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Thomas Y. Hou is the Charles Lee Powell Professor of Applied and Computational Mathematics at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. He holds a B.S. from South China University of Technology (1982), M.S. and Ph.D. from UCLA (1985-1987) under Prof. Bjorn Engquist. His research focuses on multiscale analysis, fluid dynamics, singularity formation in Euler/Navier-Stokes equations, and data analysis. Key roles include Executive Officer of Applied & Computational Math (2000-2006), and editorships of SIAM Journal on Multiscale Modeling and Simulation , Advances in Adaptive Data Analysis , and Research in Mathematical Sciences . He teaches ACM/EE 106ab: Introductory Methods of Computational Mathematics and oversees the Computing and Mathematical Sciences Colloquium. His work on singularity formation has led to groundbreaking studies in 3D Euler equations and Navier-Stokes systems, using rigorous numerics and multiscale methods. He has pioneered exponential convergence techniques in multiscale finite element methods and developed sparse time-frequency analysis for nonlinear data. Honors include the 2024 William Benter Prize, SIAM Kleinman Prize (2023), membership in the National Academy of Sciences (2024), and the 2004 Morningside Gold Medal in Applied Mathematics. His research group explores fluid dynamics, turbulence, and mathematical modeling in biological systems like keratocyte evolution. Collaborations include studies on Kolmogorov-Arnold networks (KAN) for high-dimensional data and Bayesian inference frameworks.

• Multiscale Analysis and Computation
• Fluid Dynamics
• Navier-Stokes Equations
• +6 more

Reidar B. Bratvold is a Visiting Professor at the University of Stavanger and Norwegian Institute of Technology. His research focuses on decision analysis, project valuation, and reservoir management, integrating machine learning and stochastic modeling. He has extensive industry experience, including roles as Vice President at Landmark Graphics and Managing Director at Roxar. Bratvold holds a PhD in Petroleum Engineering and an MSc in Mathematics from Stanford University, complemented by management education from INSEAD. His work emphasizes decision quality, addressing behavioral biases and uncertainty in energy investments. Notable contributions include frameworks for geosteering optimization and real-options valuation. He has authored numerous papers and books, including Making Good Decisions , and received awards such as the 2024 Energy Professional of the Year and 2017 SPE Management & Information Award. Bratvold’s research bridges academia and industry, promoting systematic decision-making methodologies. Awards: Energy Professional of the Year (2024) SPE Management & Information Award (2017) Three-time SPE Distinguished Lecturer Key Contributions: Machine learning in geosteering Value-of-information analysis Real-options valuation for oil projects His recent work explores reinforcement learning for decision-making and Bayesian methods for production optimization. Bratvold’s interdisciplinary approach combines petroleum engineering, economics, and data science to enhance industry decision-making.

• Decision Analysis
• Project Valuation
• Portfolio Analysis
• +7 more

Jef Caers is a Professor of Earth and Planetary Sciences at Stanford University, with a courtesy appointment in Geophysics. He holds a PhD (1997) and MSc (1993) in Mining Engineering and Geophysics from Katholieke Universiteit Leuven, Belgium. His research focuses on decision-making under uncertainty in critical mineral supply chains and geothermal energy development, emphasizing sustainable transitions to renewable energy. He founded the Mineral-X initiative to advance global mineral stewardship. His work integrates geostatistics, machine learning, and data science to address subsurface uncertainties in energy and mineral resources. Key contributions include Bayesian evidential learning for uncertainty quantification and AI-driven exploration tools like the Intelligent Prospector. He has authored/co-authored five seminal books in geostatistics and data science for geosciences, and received the IAMG's Krumbein Medal for career achievements. Recent research highlights include POMDP frameworks for lithium supply chain optimization, CO2 leakage quantification in carbon storage, and AI applications for mineral processing. His methodologies bridge geological modeling with decision analysis, offering actionable insights for industry and policy. Prof. Caers collaborates across disciplines, leading teams in Stanford's School of Earth, Energy & Environmental Sciences. His work emphasizes quantifying uncertainty in subsurface systems to enable robust decision-making for resource management, climate resilience, and sustainable development.

• Decision Making Under Uncertainty
• Geostatistics
• Critical Mineral Supply Chains
• +4 more

Ranadeep Daw is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He joined UWF in Fall 2024 after completing a postdoctoral fellowship at the National Institute of Environmental Health Sciences. He holds a Ph.D. in Statistics from the University of Missouri Columbia and degrees in Statistics from the Indian Statistical Institute, Kolkata. Ph.D. in Statistics, University of Missouri Columbia, 2023 M. Stat., Indian Statistical Institute, Kolkata, 2016 B. Stat., Indian Statistical Institute, Kolkata, 2014 Dr. Daw's research lies at the intersection of spatial statistics, computational methods, and machine learning. He develops low-rank spatial models, regionalization techniques using Karhunen-Loève expansions, and novel algorithms like REDS and SBoost for large-scale spatial prediction. His work applies to environmental risk modeling, exposure assessment, and biostatistics. He is actively developing computational tools in Julia, including the CAGE.jl package. His recent publications demonstrate a strong trend in scalable spatial methodologies, blending statistical theory with machine learning for environmental and public health applications. Key themes include data reduction, uncertainty quantification, and high-resolution imputation of pollutants like NO2. He has published in top journals such as Journal of Geographical Systems , Environmetrics , and Journal of Data Science . Scientific Awards: INSPIRE Scholarship, Department of Science and Technology, Government of India (2011–2016) Qualified for National Level, Regional Mathematical Olympiad (2010) Dr. Daw has served as a reviewer for journals like Spatial Statistics and Signal Processing . He has mentored at the graduate level as a Teaching Assistant and Instructor at the University of Missouri. He is actively seeking collaborations, particularly in biostatistics and Julia-based tool development. His research has been supported through academic appointments and fellowships, including at NIEHS and Deloitte. He is involved in developing open-source tools and has presented his work at major conferences including JSM and ICSA. His current projects include SBoost and MVCAGE, focusing on supervised dimension reduction and boosting for spatial data.

• Spatial Statistics
• Spatiotemporal Modeling
• Computational Statistics
• +11 more

Akil Narayan is a Professor in the Department of Mathematics and a member of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His office is located in WEB 4666 (SCI) and LCB 116 (Math). He has previously held positions as Assistant Professor at the University of Massachusetts Dartmouth (2012-2015) and Visiting Assistant Professor at Purdue University (2009-2012). His educational background includes: Ph.D. in Applied Mathematics from Brown University (2009) M.Sc. in Applied Mathematics from Brown University (2004) B.S. in Engineering Sciences and Applied Mathematics from Northwestern University (2003) B.S. in Electrical Engineering from Northwestern University (2003) Akil Narayan's primary research interests lie in numerical analysis, scientific computing, and approximation algorithms. His work spans multiple domains including uncertainty quantification, multifidelity modeling, optimization, and computational methods for partial differential equations. He has made significant contributions to the development of numerical methods for solving complex computational problems across various scientific and engineering disciplines. His research often bridges theoretical mathematics with practical applications in fields such as biomedical engineering, ecology, and power systems. Analysis of his recent publications reveals a strong focus on uncertainty quantification, multifidelity methods, and scientific machine learning. His work increasingly integrates traditional numerical methods with modern machine learning techniques, particularly in the development of physics-informed neural networks. There's also a notable emphasis on structure-preserving numerical methods and optimization techniques for computational models. His research has significant applications in biomedical imaging, particularly in electrocardiographic imaging and cardiac modeling. While specific scientific awards are not detailed in the available information, his extensive publication record in top-tier journals demonstrates recognition in his field. His work appears regularly in prestigious journals such as SIAM Journal on Scientific Computing, Journal of Computational Physics, and SIAM Review. Professor Narayan has advised numerous graduate students through the Department of Mathematics and the School of Computing at the University of Utah. His current advisees include Filip Belik, Haoyu Chen, John Turnage, and Yinqian Yu, working on topics ranging from numerical methods for PDEs to operator learning and uncertainty quantification. His former students have gone on to positions at institutions including General Motors, Amazon, Intel Corporation, and various academic institutions. He has also secured research funding supporting his work in computational mathematics and scientific computing, though specific grant details are not provided in the available text. He is actively involved with the Scientific Computing and Imaging (SCI) Institute at the University of Utah, where he collaborates with researchers across disciplines. His work through the UncertainSCI project focuses on uncertainty quantification for computational models in biomedicine and bioengineering, particularly in cardiac applications. He frequently collaborates with researchers in the Department of Mathematics, School of Computing, and the SCI Institute on interdisciplinary projects that combine mathematical theory with practical computational applications.

• Numerical Analysis
• Scientific Computing
• Approximation Algorithms
• +3 more

Malcolm Itter is an Assistant Professor in the Department of Environmental Conservation at the University of Massachusetts Amherst, where he leads the Forest Dynamics Lab. His research focuses on quantitative and applied forest ecology, with an emphasis on modeling forest responses to global change using advanced statistical methods. His research interests lie at the intersection of forest ecology, statistical modeling, and conservation. He specializes in developing Bayesian hierarchical models to predict forest dynamics, integrating data from sources such as forest inventories, LiDAR, tree rings, and remote sensing. His work supports adaptive forest management and conservation under climate change. The most recent publications highlight a strong trend in uncertainty-aware ecological modeling, joint species distribution modeling (JSDM), and applications in conservation—particularly American chestnut restoration and climate-resilient forest planning. His research increasingly emphasizes scalable, data-rich models that inform real-world management decisions. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: Malcolm Itter advises graduate students and researchers in the Forest Dynamics Lab, fostering a collaborative and inclusive research environment. His work is supported by external funding, including a project sponsored by the Northeast Climate Adaptation Science Center, indicating active grant involvement in climate adaptation research. Labs and Teams: He leads the Forest Dynamics Lab at UMass Amherst, a research group dedicated to advancing statistical models of forest change. The lab focuses on actionable science, collaborating with forest practitioners and conservation managers to co-develop decision-support tools. The team works on integrating diverse datasets to improve predictions of forest ecosystem responses under global change.

• Quantitative Ecology
• Forest Ecology
• Forest Dynamics
• +7 more

Eunshin Byon is a Professor and Richard Wilson Faculty Scholar in Industrial and Operations Engineering at the University of Michigan, serving as Director of Masters Programs. Her expertise spans reliability engineering, data-driven decision-making, and stochastic systems optimization for critical infrastructure including renewable energy grids and manufacturing processes. Education: Ph.D. in Industrial and Systems Engineering, Texas A&M University M.S. in Industrial and Systems Engineering, KAIST B.S. in Industrial and Systems Engineering, KAIST Dr. Byon's research pioneers digital twin calibration and uncertainty quantification methodologies, integrating stochastic simulation with big data analytics to solve complex reliability challenges. Her work bridges theoretical advances in sequential computer experiments with practical applications in energy sustainability and predictive maintenance, emphasizing variance reduction techniques and operational decision frameworks for interconnected systems. Recent publications reveal a concentrated focus on parameter calibration innovations and computational efficiency in stochastic modeling, demonstrating consistent advancement in digital twin technologies for energy systems. These works increasingly incorporate machine learning elements while maintaining rigorous statistical foundations for uncertainty propagation. Scientific Awards: teaching award from the QCRE division of IISE (2024) Richard Wilson Faculty Scholar (2023) 2022 Best Associate Editor award, INFORMS Journal on Data Science 2021 Department Faculty Award Dr. Byon has mentored multiple doctoral candidates to successful completion, with students placing at Clemson University and Zhejiang University. Her research is sustained by significant external funding including an NSF grant for digital twin calibration (2023-2025) and Ford Motor Company research support (2023-2025), focusing on real-world implementation of stochastic optimization frameworks. She leads a specialized research group developing next-generation reliability assessment tools, collaborating with industry partners on renewable energy integration challenges while advancing fundamental methodologies in stochastic simulation and data analytics for complex engineered systems.

• Reliability Engineering
• Fault Diagnosis
• Condition Monitoring
• +8 more