Research Overview

Our research interest is statistical machine learning and data mining. We are particularly interested in modeling and analysis of unstructured data (such as image, function, shape, direction and text data), and modeling time lapsed unstructured data for understanding and controlling time-varying processes involving changes in unstructured data, with applications to data driven discovery of advanced materials and data driven monitoring and control of advanced manufacturing processes.

Our other research is integration of theoretical models and data models through surrogate modeling, model calibration and uncertainty quantification mainly because many time-varying processes we are studying are partly explained by some theoretical models up to a certain degree with uncertainty. Many theoretical models are described as partial differential equations, and a popular choice for a surrogate model and model calibration is Gaussian process. Our interest is on how to model Gaussian process with partial differential equations.

Recent News

  • Sept 2019 - Ali Esmaieeli's paper was accepted for publication in Statistical Modeling. Thanks for the great work.
  • May 2019 - Our lab received a funding from Brookhaven National Lab for supporting our image analysis work for accelerating materials discovery. Thanks for this generous support.
  • May 2019 - Our invited review paper with Yu Ding regarding automated image analysis for materials discovery was published in MRS Communications (paper).
  • May 2019 - Garret Vo successfully defended his PhD dissertation regarding Large-scale multi-target tracking for interacting targets. He is currently working with National Geospatial Intelligence Agency as a data scientist. Congratulations!
  • April 2019 - Our work with Peihua Qiu regarding sequential adaptive sensing for jump regression estimation was submitted as a journal paper (paper).
  • August 2018 - Our paper with Daniel Apley was publisehd in Journal of Machine Learning Research (paper).
  • August 2018 - Ali's paper was publisehd in Technometrics (paper).
  • August 2018 - Xin Li received PhD degree. He will join Oak Ridge National Lab.
  • April 2018 - Vo's paper was published in Pattern Recognition (paper)
  • April 2018 - Our lab received a funding from Air Force Office of Scientific Research - Dynamic Data Driven Application Systems Program
  • March 2018 - Xin's paper was published in Annals of Applied Statistics (paper)