ARCHIVE
2023
Jul. 2023: The paper "A nonlocal theory of heat transfer and micro-phase separation of nanostructured copolymers" is published in the International Journal of Heat and Mass Transfer. Congratulations to Pratyush, Danial, and Kira! [link]
Jun. 2023: A preprint paper titled "Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate" is now available. This paper is based on a chapter of my Ph.D. dissertation. [link]
Jul. 2023: I gave a contributed talk at USNCCM17. The talk is in MS403: "Uncertainty Quantification for Learning and Data-Driven Predictive Modeling of Complex Systems". I will present works on Bayesian predictive modeling of diblock copolymer thin film self-assembly. [slides] [preprint]
Apr. 2023: A preprint paper titled "A nonlocal theory of heat transfer and micro-phase separation of nanostructured copolymers" is now available. [link]
Mar. 2023: I gave an invited talk at APS March Meeting [website] in Las Vegas, in the focus session on UQ for soft matter physics. I presented work on Bayesian predictive modeling for block copolymer self-assembly using triangular transport maps. [slides] [preprint]
Mar. 2023: I gave an invited talk at SIAM CSE 2023 [website] in Amsterdam. The talk is in the session on additive manufacturing. The session is featured on the "Industry At-a-Glance" flyer under Design & Manufacturing. I presented works on UQ using image characterizations of polymer thin films based on my dissertation work.
Mar. 2023: Two papers are accepted for publication in the Journal of Computational Physics (on the same day)! Congrats to DC, Tom, Prashant, Peng, Dr. Oden, and Omar!
Feb. 2023: My dissertation is the winner of the 2023 Oden Institute Outstanding Dissertation Award! [thesis][news]
2022
Dec. 2022 : I successfully defended my dissertation! [slides]
Nov. 2022: I will defend my dissertation on Monday, Nov. 28, 12:30~1:30 PM CT at POB 6.304 and on Zoom [link expired]. You are welcome to join!
Nov. 2022: I gave an invited talk at the 5th Annual Meeting of SIAM TX-LA Section, in MS: Recent advances in learning [website]. I presented results in our recent paper [preprint] on understanding and improving the reliability of neural operators as surrogates of parametric PDEs in Bayesian inverse problems. I was supported by SIAM Student Travel Award. [slides]
Nov. 2022: I will transition to a postdoctoral position at The Oden Institute, supervised by J. Tinsley Oden and Omar Ghattas. I will be a part of the new M2dt (Multifaceted Mathematics of Digital Twins) center, a Multifaceted Mathematics Integrated Capability Center funded by the DOE's ASCR program. [news] [website]
Oct. 2022: Our new paper (co-authored with Prashant, Tom, Dr. Oden & Omar) is now announced on arXiv! It aims to understand and improve the reliability of neural operators as surrogates of parametric PDEs in infinite-dimensional Bayesian inverse problems. [preprint]
Jun. 2022: I gave a contributed talk at USNC/TAM 2022, in MS: Phase Field Study of Microstructures and Behaviors of Advanced Materials [website]. I presented results based on our SISC paper [journal] on a globally convergent modified Newton method for the direct minimization of the Ohata--Kawasaki energy, a phase field model of diblock copolymer self-assembly. [slides]
Jun. 2022: Our new paper (co-authored by Ricardo, Josh, Fengyi, Youssef, Dr. Oden & Omar) is now announced on arXiv! In this paper, we put forth a Bayesian framework for model calibration of diblock copolymer self-assembly model under aleatoric uncertainties. The model calibration is solved using a likelihood-free inference method via measure transport in adjunct with summary statistics designs. We use expected information gain (can be easily computed) to measure the utility of summary statistics! [preprint]
May 2022: I gave a contributed talk at EMI 2022, in MS: Physics-Based Data-Driven Modeling and Uncertainty Quantification in Computational Materials Science and Engineering [website]. I presented unpublished work on a Bayesian model calibration of diblock copolymer thin film self-assembly using the power spectrum of microscopy image data. The paper containing the presented results is coming soon! [slides]