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, large language models, mixture of experts and streaming 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.
News
- (06/2024) Our paper Class-aware Initialization of Early Exits for Pre-training Large Language Models has been accepted to WANT@ICML 2024.
- (05/2024) I will be at Google in Mountain View, CA this summer as a research intern! I will work on Project Starline.
- (04/2024) Our paper Class Based Thresholding in Early Exit Semantic Segmentation Networks has been published in IEEE Signal Processing Letters.
- (12/2023) Completed the TinyML and Efficient Deep Learning Computing course from MIT Han Lab.
- (11/2023) Passed my preliminary exam. I am now a Ph.D. candidate!
- (10/2023) Presented Dataset Pruning Using Early Exit Networks in the Cohere for AI - ML Efficiency group meeting.
- (09/2023) Presented Dataset Pruning Using Early Exit Networks in Thessaloniki, Greece at the Mediterranean Machine Learning Summer School.
- (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!