With the recent advances in data science, a wide range of artificial intelligence and optimisation techniques have started to play a central role in business and society. Nowadays, breakthroughs in theory are almost immediately tested in diverse sets of applications. In this fast-moving era, Birbil’s current research focuses on mathematical optimisation approaches to data-private decision making and interpretable machine learning.

As an UvA professor, Birbil will be co-responsible for the research programme in the section Operations Management of the Amsterdam Business School. He will also participate in the Analytics for a Better World initiative, run by the UvA and Massachusetts Institute of Technology (MIT).

At the UvA, Birbil will teach courses on data-driven decision making and optimisation in machine learning.  These courses will be taught in a number of programmes, including the minor in Artificial Intelligence, the Bachelor’s in Business Analytics, and the (Research) Master’s in Business Analytics. In his teaching, Birbil makes sure to combine theory with hands-on computations, and focuses on algorithm development with implementations.

About Ilker Birbil

Birbil received his PhD in 2002 from North Carolina State University, following which he was a postodoctoral research fellow at the Erasmus Research Institute of Management. From 2004 to 2018, he was a faculty member at Sabancı University, where he was one of the founders of the Data Analytics professional degree programme. Since 2018, he has held the Chair in Data Science and Optimisation at Erasmus University Rotterdam.

Birbil is the author of numerous research articles on mathematical optimisation, operations management and data science. He has completed various research projects as the principal investigator, and participated in a number initiatives as a researcher or as a committee member.  He has also collaborated with industry to work on business problems ranging from cloud computing to airline crew planning.

Birbil has organised several schools and workshops on data science and machine learning. He writes columns and articles in newspapers about science and universities. He also contributes to two blogs on academic life and data science.