|Publications||Awards and Grants||Google Scholar||Writing|
I am a PhD student at IIIT-Delhi in ECE Department. I am advised by Dr. Saket Anand. I am working as a research intern with Prof. Pavan Turaga for 6 months (Feb - Aug 2019) and previously interned with him during Summer 2017 in the Geometric Media Lab, Arizona State University, USA.
I received my Master’s degree from IIIT-Delhi. I was awarded Best Thesis Award for my thesis titled “Signal Processing Techniques to Reduce Energy Consumption in EEG Acquisition and Transmission for WBAN” under the guidance of Dr. Angshul Majumdar.
My research interests lie in deep learning and machine learning approaches for computer vision applications, topological data analysis, differential geometry and geometry driven approaches for learning. My work as a PhD student has two aspects. The first aspect focuses on leveraging semantic and geometric constraints for developing machine learning and deep learning algorithms. This includes distance metric learning in traditional machine learning, as well as current work that focuses on geometry aware deep learning. The other aspect focuses on AI for Good, specifically towards visual wildlife monitoring applications.
Reach me at firstname.lastname@example.org or email@example.com
Updates and News
- Recieved travel grant from WiML 2019 to present my work titled “Ignorance is Bliss: Semantic Pairing for Fine-grained Visual Recognition” in the workshop.
- Paper titled “A hybrid approach for tiger re-identification” is accepted at Computer Vision for Wildlife Conservation Workshop in challenge paper track at ICCV 2019.
- “Paper titled “PrOSe: Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning” is accepted to BMVC 2019.
- Paper titled ”Primate Identification in the Wild” accepted to PRICAI 2019.
- Work titled “Geometry of Deep Generative Models for learning Disentangled Representations” accepted as poster at workshop WiCV 2019 organized in CVPR 2019.
- Selected to attend the NSF-CBMS Conference on Topological Methods in Machine Learning and Artificial Intelligence.