Khalil Al Handawi

Montréal, Québec, Canada

I am a researcher working on solving engineering and mathematics problems using numerical methods. The underlying theme of my research is numerical optimization. I believe that optimization can be used to solve a wide range of decision making problems encountered in design, manufacturing, and operations.

I love teaching and helping others solve everyday decision-making problems using optimization. I also teach optimization principles to engineering students so that they can apply them in their designs, research, and work.

In my research, I have used optimization to solve practical engineering problems, where the functions of interest are given by expensive numerical simulation. I also used the same principles to solve similar problems in other fields such as epidemiology, statistical modeling, and transportation.

Interests

	Systems optimization
	Numerical simulation
	Machine learning
	Uncertainty quantification

Education

Jan, 2017 - Dec, 2020	PhD, Mechanical Engineering McGill University
Aug, 2013 - Dec, 2015	MSc, Mechanical Engineering Khalifa University
Aug, 2009 - May, 2013	BSc, Mechanical Engineering Khalifa University

news

Nov 6, 2023	Our paper on design space exploration is accepted for publication at the ASME Journal of Mechanical Design
Jun 26, 2022	Our paper on scalable designs has been selected for the ASME Journal of Mechanical Design Editor's choice award!
Mar 23, 2022	Giving a talk on stochastic optimization in epidemiology at “Meet a GERAD Researcher!”

selected publications

IEEE TETCI

Optimization of Infectious Disease Prevention and Control Policies Using Artificial Life

Al Handawi, Khalil, and Kokkolaras, Michael

IEEE Transactions on Emerging Topics in Computational Intelligence 2021

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The spread of an infectious disease such as COVID-19 is governed by complex social interactions that are challenging to model. Policy makers must take measures to control the spread of infection despite the unknowns that accompany a novel epidemic. The principles of artificial life govern the intricacies of social interaction through which diseases can spread. Agent-based models can capture these complexities for a subset of the population by defining the behavior of individual agents. While they can be computationally expensive for large populations, their outcomes are stochastic. Therefore, they can be used to test disease prevention policies, that can be difficult to simulate using deterministic approaches. We developed an agent-based model that is inspired by several interactive simulations on the internet for describing the COVID-19 pandemic. We define metrics to estimate the socio-economic cost of disease prevention policies on the population. We present a policy-making tool based on blackbox optimization and evolutionary computation that provides well-rounded intervention measures in terms of socio-economic cost and disease control. Several intervention measures are suggested by the algorithms with varying degrees of disease control and socio-economic cost. Policy makers can choose an intervention measure based on their preference. This research recommends combining computational intelligence principles and the use of mathematical algorithms for identifying the critical amount of intervention necessary to control infectious diseases and formulate intervention policies that minimize socio-economic cost.