Statistical Sciences ARES: Justin Bois

Join us at the Statistical Sciences Applied Research and Education Seminar (ARES) with.

Justin Bois

Teaching Professor
Caltech Division of Biology and Biological Engineering
California Institute of Technology

Free Hybrid Event | Registration Required

Talk Title

Training Scientists to Perform Robust Bayesian Inference


In this talk, I will share my perspectives and experiences training scientists in Bayesian statistical inference, giving examples of what students have learned and applied. In particular, I will highlight:

  1. The importance of domain knowledge in model building
  2. Development of tailor-made models, as opposed to off-the-shelf techniques
  3. Visualization of results
  4. Robust workflows to avoid statistical pitfalls
  5. Use of real data sets in pedagogy and practice

Speaker Profile

Justin Bois is a Teaching Professor in the Division of Biology and Biological Engineering at the California Institute of Technology. He teaches nine different classes there, nearly all of which heavily feature Python. He is dedicated to empowering students in the biological sciences with quantitative tools, particularly data analysis skills.

The event is finished.

Local Time

  • Timezone: America/New_York
  • Date: Mar 13 2023
  • Time: 3:30 pm - 4:30 pm
Rooms 9014 & 9016 U of T


Rooms 9014 & 9016 U of T
700 University Avenue, Toronto, ON M5G 1X6
CANSSI Ontario


CANSSI Ontario


Boris Babic
Boris Babic

Assistant Professor, Department of Statistical Sciences, University of Toronto.