Abstract:

Machine learning (ML) is revolutionizing the way scientists and engineers conduct research. It offers powerful tools to analyze complex data, reveal patterns, and guide discovery. This seminar will provide a practical introduction to applying ML in scientific research, covering key steps such as data preparation, feature engineering, model selection, training, and validation, and result interpretation. Using real-world examples from our published works, the session will demonstrate how ML can accelerate innovation and deepen understanding across disciplines. Designed for researchers with little or no ML background, this talk emphasizes intuitive concepts, accessible tools, and best practices to help participants confidently begin using machine learning to transform their data into meaningful scientific insights.

Biography:

Dr. Yakubu Sani Wudil holds a Ph.D. in Physics from KFUPM and specializes in renewable energy harvesting and storage. He has published over 35 high-impact research papers and holds multiple U.S. patents in the field. His work has been cited more than a thousand times, with an h-index of 18, reflecting his growing influence in the field. Dr. Wudil is currently a Postdoctoral Researcher at IRC-CBM & Laser Research Group, KFUPM.

All faculty, researchers and students are invited to attend.