Moz is a doctoral candidate at University at Buffalo (UB). His research involves feature extraction and learning representations using NLP and Computer Vision algorithms to tackle real world problems. His area of interests are NLP, Computer Vision, Machine Learning and Information retrieval. He is open to discussing about opportunities in Deep Learning Research.
May'2018 - May'2019
Orlando, Florida, USA
• Research using 3D-Convolution and conv-LSTM based computer vision and NLP models to solve challenges in Lip Reading.
• Build ML models based on Time series analysis to forecast irregularly recurring expenses.
Jun'2017 – May'2018
Palo Alto, CA, USA
Research using Recurrent Neural Networks (RNN) and collaborative filtering based Deep Neural Networks to solve challenges in recommendation systems.
Hybrid feature learning - handwriting verification
We propose an effective Hybrid Deep Learning
(HDL) architecture for the task of determining the probability that a questioned handwritten word has been written by a known writer.
Writer Verification using CNN Feature Extraction
We propose an end-to-end learning method based on statistical features extracted on set-of-samples level as a step toward solving the writer verification problem which is about deciding whether two handwriting sources are identical given handwriting samples from the two sources.