Max Robotics — Global Service Robot Directory + US Certification

About

Zhaoming Kong is a leading researcher in multitask learning (MTL), a paradigm that trains models to solve multiple related tasks simultaneously by leveraging shared information. His major contributions are crystallized in a landmark three-part survey series, co-authored in 2024–2025, which comprehensively maps MTL’s evolution from its 1990s origins through deep learning and into the era of pretrained foundation models. The first installment, “Unleashing the Power of Multi-Task Learning,” has already garnered 5 citations, while the follow-up, “Part I: Fundamentals,” adds another. Together, these works provide the field’s most systematic taxonomy of MTL architectures, optimization strategies, and task-relationship modeling—offering a definitive roadmap for researchers moving beyond single-task learning. Kong’s surveys are distinguished by their historical depth and forward-looking analysis, making them essential references for anyone working in transfer learning, multi-objective optimization, or foundation model fine-tuning. By synthesizing three decades of progress, Kong has established himself as a key chronicler and conceptual architect of modern multitask learning.