Moz is a doctoral candidate at University at Buffalo (UB), trying to unearth new concepts in Deep Learning (DL). His role involves researchng in Computer Vision to build Deep Learning that can solve real world problems. He is interested in NLP, Deep Learning, Computer Vision, Machine Learning and Web Development. 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.