Michell Payano
MilaAbout
the speaker
Michell Payano started her career as an Economist in the public sector in Dominican Republic, where she developed a foundation in data driven policymaking. Recognizing the transformative potential of AI to address complex societal challenges, she made a transition to technology by pursuing a Master's degree in Computer Science with a specialization in machine learning at Université de Montréal, in collaboration with Mila. Her professional trajectory reflects an interdisciplinary vision, spanning from economic policy analysis to cutting edge research in computer vision and she is currently conducting research in multi-agent reinforcement learning for policy making.
Beyond her professional and academic background, Michell is a passionate advocate for democratizing access to technology. She has served as an adjunct professor in her home country, and in Canada, she served as a mentor to young girls through Technovation Montréal, working to close the gender gap in tech by inspiring and equipping the next generation of women in AI.
Session Spotlight
What Models Can See: Inside Modern Computer Vision
Discover how modern vision models can detect objects without ever being trained on them. In this hands-on workshop, we will explore the evolution from traditional object detection to zero-shot models like Grounding DINO, which understand natural language prompts to identify anything you can describe. After a short theory session, we’ll dive into a practical Google Colab session where the attendees will experiment with detecting custom objects using only text descriptions (no training data required). By the end, they will understand both the power and limitations of these models, and leave with practical skills to apply them to their own projects in research, industry, or creative applications.