Student mentorship

École Polytechnique (X), Harvard University, McGill University, Yale University and University of Guelph students were mentored during their tenure in the Blanchette (McGill and X), Gerstein (Harvard and Yale) and Kremer (Guelph) laboratories, respectively.

If you are interested in mentorship opportunities with Dr. Cameron, please complete this survey and email him explaining your interest.

Leo deJong Qanita Turabi Rekkab Gill
Leo deJong 
BSc, Yale
Qanita Turabi 
MBinf, Guelph
3D genome organization
Rekkab Gill 
MSc, Guelph
3D genome organization
Seb Seager David Peng Alexandra Haslund-Gourley
Seb Seager 
BSc, Yale
David Peng 
BSc, Yale
Alex Haslund-Gourley 
BSc, Yale
Matthew Kirchhof Ellen Qian Abhinav Godavarthi
Matthew Kirchhof 
MSc, Guelph
3D genome organization
Ellen Qian 
BSc, Yale
Abhinav Godavarthi 
BSc, Yale
immune profiling
Julian Rubinfien Julie Prost Seara Chen
Julian Rubinfien 
BSc, Yale
Julie Prost 
MSc, McGill
3D genome organization
Seara Chen 
BSc, McGill
3D genome organization
Jack Guo Pouriya Alikhani Côme Weber
Jack Guo 
MSc, McGill
machine learning
Pouriya Alikhani 
BSc, McGill
C. elegans
Côme Weber 
MSc, X
3D genome organization

Rotation, short-term, and internship students

Eric Ni  PhD rotation, Yale (2020–23) — cryo-EM
Tony Li  BSc short term, Yale (2023) — cryo-EM
Susanna Liu  BSc short term, Yale (2022) — cryo-EM
Maaz Syed  BComp short term, Guelph (2022) — 3D genome organization
Mihir Gowda  BSc intern, Harvard (2020) — cryo-EM

Course lecturing

COMP 364 Fall 2017: Computer Tools for Life Sciences (3 Credits)

School of Computer Science, McGill University
Co-instructed with Carlos G Oliver


Introduction to computer programming in a high level language: variables, expressions, types, functions, conditionals, loops, objects and classes. Introduction to algorithms, data structures (lists, strings), modular software design, libraries, file input/output and debugging. Emphasis on applications in the life sciences.

By the end of this course, students will be able to:

  1. Design and describe precise, unambiguous instructions that can be used [by a computer] to solve a problem or perform a task
  2. Translate these instructions into a language that a computer can understand (Python);
  3. Write programs that solve complex problems (especially those arising in Life Sciences) by decomposing them into simpler subproblems
  4. Apply programming-style and structure conventions to make your programs easy to understand, debug and modify
  5. Learn independently about new programming-language features and libraries by reading documentation and by experimenting

Python v3.6 (via Anaconda) and Sublime (or any other plain text editor)


How to Think Like a Computer Scientist: Interactive Edition (Python)

Student evaluation

      Quizzes: 5% (10 quizzes worth 0.5% each)
      Assignments: 35% (5 assignments worth 7% each)
      Midterm exam: 20%
      Final exam: 45%

Example lecture below:

An example Jupyter notebook assignment may be downloaded here.
(requires Anaconda and Python 3)

Certificate of College Teaching Preparation (CCTP)

Certificate of Completion (July 2020)

Administered by the Yale Center for Teaching and Learning (CTL) and Center for the Integration of Research, Teaching and Learning (CIRTL), the CCTP program provides comprehensive training in effective college teaching.

Requirements of the CCTP and CIRTL association:

  1. CIRTL MOOC: an introduction to evidence-based undergraduate STEM teaching
  2. Six CTL Advanced Teaching Workshops (ATWs), CIRTL workshops, or short courses
  3. Two occasions of observing others teaching with written reflections
  4. Participation in two learning communities
  5. Compiled teaching portfolio

The compiled teaching portfolio is available upon request. Please send an email with “Teaching portfolio request” as the subject line.