Experience

@ AI and Digital Science Research Center (AIRDRC)

Exploring mulitmodal large language models

Worked collaboratively in a team to develop English-Arabic large language models

Extensively evaluated the design choices for Text-To-Speech systems and eventually open-sourced state-of-the-art Text-To-Speech models for 13 Indian languages [ICASSP Paper]

Explored the Double Variational Autoencoder Network (DoENet) for unsupervised adversarial example classification in computer vision, demonstrating its target classifier and attack-agnostic nature, leading to improved performance

Enhanced the sales forecasting models for a dynamic pricing system of a prominent retail client, leveraging deep neural networks such as RNNs and LSTNet, along with a novel N-gram-based method [US Patent]

Implemented RNN-based spatiotemporal travel time prediction models to evaluate against temporal difference-based methods [IJCNN Paper]

Developed an employee profile retrieval system that parses information documents and responds to input text queries, as part of an internal profile hunt challenge [Special Initiative Award]

Developed a yield prediction model for hybrid corn crops to recommend effective crossing of species, winning an internal hackathon out of 8 teams [Innovation Pride Award]

Developed computer vision pipelines to solve curved text extraction, perspective correction, etc., winning an internal image enrichment hackathon with over 100 participants [Innovation Pride Award]

Created a scene text translation system for Indian languages using synthetic datasets, incorporating an Efficient and Accurate Scene Text Detector (EAST) for detection, and Convolutional Recurrent Neural Networks (CRNN) for Classification and Recognition [Dataset] [Detection] [Classification] [Recognition]

Set up programming competitions for the One Fourth Labs Deep Learning course [Course]

Investigated attention models in Deep Learning, analyzing foreground region detection through proxy data with distinct statistical properties [Report]