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, knowledge distillation, quantization, semantic segmentation.

Once upon a time, I was writing about Formula 1 news at damalibayrak.com. Nowadays, I am into running and triathlon (and a little bit of paragliding). I enjoy playing correspondence chess.


  • (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.