My research interests are at the intersection of machine learning, optimization, statistical learning, and computational neuroscience.

My PhD research is divided into three tracks:

• Interpretable deep learning: I am interested in a class of machine learning algorithms referred to as unrolled learning. This is to develop deep interpretable and efficient inference models based on iterative optimizations or generative models. For deep sparse coding unrolled networks, we can derive a mathematical relation between network weights, its predictions, and the training data. The relation helps to reason about representations of a new test example and extract similar/dissimilar data from the training set. Check out this work for the interpretability of deep unrolled networks as well as their theoretical properties for model recovery. Moreover, I have demonstrated the efficiency and state-of-the-art competitive performance of such architectures in the supervised task of Poisson image denoising in my recent ICML publication.
• Computational neuroscience: I develop interpretable networks to learn features driving neural activities. The network is based on unrolled learning allowing its application to data limited regimes such as single neurons. We studied piriform cortex neurons in this abstract, and showcased a sparse deconvolutional method for the analysis of dopaminergic neurons in this abstract. For this track, I closely collaborate with Paul Masset from Murthy Lab.

During my two fantastic research internship experiences, I worked on speech enhancement. Here, I proposed channel-attention to improve multichannel speech enhancement. In another publication, a joint work with Kazuhito Koishida from Microsoft, I proposed a training framework for perceptual enhancement of stereo speech.