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Research interests: Bayesian statistics, Monte Carlo methods, deep learning, reinforcement learning, variational inference, information fusion, model uncertainty, stochastic optimization.

Latest News

• Three of our manuscripts have been accepted for presentation at ICASSP 2020 in Barcelona. Hats off to my colleagues and co-authors (Marija Iloska, Liu Yang, Hechuan Wang) on the fantastic effort. Here are the manuscript titles:
  • “Enhanced Mixture Population Monte Carlo Via Stochastic Optimization and Markov Chain Monte Carlo Sampling”
  • “A Particle Gibbs Sampling Approach to Topology Inference in Gene Regulatory Networks”
  • “Indoor Altitude Estimation of Unmanned Aerial Vehicles Using a Bank of Kalman Filters”
• Our journal article entitled “Stochastic Gradient Population Monte Carlo” has been accepted for publication in the next issue of the IEEE Signal Processing Letters.
• I was the recipient of the IACS Junior Researcher Award.
• Our manuscript entitled “Efficient Adaptive Multiple Importance Sampling” has been accepted for presentation at EUSIPCO 2019.
• Our manuscript entitled “A Novel Particle Filter for High-Dimensional Systems Using Penalized Perturbations” has been accepted for presentation at EUSIPCO 2019.
• I was the recipient of a Best Student Paper Award at ICASSP 2019!
• Our manuscript entitled “A Variational Adaptive Population Importance Sampler” has been accepted for presentation at ICASSP 2019.
• Check out our journal paper “Robust Covariance Adaptation in Adaptive Importance Sampling“, which was published to the June 2018 issue of the IEEE Signal Processing Letters.

Links: LinkedIn, GitHub