11月4日，应伟德betvlctor1946邀请，卡内基梅隆大学张坤副教授于长清湖校区文宗楼506会议室做了题为“Learning Causality and Learning with Causality: A Road to Intelligence”的学术报告。报告会由伟德betvlctor1946副院长孙建德教授主持，学院部分教师、硕博士研究生聆听了此次报告，并参与讨论。
报告会中，张坤副教授首先通过分析观察值找到两个变量的因果关系向我们介绍因果关系在机器学习、统计数据、哲学等方面的重要作用。此外，张坤副教授强调可以利用因果关系解决在迁移学习和半监督学习方面存在的问题。随后，张坤副教授主要和老师、同学们探讨了如何从观测数据中学习因果关系以及因果关系如何帮助机器学习和其他任务。最后，张坤副教授讨论了为什么因果关系代表普遍性的智能。张坤副教授的精彩报告，深入浅出，赢得了老师和同学们的阵阵掌声。
报告会交流环节，张坤副教授对同学们提出的因果关系在股票分析中如何应用等问题进行了详细的解答，使同学们受益匪浅。通过此次报告会，我院师生对因果关系研究方向有了新的认识，对今后的科学研究有着重要意义。
KunZhang is an associate professor in the philosophy department and an affiliate faculty member in the machine learning department at CamegieMellon University, and a senior research scientist at Max Planck Institute for Intelligent System,Germany. His research interests lie in machine learning and artificial intelligence especially in discovery,causality-based learningand general-purpose artificial intelligence. He develops methods for automated causal discovery from various kinds of data, investigate learning problems, especially transfer learning, concept learning, and deep learning, from a causal view, and study philosophical foundations of causation and various machine learning tasks. He coauthored a best student paper for UAI and received the best benchmark award of the causality challenge, and has served as an area chair or seniorprogram committee member for major conferences in machine learning or artificial intelligence including NeurlPS, UAI, ICML.AISTATS. AAAI, and IJCAI. He has organized various academicactivitiesto foster interdisciplinary research in causality.
