External Links

Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine Translation Permalink

Research Project, Carnegie Mellon University, LTI, 2020

Investigated the effect of out-of-domain data and monolingual data size on the quality of Unsupervised MT
Compared the efficacy of back-translation vs language transfer techniques using the mBART model on similar (English-Romanian) and dissimilar (English-Nepali) language pairs

Research Project, Carnegie Mellon University, LTI, 2020

Adapted a Transformer based VQA model (LXMERT) for the task of Image-Set VQA and developed an Adversarial Regularization method to reduce dependance on language biases & improve performance on out-of-domain data
Introduced a new pre-training objective which utilized object bounding boxes extracted from an RCNN to improve model performance on object description questions by 4%

Course Project, Carnegie Mellon University, LTI, 2020

Introduced a generative approach that uses a Beta-VAE to identify scenes changes in videos by measuring KL divergence between images and Eliminated manual effort in annotating data for downstream tasks (Super-Slomo)

Course Project, Carnegie Mellon University, LTI, 2019

Investigated factors that govern the YouTube trending page and visualized the presence of user and platform bias
Hypothesized the reasons for existence of bias and demonstrated their variability across different countries
Constructed a Machine Learning pipeline with XGBoost classifier to predict the likelihood of a video to trend
Video Demo: https://www.youtube.com/watch?v=UlanYkq_VII

Course Project, PES University, Computer Science Dept., 2019

Developed a Bi-LSTM model which utilized Contextualized word Embeddings (ELMo), to detect discrepancies between claim present in a news article and other authoritative news sources to identify potential fake news
Demonstrated the superiority of the approach over existing online APIs for stance detection
Multidimensional Analysis of fake news including identifying fake accounts on social media, fake websties detection and analysis of spliced images to spread fake news.

Course Project, PES University, Computer Science Dept., 2018

Introduced a novel pipeline architecture for face recognition which used the highly optimized CloudForest algorithm to achieve 10-15x training time improvement over other ensemble classifiers such as Random Forest