Panayiotis Petousis

PhD Candidate
  • UCLA Bioengineering

Office: 924 Westwood Blvd, Suite 420, Room P



Panayiotis Petousis is a PhD student at the Medical Imaging Informatics Laboratory. He obtain his bachelors and masters in Biomedical engineering from Imperial College London. Earlier research interests and in particular his master thesis involved neural networks and Image Processing techniques mimicking the Human Visual System. Currently, his research interests involve the development of predictive Probabilistic Graphical Models (PGM) from observational clinical data. Additional research interest are the translation of these PGMs in the clinical domain and the visualization of their inferences.


  1. Hsu W, Petousis P, Yu W, Meng F, Bui AAT. A web-based platform for evaluating and disseminating predictive models. 2017 AMIA Joint Summits on Translational Science. San Francisco,California, USA; 2017.
  2. Shen S, Han SX, Petousis P, Weiss RE, Meng F, Bui AAT, Hsu W. A Bayesian model for estimating multi-state disease progression. Comput Biol Med. 2017;81:1111–120. DOI: 10.1016/j.compbiomed.2016.12.011. PDF
  1. Petousis P, Han SX, Aberle D, Bui AAT. Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network . Artificial Intelligence in Medicine [Internet]. 2016;72:42–55. Retrieved from: DOI: PDF
  1. Shen S, Han SX, Petousis P, Meng F, Bui AAT, Hsu W. Continuous Markov Model Approach Using Individual Patient Data to Estimate Mean Sojourn Time of Lung Cancer. AMIA Annu Symp Proc. San Francisco, CA, USA; 2015.