Christian Kästner
AI + SE Seminar Series (EST).
Christian Kästner is an associate professor and the director of the Software Engineering PhD program at the School of Computer Science at Carnegie Mellon University. His research originally focused on software analysis and the boundaries of modularity, especially in the context of highly-configurable systems. He also conducts research on sustainability of open source software and communities. His research often used artificial intelligence and machine-learning tools, such as when predicting how configuration options change the performance of a software system or when modeling the benefit of donations in open source projects, though he personally never cared much about machine learning as a topic in itself (and even though unlikely, he wouldn’t mind another AI winter). Since 2019, he regularly co-teaches a new course “Machine Learning in Production” at the intersection of software engineering and machine learning to better prepare the large number of students who, after graduation, start to work on software systems that integrate more and more machine learning (e.g., mobile apps, web applications, IoT devices) — and he has written a textbook on the topic. Since then, he also conducted research on collaboration, documentation, and quality assurance in teams where software engineers and data scientists interact