David Beauchemin
An actuary by training, David decided to continue his studies at the master's level in computer science to become familiar with machine learning. His master's degree in natural language processing focused on external source extraction in an insurance business process. He is now pursuing his studies at the Ph.D. level, where he is interested in personalizing automatically generated content from insurance contracts.
Through his active participation in several projects in the machine learning community, David is committed to bringing the local Artificial Intelligence scene to the forefront. In particular, he is the creator and developer of Deepparse, a library for multilingual address segmentation using deep learning. He is the cofounder of the knowledge sharing organization Meetup Machine Learning Québec. He also acts as president of .Layer, an NPO he co-founded to promote data science. He has also launched a podcast on artificial intelligence, OpenLayer, and acts as a collaborator for the IA café podcast. Finally, David has presented the natural language processing at the Canadian Institute of Actuaries webinar, the issue of reproducibility in machine learning at the Intelligence and Data Institute public webinars, and many other technical topics in machine learning at over twenty events.
Olivier Belhumeur
Passionate about mathematics and computer science, Olivier obtained his bachelor's degree in actuarial science before pursuing a master's degree in artificial intelligence at Laval University. While obtaining his bachelor's degree, Olivier also had the opportunity to study in Lyon to expand his knowledge.
He is particularly interested in automatic natural language processing, but he also has knowledge in the field of insurance and finance. Olivier is also a contributing member of the Deepparse open source library. Mutual aid and solidarity are at the core of his values.
Mathieu Godbout
Mathieu is currently pursuing a doctorate's degree in artificial intelligence (AI). Fascinated by the promises of AI, but mostly by the mathematical mechanics behind these promises, he chooses to start a Ph.D at the winter of 2021. His project focuses on analyzing his main field of interest: reinforcement learning and its certification for deployment in real-world contexts.
A motivated pedagogue, he has created much learning material for courses at all levels and has acted as a teaching assistant in university-level training. In the academic world, Mathieu has also participated in several conferences on AI to present his work. His accomplishments have recently allowed him to be a guest speaker to present AI to high school students in Quebec City in collaboration with the Musée de la Civilisation.
Jean-Samuel Leboeuf
After obtaining his bachelor's and master's degrees in physics at Université Laval, Jean-Samuel redirected his studies towards artificial intelligence, where he became interested in machine learning theory. His interests are varied, particularly in computer vision and automatic natural language processing. His most recent work on decision trees has been published at NeurIPS 2020, one of the most important conferences in the field of AI. Jean-Samuel is also an expert in Python and LaTeX and is the main author of the python2latex library, which simplifies the writing of reports and articles using Python.
Gaël Letarte
After an integrated bachelor's degree in mathematics and computer science, Gaël began graduate studies in machine learning. He is currently completing his Ph.D., during which he has published in various fields, including bioinformatics, natural language processing and machine learning theory. His research concerns the use of the PAC-Bayesian theory for representation learning. His latest work proposing statistical guarantees for binary activated neural networks has been published at the prestigious international conference Neural Information Processing Systems (NeurIPS).
Frédérik Paradis
With a bachelor's degree in computer science at Université Laval and a master's degree at the University of Ottawa, Frédérik is currently a Ph.D. candidate at Université Laval in the field of deep learning. In conjunction with his Ph.D., he is the lead developer of the deep learning library Poutyne, which aims to simplify the development of deep neural networks and eliminate repetitive code.
Frédérik has many experiences as a teacher ranging from assistant to lecturer. Among others, he was a teacher for the 2019 Winter School in Machine Learning at Université Laval and was a teacher for the Introduction to Programming course at Laval University. On the academic side, he has been at multiple times a referee to select papers for the prestigious NeurIPS conference.
Pierre Pothier
As a manager, Pierre constantly seeks to establish the teams he oversees as the internal business partner for digital and technological transformation matters, aiming for the adoption of the best tools and practices in the field, while developing a keen sense of outreach and attentiveness to clients. As part of management teams, he has helped define the business vision, identify the challenges of digital transformation, and orchestrate these initiatives, which are essential for technological perspectives.
His career path has allowed him to work in the high-tech manufacturing sector (EXFO), in the general insurance sector (L'Union Canadienne division of Co-Operators), in consumer services at CAA-Quebec, as vice-president of the IT department, at Revenu Québec, as a senior director in the technology and processing branch and recently as head of the software and network integration program for the Quebec City tramway at the Réseau de transport de la Capitale. He has worked in the management of IT teams, in support, in operations, in the management of relations with suppliers and business units. He has also accompanied these organizations in the realization of major projects such as the implementation of integrated management solutions like SAP, numerous technological infrastructure projects, implementation of information technology solutions, deployment of telephony and collaboration solutions, restructuring of services, implementation of technological and methodological elements for the achievement of compliance such as SOX, PCI.
Pierre holds a Bachelor's degree in Management Information Systems from Laval University obtained in 1986.
Dominique Pothier
Dominique is originally from Quebec City, where he graduated from Laval University in 2015 with a Bachelor's degree in Software Engineering. He then worked as a software developer, both as an internal employee and as a consultant. In 2017, he returned to university to undertake a master's degree in Artificial Intelligence and Deep Learning. He is studying the application of recurrent models to non-sequential data. In 2020, he joined the Baseline foundation team. He now wants to combine these different experiences to enable his clients to develop robust, scalable and transparent artificial intelligence solutions.
On a personal level, Dominique has been continuously involved in Quebec's college debate scene since 2015. His involvement has led him to develop, then teach, a rigorous analytical method and clear communication strategies. In 2019, he won the Canadian national championship in French-speaking academic debate and represented Laval University at the World Championships in English-speaking academic debate in Bangkok, Thailand.
Simon Provencher
Simon is an actuary, computer scientist, expert in machine learning and business intelligence through his training and experience. He acquired his academic experience through a bachelor's degree in actuarial science and computer science and a master's degree in artificial intelligence. He also acquired a strong experience in software engineering at Arcbees, worked in artificial intelligence for Coveo and is currently a research consultant for Cooperators, an insurer.
His technical expertise is in natural language processing, and his preferred fields are insurance and finance, which he knows from programming to distribution. This unique combination has led him to contribute to the new machine learning class offered at Université Laval's actuarial science department.
Listen to this interview where Simon shares his atypical background.
François-Alexandre Tremblay
With a Master's degree in Industrial Relations from Université de Montréal and a Master's degree in Computer Science from Université Laval, François-Alexandre has professional experience in management and artificial intelligence. He has worked on the development and negotiation of large-scale compensation programs as well as on applied projects in People Analytics and Industry 4.0. He has received several honors and merits such as the Natural Sciences and Engineering Research Council of Canada (NSERC) research grant. In 2022, he joins Baseline to share his expertise and interests in reinforcement learning, deep learning, simulation and combinatorial optimization, particularly in the manufacturing and mass industry. He also has a strong background in software engineering, algorithms and programming.
On a personal level, François-Alexandre is fond of traveling, climbing, diving and the outdoors. Passion that he intends to pass on to his twin babies.
