PeopleofSuperintelligence

Researcher

Hanane Dagdougui

Hanane Dagdougui is an energy-systems researcher who applies optimization and machine learning to smart grids, microgrids and smart buildings. After a 2011 PhD in Systems Engineering from a joint Genoa and Mines ParisTech program, she held research posts in Genoa and at ÉTS Montréal and joined Polytechnique Montréal in 2017. With Vincent Taboga she combined distributed ADMM optimization with deep learning to plan building temperature setpoints in 2023, and in 2024 she coauthored an Applied Energy paper on neural differential equations for temperature control under demand response. She was promoted to full professor in June 2024 and coauthored a Conversation article on digital intelligence and greenhouse gas emissions in March 2026. Today she is a Full Professor at Polytechnique Montréal and an Associate Academic Member of Mila.

Timeline

  1. 2011

    A joint doctorate in systems engineering

    She earns a PhD in Systems Engineering in 2011 through an international joint program of the Faculty of Engineering of Genoa and Mines ParisTech. Her thesis covers the optimization of a green hydrogen supply chain and decision support systems for selecting sites for hydrogen production from renewables.

  2. 2013–2016

    Researcher in Genoa and Montréal

    In 2013, she is a research assistant in the Department of Informatics, Bioengineering, Robotics and System Engineering at the University of Genoa. From 2013 to 2016, she is an institutional researcher in the Department of Electrical Engineering at ÉTS Montréal, where she develops optimization models for power systems and renewable energy-based systems.

  3. 2017

    Joining Polytechnique Montréal

    She joins the Department of Mathematical and Industrial Engineering at Polytechnique Montréal in 2017. Her research there covers distributed optimization theory and the use of mathematical optimization and machine learning on problems of smart grids, microgrids and smart buildings.

  4. December 2023

    Distributed Planning Networks

    In December 2023, she and Vincent Taboga post an arXiv paper that combines distributed optimization using ADMM with deep learning models to plan indoor temperature setpoints. Their algorithm, called Distributed Planning Networks, is tested on an 18-zone building modeled in EnergyPlus and manages Demand Response peak events.

  5. June 2024

    Promotion to full professor

    In June 2024, she is promoted to Full Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal.

  6. July 2024

    Neural differential equations for buildings

    In 2024, she coauthors an Applied Energy paper with Vincent Taboga, Clement Gehring, Mathieu Le Cam and Pierre-Luc Bacon that uses continuous-time neural differential equation models to predict building temperature and HVAC power use. A planning algorithm inside an MPC framework controls the temperature setpoint to limit HVAC power consumption, and the continuous-time models are more sample-efficient and robust to missing and irregular observations. A Mila blog post presents the work in July 2024.

  7. March 2026

    A Conversation article on emissions

    In March 2026, she coauthors an article in The Conversation with Diane Gorcy, Annie Levasseur and Olivier Bahn on digital intelligence, which covers AI, algorithms, big data and connected objects, as a lever for the shift to low-carbon energy systems. The article also addresses the emissions and material impacts that come from producing and running these tools.

  8. As of October 2026

    Professor at Polytechnique Montréal

    She is a Full Professor at Polytechnique Montréal and an Associate Academic Member of Mila - Quebec Artificial Intelligence Institute. Her research topics at Mila include deep learning, distributed systems and optimization.

Career

  1. Since 2017Polytechnique MontréalFull Professor
  2. NowMilaAssociate Academic Member
  3. From 2013University of GenoaResearch Assistant
  4. 2013–2016École de Technologie SupérieureInstitutional Researcher

Education

  1. 2011University of GenoaPhD
  2. 2011MINES ParisTechPhD

From the biography.

  1. Universities (31%) and startups and other companies (25%) employ the largest shares of 2020–2026 entrantsIncludes Mila. Among 819 people whose first recorded role started in 2020–2026 and who have a known current organization, 60% work in commercial sectors (25% at startups and other companies, 23% at frontier labs and 13% at big tech, with 9 in two), and 31% work at universities, compared with 66% for earlier entrants.
  2. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes Mila. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  3. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Mila. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Polytechnique Montréal. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Hanane Dagdougui yet. Send us a free-licensed photo.

  1. Hanane Dagdougui | Mila mila.quebec. Checked 21 October 2026.
  2. Hanane Dagdougui : GERAD gerad.ca. Checked 21 October 2026.
  3. A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events arxiv.org. Checked 21 October 2026.
  4. Hanane Dagdougui promoted to full professor : GERAD gerad.ca. Checked 21 October 2026.
  5. Neural differential equations for temperature control in buildings under demand response programs ideas.repec.org. Checked 21 October 2026.
  6. Neural Differential Equations for Temperature Control in Buildings Under Demand Response Programs | Mila mila.quebec. Checked 21 October 2026.
  7. L'intelligence numérique peut aider à réduire les GES… et les aggraver. Voici comment jongler avec ce paradoxe theconversation.com. Checked 21 October 2026.