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FSU Mathematics

Feng Bao

Assistant Professor

Department of Mathematics, Florida State University

Email: bao@math.fsu.edu

Office: LOVE Building 312







I am an assistant professor of mathematics at Florida State University. My research interests include analysis and numerical solutions for stochastic (partial) differential equations, data assimilation and stochastic inference, stochastic optimal control, uncertainty quantification, and mathematical foundations for machine learning.







Ph.D. in Mathematics, Auburn University, Auburn, USA

M.S. in Mathematics, Shandong University, Shandong, China

B.S. in Mathematics, Zhejiang University, Hangzhou, China



Professional Experience




Florida State University, USA

Assistant Professor, Department of Mathematics, 2018 - present

University of Tennessee at Chattanooga, USA

Assistant Professor, Department of Mathematics, 2016 - 2018

Oak Ridge National Laboratory, USA

Postdoc, Division of Computational Science and Mathematics, 2014-2016



Teaching in Spring 2023




Applied Math Seminar: Stochastic Computing and Data Science

Stochastic Computing and Optimization



Active Research Grants




-       CAREER: An Efficient Computational Framework for Data Driven Feedback Control (DMS-2142672)

NSF - CAREER Program (PI, $424,053)

-       Reliable and Efficient Machine Learning for Leadership Facility Scientific Data Analytics (DE-SC0022297)

DOE - Advanced Scientific Computing Research (PI, $301,222)

-       Frameworks, Algorithms and Scalable Technologies for Mathematics (FASTMath-5)

DOE - Advanced Scientific Computing Research (University PI, $345,471)

-       Computational Framework for Unbiased Studies of Correlated Electron Systems (CompFUSE)

DOE - Advanced Scientific Computing Research (University PI, $225,000)

-       Accurate Quantified Mathematical Methods for Neutron and Experimental Sciences (ACUMEN)

DOE - Advanced Scientific Computing Research (University PI, $180,914)



Delivered Research Projects




-       Efficient Adaptive Backward SDE Methods for Nonlinear Filtering problems (DMS-1720222)

NSF - Computational Mathematics Program (PI, $124,995)



Honors and Awards




-       NSF CAREER Award, 2022

-       ORAU Ralph E. Powe Junior Faculty Enhancement Award, 2017

-       Winner of 37th SIAM SEAS Conference Student Paper Competition, 2013

-       Don and Sandy Logan Fellowship, Auburn University, 2012-2013


Graduate Students




-       Current Ph.D. students: Siming Liang, Yunzheng Lyu, Azaryah Wilson, Zezhong Zhang, Hui Sun

-       Former Ph.D. students: Xin Li (2021), placement: data scientist at Citi Bank


Editorial Board




-       Foundations of Data Science

-       Discrete and Continuous Dynamical System - series S

-       Numerical Methods for Partial Differential Equations









1.     Z. Zhang, R. Archibald, F. Bao, A PDE-based Adaptive Kernel Method for Solving Optimal Filtering Problems, Journal of Machine Learning for Modeling and Computing, to appear, 2022

2.     O. Dyck, F. Bao, M. Ziatdinov, Ali Y. Nobakht, K. Law, A. Maksov, B.G. Sumpter, R. Archibald, S. Jesse, S.V. Kalinin, and D.B. Lingerfelt, Strain-Induced Asymmetry and Om-Site Dynamics of Silicon Defects in Graphene, Carbon Trends, 9, 100189, 2022

3.     R. Archibald, F. Bao, Y. Cao, and H. Zhang, A Backward SDE Method for Uncertainty Quantification in Deep Learning, Discrete and Continuous Dynamical Systems - Series S, to appear, 2022

4.     R. Archibald, and F. Bao, Kernel Learning Backward SDE filter for Data Assimilation, Journal of Computational Physics, 455, 111009, 2022

5.     X. Li, F. Bao, and K. Gallivan, A Drift Homotopy Implicit Particle Filter Method for Nonlinear Filtering Problems, Discrete and Continuous Dynamical Systems - Series S, 15(4), 727-746, 2022

6.     X. Xie, F. Bao, T. Maier, and C. Webster, Analytic Continuation of Noisy Data Using Adams Bashforth Residual Neural Network, Discrete and Continuous Dynamical Systems - Series S, 15(4), 877-892, 2022

7.     N.G. Cogan, F. Bao, R. Paus, and A. Dobreva, Data Assimilation of Synthetic Data as a Novel Strategy for Predicting Disease Progression in Alopecia Areata, Mathematical Medicine and Biology: A Journal of the IMA, 06,14778602 2021

8.     H. Sun, and F. Bao, Meshfree Approximation for Stochastic Optimal Control Problems, Communications in Mathematical Research, 37(3), pp. 387-420, 2021

9.     F. Bao, Y. Cao, and J. Yong, Data Informed Solution Estimation for Forward Backward Stochastic Differential Equations, Analysis and Applications, 19(3), pp. 439-464, 2021

10.   O. Dyck, M. Ziatdinov, S. Jesse, F. Bao, A.Y. Nobakht, A. Maksov, S. Shin, B.G. Sumpter, R. Archibald, K.J.H. Law, and S.V. Kalinin, Probing potential energy landscapes via electron-beam-induced single atom dynamics, Acta Materialia, 203, 116508, 2021

11.   R. Archibald, F. Bao, J. Yong, and T. Zhou, An Efficient Numerical Algorithm for Solving Data Driven Feedback Control Problems, Journal of Scientific Computing, 85-51, 2020

12.   A.Y. Nobakht, O. Dyck, D.B. Lingerfelt, F. Bao, M. Ziatdinov, A. Maksov, B.G. Sumpter, R. Archibald, S. Jesse, S.V. Kalinin, and K.J.H. Law, Reconstruction of Effective potential from statistical analysis of dynamic trajectories, AIP Advances, 10(6), 065034, 2020

13.   R. Archibald, F. Bao, and J. Yong, A Stochastic Gradient Descent Approach for Stochastic Optimal Control, East Asian Journal of Applied Mathematics, 10(4), 635-658, 2020

14.   F. Bao, Y. Cao and X. Han, Forward Backward Doubly Stochastic Differential Equations and Optimal Filtering of Diffusion Processes, Communications in Mathematical Sciences, 18(3), 635-661, 2020

15.   F. Bao and T. Maier, Stochastic Gradient Descent Algorithm for Stochastic Optimization in Solving Analytic Continuation Problems, AIMS Foundations of Data Science, 2(1), 1-17, 2020

16.   F. Bao, R. Archibald and P. Maksymovych, Backward SDE Filter for Jump Diffusion Processes and Its Applications in Material Sciences, Communications in Computational Physics, (27), 589-618, 2020

17.   R. Archibald, R. Bao and X. Tu, A Direct Filter Method for Parameter Estimation, Journal of Computational Physics, (398), 108871, 2019

18.   F. Bao and Y. Cao, Adjoint Forward Backward Stochastic Differential Equations Driven by Jump Processes and Its Application to Nonlinear Filtering Problems, International Journal for Uncertainty Quantification, 9(2), 143-159, 2019

19.   F. Bao, L. Mu and J. Wang, A Fully Computable Posteriori Error Estimation for the Stokes Equations on Polytopal Meshes, SIAM Journal on Numerical Analysis, 57(1), 458-477, 2019

20.   O. Dyck, F. Bao, M. Ziatdinov, A. Y. Nobakht, S. Shin, K. Law, A. Maksov, B.G. Sumpter, R. Archibald, S. Jesse, and S.V. Kalinin, Leveraging Single Atom Dynamics to Measure the Electron-Beam-Induced Force and Atomic Potentials, Proceedings of Microscopy and Microanalysis, 24, pp. 96-97, 2018

21.   X. Xie, F. Bao and C. Webster, Evolve Filter Stabilization Reduced-Order Model for Stochastic Burgers Equation, Fluids, 3(4), 3040084, 2018

22.   C. Yang, D. Posny, F. Bao and J. Wang, A Multi-scale Cholera Model Linking Between-host and Within-host Dynamics, International Journal of Biomathematics, 11(3), 1850034, 2018. 

23.   F. Bao, Y. Cao and W. Zhao, A Backward Doubly Stochastic Differential Equation Approach for Nonlinear Filtering Problems, Communications in Computational Physics, 23(5), pp. 1573-1601, 2018. 

24.    K. Kang, V. Maroulas, I. Schizas and F. Bao, Improved Distributed Particle Filters for Tracking in Wireless Sensor Network, Computational Statistics and Data Analysis, 117: 90-108, 2018. 

25.   F. Bao and V. Maroulas, Adaptive Meshfree Backward SDE Filter, SIAM Journal on Scientific Computing, 39(6), A2664-A2683, 2017 

26.   F. Bao, Y. Cao, X. Han and J. Li, Efficient Particle Filtering for Stochastic Korteweg-De Vries Equations, Stochastics and Dynamics, 17(2),1750008, 2017. 

27.   F. Bao, R. Archibald, J. Niedziela, D. Bansal and O. Delaire, Complex Optimization for Big Computational and Experimental Neutron Datasets, Nanotechnology, 27(48), 484002, 2016. 

28.   F. Bao, Y. Tang, M. Summers, G. Zhang, C. Webster, V. Scarola and T.A. Maier, Fast and Efficient Stochastic Optimization for Analytic Continuation, Physical Review-B, 94: 125149, 2016. 

29.   F. Bao, R. Archibald, D. Bansal and O. Delaire, Hierarchical Optimization for Neutron Scattering Problems, Journal of Computational Physics, 315: 39-51, 2016. 

30.   F. Bao, Y. Cao, C. Webster and G. Zhang, An Efficient Meshfree Implicit Filter for Nonlinear Filtering Problems, International Journal for Uncertainty Quantification, 6(1), 19-33 2016. 

31.   F. Bao, Y. Cao, A. Meir and W. Zhao, A First Order Fully Discretized Numerical Algorithm for Backward Doubly Stochastic Differential Equations, SIAM/ASA Journal on Uncertainty Quantification, 4(1), 413-445, 2016. 

32.   B. Hu, Y. Cao, W. Zhao and F. Bao, Identification of hydraulic conductivity distributions in density dependent flow fields of submarine groundwater discharge modelling using adjoint-state sensitivities, SCIENCE CHINA Earth Sciences, 59(4): 770-779, 2016. 

33.   F. Bao, Y. Cao and W. Zhao, A First Order Semi-Discrete Algorithm for Backward Doubly Stochastic Differential Equations, Discrete and Continuous Dynamical Systems - Series B, 2(5), pp. 1297-1313, 2015. 

34.   F. Bao, Y. Cao, C. Webster and G. Zhang, A Hybrid Sparse-Grid Approach for Nonlinear Filtering Problems Based on Adaptive-Domain of the Zakai Equation Approximations, SIAM/ASA Journal on Uncertainty Quantification, 2(1), pp.784-804, 2014. 

35.   F. Bao, Y. Cao and X. Han, An Implicit Algorithm of Solving Nonlinear Filtering Problems, Communications in Computational Physics, 16(2), pp. 382-402, 2014. 

36.   F. Bao, Y. Cao and W. Zhao, Numerical Solutions for Forward Backward Doubly Stochastic Differential Equations and Zakai Equations, International Journal for Uncertainty Quantification, 1(4), pp. 351-367, 2011.