, Navigating Risk, and infrastructure resilience initiatives like BIG-ROHU and INFRA.RELEARN, focusing on probabilistic risk modeling across civil and mechanical engineering domains.

Harald Friedrich Hans Volker Sigmar UhligView profile

Professor

• Macroeconomics
• Monetary Economics
• Financial Economics
• +7 more

Harald Uhlig holds the Bruce Allen and Barbara Ritzenthaler Professorship at the University of Chicago Department of Economics. He is a leading expert in macroeconomics, monetary economics, and financial economics with a focus on dynamic stochastic general equilibrium models, Bayesian econometrics, and economic policy analysis. Ph.D. in Economics, University of Minnesota (1990) Diplom in Mathematics, TU Berlin (1985) His research spans business cycles, growth theory, financial crises, and economic policy, particularly analyzing monetary-fiscal interactions, sovereign debt dynamics, and the impact of financial markets on macroeconomic stability. He has pioneered methods in vector autoregressions and numerical solution techniques for economic models. The 15 most recent articles show a focus on cryptocurrency economics, sovereign debt crises in monetary unions, fiscal stimulus effects, and financial health economics. Subfields include DSGE modeling, asset pricing, and policy analysis under uncertainty. Frank P. Ramsey Prize (2005) Fellow of the Econometric Society (2003) Alfred P. Sloan Fellowship (1989-1990) Fulbright Scholarship (1985-1986) He has advised 21 PhD students across Tilburg University, Humboldt University Berlin, and University of Chicago. Grants include NSF awards for macroeconomic risk analysis and INET funding for research on economic fragility.

• Glaciology
• Climate Change
• Ice Sheet Dynamics
• +3 more

Dr Amber Leeson is a Reader in Glaciology at Lancaster Environment Centre , Lancaster University. She serves as Associate Director for Research and leads projects integrating data science , numerical modeling , and remote sensing to study climate change impacts on the Greenland and Antarctic Ice Sheets . Her current research focuses on supraglacial hydrology , digital twinning of ice sheets , and firn evolution . Current Research Themes Ice sheet surface climate representation in models Supraglacial hydrology and ice sheet dynamics Firn evolution (past, present, future) Leadership Roles Associate Director for Research, Lancaster Environment Centre Treasurer and Secretary, International Glaciological Society Theme Lead for Environment, Data Science Institute (DSI) Her teaching includes courses on Glacial Systems, Environmental Processes, and Global Change. She supervises undergraduate and master's dissertations in Polar Science . Recent projects include Arctic Extremes (ARCTEX) , 5D Antarctica (5DAIS) , and AI Forecasting for Ice Shelf Collapse (AI4IS) . Amber's scientific contributions span climate model calibration, firn densification studies, and supraglacial lake dynamics. She has held advisory roles for the UK Polar Network , AGU Cryosphere Committee , and UKNCAR . Her work unites glaciology , data science , and climate modeling to address planetary-scale environmental challenges.

Aaron A. KingView profile

Professor

• Theoretical Ecology
• Epidemiological Dynamics
• Evolutionary Biology
• +5 more

Aaron A. King, Ph.D. , is the Nelson G. Hairston Collegiate Professor of Ecology and Evolutionary Biology, Complex Systems, and Mathematics at the University of Michigan and an External Professor at the Santa Fe Institute . He is a Fellow of the American Association for the Advancement of Science and a Biological Sciences Scholar at the University of Michigan. His research integrates mathematical modeling, statistical inference, and empirical data to study ecological and epidemiological systems. Education: Ph.D. in Applied Mathematics, University of Arizona, 1999 M.A. in Mathematics, University of Hawai'i, 1992 B.A. summa cum laude in Mathematics, Rice University, 1989 Research Interests: Dr. King's lab focuses on the dynamics of ecological, epidemiological, and evolutionary systems . His work includes: Modeling host-pathogen systems (COVID-19, influenza, dengue, pertussis, cholera, malaria) Antibiotic resistance in hospital settings Statistical inference for ecological and epidemiological data Integration of genomic and epidemiological data Mathematical frameworks for understanding parasite infections and immune responses Scientific Awards: Fellow of the American Association for the Advancement of Science Biological Sciences Scholar, University of Michigan Teaching and Mentorship: Dr. King teaches courses in Mathematical Ecology , Adaptive Systems , and Statistical Inference . He mentors graduate students and postdoctoral fellows, including Avinash Subramanian and Madeline Peters , and is affiliated with multiple interdisciplinary centers at the University of Michigan. Lab and Collaborations: He leads the King Laboratory of Theoretical Ecology & Evolution , which emphasizes reproducible research and rigorous theoretical approaches. The lab collaborates with institutions like the Santa Fe Institute and accepts students from programs such as Ecology & Evolutionary Biology , Applied & Interdisciplinary Mathematics , and Data Science .

Sahani PathirajaView profile

Lecturer

• Data Science
• Bayesian Inference
• Stochastic Analysis
• +7 more

Dr. Sahani Pathiraja is a Lecturer (tenure track assistant professor) at UNSW Sydney , specializing in Data Science . Her research bridges mathematical and statistical foundations with practical applications in environmental and biomedical sciences. Research Focus : Sequential Bayesian inference, Monte Carlo methods, stochastic analysis of non-linear filtering, uncertainty quantification, and real-time parameter estimation. Current Projects : Co-investigator in the ARC Industrial Transformation Training Centre: Data Analytics for Resources and Environment (DARE) and the Next Generation Graduate Program (NGGP) in Sports Data Science and AI . Research Supervision : Dr. Pathiraja supervises PhD students in areas including: Bayesian inference Stochastic differential equations Data assimilation Non-linear filtering Scientific Collaborations : Her work intersects with environmental science, biomedical applications, and machine learning. Projects include stochastic hydrology, SDEs, and operator learning for environmental systems. Contact Information : Email: s.pathiraja@unsw.edu.au Phone: +61 2 8065 0836 Office: Room 2070, Level 2, The Red Centre, UNSW Sydney

Alex ArchibaldView profile

Professor

• Atmospheric Chemistry
• Climate Science
• Environmental Science
• +5 more

Professor Alex Archibald is the Professor of Atmospheric Chemistry in the Yusuf Hamied Department of Chemistry at the University of Cambridge. His research group investigates atmospheric chemistry-climate interactions through fundamental laboratory studies atmospheric observations numerical model simulations . Research interests focus on chemistry-climate feedbacks , including hydrogen economy impacts biogenic hydrocarbon oxidation air pollution mitigation marine sulfur cycling machine learning applications . Recent publications emphasize hydrogen-soil deposition dynamics ozone-temperature relationships DMS chemistry in Earth systems hydrogen economy climate implications AI-driven climate modeling transboundary pollution studies . Teaching includes Part I Kinetics of Chemical Reactions Part II Chemistry in the Atmosphere Part III IDP1 projects . The research team has 15+ current and former students working on topics from Martian atmospheric modeling to urban temperature extremes.

Eric BarthView profile

Professor

• Dynamic systems and control
• Mechatronic systems
• Fluid power systems
• +7 more

Eric Barth is Professor of Mechanical Engineering and Professor of Neurological Surgery at Vanderbilt University's School of Engineering. He serves as Director of the C* Control laboratory (also known as the Laboratory for the Design and Control of Energetic Systems) and is affiliated with the Vanderbilt Institute for Surgery and Engineering (VISE), an interdisciplinary entity bringing engineers and physicians together to impact healthcare. His educational background includes: Ph.D. in Mechanical Engineering from Georgia Institute of Technology M.S. in Mechanical Engineering from Georgia Institute of Technology B.S. in Engineering Physics from University of California - Berkeley Professor Barth's research focuses on dynamic systems and control with applications spanning multiple domains. His primary interests include the design, modeling and control of mechatronic and fluid power systems, free-piston internal combustion and free-piston Stirling engines, energy storage and harvesting systems, and MRI compatible pneumatic robots for medical applications. His work applies a system dynamics and control perspective to problems involving the control and transduction of energy, encompassing multi-physics modeling, control methodologies formulation, and model-based design. His recent publications reveal a strong trajectory connecting mechanical engineering principles with medical applications, particularly in neurosurgery. The research spans energy systems (especially Stirling engines and novel energy storage approaches) and advanced medical robotics for MRI-guided interventions. This dual focus demonstrates his ability to bridge theoretical control systems with practical applications in both energy and healthcare domains. Professor Barth actively advises several doctoral students including David Comber, Joshua J Cummins, Alexander V. Pedchenko, and E. Bryn Pitt. His research is supported by significant funding, notably from the Center for Compact and Efficient Fluid Power, an NSF Engineering Research Center. The C* Control laboratory he directs occupies approximately 1000 square feet and contains specialized equipment including an 8-camera high-bandwidth optical tracking system, mechanical breadboard tables, pneumatic equipment with high-bandwidth servo-valves, specialized pressure sensors, a thermographic camera, high-speed video equipment, 3D printers, and a 2D laser cutter. Computational facilities include a network of approximately 20 machines running MATLAB/Simulink and SolidWorks, with access to additional CNC machining resources through the School of Engineering and the University.

Leanna L. HouseView profile

Associate Professor

• Bayesian Statistical Modeling
• Uncertainty Analysis
• Data Mining
• +5 more

Leanna L. House is an Associate Professor in the Department of Statistics at Virginia Tech's College of Science. She specializes in Bayesian statistical modeling, with a focus on model averaging, kernel regression, and uncertainty analysis of computer models. Her research spans applications in proteomics, bioinformatics, cosmology, climatology, and hydrology. She teaches courses such as Applied Bayesian Statistics and Hierarchical Models, emphasizing data visualization and interactive analytics. Education: Ph.D. in Statistics, Duke University (2006) M.S. in Statistics, Duke University (2003) M.A.T. in Curriculum Development, Cornell University (1999) B.S. in Biometry and Statistics, Cornell University (1998) Her work integrates data mining and visualization to enhance human-data interaction, with notable contributions to environmental modeling and proteomic analysis. She collaborates on interdisciplinary projects, including climate change studies and route choice models in transportation systems.

Rohan AlexanderView profile

Assistant Professor

• Quantitative Social Science
• Bayesian Methods
• Text as Data
• +4 more

Rohan Alexander is an Assistant Professor jointly appointed in the Faculty of Information and the Department of Statistical Sciences at the University of Toronto. His research focuses on improving the trustworthiness of data science through rigorous workflows, including reproducibility and bias mitigation. He co-founded The Data Workshop, a platform for data science best practices, and authored Telling Stories With Data , a book emphasizing reproducible methods. Rohan holds a PhD in Economics from the Australian National University, specializing in economic history. Rohan’s academic roles include Assistant Director of CANSSI Ontario and Senior Fellow at Massey College. His teaching includes courses like Experimental Design for Data Science and Worlds Become Data . He actively contributes to interdisciplinary initiatives like the Schwartz Reisman Institute for Technology and Society. His research interests span quantitative social science, Bayesian methods, text analysis, and computational reproducibility. He emphasizes code transparency and testing in data science projects. Rohan’s work addresses challenges in data measurement, such as systematic missingness, and explores AI’s societal impacts through collaborative projects with academia and industry. Rohan is affiliated with the Data Sciences Institute and leads initiatives on reproducibility. His current supervision includes PhD student Ciara Zogheib. His institutional roles extend to strategic projects like the Climate Positive Energy initiative and School of Cities.

David JensenView profile

Professor

• Machine Learning
• Causal Modeling
• Artificial Intelligence
• +3 more

David Jensen is a Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He directs the Knowledge Discovery Laboratory and the Computational Social Science Institute. His research focuses on machine learning, causal modeling, and analyzing large social, technological, and computational systems. Jensen's work is supported by organizations like the National Science Foundation and DARPA. Education: DSc in Engineering and Policy, Washington University in St. Louis (1992) MS in Engineering and Policy, Washington University in St. Louis (1988) BS in Mechanical Engineering, University of Nebraska (1986) Research Interests: Causal inference in relational and dynamic systems Machine learning applications in security and privacy Computational social science Large-scale network analysis Achievements: Recipient of teaching awards from UMass College of Natural Sciences (2011) and CICS (2022) 2017 IEEE INFOCOM Test of Time Paper Award Leadership roles in conferences and journals, including action editor for the Journal of Machine Learning Research Labs and Affiliations: Founder of the Knowledge Discovery Laboratory (2000) Director of the Computational Social Science Institute (2018-2022) Member of the Computing Community Consortium (CCC) Council

David CastañónView profile

Professor

• Stochastic Control
• Estimation Theory
• Game Theory
• +6 more

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

Ted MouwView profile

Professor

• Demography
• Social Stratification
• Economic Sociology
• +5 more

Ted Mouw is a Professor of Sociology at the University of North Carolina at Chapel Hill. He holds a B.A. in English Literature from Oberlin College, an M.A. in Economics, and a Ph.D. in Sociology from the University of Michigan. His research focuses on demography, social stratification, and economic sociology, with emphasis on labor markets, globalization’s impact, and immigration. Current projects include studies on social mobility in the U.S., economic effects of globalization in Indonesia and Mexico, and labor market dynamics for Hispanic immigrants in North Carolina. He has taught courses such as Social Stratification, Economy and Society, and Applied Regression Analysis, reflecting his expertise in quantitative methods and social theory. His work frequently intersects with the Carolina Population Center and addresses topics like occupational segregation, wage inequality, and transnational social networks. Mouw’s research also explores the interplay between spatial diffusion, migration patterns, and racial/ethnic integration in urban areas.

Win WahView profile

Academic

• Occupational Health
• Environmental Health
• Wildfire Impact on Health
• +3 more

Dr. Win Wah is a Senior Research Fellow at Monash University specializing in occupational and environmental health impacts related to extreme weather events, particularly bushfires. Contributes to UN Sustainable Development Goals through research on climate change health impacts Lead investigator in projects examining first responders' health risks His work spans: Systematic reviews of wildfire smoke exposure effects Statistical modeling of post-cancer surgery quality of life Construction worker health assessments Research outputs show collaboration patterns across: Public health epidemiology Occupational risk assessment Environmental health policy

Michael ChoiView profile

Assistant Professor

• Markov Chains
• Stochastic Algorithms
• Statistical Physics
• +6 more

Michael Choi is an Assistant Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA). Previously, he held a joint appointment with Yale-NUS College (2022–2025) and worked at the School of Data Science (SDS) at the Chinese University of Hong Kong, Shenzhen. He earned his PhD from Cornell University’s School of Operations Research and Information Engineering (ORIE), advised by Prof. Pierre Patie, and holds an undergraduate degree in Actuarial Science from the University of Hong Kong. His research focuses on Markov chains and processes, stochastic algorithms (e.g., MCMC, simulated annealing, Langevin dynamics), and their applications in statistical physics, optimization, Bayesian statistics, information theory, game theory, and theoretical computer science. He explores interdisciplinary connections with quantum computation, control theory, and computational chemistry. Choi actively contributes to the academic community, serving as an Associate Editor of Statistics and Computing since August 2023. He has presented at numerous international conferences and workshops, including the IMS APRM 2026 (Hong Kong), INFORMS International Meeting 2025 (Singapore), and BayesComp 2025 (Singapore). His work bridges theoretical foundations with practical computational methods in stochastic systems.

• Biostatistics
• Public Health
• Epidemiology
• +9 more

Dr. Shuvo Bakar is a Senior Lecturer in the Sydney School of Public Health at the University of Sydney, within the Faculty of Medicine and Health. He holds a PhD in Statistics from the University of Southampton, UK, and has prior experience as an Assistant Professor at Yale University, Lecturer at the Australian National University, and Scientist at Data61 (CSIRO). His research focuses on statistical methods applied to public health challenges, including Bayesian hierarchical modeling, machine learning, spatio-temporal analysis, and their applications in epidemiology, clinical trials, and environmental health. Dr. Bakar's research interests span statistical methodologies such as Bayesian adaptive designs, small area estimation, and spatial risk modeling, alongside applications in child health, infectious diseases, and extreme weather impacts on health. He is an active member of academic communities, including the Royal Statistical Society (RSS Fellow), Statistical Society of Australia, and the Australian Trials Methodology Research Network. His work also involves collaborations on grants totaling millions in funding, addressing topics like climate change impacts on health inequity and cardiovascular disease prevention in remote regions. Education: PhD in Statistics (University of Southampton, UK) Key Research Themes: Obesity, Diabetes, Cardiovascular Disease; Reproductive, Maternal & Child Health Grants/Projects: Includes NHMRC-funded trials on respiratory infections in First Nations children and MRFF grants for cardiovascular risk reduction in regional Australia. Dr. Bakar's contributions extend to editorial roles for Nature Scientific Reports and Discover Public Health , and his research has been published in journals like PloS One , Climatic Change , and Journal of the Royal Statistical Society .