I am a PhD student at the University of Central Florida, where I research in machine learning. Recently, I interned in the Computational Social Science team at Snap Inc., Los Angeles, CA where I was involved in Learning Representations and Sentiment Analysis for social-media posts, micro-blogs, and short messages. Previously, I interned in the NLP group at Siemens, Princeton, NJ where I was involved in improving the performance of a production-level NLP model and also produced a new state-of-the-art in another project on extracting key-value information from reports and contributed to publishing a conference paper on the same.
Previously, I have worked on Image and Signal Processing, Computer Vision, Machine Learning, and Deep Learning in the Imaging Team under the supervision of Keerthi Ram at HTIC, Indian Institute of Technology Madras, Chennai, India.
I completed my bachelor’s degree in Biomedical Engineering from the Department of ECE in College of Engineering, Guindy in 2014 with a concentration in Signal Processing and Pattern Recognition under the supervision of Prof. Shenbaga Devi (Director, Centre for Medical Electronics, Anna University).
I am always interested in new challenges. Feel free to reach out to discuss opportunities, technologies, engineering, and research ideas.
PhD Student in Computer Engineering
University of Central Florida
B.E. in Biomedical Engineering, 2014
College of Engineering, Guindy
One of the key challenges in internet content management is the filtering of the profane language. What forms the profane language is the question we got to answer. To do this we categorize it into classes such as offensive language, hate speech, and neither. This categorization is done by...
Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting working age population in the world. The aim of this challenge is to evaluate algorithms for automated detection and grading of diabetic retinopathy and diabetic macular edema using retinal fundus images.
Breast cancer is one of the leading cancer-related death causes worldwide, specially woman. However, early diagnosis significantly increases treatment success. The challenge is to automatically classifying H&E stained breast histology microscopy images in four classes: normal, benign, in situ carcinoma, and invasive carcinoma.
As the autonomous navigation is getting closer to reality and in order address, the safety issues in driving an autorickshaw on Indian roads, the first step would be the high accuracy detection of autorickshaws having a variety of shapes and sizes found on Indian roads, within an image.
This is a high-throughput experimental project, the goal of which is to systematically do injection-based tractography in whole mouse brains, on a grid of injections that cover the whole brain.
In this challenge, one of the largest retailers in Germany wants to improve their inventory-management process in its Food and Groceries business. The company is looking for intelligent solutions that can reduce the amount of human effort in its warehouse and retail outlets.
The goal of this project is to produce a system that takes colonoscopy images as input and detects the presence/absence of polyps in each frame using Convolutional Neural Networks (CNN) so as to be used in the prevention of colorectal cancer clinically.
The goal was to find the likelihood of a given 3D CT image having Lung cancer.
The objective of the project is to develop applications to process and reconstruct three-dimensional Electron Paramagnetic Resonance (EPR), Nuclear Magnetic Resonance (NMR) / Dynamic Nuclear Polarization (DNP) datasets.
The objective of this work is to assess the possibility of using (Electroencephalogram) EEG for communication between different subjects.
Speech impaired people have difficulty in communicating with normal people because the hand gestures used by them to communicate their information is not easily understandable; only trained people can understand these. Most expressions and emotions remain unconveyed, sometimes even misinterpreted.
Research & Development groups