报告题目：Theory + Data: Building Hybrid Models with Stoichiometry and Machine Learning

报告人： 王皓 教授

单位：University of Alberta

时间：2026年5月21日下午3：30-4：30

地点：脱衣舞-性感脱衣舞-性感衣舞 正新楼209室

校内联系人：刘素莉 [email protected]

摘要：Stoichiometric principles provide a rigorous, law-based backbone for building mechanistic models that are robust and empirically testable through conservation and physiological constraints. In this talk, I will introduce stoichiometric models that resolve key biological paradoxes by explicitly tracking elemental and quota-driven limitations, then demonstrate how data-driven components can (i) learn missing processes, (ii) improve short-term forecasting, and (iii) enable real-time monitoring. Methane biogenesis in oil sands tailings serves as a central application: we couple stoichiometric biodegradation dynamics with ML-enabled monitoring to better predict emissions trajectories. This research contributes to the broader goal of carbon neutrality.

报告人简介：王皓(Hao Howard Wang)，加拿大生物数学首席科学家（Tier 1 Canada Research Chair in Mathematical Biosciences），加拿大阿尔伯塔大学终身正教授，阿尔伯塔大学数学生态学和传染病学交叉研究实验室主任。现任11个国际生物数学和动力系统主流杂志的主编或编委，已在高水平SCI期刊发表论文超过两百篇。在化学计量学，动物认知移动，环境污染毒素，微生物分解，物种入侵，传染病传播机制和预测等都做出了开创性和突破性研究。目前主导加拿大Alliance Missions等多个国家重点基金。荣获加拿大应用与工业数学会科研突破奖（CAIMS-SCMAI Research Prize），阿尔伯塔大学杰出导师奖，约瑟夫·米歇尔指导奖，美国数学生物科学研究所杰出青年学者奖，以及加拿大国家基金科研加速资助奖等。

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