I am a final year Ph.D. candidate at Mila and Université Laval supervised by Prof. Jean-François Lalonde and Prof. Christian Gagné. I am working on meta-learning, representation learning, and self-supervised learning in low supervision conditions. With my supervisors, we first proposed the idea of associative alignment for few-shot image classification at the European Conference on Computer Vision (ECCV) 2020 to increase the model capacity of the conventional few-shot learning methods. Second, we proposed a mixture model-based representation learning approach at International Conference on Computer Vision (ICCV) 2021 to learn a robust multi-modal feature space in a semi-supervised way which improves few-shot image classification. While working with my supervisors, I finished my third Ph.D. project in collaboration with Prof. Hugo Larochelle. The work proposes a novel representation learning method based on a hybrid CNN/transformer-type architecture, which is under review now.

Before the Ph.D., I was an M.S. student at biomedical and computer engineering at METU under the supervision of Prof. Fatos T. Y. Vural. I was a research assistant at METU-fMRI and worked on several fully-funded projects on medical image analysis using machine learning.

I was also a teaching assistant for two related graduate-level courses: a) Pattern Recognition and Machine Learning (Bioshp, 2008 based), and b) Deep Learning (Goodfellow et al. 2016 based). I am toward publicly sharing my course slides for Bishop's book.