About Me

I am a Ph.D. student at University of Illinois Chicago in the Electrical and Computer Engineering Department. Currently, I am working on the trade-off between the computational cost and the performance of deep learning models. More specifically, I have experience in the following areas: early exit networks, pruning, sparsity, quantization, knowledge distillation, semantic segmentation and large language models.

In the past, I had the pleasure of writing about Formula 1 news for damalibayrak.com. Nowadays, I am passionate about running, triathlon, and a little bit of paragliding. I enjoy watching all sorts of sporting events, but in particular I like snooker. I think correspondence chess is the best type of chess. Beyond these, I try my best to read and travel as much as possible.


  • (07/2023) Published a new blog post about data races.
  • (06/2023) Coming to Honolulu, HI for ICML 2023! I will present Dataset Pruning Using Early Exit Networks.
  • (02/2023) I will be interning at  in the Core ML Tools team in Seattle, WA this summer! Super exciting!
  • (11/2022) I will be in New Orleans, LA for NeurIPS 2022!
  • (07/2022) Our paper "Pruning Early Exit Networks" has been accepted to Sparsity in Neural Networks 2022.
  • (05/2022) I have been selected to attend the Eastern European Machine Learning Summer School 2022. I will be presenting our work E2CM. Update: Received top-voted poster award.
  • (04/2022) Our paper "E2CM: Early Exit via Class Means for Efficient Supervised and Unsupervised Learning" has been accepted to IJCNN 2022.
  • (08/2021) I worked as a machine learning intern at Roku in the Advertising Engineering team in Summer 2021.