Nil-Jana Akpinar
I am a PhD student in the joint program between the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University, advised by Alexandra Chouldechova and Zachary Lipton.

My research interests lie in statistical methodology, machine learning in high-stakes decision settings, and fairness, accountability and transparency in machine learning. My work is generously supported by an Amazon Graduate Research Fellowship (2021).

I graduated with a Master of Science in Mathematics from the University of Freiburg in Germany in 2018. Before that, I obtained a Bachelor of Science in Economics (2017) and a Bachelor of Science in Mathematics (2015) from the University of Freiburg.


  • May 2021: Amazon awarded me a Graduate Research Fellowship.

  • Mar 2021: I am joining LinkedIn as a Machine Learning Engineering intern for this summer.

  • Feb 2021: Our FAccT 2021 paper got featured by MIT Technology Review here.

  • Jan 2021: I joined the editorial board of the ML@CMU blog.

  • Dec 2020: Our paper 'The effect of differential victim crime reporting on predictive policing systems' was accepted for publication at the Conference on Fairness, Accountability, and Transparency (FAccT 2021).

  • July 2020: I am presenting our paper 'Analyzing Student Strategies In Blended Courses Using Clickstream Data' at the Educational Data Mining conference 2020. A preprint can be found here, and a video of the presentation is also available.

  • May 2020: CMU just awarded me the degree of Master of Science in Statistics.

  • Feb 2020: I will be joining LinkedIn as a Fairness and Privacy Research Engineering Intern this summer.

  • Feb 2020: My poster presentation won the best 3-minute student presentation award at AAAI. The poster can be found here.

  • Dec 2019: I will be presenting a poster at AAAI-20 featuring joint work with Bernhard Kratzwald and Stefan Feuerriegel. A longer version of the paper can be found here.

Last updated: June 2021