Researchers-in-AI4S

This is the summary for researchers in AI for Science. Currently, the updating list only contained people in the field of AI + applied mathematics and mechanics sorted by Shizheng Wen. Other fields like AI + Molecular dynamics, materials/chemistry/biology will be sorted out soon.

(:notebook: Please notice that this list is very subjective, it only contained the researchers the creator so far. Also, please ignore the note, which is only some characteristics for me to remember.)

👨‍🔬Mainstream ML

Most of the following researchers have a strong background in applied mathematics (numerical analysis and dynamical systems) and data science (machine learning, optimization and statistics), and come from the department of computer science and mathematics in different universities or research institutes (DeepMind, MSR, Google and etc.). They are in the mainstream machine learning community and perfer to publish paper in machine learning conference.

Name	Institution	Research Field	Note
Michael Bronstein	Prof @ Oxford/Deepmind/twitter	Geometric DL, GNN	Online Course 2022
Anima Anandkumar	Prof @Caltech/Nvidia	AI+Science (FNO)	Received her PhD degree at 2009
Max Welling	Prof @UoA/MSR	AI+Science(PDE, MD);GNN;Bayes	Received his PhD in Physics
Siddhartha Mishra	Prof @ETH	Numerical Analysis; AI for physics; physics for AI	Approximation Theory and error bound of PINN, DeepOnet, FNO; DS for GNN
Michael Mahoney	Prof. @UCBerkely	Statics, SciML	Experienced in Graph, HPC, SciML ...
Stephan Günnemann	Prof. @TUM	GNN; ML4DS	Author of GNN FEM network for DS
Benjamin Erichson	Asist. Prof. @ U Pitts	SciML, dynamical systems, deep learning	Postdoc. of Mahoney at Berkeley and Kutz brunton at UW
Jure Leskovec	Prof. @ Stanford	Graph Neural Network	Postdoc. of Mahoney at Berkeley. Leading researcher in the field of Graph. recording courses for GNN
Tobias Pfaff	Researcher @Deepmind	GNN for Physical Simulation	Earlier works are related to CG. Markus Gross's students. Collaborator of Nils Thuerey. First-author of meshgraphnet
Alvaro Sanchez Gonzalez	Researcher @Deepmind	GNN for Physical Simulation	First author of GNS. Finished many GNN related work. Have worked with Miles Cranmer ever.
Peter Battaglia	Research Scientist @Deepmind	GNN, Physics Engine based on GNN	Collobrate with Pfaff and Gonzalez for many GNN works.
Stephan Hoyer	Researcher @GoogleAI	DL for Physicals Science	Active in Twitter. Many representative works include Lagrangian NN, ML accelerated CFD, etc.
Yarin Gal	Prof. @Oxford	Theory:Bayesian Deep Learning; Application	Looking for students
Molei Tao	Associate Prof. @Gatech	Dynamical ML, AI4Science, Physics4AI	Applied/Computational science. PhD student of Houman Owhadi
Houman Owhadi	Prof. @Caltech	Sci. ML, Physics informed learing and mathematics of ML	Advisor of Tao Molei, mathematician
Thomas Kipf	Researcher @Google Brain	Graph Neural Network	Student of Max Welling, first-auth. of ConvolutionalGNN (2017ICRL)
Johannes Brandstetter	Researcher @Microsoft Research Amsterdam	Physics Geometric Deep Learning	Max Welling's Student
Rose Yu	Prof. @UCSD	Physics-informed ML for modeling of sptiotemporal Dynamics	from Northeastern to UCSD. Postdoc of Anime。
Aditi S. Krishnapriyan	Assistant Prof. @UCB	physics-inspired machine learning methods; GNN; development of machine learning methods informed by physical sciences applications including molecular dynamics, fluid mechanics, climate science	Mahoney's PhD Student
Andrew M. Stuart	Prof @Caltech	Data Assimilation; Inverse Problem; Scientific ML	Samuel Lanthaler's (mishra's PhD) advisor for PostDoc
Max Tegmark	Prof. @MIT	AI for Physics; Physics for AI; Physics (Cosmology, quantum Informaiton)	Supervisor of AI Feynman and AI Poincaré (symbolic regression)
Lexing Ying	Prof. @Stanford	PDE + Neural Network	Yiping Lu's advisor at Stanford
Jianfeng Lu	Associate Prof. @Duke	AI+PDE/Science	Phd Students of Weinan E
Weinan E	Prof. @Princeton	AI+Science(PDE/MD)	Propose the concept of AI + Science
Hod Lipson	Prof. @Columbia	Robotics+Hidden variables discovery	Distill natural laws from data Science Nature CS
Evangelos Theodorou	Prof. @ Gatech	Deep Learning, Dynamic & Control, Stochastic process	in the department of Aerospace Engineering

👨‍🔬Domain field ML

Follwing researchers often have a strong domain knowledge in mechanics (solid or fluid) and work on developing data-driven modeling in science and technology by utilizing algorithms from machine learning community. They are not interested in advancing new machine learning algorithm, and prefer publishing their works in top-tier journals.

Name	Institution	Research Field	Note
Peter Benner	Prof. @Max Planck Institute	SciML,model reduction,dynamical system	Researchgate
George Karniadakis	Prof. @Brown	Math+Machine Learning; PINN; Numerical Analysis	PINN, DeepOnet
Themis Sapsis	Prof. @MIT	Analytical, computational and data-driven methods for modeling high-dimensional nonlinear systems	Know George Haller. Phd advisor of Zhongyi Wan
petros koumoutsakos	Prof. @Harvard	Physical Simulation (fluid, molecular), Machine Learning, HPC	Golden Bell Prize
Steven Brunton	Prof. @UW	Data-driven dynamics and control, fluid mechanics	first-author of ARFM (fluid and ML); sparse identification; POD/DMD;
Nathan Kutz	Prof. @UW	Applied Mathematics; Data-driven dynamics	Closely worked with Brunton
Bernd R Noack	Prof. @HIT(sz) @TU Berlin	Fluid Mechanics	Closely collobrated with Brunton, Weiwei Zhang
George Haller	Prof. @ETHz	Dynamical System + Machine Learning	Manifold-based ROM
Rajeev Jaiman	Associate Prof. @UBC	Fluid-structure interaction + Machine Learning	Once at NUS, know Dowell
Ricardo Vinuesa	Assistant Prof. @KTH	Fluid Mechanics + ML	collobrated with Brunton
Dennis M. Kochmann	Prof. @ETH	Solid Mechanics, Materials + ML	Once at Caltech
Karthik Duraisamy	Prof. @Umich	Turbulence + ML	First-author of ARFM ('Turbulence modeling in the age of data')
WaiChing Sun	Prof. @Columbia	Solid Mechanics; computational mechanics + ML	Author of Manifold embedding data-driven mechanics; especially like publishing paper in CMAME
Heng Xiao	Prof. @University of Stuttgart	Fluid Mechanics (Aerospace) + ML	once at VT, co-author of ARFM with Duraisamy
Eleni Chatzi	Prof. @ETH	Structural Mechanics & Monitoring + ML	Got her PhD in Columbia
Laura De Lorenzis	Prof. @ETH	Solid/Computational Mechanics + ML	New Editor in CMAME
Sid Kumar	Assistant Prof. @Delft	Mechanics and Materials + ML	Student of Lorenzis
Jianxun Wang	Assistant Professor @UND	Fluid Mechanics + ML	Students of Hang Xiao
Jinlong Wu	Assistant Professor @UWM	Data-driven dynamics (mechanics)	Students of Hang Xiao, Postdoc of Andrew M. Stuart
Lailai Zhu	Assistant Prof. @NUS	low-Reynolds-number fluid-structure interactions (bio, microfluidics) NUS	Postdoc of Howard stone
Gianluca Iaccarino	Prof. @Stanford	Aerospace + Datadriven Modeling	co-author of ARFM with Duraisamy
Leonardo Andrés Zepeda Núñez

👨‍🔬Chinese Researchers

Following researchers are working at Universities in China now. One of their research fields is scientific machine learning.

Name	Institution	Research Field
孙浩	**人民大学	科学机器学习，符号回归
董彬	北京大学	数值，机器学习理论
张伟伟	西北工业大学	流固相互作用，数据驱动的机器学习
王立威	北京大学	机器学习理论
毛志平	厦门大学	物理信息神经网络

👨‍🎓Young Researchers

Following people are PhD candidates or just have finished their PhD degrees recently. Some of them are very young but have already conducted representative works.

Name	Institution	Mainly Research Field	Representatives
Ziming Liu	MIT	AI4Physics; Physics4AI	AI Poincaré
Yiping Lu	Stanford	Differential equations Neural Network	PDE-Net; Bridging NN and DF
Zongyi Li	Caltech	Scientific ML	FNO
Silviu-Marian Udrescu	MIT	Particle Physics and Artificial Intelligence	AI Feynman
Konstantin Rusch	ETH	Physcis (DS) for ML (GNN)	Graph-Coupled Oscillator Networks
Ben Moseley	ETH	PINN; The application of PINN is real influential field	Finite Basis PINN (FBPINNS):
Miles Cranmer	Princeton	Symbolic regression; AI; Astrophysics;	Rediscovering orbital mechanics with machine learning; Lagrangian NN
Samuel Lanthaler	Caltech	Numerical Analysis (NA) and NA for operator learning	Error estimates for DeepOnet
Nikola B. Kovachki	Caltech	machine learning methods for the physical sciences in theory and practice	Approximation theory and error bound for FNO
Shaowu Pan	RPI	Scientific ML; Turbulence	Neural Implicit Flow
Hannes Stärk	MIT	geometric deep learning and its application in molecular biology	Estabilish the GNN reading group LOG2
Xiaoyu Xie	Northwestern University	Data-driven (AI) in manufacturing, fluid/solid mechanics	Data-driven discovery of dimensionless numbers and govern laws , Northwestern
Han Gao	University of Notre Dame	ML+fluid	Surrogate modeling of fluid without data; PhyGeoNet
Pu Ren	UCB	Scientific ML	PhyCRNet
Eric Qu	Duke Kunshan University	Geometric Deep Learning and AI for Science	Currently None
Jiaqing Kou	RWTH Aachen University	ROM; AI in fluid-structure interaction	Data-driven modeling for unsteady aerodynamics and aeroelasticity
Zhong Yi Wan	Google Research	SML; ROM	Data-assisted reduced-order modeling/forecasting of high-dimensional dynamical/chaotic system.
Prakash Thakolkaran	Delft	Deep Learning; Computational Mechanics and Design	NN-EUCLID
Mattia Cenedese	UBS	Data-driven Dynamical System	Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds
Boyuan Chen	Duke	Robotics+Hidden variables discovery+AI4Science	AP at duke, Hod Lipson's PhD student
Zhengyu Huang	Caltech	Numerical Methods+Machine Learning+Application (Fluid-structure interaction)	PhD advisor: Charbel Farhat PostDoc advisor: Andrew Stuart
Guan-Horng Liu	Gatech	Scalable computational methods for Neural Dyanmics	PhD advisor: Evangelos Theodorou and Molei Tao
Mike Yan Michelis	ETH	AI for robotics	Differentiable Simulation for soft robotics

Thomas Kipf

graph TB
GNS,ICML2020 --mesh, rather than points--> Meshgraphenet,ICLR2021

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