I am a freelance programmer and Deep-learning practitioner.
I completed my bachelor's in Computer Science and Engineering at the
Chittagong University of Engineering and Technology. There I worked
on IoT Based Smart Food Monitoring System under the supervision of
Dr. M. Moshiul Hoque.
I am pursuing my MS in Computer Science with AI Concentration at the
University of Nebraska at Omaha. I'm a Graduate Research Assistant at UNO's RNA Lab
under the supervision of
Dr. Xin Zhong.
I do Computer Vision research, specializing on Self-Supervised Representation Learning.
Projects
Self-Supervised Representation Learning
Title Hidden: Work Under Review
PyTorch
Proposed a new augmentation. Applying with different Joint Embedding Architectures, results in higher linear evaluation accuracy along with Distortion Robustness on previously unseen augmentations. Below chart shows the comparison of VICReg Baseline and VICReg with our proposed augmentation.
Deep Learning-based Audio in Image Watermarking (Deep AIWM)
Tensorflow
Deep AIWM is a Deep Learning model that can embed audio data inside RGB image without degrading the quality and extract the audio data from the marked image. The model's Siamese Network is able to distinguish noisy extracted audio from the marked image which can be used for speech command recognition simply by scanning image.
Simple Sentiment PyTorch | Flask
Simple Sentiment is a DistilBERT based classifier that can recognize positive and negative vibes from sentences. It was trained using the Sentiment140 Dataset comprising of 1.6 Million tweets labeled positive or negative.
Simple MANN (using LSTM) TensorFlow
Simple MANN is a meta-learning implementation inspired by the paper Optimization as a Model for Few-Shot Learning. Even though the name suggests MANN(Memory Augmented Neural Networks), instead of NTM this implementation uses a single layer LSTM as memory unit. The model is evaluated on the Omniglot Dataset.
Facebook Hateful Meme Challenge PyTorch | MMF
Submission for Facebook Hateful Meme Challenge at DrivenData using VisualBERT. The model scored 0.7214(AUC-ROC) without ensemble and task specific pre-training. Model's MMF configuration is given below.
COVID-19 Identification from Chest X-Ray PyTorch
Classifier based on ResNet-18 model which has been finetuned on the Chest X-Rays from COVID-19 Radiography Database. The classifier can differentiate X-Rays of Normal Condition, Viral Pneumonia and COVID-19 patients.
Stock Scraper MongoDB | Express | React.js | Node.js
Stock Scraper is a tweet scraping server that counts number of times any stock has been tweeted by targeted users at Stocktwits and visualizes the data. This project is a excellent groundwork for analyzing and extracting values from social investment platforms.
IoT Based Smart Food Monitoring System
Raspberry Pi | Swift | Python | MongoDB | Express | Node.js
The project facilitates food monitoring of refrigerators and food cold storage systems using mobile devices, with high scalability. The system is developed using Raspberry Pi device and transformed into a small form factor IoT device. One of the pros is it's ability to scale up which can facilitate large scale cold storage monitoring.
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Code reproduction of various papers PyTorch
Code reproducibility of papers is becoming more and more challenging as models are becoming more complex and abstract. Here are some of my practice attempts to reproduce models from papers with help from various online forums using popular machine learning frameworks.
Online Job Market (Job Engine) React.js | Javascript
Job Engine is a single page web app for searching and posting jobs online. The browser based app’s front end is implemented using Javascript, React.js and server intergration using REST API.
Car Rental System (EasyRide) Javascript | PHP | HTML
Easyride is a database project for renting cars in a particular area, facilitating CRUD functions to the end-user. The project is implemented using PHP, HTML, CSS, MySQL, Javascript.
Direct 2D Swift
Direct 2D is a self initiated game development project. The game was implemented using Swift with basic physics written from scratch. The app was available on Apple AppStore from July 2017 to July 2018 with over 2.6K downloads.