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About

Dazheng Zhang is a leading researcher in multitask learning (MTL), a paradigm that trains models to solve multiple related tasks simultaneously by sharing information across them. Zhang’s major contribution is a landmark three-part survey, *Multitask Learning 1997–2024*, which traces MTL from its origins in the 1990s through the present day. The first part, “Fundamentals,” published in 2025, systematically reviews the core principles, architectures, and algorithms that distinguish MTL from single-task learning, highlighting how shared representations improve efficiency and generalization. This comprehensive work has already garnered early citations, signaling its importance as a definitive reference for the field. By synthesizing nearly three decades of research, Zhang provides an invaluable roadmap for students and practitioners seeking to understand MTL’s evolution, key breakthroughs, and open challenges. The survey’s scope and clarity position Zhang as a key synthesizer and historian of this rapidly advancing area, offering both newcomers and experts a clear, structured foundation for future work in multitask learning.