Scientific Machine Learning (SciML) Professors and Supervisors

Sashikumaar Ganesanمشاهده پروفایل

استاد

Indian Institute of ScienceIndia

Prof. Sashikumaar Ganesan is a Professor at the Indian Institute of Science (IISc), Bangalore, where he leads the AI for Research and Engineering eXcellence (AiREX) Lab. He also serves as the Founder and Chief AI Officer of Zenteiq AiTech Innovations and has held leadership positions including Chairman of the CDS Department (2018-2024) and Programme Director for Advanced Programs in Gen AI, Computational Data Science and AI, and AI & MLOps since 2020. His research interests include: Scientific Machine Learning (SciML) Physics-Informed Neural Networks (PINNs) High-performance computing Hardware-aware numerical methods Machine Learning Operations (MLOps) Computational Science Prof. Ganesan's work focuses on integrating scientific computing with machine learning to advance AI applications in scientific and engineering domains. His research spans AI for aerospace and defense applications, scientific machine learning, MLOps frameworks, and computational science including finite element analysis and fluid dynamics modeling. He has developed science-native Foundation Models through his company Zenteiq.ai to enhance AI-driven R&D in aerospace and defense sectors. His significant projects include the Indo-German Partnership Program (2020-2024) focused on hardware-aware numerical schemes for multi-GPU supercomputers and the Shell India project on Large Scale Simulation augmented by AI for wind turbine farms. He has mentored over 25 students and published more than 60 works, including the book 'Finite Elements: Theory and Algorithms' (Cambridge University Press, 2017). Prof. Ganesan teaches courses including 'Generative and Agentic AI in Practice' (2025), 'AI and Machine Learning' (2024), and 'High-Performance Computing and Data Science,' while also contributing to societal discourse through COVID-19 projections featured in major Indian media outlets.

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Rui Zhangمشاهده پروفایل

پژوهشگر ارشد

ETH ZurichSwitzerland

Dr. Rui Zhang is a Research Fellow at the Chair of Structural Mechanics and Monitoring within the Department of Civil, Environmental, and Geomatic Engineering (D-BAUG) at ETH Zurich. He holds a Ph.D. in Civil Engineering from Penn State University (2024), a Master’s from Tongji University (2019), and a Bachelor’s from Northwest A&F University (2012). His research focuses on advancing Uncertainty Quantification-integrated Scientific Machine Learning (UQ-SciML) frameworks to enhance structural identification and health monitoring, aiming to improve infrastructure safety and sustainability. Education: B.Sc. (Civil Eng., 2012) – Northwest A&F University; M.Sc. (Civil Eng., 2019) – Tongji University; Ph.D. (Civil Eng., 2024) – Penn State University. Current Role: Postdoctoral Fellow at ETH Zurich since 2025, supported by an ETH Zurich Postdoctoral Fellowship. Research Themes: Machine learning applications in structural engineering, computational modeling, and domain-specific insights for infrastructure resilience.

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Wuyang Chenمشاهده پروفایل

استادیار

Simon Fraser UniversityCanada

Wuyang Chen is an Assistant Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2023). His research focuses on integrating AI with physics, developing principled deep learning methods, and advancing areas like scientific machine learning (SciML), computer vision, and foundation models. Notable contributions include PDE-Controller for LLM-based scientific reasoning and neural operators for fluid reconstruction. He has received prestigious awards, including the INNS Doctoral Dissertation Award (2024) and the NSERC Discovery Grant. Dr. Chen serves as an Area Chair for NeurIPS 2025 and ICML 2025. His lab, DeLTA, emphasizes AI applications in science and hosts active collaborations. He teaches CMPT 419/983 (Special Topics in AI) and mentors students in research areas like model efficiency and self-supervised learning. Education: Ph.D., Electrical and Computer Engineering, The University of Texas at Austin (2023) Recent Awards: INNS 2024 Doctoral Dissertation Award, iSchools Doctoral Dissertation Award 2024, Google GCP Credit Award Key Activities: Organized NeurIPS 2024 workshop on Foundation Models for Science; presented at AAAI 2025, SIAM CSE25, and Berkeley BLISS seminar

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Hendrik Ranochaمشاهده پروفایل

استاد

Max Planck Institute for ChemistryGermany

Hendrik Ranocha is a Professor in Numerical Mathematics at Johannes Gutenberg University Mainz, Germany. His research focuses on the analysis and development of numerical methods for partial and ordinary differential equations, with particular emphasis on stability and structure-preserving techniques that transfer results from continuous to discrete levels. His educational background includes: PhD in Mathematics from TU Braunschweig (2016-2018), advised by Thomas Sonar MSc in Mathematics from TU Braunschweig (2014-2016) BSc in Mathematics from TU Braunschweig (2011-2014) Exchange student at Yonsei University, Seoul (2013) BSc in Physics from TU Braunschweig (2010-2013) Hendrik Ranocha's research spans Numerical Analysis and Scientific Computing . His work focuses on developing numerical schemes for hyperbolic balance laws and dispersive-dissipative equations, including Discontinuous Galerkin methods, spectral element methods, finite difference schemes, and flux reconstruction. He specializes in structure-preserving methods that conserve entropy/energy, utilizing summation by parts operators and mimetic properties. His research also encompasses Runge-Kutta methods, stability of time integration schemes, adaptivity in time and space, data-driven approaches, and uncertainty quantification. His recent publications demonstrate a strong focus on entropy-stable numerical methods, structure-preserving discretizations, and high-performance computing implementations in Julia. The research trends show increasing emphasis on practical software implementations (Trixi.jl, SummationByPartsOperators.jl), applications to physical systems like compressible Euler equations and shallow water equations, and addressing fundamental numerical challenges in stability and convergence. Hendrik Ranocha leads a research group at Johannes Gutenberg University Mainz with several PhD students and postdocs, including Louis Petri, Marco Artiano, Sebastian Bleecke, Saurav Samantaray, Arpit Babbar, and Valentin Churavy. He collaborates extensively with researchers such as Gregor Gassner, Andrew R. Winters, Michael Schlottke-Lakemper, and Jesse Chan on numerical methods and software development. He is actively involved in open-source scientific computing, contributing to projects like Trixi.jl (a Julia package for adaptive high-order numerical simulations of conservation laws), SummationByPartsOperators.jl, OrdinaryDiffEq.jl, NodePy, and RK-Opt. He is part of the SciML organization, which develops high-performance Julia libraries for scientific machine learning and computational science.

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Christopher Rackauckasمشاهده پروفایل

پژوهشگر

Stony Brook UniversityUnited States

Christopher Rackauckas is a Research Affiliate and Co-PI of the Julia Lab at the Massachusetts Institute of Technology. He serves as Director of Modeling and Simulation at Julia Computing and Lead Developer of the SciML Open Source Software Organization. His work bridges Scientific Machine Learning (SciML) with high-performance computing, focusing on accelerating differential equation solvers, pharmacometric modeling, and integrating mechanistic models with machine learning. PhD in Mathematics from University of California, Irvine Undergraduate at Oberlin College His research spans computational biology, pharmacometrics, quantitative systems pharmacology, and GPU-accelerated computing. He developed the Pumas platform for personalized medicine and the DifferentialEquations.jl solver suite. Key awards include the ISoP Emerging Scientist Award, ACoP 2020 Quality Award, and Ford Predoctoral Fellowship. Collaborations include Moderna and Pfizer for drug development acceleration. Scientific Excellence Award (US Air Force AI Accelerator) IEEE-HPEC Outstanding Paper Award Kovalevsky Outstanding Ph.D. Thesis Award He teaches the MIT course 18.337 Parallel Computing and Scientific Machine Learning and contributes to open-source scientific software in Julia.

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Mamzi Afrasiabiمشاهده پروفایل

مدرس

ETH ZurichSwitzerland

Mamzi Afrasiabi is a Lecturer at the Department of Mechanical and Process Engineering, ETH Zurich. His research focuses on computational mechanics, fluid dynamics, and advanced manufacturing technologies. Computational Mechanics & Fluid Dynamics Manufacturing Process Simulation Multiphysics and Multiscale Modeling High-Performance Computing (HPC) Scientific Machine Learning (SciML) Dr. Afrasiabi holds a GRA Fellowship and Zienkiewicz Scholarship , with editorial roles in journals like the International Journal of Hydromechatronics . He received the CIRP Best Paper Award and is a Corporate Member of the International Academy for Production Engineering (CIRP).

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