Doina Precup

Doina Precup
Name	Doina Precup
Nationality	Canadian–Romanian
Alma mater	McGill University; Université de Montréal; Massachusetts Institute of Technology
Occupation	Computer scientist; researcher; executive
Known for	Reinforcement learning; artificial intelligence; machine learning

Doina Precup is a Canadian–Romanian computer scientist and AI researcher known for work in reinforcement learning, hierarchical learning, and ethical deployment of artificial intelligence. She has held academic positions, led research groups, and served in industry and policy advisory roles connecting institutions in North America and Europe. Precup's career spans contributions to robotics, healthcare applications, and AI governance through collaborations with universities, research labs, and corporations.

Early life and education

Precup was born in Romania and completed undergraduate studies before immigrating to Canada, where she pursued graduate training at McGill University and the Université de Montréal, later obtaining a Ph.D. from the Massachusetts Institute of Technology under advisors connected to the fields represented by contemporaries at McGill University, Université de Montréal, and Massachusetts Institute of Technology. Her doctoral work built on foundations established by researchers from University of Alberta, University of Toronto, Carnegie Mellon University, and influenced by algorithms appearing in publications at NeurIPS, ICML, AAAI, and IJCAI. During her education she engaged with labs and centers such as those at Montreal Institute for Learning Algorithms, Vector Institute, MIT Computer Science and Artificial Intelligence Laboratory, and initiatives related to European Research Council funded projects.

Academic career and research

As a professor and researcher, Precup has supervised students and postdoctoral fellows who later joined faculties at institutions including McGill University, Montréal Neurological Institute and Hospital, University College London, and University of Oxford. Her publications appeared in venues such as Journal of Machine Learning Research, IEEE Transactions on Neural Networks and Learning Systems, and conference proceedings of NeurIPS, ICML, AAAI, and IJCAI. Research themes include hierarchical reinforcement learning influenced by frameworks from Temporal Difference Learning, Markov decision processes, and methodologies aligned with work at DeepMind, OpenAI, Google Research, and Facebook AI Research. Collaborations connected her to researchers at University of California, Berkeley, Stanford University, Princeton University, Harvard University, and industry labs focused on robotics like Boston Dynamics, Toyota Research Institute, and NVIDIA Research.

Her group explored applications in healthcare and decision-making similar to efforts at Massachusetts General Hospital, Johns Hopkins University, and projects linked to National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, and Canadian Institute for Advanced Research. Methodological contributions touched on topics studied by peers from University of Pittsburgh, University of Washington, University of Michigan, and University of Pennsylvania and were cited alongside work from authors affiliated with Columbia University, Yale University, Imperial College London, and ETH Zurich.

Industry roles and contributions

Precup transitioned between academia and industry, taking leadership roles comparable to positions at Google DeepMind, Facebook AI Research, Microsoft Research, and Amazon Web Services research groups. She has engaged with corporate and governmental stakeholders including Government of Canada initiatives, European Union advisory bodies such as Horizon 2020, and multinational partnerships aligned with standards set by organizations like IEEE and ISO. Her industry contributions span projects with corporations and consortia including IBM Research, Apple Machine Learning Research, SAP, and startup ecosystems linked to MaRS Discovery District and Element AI-related networks.

Precup participated in policy discussions alongside figures from United Nations, Organisation for Economic Co-operation and Development, and panels connected to World Economic Forum where debates involved representatives from European Commission, U.S. National Science Foundation, and Canadian Institute for Advanced Research. She has also consulted for healthcare technology ventures, regulatory bodies, and non-profit initiatives collaborating with institutions such as Hospitals of Ontario Physician Services and professional societies like Association for the Advancement of Artificial Intelligence.

Awards and honors

Throughout her career Precup received recognitions from academic and professional organizations similar to prizes and fellowships awarded by Royal Society of Canada, Canadian Institute for Advanced Research, IEEE, and funding from agencies including Natural Sciences and Engineering Research Council of Canada and Social Sciences and Humanities Research Council. She has been invited to deliver plenary talks and keynote lectures at conferences such as NeurIPS, ICML, AAAI, IJCAI, and symposiums organized by Royal Society, Engineering and Physical Sciences Research Council, and regional academies in Europe and North America. Honors include memberships and distinctions aligned with leadership roles in consortia like Vector Institute and advisory appointments paralleling those held at Canadian Institute for Advanced Research and international research councils.

Personal life and affiliations

Precup maintains affiliations with academic institutions including McGill University and with research networks such as Canadian Institute for Advanced Research, Vector Institute, Montreal Institute for Learning Algorithms, and international collaborations tied to European Research Council and Horizon Europe. She serves on editorial boards and program committees for journals and conferences including Journal of Machine Learning Research, NeurIPS, ICML, and AAAI. Her personal interests intersect with community engagement and mentorship programs associated with organizations like Association for Women in Science, Women in Machine Learning, Society for Industrial and Applied Mathematics, and outreach initiatives coordinated with MIT Media Lab and regional innovation hubs.

Category:Computer scientists Category:Artificial intelligence researchers