About Me

(in about 744 words)

I am currently an Assistant Computational Statistician jointly in the Decision and Infrastructure Sciences and the Mathematics and Computer Science divisions at Argonne National Laboratory. I am also a scientist at-Large fellow at University of Chicago Consortium for Advanced Science and Engineering (UChicago CASE) Before that, I spent three years as a postdoc with Stefan Wild at Argonne National Laboratory. I obtained my PhD in Statistics from Virginia Tech in July 2019, where I worked on Stochastic Computer Model Calibration and Uncertainty Quantification with Prof. David Higdon.

Research Interest

My broad research interests include, but not limited to, scalable algorithm for calibrating large scale computer models, inverse problem, uncertainty quantification, and bayesian optimization. I am particularly interested in finding ways to represent model inadequacy that is meaningful and facilitates extrapolation. Check project page for more details into my research.

Google Scholar







[Thesis] Stochastic Computer Model Calibration and Uncertainty Quantification - Virginia Tech.

Invited and contributed talks

  • Double Sequential Calibration Strategy For Stochastic Simulation Models. ASA Joint Statistical Meeting, Washington DC, August, 2022.

  • Combining Calibrated Simulator With Observations to Infer Photometric Redshift. SIAM Conference on Uncertainty Quantification (UQ22), Atlanta GA, April, 2022.

  • Scalable Statistical Inference of Photometric Redshift via Data Subsampling. International Conference on Computational Science (ICCS 2021), Virtual, June, 2021.

  • Scalable Gaussian Process Regression Based on Data Subsampling.
    SIAM Conference on Computational Science and Engineering (CSE21), Virtual, March, 2021.

  • Characterization of Traffic Flow Using Large Scale User Data.
    Joint Statistical Meeting (JSM2020), Virtual, August, 2020.

  • Clustering based Gaussian Process Emulation and Calibration of a Stochastic Agent based Model.
    SIAM Conference on Mathematics of Data Science (MDS20), Virtual, June, 2020.

  • Emulation, Calibration and UQ for Stochastic Agent Based Model.
    Los Alamos National Laboratory, NM, March, 2020.

  • Dirichlet Process Gaussian Process Model for Photometric Redshift.
    Joint Statistical Meeting (JSM2019), Denver CO, July-August, 2019.

  • Stochastic Agent Based Model Calibration.
    Spring Research Conference, VT Blacksburg VA, May, 2019.

  • Clustering based Gaussian Process Emulation and Calibration of a Stochastic Agent based Model.
    Statistical Perspective on Uncertainty Quantification, UNC Chappel Hill, May, 2019.

In news

Other stuff I enjoy doing

  • Running, biking, cross-country skiing - check my Strava profile.
  • Photography - my old album at flickr.

Who inspires me (from all disciplines in life) and has a homepage