Babak Taati

Dr. Taati is a Senior Scientist at KITE, the research arm of the Toronto Rehabilitation Institute, and an Associate Professor at the University of Toronto, with affiliations at the Vector Institute, Intelligent Assistive Technology and Systems Lab (IATSL), and Computational Vision group. As the lead of KITE’s Aging team, he focuses on advancing assistive technologies for aging populations.

Talk Title: Computer Vision in Geriatric Care
As the global population ages, innovative technologies are crucial for providing quality care to individuals with physical or cognitive disabilities. This presentation explores cutting-edge computer vision systems designed to support and enhance elder care. We will discuss two applications: an ambient monitoring system for pain assessment in older adults with severe dementia, addressing the challenge of pain management in non-communicative patients, and a vision-based fall risk monitoring system for long-term care facilities, aimed at preventing falls and improving safety. These examples demonstrate the potential of computer vision to improve geriatric care, offering non-invasive, continuous monitoring solutions that can significantly improve quality of life and care efficiency for aging populations.

Satya Mallick

As the CEO of OpenCV.org, Dr. Satya Mallick leads the world’s largest open-source computer vision library, fostering its growth and success. Dr. Mallick is also the founder and partner of Big Vision LLC, a consulting company specializing in services and products across various domains, including computer vision, AI, deep learning, machine learning, ChatGPT & LLMs, augmented reality, and video analytics. Additionally, he runs LearnOpenCV.com, a widely recognized computer vision blog where he shares expertise and insights with a vast audience of readers.

Title: Small Data, Big Impact: Building Computer Vision Solutions for Underrepresented Populations.

In an era dominated by large-scale datasets, designing computer vision solutions for unique and often underrepresented populations demands rethinking technology and methodology. Infants, toddlers, and the elderly represent groups where data is scarce due to ethical, practical, and privacy concerns but where the potential for positive impact is immense. This keynote will explore the core challenges of building and deploying computer vision systems with limited yet precious data. We will see how approaches involving synthetic data, GenAI, stereo vision, and even models built using big data can immensely help in solutions where data is sparse, and privacy is critical.