Faraz Khoshbakhtian

About

I am an Industrial Engineering PhD candidate at University of Toronto. My specialized area of research delves into the symbiotic relationship between machine learning and combinatorial optimization, with particular emphasis on applications in healthcare and pandemic mitigation strategies. I develop novel learning algorithms that can be used to solve large-scale optimization problems such as the Critical Node Detection Problem.

I am a member of morLAB and I am advised by Prof. Dionne M. Aleman.

I am currently a research intern at Mastercard, where my work centers on developing new self-supervised learning algorithms for universal node embeddings on large-scale graphs.

Beyond the realm of engineering, my academic foundation is rooted in Philosophy from my undergraduate studies, and I continue to regard myself as an ongoing student in this discipline. Outside of research, I enjoy making music, photography, wandering on long walks, and cinema .

My resume is available here.

Research

I am deeply curious about the intricate dynamics of influence and power in complex systems. Right now, my research is zoomed in on large-scale pandemic modeling, especially where network representation learning meets the Critical Node Detection Problem (CNDP).

In my MASc program during 2020-21, I worked on some cool machine learning applications, mainly focusing on the early prognosis of COVID-19 patients. This experience gave me a solid foundation, propelling me into my current PhD journey.

I earned my HBSc in Philosophy, Computer Science, and Statistics from the University of Toronto in 2020. Along the way, I picked up a couple of awards—Woodsworth College Brookfield’s Leadership Scholarship in 2018 and the Scholars in Residence award in 2017. I also had the chance to be part of the Socrates project at the Philosophy Department in 2018, which was an incredible experience.

Academic writings and publications

  • Khoshbakhtian, F., Validi, H., Ventresca, M., Aleman, D. M. (2023) Distance-based critical node detection for effective vaccine policies. submitted to Operations Research Letters. (preprint, code)
  • Pirmorad, E., Khoshbakhtian, F., Mansouri, F., and Farahmand, A. M. (2021) Deep reinforcement learning for online control of stochastic partial differential equations. The Symbiosis of Deep Learning and Differential Equations. (pdf)
  • Khoshbakhtian, F., Lagman, A., Aleman, D. M., Giffen, R., and Rahman, P. (2021). Prediction of severe COVID-19 infection at the time of testing: A machine learning approach. (preprint)
  • Khan, S. S., Khoshbakhtian, F., and Ashraf, A. B. (2021). Anomaly detection approach to identify early cases in a pandemic using chest X-rays. Proceedings of the Canadian Conference on Artificial Intelligence. (pdf)

Conference and workshop presentations

(bold for the presenter)

  • Khoshbakhtian, F., Validi, H., Ventresca, M., Aleman, D. M. Distance-based critical node detection for effective vaccine policies. INFORMS Annual Meeting. Phoenix, US. October 2023 (scheduled)
  • Khoshbakhtian, F., Validi, H., Ventresca, M., Aleman, D. M. Distance-based critical node detection for effective vaccine policies. INFORMS Healthcare. Toronto, Canada. July 2023
  • Khoshbakhtian, F., Validi, H., Ventresca, M., Aleman, D. M. Distance-based critical node detection for effective vaccine policies. CORS / Optimization Days. Montreal, Canada. May 2023.
  • Khoshbakhtian, F., Validi, H., Ventresca, M., Aleman, D. M. Distance-based critical node detection for effective vaccine policies. Panoptic: view on global optimization. Florida, US. March 2023.
  • Khoshbakhtian, F., Lagman, A., Aleman, D. M., Giffen, R., and Rahman, P. Prediction of severe COVID-19 infection at the time of testing: A machine learning approach. CORS/INFORMS International Conference. Vancouver, Canada. June 2022.
  • Pirmorad, E., Khoshbakhtian, F., Mansouri, F., and Farahmand, A. M. Deep reinforcement learning for online control of stochastic partial differential equations. Spotlight presentation at The Symbiosis of Deep Learning and Differential Equations. virtual. Dec 2021.

Other writings

  • Stories of Autonomy: A Critical Introduction in Defense of a Belief in the Indifference of the Will (pdf)

Teaching

At the university of Toronto, I have served as a Teaching Assistant and Head Teaching Assistant for a series of programming, data analytics, and philosophy courses:

  • MIE1624: Introduction to Data Science and Analytics
  • MIE253: Data Modelling
  • MIE250: Introduction to Object Oriented Programming
  • APS106: Fundamentals of Computer Programming
  • PHL101: Introduction to Philosophy

News

  • August 2023: New personal website is now live!
  • August 2023: Won Ontario Gratuade Scholarship (OGS) (value: 15,000CAD)
  • June 2023: Started an applied science internship at Mastercard
  • May 2023: Won Mitacs Accelerate award (value: 15,000CAD)
  • April 2023: Won Emerging and Pandemic Infections Consortium doctoral award (value: 10,000CAD)
  • October 2022: Co-founded University of Toronto Students for a Free Iran (UTSFI)