Curriculum Vitae 

Education

• Ph.D., Mathematics, University of Pittsburgh, PA, USA, 2013.

• M.S., Applied Mathematics, Université d’Orléans, Orléans, France, 2008.

• B.S., Mathematics, Honor Program, University of Science, Ho Chi Minh City, Vietnam, 2006.

Professional Experience

• 2016 – now: Research Staff, Computer Science and Mathematics Division, Oak Ridge National Laboratory.

• 2013 – 2016: Postdoctoral Research Associate, Computer Science and Mathematics Division, Oak Ridge National Laboratory.

• 2008 – 2013: Teaching/Research Assistant, Department of Mathematics, University of Pittsburgh.

• 2006 – 2008: Instructor, Department of Mathematics, University of Science, Vietnam.

Research Interests

• Compressed sensing

• Optimization for machine learning

• High-dimensional approximations

• Numerical solution for partial differential equations

• Turbulence modeling, coupling free flow and porous media flow

Current Funding Support

• DOE Advanced Scientific Computing Research, Reliable and efficient machine learning for leadership facility scientific data analytics, Senior Investigator, 2021-2024.

• DOE SCiDAC-FES partnership, Center for Simulation of Plasma – Liquid Metal Interactions in Plasma Facing Components and Breeding Blankets of a Fusion Power Reactor, Senior Investigator, 2023-2027.

• DOE SciDAC, Frameworks, Algorithms and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute, Team Member, 2020-2025. 

• ORNL Laboratory Directed Research and Development, Assurance of Artificial Intelligence for Science Applications, Co-Principle Investigator, 2021-2024. 

Softwares

• AdaDGS: An adaptive black-box optimization method with directional Gaussian smoothing for high-dimensional functions (https://github.com/HoangATran/AdaDGS)

• BABIES: Black-box Attack Based on IntErpolation Scheme (https://github.com/HoangATran/BABIES)

• OWL: Orthogonally Weighted L_{2,1} Regularizer (https://github.com/a-petr/OWL)

Publications

My Google Scholar profile can be found here. 

Journal papers 

1. Hoang Tran, Qiang Du, Guannan Zhang. Convergence analysis for a nonlocal gradient descent method via directional Gaussian smoothing, submitted, https://arxiv.org/abs/2302.06404 (2023).

2. Armenak Petrosyan, Konstantin Pieper, Hoang Tran. Orthogonally weighted L_{2,1} regularization for rank-aware joint sparse recovery: algorithm and analysis, submitted, https://arxiv.org/pdf/2311.12282 (2023).

3. Shi, Zhongshun, Hang Ma, Hoang Tran, and Guannan Zhang. Compressive-sensing-assisted mixed integer optimization for dynamical system discovery with highly noisy data, submitted, https://arxiv.org/abs/2209.12663 (2023).

4. A. J. Wilson, H. Tran and D. Lu. Uncertainty Quantification of Capacitor Switching Transient Location using Machine Learning, in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2023.3286173 (2023).

5. Hoang Tran, Clayton Webster. Analysis of sparse recovery for Legendre expansions using envelope bound, Numer. Methods Partial Differ. Eq. 38 (2022),  2163–2198.

6. Majdi Radaideh, Hoang Tran, Lianshan Lin, Hao Jiang, Drew Winder, Sarma Gorti, Guannan Zhang, Justin Mach, Sarah Cousineau, Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions, Nuclear Instruments and Methods in Physics Research Section B, volume 525, pp. 41- 54, 2022. 

7. Nick Dexter, Hoang Tran, Clayton Webster. On the Strong Convergence of Forward-Backward Splitting in Reconstructing Jointly Sparse Signals, Set-Valued Var. Anal 30, 543–557 (2022).

8. Richard Archibald, Hoang Tran. A dictionary learning algorithm for compression and reconstruction of streaming data in preset order, Discrete and Continuous Dynamical Systems Series S, 15 (4): 655-668, 2022.

9. Jiaxin Zhang, Hoang Tran, Guannan Zhang. Accelerating reinforcement learning with a Directional-Gaussian-Smoothing evolution strategy. Electronic Research Archive, 2021, 29(6): 4119-4135. doi: 10.3934/era.2021075

10. Yiming Xu, Akil Narayan, Hoang Tran, Clayton Webster. Analysis of the ratio of l1 and l2 norms in compressed sensing, Applied and Computational Harmonic Analysis, Volume 55, pp. 486-511, 2021. 

11. Nick Dexter, Hoang Tran, Clayton Webster. A mixed l1 regularization approach for sparse simultaneous approximation of parameterized PDEs, ESAIM: Mathematical Modelling and Numerical Analysis, 53(6), pp. 2025-2045, 2019. 

12. Armenak Petrosyan, Hoang Tran, Clayton Webster. Reconstruction of jointly sparse vectors via manifold optimization, Applied Numerical Mathematics 144, pp. 140-150, 2019. 

13. Anh Tran, Hoang Tran. Data-driven high-fidelity 2D microstructure reconstruction via non-local patch-based image inpainting, Acta Materialia 178, pp. 207-218, 2019.

14. Anh Tran, Dehao Liu, Hoang Tran, Yan Wang. Quantifying uncertainty in the process-structure relationship for Al-Cu solidification, Modelling and Simulation in Material Science and Engineering 27 (2019) 064005.

15. Hoang Tran, Clayton Webster. A class of null space conditions for sparse recovery via nonconvex, non-separable minimizations, Results in Applied Mathematics 3 (2019) 100011. 

16. Abdellah Chkifa, Nick Dexter, Hoang Tran, Clayton Webster. Polynomial Approximation via Compressed Sensing of High-dimensional Functions on Lower Sets, Math. Comp., 87 (2018), pp. 1415-1450.

17. Michaela Kubacki, Hoang Tran. Non-iterative Partitioned Methods for Uncoupling Evolutionary Groundwater-Surface Water Flows, Fluids 2017, 3, 47; doi:10.3390/fluids2030047. 

18. Hoang Tran, Clayton Webster, Guannan Zhang. Analysis of Quasi-Optimal Polynomial Approximations for Parameterized PDEs with Deterministic and Stochastic Coefficients, Numer. Math. (2017), 137:451-493. 

19. Martina Bukac, William Layton, Catalin Trenchea, Marina Moraiti, Hoang Tran. Analysis of Partitioned Methods for Biot System, Numer. Methods Partial Differential Equations, 31: 1769–1813, 2015. 

20. Nan Jiang, Hoang Tran. Analysis of A Stabilized CNLF Method with Fast Slow Wave Splittings for Flow Problems, Comput. Methods Appl. Math., 15(3), pp. 307–330, 2015.

21. Nan Jiang, Michaela Kubacki, William Layton, Marina Moraiti and Hoang Tran. Unconditional Stability of a Crank-Nicolson Leap-Frog Stabilization and Applications, J. Comput. Appl. Math., 281 (2015), 263-276. 

22. William Layton, Hoang Tran, Catalin Trenchea. Numerical Analysis of Two Partitioned Methods for Uncoupling Evolutionary MHD Flows, Numer. Methods Partial Differential Equations, 30(4), 1083-1102, 2014. 

23. William Layton, Hoang Tran, Catalin Trenchea. Analysis of Long Time Stability and Errors of Two Partitioned Methods for Uncoupling Evolutionary Groundwater - Surface Water Flows, SIAM J. Numer. Anal., 51(1), 248-272, 2013.  

24. William Layton, Hoang Tran, Xin Xiong. Long Time Stability of Four Methods for Splitting the Evolutionary Stokes-Darcy Problem into Stokes and Darcy Sub-problems, J. Comput. Appl. Math., 236 (13) (2012), 3198-3217.

25. William Layton, Lars Roehe, Hoang Tran. Explicitly Uncoupled Variational Multiscale Stabilization of Fluid Flow, Comput. Methods Appl. Mech. Engrg. 200 (2011), No. 45-46, pp. 3183-3199.

Conference Papers 

26. Lianshan Lin, Hoang Tran, Majdi Radaideh, Sarma Gorti, Srdjan Simunovic, Hao Jiang, Drew Winder, Sarah Cousineau, Material Model Parameters Optimization in Liquid Mercury Target Dynamic Simulation with Machine Learning Surrogates, 2023 International Mechanical Engineering Congress & Exposition (ICEME 2023), New Orleans, LA, USA.

27. Lianshan Lin, Hoang Tran, Majdi Raddaideh, Austin Hoover, Drew Winder, Sarah Cousineau. Calibration of the 2-phase bubble tracking model for liquid mercury target simulation with machine learning surrogate models, Particle Accelerator Conference (IPAC’23), Venice, Italy.

28. Hoang Tran, Dan Lu, Guannan Zhang (2022). Exploiting the Local Parabolic Landscapes of Adversarial Losses to Accelerate Black-Box Adversarial Attack. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13665. Springer, Cham.

29. Lianshan Lin, Hoang Tran, Majdi Radaideh, Drew Winder, Benchmarking and Exploring Parameter Space of the 2-Phase Bubble Tracking Model for Liquid Mercury Target Simulation, North American Particle Accelerator Conference (NAPAC 2022), Albuquerque, NM, USA.

30. Lianshan Lin, Hoang Tran, Sarma Gorti, Justin Mach, Drew Winder, Application of Machine Learning to Predict the Response of the Liquid Mercury Target at the Spallation Neutron Source, 12th International Particle Accelerator Conference - IPAC’21.

31. Hoang Tran, Dan Lu and Guannan Zhang, Boosting black-box adversarial attack via exploiting loss smoothness, Proceedings of ICLR Workshop on Security and Safety in Machine Learning Systems, 2021.

32. Jiaxin Zhang, Hoang Tran, Dan Lu, Guannan Zhang. Enabling long-range exploration in minimization of multimodal functions, Proceedings of 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021.  https://arxiv.org/abs/2002.03001 

33. Anh Tran, Hoang Tran, 2D Microstructure Reconstruction for SEM via Non-local Patch-Based Image Inpainting, TMS 2021 150th Annual Meeting & Exhibition Supplemental Proceedings, 495. 

34. Nick Dexter, Hoang Tran, Clayton Webster. Reconstructing high-dimensional Hilbert-valued functions via compressed sensing, 13th International Conference on Sampling Theory and Applications (SampTA 2019).

35. William Layton, Hoang Tran, and Catalin Trenchea. Stability of partitioned methods for magnetohydrodynamics flows at small magnetic Reynolds number, Contemp. Math., vol. 586, pp. 231-238, 2013.

36. Timothy Luciani, Adrian Maries, Hoang Tran, Mehdi Nik, Levent Yilmaz, Elisabeta Marai. A Novel Method for Tracking Tensor-based Regions of Interest in Large-Scale, Spatially-Dense Turbulent Combustion Data, IEEE VisWeek 2012, Poster Abstracts with System Demonstration, pp. 1-2, 2012.

Book Chapters 

37. Hoang Tran, Clayton Webster, Guannan Zhang. A Sparse-Grid Method for Bayesian Uncertainty Quantification with Application to Large Eddy Simulation Turbulence Models, In: Garcke J., Pflüger D. (eds) Sparse Grids and Applications - Stuttgart 2014. Springer Lecture Notes in Computational Science and Engineering, vol 109, pp. 291-313, 2016.

Technical Reports 

38. Hoang Tran, Guannan Zhang. AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient, preprint. https://arxiv.org/abs/2011.02009.

39. Hoang Tran, Catalin Trenchea, Clayton Webster. A Convergence Analysis of Stochastic Collocation Method for Navier-Stokes Equations with Random Input Data, ORNL Technical Report, Oak Ridge National Laboratory, 2014.