Congratulations to Hongyu Fu for passing his defense today. Hongyu’s thesis is titled “Efficient Machine Learning Techniques for Low- and High-Dimensional Data Sources.” His Dissertation Committee includes Jay Kuo (Chair), Antonio Ortega, and Aiichiro Nakano (Outside Member). The Committee members were very pleased with Hongyu’s high-quality work and professional presentation skills. MCL News team invited Hongyu for a short talk on her thesis and PhD experience, and here is the summary. We thankHongyu for his kind sharing, and wish him all the best in the next journey. A high-level abstract of Hongyu’s thesis is given below:

“This thesis concentrates on the development of efficient machine learning methodologies for both low and high-dimensional data. It presents a novel, feature-based machine learning technique, noted the Subspace Learning Machine (SLM), specifically designed for low-dimensional data. SLM combines the efficiency of decision trees and the effectiveness of multi-layer perceptrons to solve classification and regression problems with high performance and low model complexity. For high-dimensional data, the thesis proposes an efficient feed-forward machine learning framework and an adaptive SLM design with soft partitioning for image classification. These methods offer lightweight, adaptive models with low computational requirements and high performance.”

Hongyu shared his Ph.D. experience at MCL as follows :
“My PhD experience at the USC Media Communications Lab under the guidance of Prof. Kuo was both challenging and rewarding, providing me with a solid foundation in machine learning and multimedia data processing. The PhD training at MCL with the weekly report and seminar series was quite unique,  we practiced clear summarization ability, writing skills, and sharpened our presentation and oral communication abilities extensively. Prof. Kuo was extremely energetic, and working with constantly high efficiency, with more than 10 students in the lab all year round, Prof. Kuo was still capable of reading the reports, having individual meetings with every PhD student at least once a week, and providing valuable feedback. During my years at MCL, I could constantly feel the desire and passion that Prof. Kuo shed on doing the good research. For the job hunting, either industry or academia, Prof. Kuo cares for all his students and spares no effort to help with a good landing or position for everyone. All the best wishes to Prof. Kuo, MCL, and green learning to have a prosperous future.”