I am a Data Science and Artificial Intelligence, Ph.D. student at the University of Edinburgh. I am a part of BayesWatch research group where I am supervised by Prof. Amos Storkey. I am interested in developing topology and geometry aware machine learning methods to understand the underlying structure of data.
Thesis:Generative Adversarial Networks for Unsupervised Cross-Domain Speech-to-Speech Synthesis
The idea of unsupervised image-to-image translation has been extensively explored for style transfer with an application like translating a photograph to the “Monet Impression, Sunrise”. In our work, we build on this idea and extend it to develop a speech-to-speech synthesis framework. We define speech-to-speech synthesis as the task of translating the voice of the male speaker to that of the female speaker and vice versa. We work with the time-frequency (TF) representation of speech. We further present the key challenge associated with using the TF representation in the training of neural networks. Finally, we present our solution to a problem using the consistency condition of TF as an additional constraint in the optimization problem.
Worked on spectral methods for network alignment.
Worked on developing machine learning methods for knowledge graph analysis.
Worked at the intersection of Artificial Intelligence & Bioinformatics with focus on developing Machine Learning solutions for Life Science problems. Projects, I worked on:
· Protein function prediction
· Multi-modal learning for disease-gene prioritization
· Deep Learning model to predict plants traits from raw Images
Fine-grained object classification : Worked with deep CNNs to prevent the classification of an object to its visually similar class with focus on dataset of visually similar handbags.
Source camera identification : Worked with probabilistic methods for modelling noise distribution of cameras and
use it with a manifold-based learning to identify the source camera of an image.
Image Forensic : Worked on an algorithm for detection of double compressed JPEG images using gaussian mixture model (GMM) and support vector machine (SVM).
GPA: 1.1/1.0 (1:Best,5:Worst)