About me
Hello. My name is Nikola Janjušević. I am a fourth year PhD candidate in Electrical Engineering at New York University under the advisory of Professor Yao Wang, NYU VideoLab. I received my Bachelors in Electrical Engineering from The Cooper Union for Advancement of Science and Art in 2019.
My current interests are in interpretable deep-learning models for solving inverse-problems and low-level computer-vision/image-processing tasks. My background is in signal-processing and non-smooth, convex optimization. Outside of academia, I go biking and skateboarding with my friends.
Updates
July 2022: "Gabor is Enough" (IVMSP22) up on IEEE Explore.
June 2022: Intering with Apple's Video Engineering team in Cupertino, CA this summer!
Recent blog posts
So-called interpretably constructed deep neural networks often sell their methods by showing near state-of-the-art performance for only a fraction of the parameter count of black-box networks. However, can we consider these fair comparisons when the number of learned parameter counts are not matched? ...
A walkthrough of implementing Total Variation color image denoising in the Julia programming language, starring Fabio and Masa. 
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I often find my downloads folder filling up with tons of research papers with nondescript (ID) names, such as "1909.05742.pdf". Keeping these PDFs open allows me to keep track of them, but once I close those windows they seem as good as lost. To remedy this, I've written a short Python script employing a wrapper for the arXiv.org API. ...
The iterative soft thresholding algorithm is one of the simplest algorithms for sparse coding (in this case, solving the basis-pursuit denoising functional). Understanding its derivation as a special case of the Proximal Gradient Method is a great introduction into the world of proximal methods. ...
Zathura + latexmk -> :). Latest update: 17th January 2021. ...