I am a Postdoctoral Research Associate at the Aerospace Computational Design Lab (ACDL), where I work under the supervision of Professor Karen Willcox. My research program is focused on computational methods and numerical analysis for control, optimization, design and uncertainty quantification of large-scale dynamical systems. To enable—or speed-up—these computationally expensive tasks, I am interested in developing new methods and algorithms that use reduced-order surrogate models. With an increased availablilty of data available, I am interested in integrating information from data into decades-old, accurate, first-principles computational models.
March 5-9, 2018: The workshop Reducing Dimensions and Cost for UQ in Complex Systems is coming up, and I am looking forward to present recent results on Conditional-Value-at-Risk estimation there.
April 10-13, 2018: I will present at the great MoRePaS IV conference (Model Reduction of Paramatrized Systems) in Nantes, France.
A preprint of the paper Conditional-Value-at-Risk estimation via Reduced-Order Models with Timur Takhtaganov (Rice), Matthias Heinkenschloss (Rice) and Karen Willcox (MIT) is now posted.
11/21/2017: I gave a presentation about "Model reduction for uncertainty quantification of high-dimensional systems" at the Department of Mathematics at the Johannes Gutenberg University Mainz.
A preprint of the paper Multifidelity probability estimation via fusion of estimators with Alexandre Marques (MIT), Benjamin Peherstorfer (Wisconsin), Umberto Villa (UT Austin) and Karen Willcox (MIT) is now posted.
10/9-13/2017, I visited Professor Matthias Heinkenschloss at Rice University to work on efficient computation of risk measures in optimization.
07/10/2017: During the SIAM Conference on Control and its Applications in Pittsburgh I presented work on "Data-Driven Reduced-Order Models for Control of PDEs with Uncertain Parameters".
06/26/2017: I presented work on "Stabilization of reduced-order flow models through learning-based closure modeling" at the Conference on Classical and Geophysical Fluid Dynamics: Modeling, Reduction and Simulation at Virginia Tech.
05/23/17: At the SIAM Optimization conference in Vancouver, I presented our work on "Data-Driven Model Reduction via CUR-Factored Hankel Approximation".
04/18/2017: I gave a talk at the Sibley School of Mechanical and Aerospace Engineering at Cornell about "Exploiting Low-Dimensional Structures for Sensing and Control of Fluids via Data-Driven Reduced-Order Modeling".
04/03/17: We organized a minisymposium on "Uncertainty Quantification and Model Inadequacy in Combustion Simulations" at the 2017 SIAM International Conference on Numerical Combustion (with Todd Oliver). There, I presented "Multifidelity Failure Probability Estimation in Combustion Modeling".
03/30-31/17: I visited Professor Karthik Duraisamy at the University of Michigan.
03/17/17: I gave a presentation at the Department of Mathematics Colloquium at Virginia Tech.
03/15/17: I gave a talk on "Data-driven low-dimensional modeling for analysis and decision-making" at Sandia National Laboratories in Livermore, CA.
03/02/2017 I presented our work on "Multifidelity Computation of Failure Probabilities" at the SIAM CSE 2017 in Atlanta, GA.
02/28/2017 We co-organized a minisymposium on "Model Order Reduction: Perspectives from Junior Researchers" at SIAM CSE 2017 (with Alessandro Alla ).
02/15/2017: I gave a presentation about "Exploiting low-dimensional structures for sensing and control of fluids via data-driven reduced-order modeling at the "Computational Science Seminar", Department of Mathematics, University of Massachusetts Dartmouth, February 15, 2017.
02/02/2017: I presented our work on "Data-driven modeling for control of systems with time-varying and uncertain parameters" at the workshop on "Data-Driven Methods for Reduced-Order Modeling and Stochastic Partial Differential Equations" at the Banff International Research Station in Canada.
Jan 16-20,2017: I visited Professor Matthias Heinkenschloss at Rice University to work on risk measure estimation and computation.