Abstract: Scientific discovery increasingly relies on the ability to interpret complex, high-dimensional data. Visual discovery—using computer vision and AI-driven techniques to uncover patterns, structures, and insights—has emerged as a powerful tool across disciplines. By enabling automated analysis of images, videos, and visual signals, researchers can accelerate breakthroughs in areas ranging from biology and medicine to physics and environmental science.
In this talk, Dr. Saket Anand and Dr. Utkarsh Mall from MBZUAI will present how visual discovery methods are reshaping scientific inquiry. The session will highlight advances in computer vision, machine learning, and multimodal analysis that allow scientists to move beyond traditional observation toward scalable, data-driven exploration.
Key themes will include:
The role of visual discovery in analyzing scientific datasets across domains.
Techniques for extracting meaningful features and patterns from complex visual inputs.
Applications in healthcare imaging, environmental monitoring, and experimental sciences.
Challenges of interpretability, scalability, and domain adaptation in scientific contexts.
Future directions for integrating visual discovery with generative and predictive AI models.
The presentation will emphasize case studies and ongoing research, showing how visual discovery is becoming a cornerstone of modern science. By combining computational vision with scientific reasoning, this approach opens new pathways for accelerating discovery and deepening our understanding of the natural world.