جستجوی استاد راهنمای دانشگاهی

• Scientific Machine Learning
• Climate Science
• Earthquake Seismology
• +۳ مورد دیگر

Karianne Bergen is an Assistant Professor of Data Science and Earth, Environmental & Planetary Sciences at Brown University, with a courtesy appointment in Computer Science. She leads the Scientific Machine Learning (SciML) Research Group, affiliated with the Data Science Institute (DSI), DEEPS, and the SciAI Center. Her research focuses on scientific machine learning (SciML), including surrogate models for climate science, explainable AI (XAI), and foundation models. She holds a Ph.D. and M.Sc. in Computational and Mathematical Engineering from Stanford University and a B.Sc. in Applied Mathematics from Brown University. Her postdoctoral training included a Harvard University HDSI fellowship in Computer Science. Education: B.Sc. Applied Mathematics, Brown University (2009) M.Sc. Computational and Mathematical Engineering, Stanford University (2015) Ph.D. Computational and Mathematical Engineering, Stanford University (2018) Research Interests: Dr. Bergen’s work bridges machine learning and Earth sciences, emphasizing scalable methods for climate modeling and geophysical data analysis. Her group develops emulators for Antarctic ice sheet dynamics, XAI frameworks for climate data interpretation, and scientific foundation models for geoscience applications. Recent projects include GAN-based sea ice resolution enhancement and flow-based neural networks for sea level projections. Awards: Harvard Data Science Initiative Postdoctoral Fellowship (2018–2020) Stanford Graduate Fellowship in Science and Engineering (2011–2015) Outstanding Student Paper Award, American Geophysical Union (2015) Advising & Collaborations: She mentors PhD students in Earth, Environmental, and Planetary Sciences and collaborates with institutions like MIT-Lincoln Laboratory, SciAI Center, and international geoscience groups. Her lab includes postdocs (e.g., Hilarie Sit) and alumni from data science practicum programs. Labs/Teams: The SciML group at Brown University focuses on multidisciplinary projects at the intersection of AI and Earth sciences, with recent presentations at AGU Fall Meetings and the AI for Science Workshop at ICML.

Qi Tangمشاهده پروفایل

استادیار

• High-Performance Computing
• Computational Plasma Physics
• Fusion Simulations
• +۳ مورد دیگر

Qi Tang is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology, part of the College of Computing. He joined Georgia Tech in 2024 after serving as a Staff Scientist at Los Alamos National Laboratory (LANL) from 2018 to 2024. His research focuses on computational plasma physics, high-performance computing, and scientific machine learning, with applications in fusion energy, plasma simulations, and structure-preserving neural networks. Education: Ph.D. in Applied Mathematics, Michigan State University, 2015 B.S. in Mathematics & Applied Mathematics, Zhejiang University, 2010 Research Interests: Qi’s work spans scalable numerical algorithms for exascale computing, fusion modeling, and scientific machine learning. Key areas include: High-order schemes, adaptive mesh refinement, and GPU acceleration for MHD and plasma simulations Structure-preserving neural networks for dynamical systems and multiscale physics Multi-physics modeling of tokamak disruptions and magnetic reconnection Grants & Collaborations: Principal Investigator (PI) for multiple DOE grants, including ASCR MMICC Center (CHaRMNET) Led a multi-institutional ASCR SciML team with LANL, ANL, and universities Recipient of LANL LDRD and NSF grants for fusion and plasma research Advising & Teaching: Advises Ph.D., master’s, and undergraduate students in CSE, physics, and engineering Teaches Parallel Computing Programming and Applications (CSE-6230) Co-advises students in DOE-funded programs and LANL collaborations Labs & Teams: Qi is affiliated with the DOE ASCR MMICC Center (CHaRMNET) and LANL’s Applied Mathematics and Plasma Physics Group. His team develops open-source tools like MFEM-based MHD solvers and structure-preserving ML frameworks.

Ján Drgonaمشاهده پروفایل

دانشیار

• Differentiable programming
• Scientific machine learning
• Dynamical systems
• +۳ مورد دیگر

Ján Drgona is an Associate Professor in the Department of Civil and Systems Engineering at Johns Hopkins University's Whiting School of Engineering and a member of the Ralph S. O'Connor Sustainable Energy Institute (ROSEI). Previously, he served as a Principal Investigator and Research Data Scientist at Pacific Northwest National Laboratory (PNNL) and held a postdoctoral position at KU Leuven in Belgium. Dr. Drgona earned his BSc, MSc, and PhD in control engineering from the Slovak University of Technology. His research centers on differentiable programming and scientific machine learning (SciML) for dynamical systems, optimization, and control, with particular applications to building energy systems and industrial process control. He is the lead developer of the Neuromancer SciML library in PyTorch for solving constrained optimization, physics-informed machine learning, and optimal control problems, which became PNNL's most popular open-source repository within two years of its release. Dr. Drgona's publication record demonstrates a strong focus on bridging machine learning with physical systems and control theory. His recent work spans differentiable predictive control, physics-informed neural networks, optimization algorithms, and applications to energy systems. A recurring theme across his publications is the integration of domain knowledge with data-driven approaches to create more efficient, reliable, and interpretable systems for real-world applications, particularly in sustainable energy and building systems. As an active member of the scientific community, Dr. Drgona regularly serves as a reviewer for prestigious journals including Applied Energy, Automatica, IEEE Control Systems Letters, IEEE Transactions on Control Systems Technology, IEEE Transactions on Industrial Informatics, Control Engineering Practice, Journal of Process Control, Energy and Buildings, Journal of Control Automation and Electrical Systems, and Electric Power Systems Research. Dr. Drgona has been involved in several high-impact projects, including developing AI models that slash HVAC energy costs while predicting them with precision. He has participated in Johns Hopkins' International Energy Summit to accelerate clean technology innovation and has presented at major conferences including ACC 2025 where he co-organized workshops on Physics-Informed Machine Learning in Control and Safe Physics-Informed Machine Learning for Dynamics and Control.

• Data Science
• Machine Learning
• Process Systems Engineering
• +۵ مورد دیگر

Vinicius Viena Santana is a postdoctoral researcher at the Norwegian University of Science and Technology (NTNU) specializing in data science, machine learning, and process systems engineering. His work bridges hybrid modeling and scientific machine learning (SciML) to optimize chemical processes, particularly in blue hydrogen production through the FME Hydrogeni consortium. He collaborates with leading institutions like MIT Portugal program and the University of Porto. Ph.D. in Chemical Engineering (University of Porto, MIT Portugal program) Master's in Industrial Engineering (PEI UFBA) Master's in Chemical Engineering (University of Porto) Bachelor's in Chemical Engineering (Federal University of Bahia, UFBA) His research spans: Scientific Machine Learning (SciML) for industrial AI Hybrid modeling combining physical and data-driven approaches Reinforcement learning for chemical process design Applications in energy systems (wind/solar forecasting, hydrogen production) Julia programming for scientific computing His publications focus on integrating machine learning with chemical engineering challenges, including uncertainty quantification, molecule generation, and predictive modeling for energy systems. Recent work emphasizes scalable AI-driven solutions for sustainable chemical processes. Scientific awards: 1st place in poster award for TKP4170 Process Design Project Supervision and teaching: Co-supervisor for TKP4170 Process Design Project (2023–2024) Teaches data science and machine learning in natural sciences (KP8907, TKP4198) Introduction to separation processes (KP3010)

Andrej Risteskiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistics
• Theoretical Computer Science
• +۵ مورد دیگر

Andrej Risteski is an Associate Professor at the Machine Learning Department of Carnegie Mellon University (CMU) since 2025. He previously held the Norbert Wiener Research Fellow position jointly between the Applied Math Department and IDSS at MIT (2017–2019) after completing his PhD in Computer Science at Princeton University (2012–2017) under Sanjeev Arora . His research focuses on the intersection of machine learning, statistics, and theoretical computer science , emphasizing generative models, representation learning, and out-of-distribution generalization with applications to natural language processing and scientific domains . His recent publications explore edge embeddings in Graph Neural Networks (GNNs) , score matching efficiency , and theoretical foundations of diffusion models . Key contributions include analyzing computational bottlene.com/activities/statistical-and-computational-challenges-in-probabilistic-scientific-machine-learning-sciml/">NSF CAREER Award , DOE Computational Science Graduate Fellowship for Stephen Huan, and co-organizing the COLT workshop on Theory of AI for Scientific Computing . He advises PhD students across Machine Learning, Computer Science, and Mathematics , including Bingbin Liu (Kempner Institute Fellow) and Elan Rosenfeld (Google Research Scientist). Teaching includes Probabilistic Graphical Models and Advanced Deep Learning at CMU, plus Applied Mathematics at MIT. His work is supported by NSF, DoD , and OpenAI Superalignment grants. Education : PhD in Computer Science (Princeton), BSc in Computer Science (Princeton) Current Positions : Associate Professor, CMU Machine Learning Department Former Positions : Norbert Wiener Fellow, MIT IDSS & Applied Mathematics Research Areas : Generative Models (GANs, Diffusion Models) Representation Learning Out-of-Distribution Generalization Neural Language Models AI for Scientific Applications Sampling and Optimization Algorithms Scientific Awards : NSF CAREER Award (2023) Google Research Award (2024) Amazon Research Award (2022) OpenAI Superalignment Grant (2023) Recent Talks (2023–2025): "Architectural Nuances and Benchmark Gaps in Scientific ML" (UC Berkeley, 2025) "The Statistical Cost of Score-Based Losses" (Simons Institute Boot Camp, 2024) "Neural Networks for PDEs" (ETH Zurich, 2024) "Discernible Patterns in Transformers" (Theory of Interpretable AI, 2024) He leads a research group producing work at the interface of computational complexity and graph learning , with empirical validation on topological bottlenecks and hub node dynamics . Current projects include ICML 2025 paper on edge embeddings in GNNs and COLT 2025 workshop on AI for Scientific Computing co-organized with MIT, Duke, and ETH Zurich collaborators.

Salvatore Cuomoمشاهده پروفایل

دانشیار

• Numerical Analysis
• Mathematical Aspects of Artificial Intelligence
• Scientific Machine Learning
• +۲ مورد دیگر

Salvatore Cuomo, Ph.D., is an Associate Professor of Numerical Analysis at the Department of Mathematics and Applications 'Renato Caccioppoli' of the University of Naples Federico II. He co-founded the MODAL Laboratory and the academic spin-off Predico s.r.l., focusing on mathematical modeling, data analysis, and AI applications. His research bridges theoretical numerical methods with practical AI solutions. Research Focus: Numerical Approximation (NA), Mathematical Aspects of Artificial Intelligence (MAI), and Scientific Machine Learning (SciML). Professional Contributions: Co-inventor of the PredicTS prediction patent, emphasizing real-world applications of numerical analysis and AI. His work spans data-driven modeling, B-spline functions, optimization landscapes in AI, and physics-informed neural networks for interdisciplinary problems.

• Machine Learning
• Geosciences
• Seismology
• +۵ مورد دیگر

Karianne Bergen is an Assistant Professor at Brown University, affiliated with the Departments of Earth, Environmental and Planetary Sciences and Data Science. She holds a courtesy appointment in Computer Science and is the founder of the Scientific Machine Learning (SciML) Research Group. Her work bridges geosciences and AI, focusing on interpretable machine learning for seismology and climate science. Research interests include earthquake monitoring via AI, climate modeling using neural networks, and explainable AI for geoscience applications. Recent projects involve improving sea level projections and analyzing carbon capture materials using GANs and LSTM emulators. Her talk at the 2024 'Exploring System Dynamics in the Natural World with AI' conference highlighted AI-driven climate science. Key achievements include the 2021 Sleeping Bear Award. She advises PhD students in geosciences and data science, focusing on topics like sea ice dynamics and uncertainty quantification. Labs/Teams: SciML Group at Brown University, collaborating with institutions like the SciAI Center. Current research includes prototype-based explainability and flow-based neural networks for environmental projections.

• Machine Learning
• Scientific Machine Learning
• Data Processing Algorithms
• +۲ مورد دیگر

Jeyan Thiyagalingam is a Visitor at the Oxford e-Research Centre and heads the Scientific Machine Learning (SciML) research group at the Rutherford Appleton Laboratory (STFC-RAL), Harwell. His research focuses on applying machine learning and signal processing techniques to solve fundamental scientific challenges. Previously, he served as an Assistant Professor at the University of Liverpool’s School of Electrical Engineering and Electronics and Computer Sciences, and held roles at the University of Oxford as a post-doctoral researcher and James Martin Fellow. He has also worked in industry with MathWorks UK. Dr. Thiyagalingam’s expertise spans machine learning models, data processing algorithms, and signal processing. He holds prestigious fellowships including the British Computer Society Fellowship and is a Senior Member of IEEE. He currently serves as an Associate Editor for the Patterns Journal . His research group at STFC-RAL (SciML) develops and applies advanced computational methods to address scientific problems. While no specific articles are listed here, his work bridges academia and industry, emphasizing practical applications of machine learning in scientific domains.

Baskar Ganapathysubramanianمشاهده پروفایل

استاد

• Computational physics
• Computational mechanics (fluid mechanics and heat transfer)
• Stochastic analysis
• +۹ مورد دیگر

Baskar Ganapathysubramanian is the Joseph C. and Elizabeth Anderlik Professor in Engineering and Professor of Mechanical Engineering at Iowa State University. His research focuses on computational mechanics, stochastic analysis, and AI-driven solutions for complex systems in agriculture and materials science. He holds an Affiliate Professor appointment and leads interdisciplinary projects at the intersection of engineering and data science. Education: Ph.D. and M.S. in Mechanical and Aerospace Engineering from Cornell University (2008, 2006); B.Tech. in Mechanical Engineering from Indian Institute of Technology Madras (2003). Research Interests: Scientific Machine Learning (SciML) for flow simulations and materials design AI in agriculture: precision phenotyping, crop modeling, and pest detection Advanced computational methods: adaptive mesh refinement, physics-informed neural networks Additive manufacturing cybersecurity and process optimization Key Research Themes: His work integrates high-performance computing with AI to address challenges in plant phenomics, organic semiconductor materials, and multiphase flow dynamics. Recent projects include TerraIncognita (species discovery), AgriField3D (maize datasets), and AIIRA (AI for resilient agriculture). Collaborations and Infrastructure: Active in the AI Institute for Resilient Agriculture (AIIRA), developing digital twins for crop systems. Co-developed tools like GRATE2.0 for polymer analysis and FlowBench for fluid dynamics benchmarking.

Peng Chenمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Uncertainty Quantification
• Bayesian Inference
• +۵ مورد دیگر

Peng Chen is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology, part of the College of Computing. His research focuses on Scientific Machine Learning (SciML), Uncertainty Quantification (UQ), and their applications in computational science and engineering. He holds a PhD and Master's from École Polytechnique Fédérale de Lausanne (EPFL) and a Bachelor's from Xi’an Jiaotong University. Education: PhD in Computational Mathematics (EPFL, 2014), Master in Computational Mathematics (EPFL, 2011), Bachelor in Mathematics (Xi’an Jiaotong University, 2009). Research interests include Bayesian inference, stochastic optimization, parallel computing, and scalable algorithms for high-dimensional problems. His work addresses challenges in data-driven modeling of complex systems, such as metamaterial design, plasma fusion optimization, and epidemic modeling. Advising: Supervises PhD and Master’s students in SciML and UQ. Notable students include Jinwoo Go, Yuan Qiu, and Phillip Si. Alumni include Irene Simo Munoz and Leyao Huang. Labs/Teams: Leads the SciML & UQ group at Georgia Tech, developing software like SOUPy and pSVGD for stochastic optimization and Bayesian inference. Collaborates on NSF-funded projects, including a $1.5M award for variational neural networks in physical systems.

• Signal Processing
• Scientific Machine Learning
• Inverse Problems
• +۷ مورد دیگر

Prof. Dr. Ivan Dokmanić is an Associate Professor for Data Analytics at the Department of Mathematics and Computer Science, University of Basel, where he leads the Research Group Signal and Data Analytics. His work bridges data science, signal processing, and physics to address challenges in computational imaging, acoustics, and audiovisual reasoning. His research focuses on developing principled machine learning and signal processing techniques grounded in physical models. Key interests include: Signal Processing (especially scientific and wave-based) Scientific Machine Learning (SciML) Inverse Problems and Imaging Computational Acoustics and Audio Seismic Signal Processing Dokmanić is the principal investigator of the ERC Starting Grant project SWING (Signals, Waves, and Learning), which aims to create a data-driven framework for wave-based inverse problems. This project underscores his leadership in integrating physical models with modern learning techniques. He has received significant recognition through competitive funding, most notably the ERC Starting Grant, highlighting the innovative and high-impact nature of his research. While no detailed list of publications is provided in the text, his work appears to trend toward interdisciplinary methodologies combining physics, signal processing, and machine learning for scientific discovery. Dokmanić is actively involved in research and academic leadership. He supervises a research group and contributes to the academic mission of the University of Basel. Although specific advisees are not listed, his group likely includes PhD and master’s students working on advanced topics in data analytics and signal processing. He has secured substantial grant funding, indicating active research projects and collaboration opportunities. He is affiliated with the Research Group Signal and Data Analytics, which serves as the hub for his interdisciplinary investigations into wave phenomena, inverse modeling, and data-driven scientific computing.

Hadrien Montanelliمشاهده پروفایل

پژوهشگر

• Numerical methods for PDEs
• Integral equations
• Inverse problems
• +۸ مورد دیگر

Hadrien Montanelli is a Researcher in applied mathematics at Inria with the IDEFIX team and a Lecturer at École Polytechnique . His work bridges numerical methods, scientific machine learning, and approximation theory, with applications in wave propagation, cell polarization modeling, and quantum computing theory. His research explores: Numerical solutions for stiff PDEs and nonlocal operators Deep ReLU networks' ability to overcome dimensionality challenges Computer-assisted proofs for PDE existence Quantum logic gates and Schrödinger equation implementation Pattern formation on spheres with applications in developmental biology Recent publications focus on SciML (Scientific Machine Learning), nonlocal calculus on spheres, and advanced numerical methods for inverse problems. Collaborations include prominent researchers like Houssem Haddar , Qiang Du , and Stanislav Shvartsman .

Alexander Heinleinمشاهده پروفایل

استادیار

• Numerical methods for partial differential equations
• Scientific computing
• Domain decomposition methods
• +۶ مورد دیگر

Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.

• Machine Learning
• Scientific Computing
• Artificial Intelligence
• +۳ مورد دیگر

Zongren Zou is a Postdoctoral Scholar Research Associate at the California Institute of Technology, specializing in computational methods and machine learning applications in science and engineering. His work includes contributions to physics-informed neural networks (PINNs), uncertainty quantification (UQ), and scientific machine learning (SciML). Key research areas include: Neural networks for solving Hamilton-Jacobi PDEs Multi-time uncertainty quantification in scientific computing Computational mathematics applications in engineering He has developed open-source repositories such as NeuroSEM and MHPINN to advance methodologies in these domains.

Ke Chenمشاهده پروفایل

استادیار

• Scientific Machine Learning
• PDE Inverse Problems
• Numerical Analysis
• +۴ مورد دیگر

Ke Chen is an Assistant Professor in the Department of Mathematical Sciences at the University of Delaware, affiliated with the Data Science Institute. His research focuses on Scientific Machine Learning (SciML) and PDE Inverse Problems, with applications in biomedical imaging and geophysical sciences. Prior to UD, he held postdoctoral positions at the University of Texas at Austin and the University of Maryland. He earned his Ph.D. in Mathematics from the University of Wisconsin-Madison (2019) and a B.A. from Shanghai Jiao Tong University. Education: Ph.D. in Mathematics, University of Wisconsin-Madison (2016–2019) M.S. in Mathematics, University of Wisconsin-Madison (2015–2016) B.A. in Mathematics and Applied Mathematics, Shanghai Jiao Tong University (2011–2015) Research Interests: Dr. Chen combines numerical analysis and deep learning to address inverse problems in imaging and environmental sciences. His work bridges traditional computational methods with modern AI-driven approaches, aiming to solve challenges in biomedical and geophysical domains. Recent efforts include developing pseudo-differential autoencoder networks for inverse PDE operators and exploring weight decay biases in neural networks for better generalization. Recent Activities: In 2024, he co-organized a minisymposium at the SIAM Conference on Mathematics of Data Science and presented at the SIAM Central States Section Annual Meeting. His research has led to publications on topics ranging from microbial ecology in flooded rice paddies to computational methods for pathogen detection in food safety. Professional Affiliations: Brin Postdoctoral Fellow, University of Maryland (2022–2024) R.H. Bing Instructor, University of Texas at Austin (2019–2022) Labs/Teams: Affiliated with the Data Science Institute at UD, focusing on interdisciplinary collaborations in computational mathematics and machine learning.