Meet our Team

Jacob completed his B.Sc. in Environmental Engineering and Ph.D. in Civil Engineering at North Carolina State University in the United States in 2016 and 2020, respectively. His Ph.D. dissertation was on simulating evolution of low-voltage electric grids with distributed energy technology adoption using agent-based modelling techniques. His dissertation was funded by the United States National Science Foundation through the Graduate Research Fellowship Program. Jacob’s research applies sociotechnical modelling techniques to simulate water and energy networks as complex adaptive systems. His work has developed an array of agent-based modelling frameworks to analyze critical infrastructure protection, ecotourism, solar PV technology diffusion, decentralized water systems, and peer-to-peer energy markets. He is currently a Research Analyst at the Institute and performs energy system modelling and optimization research to analyze electrification and decarbonization pathways for the Canadian economy.

Josée Provençal holds a Ph.D. in political science from the University of Ottawa, specialising in energy. She was a post-doctoral fellow at the Montreal Climate Lab of the Institut National de la Recherche Scientifique (INRS), working with the City of Montreal and the Ouranos research consortium on urban adaptation to climate change. She has more than ten years of experience in the energy industry in Canada and internationally. She established a knowledge management platform at the Quebec Association for Energy Efficiency (AQME), advised international clients on their strategic positioning at her company AETOS Energy, Environment, and influenced public decision-makers to accelerate the energy transition as a senior analyst at the Quebec Association for the Production of Renewable Energy (AQPER). She is active in two research groups, Polémos, an independent research group on degrowth, and the Observatoire de la post-croissance et la décroissance (OPCD), where she researches the political economy of energy, energy sufficiency, and the social relations that structure our energy consumption.

Pierre Cilpa is a seasoned systems and data expert, holding a Bachelor's in Electrical Engineering from Polytechnique Montréal and certifications in SAFe 5, Microsoft Azure, and SQL. As the Founder of SOLR Consultation Inc., he has demonstrated prowess in project management, cloud infrastructure, and IoT systems development. His deep understanding of network, security, and data management has been crucial in navigating complex information systems. At Hydro-Quebec, as an ETL Python Developer Consultant, Pierre led projects with precision, ensuring top-quality reporting and efficient ETL pipeline operations. His tenure at the National Bank of Canada was marked by his management of data center operations and skillful use of SQL with Snowflake, enhancing strategic process and project planning in the Chief Data Office. With a strong background in electronics design and product development, Pierre has contributed to innovations in wireless power transfer and ultrasonic signal technology. Notably, as a collaborator on patented floating photovoltaic power generator technology, he continues to be an influential figure at the convergence of innovation, data, and technology, upholding the highest standards in his field.

Mahdis, a Polytechnique Montreal alumna, holds a Ph.D. in Applied Mathematics and has been affiliated with various Canada Research Chairs, including CIRRELT and GERAD. Her academic journey began at the Polytechnic of Tehran (Amirkabir), where she completed her B.Sc. and M.Sc. in Industrial Engineering. Her research focuses on mathematical modeling, data-driven optimization, and network optimization across different industries. Mahdis brings eight years of expertise in mixed integer linear programming, non-linear programming, and binary quadratic programming, coupled with strong skills in visual reports and business intelligence. With over six years of experience in Machine Learning, she excels in data extraction, cleaning, and application of diverse techniques, including supervised and unsupervised learning, reinforcement learning, deep learning, and time series forecasting. Her programming proficiencies span Python, Java, Matlab, SQL, PyTorch, and TensorFlow. Mahdis is also adept in using tools and frameworks like Pyomo, Plotly, SciPy, Spark, and Databricks.