FSU Mathematics
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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 differential equations, data assimilation and stochastic inferences, stochastic optimal control, uncertainty quantification for machine learning, and application of machine learning in sciences.
Auburn University, Auburn, USA Ph.D. in Mathematics, 2014 Shandong University, Shandong, China M.S. in Mathematics, 2009 Zhejiang University, Hangzhou, China B.S. in Mathematics, 2006
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
Calculus I, Applied Math Seminar: Advances in Numerical Methods for Data Science
- Frameworks, Algorithms and Scalable Technologies for Mathematics (FASTMath-5) DOE - Advanced Scientific Computing Research (University PI) - Computational Framework for Unbiased Studies of Correlated Electron Systems (CompFUSE) DOE - Advanced Scientific Computing Research (University PI) - Efficient Adaptive Backward SDE Methods for Nonlinear Filtering problems NSF - Computational Mathematics Program (DMS-1720222 PI) - Accurate Quantified Mathematical Methods for Neutron and Experimental Sciences (ACUMEN) DOE - Advanced Scientific Computing Research (University PI)
- 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
Current Ph.D. students: Azaryah Wilson, Zezhong Zhang, Hui Sun Former Ph.D. students: Xin Li (2021); placement: data scientist at Wells Fargo Bank |
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1. 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, to appear, 2021
2. 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, to appear, 2021
3. H. Sun, and F. Bao, Meshfree Approximation for Stochastic Optimal Control Problems, Communications in Mathematical Research, to appear, 2021
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. R. Archibald, R. Bao and X. Tu, A Direct Filter Method for Parameter Estimation, Journal of Computational Physics, (398), 108871, 2019
13. 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
14. 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
15. 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
16. X. Xie, F. Bao and C. Webster, Evolve Filter Stabilization Reduced-Order Model for Stochastic Burgers Equation, Fluids, 3(4), 3040084, 2018
17. 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.
18. 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.
19. 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.
20. F. Bao and V. Maroulas, Adaptive Meshfree Backward SDE Filter, SIAM Journal on Scientific Computing, 39(6), A2664-A2683, 2017
21. 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.
22. 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.
23. 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.
24. F. Bao, R. Archibald, D. Bansal and O. Delaire, Hierarchical Optimization for Neutron Scattering Problems, Journal of Computational Physics, 315: 39-51, 2016.
25. 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.
26. 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.
27. 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.
28. 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.
29. 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.
30. 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.
31. 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.