Rujian Chen

I am a PhD student in the EECS department at MIT and a member of the Sensing, Learning and Inference (SLI) group within the MIT Computer Science and Artificial Intelligence Lab (CSAIL). I am advised by John W. Fisher III.

My research interests include Bayesian models and inference, machine learning and decision making. I am currently working on analysis of Bayesian nonparametric models, methods for model selection and optimization, and scalable approximate inference in complex physical systems.

Separately, I have worked on Bayesian methods for time series and image data analysis, applied to smart sensors and geoscience problems. Prior to my PhD research, I worked on video-based drone control and navigation, advised by Nick Kingsbury at Cambridge University; and on computational biology advised by David Gifford at MIT.


Publications / Talks

  • Posterior Consistency for Gaussian Process Surrogate Models with Generalized Observations.
    R. Chen and J.W. Fisher III.
    Neural Information Processing Systems (NeurIPS) 2022, Workshop on Gaussian Processes, Spatiotemporal Modeling and Decision Systems. Paper Poster Code

  • Bayesian Modeling and Decision Making for a Well System.
    R. Chen, J. Pacheco and J.W. Fisher III.
    INFORMS Annual Meeting 2018. Abstract

  • Model-based Information-theoretic Planning and Optimization.
    R. Chen, J. Pacheco and J.W. Fisher.
    Poster, ONR Decentralized Perception Basic Research Challenge, Cambridge MA, 2018.

  • Differential Chromatin Profiles Partially Determine Transcription Factor Binding.
    R. Chen and D. Gifford.
    PLOS ONE 12(7). Jul 2017. Paper Code

  • Bayesian Well Rate Estimation.
    R. Chen, J. Pacheco, G. Rosman and J.W. Fisher III.
    Technical talk at ExxonMobil Upstream Research Company, Houston TX, 2017.

  • Demonstrating UAV Navigation, Environment Recognition and Exploration.
    R. Chen.   MEng Thesis, Cambridge University, 2013.

Work Experience

  • Quantitative Research Intern, Citadel LLC.
  • Summer Undergraduate Research Fellow (SURF), California Institute of Technology
  • Summer Intern, Intersil UK.


  • Teaching Assistant, MIT 6.438, Algorithms for Inference
  • Teaching Assistant, MIT 6.C01, Modeling with Machine Learning: From Algorithms to Applications

Selected Awards

Technical Skills

machine learning, Bayesian inference, deep learning, time series modeling, image processing, optimization, python, matlab, R, pytorch, git, latex, linux.