Hengjie Yu, Ph.D.
Profile
Dr. Yu’s current research centers on Explainable AI (XAI) for Science, specifically investigating nano-protein/plant-environment interactions. His goal is to integrate domain knowledge with advanced AI to create domain datasets and foundational models, ultimately improving our understanding of how nanomaterials interact with biological systems in the contexts of healthcare and sustainable agriculture.
Education background
Dr. Yu is an Assistant Researcher and Postdoc Fellow in the Department of AI, School of Engineering, Westlake University, working under Chair Prof. Yaochu Jin. He earned his Ph.D. in Engineering from College of Biosystems Engineering and Food Science, Zhejiang University in 2024, advised by Prof. Fang Cheng. During his doctoral studies, he was also a visiting Ph.D. student at the Department of Chemistry, National University of Singapore (2023-2024), mentored by Prof. Sam F. Y. Li.
Research experience
Dr. Yu’s research initially focused on XAI for nano-plant-environment interactions and nano-enabled agriculture at Zhejiang University. His expertise expanded to integrate XAI with chemistry during his time at the National University of Singapore.
Currently, he is dedicated to AI for Science, specifically applying XAI to challenges in nanotherapeutics, nanoagriculture, and the functional prediction of biomacromolecules. His work synergizes domain knowledge with advanced AI to create datasets and foundational models, aiming to enhance our understanding of nanomaterial and biosystem interactions for safe and efficient applications in healthcare and sustainable agriculture.
He has authored/co-authored over ten publications in international peer-reviewed journals. More details are available in the Publications.
News
Empowering scientific discovery with explainable small domain-specific and large language models. Our article is now online in Artificial Intelligence Review！We hope that our research experience in AI and Science can provide some unique perspectives on AI for Science from a knowledge perspective.
Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing?.Drawing on the Diffusion of Innovation theory, we project that AI4Science’s share of total publications will rise from 3.57% in 2024 to approximately 25% by 2050. Unlocking the potential of AI researchers is essential for driving this shift and fostering deeper integration of AI expertise into the research ecosystem. To this end, we propose structured and actionable workflows, alongside key strategies to position AI researchers at the forefront of scientific discovery.
Congratulations to Prof. Yaochu Jin for winning the 2025 IEEE Frank Rosenblatt Award!!!
Optimizing benefit-risk trade-off in nano-agrochemicals through explainable machine learning: Beyond concentration. Our article is now online in Environmental Science: Nano！This study proposes an explainable optimization method for accelerating the screening and design of nano-agrochemicals.
Contact
Email: yuhengjie@westlake.edu.cn