Hello, this is
 Mohammad Abuzar Shaikh (Moz)

PhD Candidate at University at Buffalo

About Moz

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.

Timeline

Jan'2018 - Current
Buffalo, NY, USA

Teaching Assistant

• Deep Learning CSE676 (Fall'2019, Spring '2020)
• Advanced ML CSE674 (Spring'2018, Spring'2019)
• Intro to Machine Learning (ML) CSE574 (Spring'2018)

May'2019 - Aug'2019
Palo Alto, CA, USA

PhD Research AI, EY Labs

Deep Learning Research to overcome challenges in Document Intelligence using Self-Attention; Contextual-Attention; Generative Adversarial Networks

May'2018 - May'2019
Orlando, Florida, USA

AI R&D, RBC Innovation Lab

• 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

ML Research Intern, SAP Labs

Research using Recurrent Neural Networks (RNN) and collaborative filtering based Deep Neural Networks to solve challenges in recommendation systems.

Publications

Sep'2019

Explanation based Handwriting Verification
BMVC'19
We propose a method to generate explanations for the confidence provided by convolu-tional neural network (CNN) which maps the input image to 15 annotations (features) provided by experts

Sep'2018

Hybrid feature learning - handwriting verification
ICFHR'18
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.

Sep'2018

Writer Verification using CNN Feature Extraction
ICFHR'18
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.

Projects

Conf 2020

Document Intelligence

Coming soon

Conf 2020

Claim Identification 

Coming soon

CSE676 Fall'2019

Self Attention GAN

Implementation of SAGAN to learn the concept of self-attention

ICFHR'2018

Handwriting Analysis

Handwriting Verification and Explanation

How to Find me