PROJECTS
I have worked on several projects in the field of Database Systems, Distributed Systems,Computer Vision and Image Processing, Machine Learning, and Data Intensive Computing during my course of study at University at Buffalo. Below is the list of all the projects that I have worked on so far.
It is a simple SQL query evaluator with support for Insert, Delete, Select, Nested-Select, Project, Join, Bag Union, Limit, Aggregate functions (COUNT, MIN, MAX, AVG, SUM), GROUP BY,
GROUP BY Aggregates and ORDER BY clause on Big Data (TPCH). It also supports Indexing to some extent.
JAVA JSQLParser EvalLib
It is a Dynamo-style key-value storage implementing partitioning, replication and failure handling
to provide per-key linearizability and availability even under failure.
JAVA Android Socket Programming
A Big Data pipeline to perform Data Cleaning and then word count and word co-occurrence algorithms on the text data collected from Twitter REST API, New York Times API, and Common Crawl Data on Sports and then performed visualization in Tableau.
Python Tableau MapReduce
To analyze Influenza outbreak by performing EDA on the Twitter data extracted by using Twitter REST APIs and comparing the data with Official Influenza Statistics using charts and heat map.
R Shiny Twitter API
A distributed hash-table based on Chord that provides node joins, ID space partitioning, and ring based routing.
JAVA Android Socket Programming
A group messaging android application with decentralized TOTAL and FIFO message ordering guarantees.
JAVA Android Socket Programming
Implemented hough transform algorithm to detect lines, vertical and diagonal, and circles in the image.
Python
Implemented template matching algorithm to find the template in the given image, invariant of template size.
Python OpenCV
Implemented K-means clustering algorithm to select the color palette for color quantization of an image.
Python
Implemented GMM using Expectation Maximization Algorithm on Old Faithful Dataset.
Python
Implemented morphological operations, Opening, Closing, Dilation and Erosion and then using them to remove noise from an image and extract boundaries.
Python
A basic study on the effects of various hyper-parameters on a neural network model used for the task of classification.
Python Keras
Using Deep Reinforcement Learning Algorithm – Deep Q-Network to teach the agent to navigate in the grid world environment in order to reach the goal.
Python
Using Logistic regression, Neural Network, Random Forest and SVM on the MNIST and USPS Dataset. Further, implemented ensemble of these four classifiers using Majority Voting.
Python Keras
Implemented both the closed form solution and Gradient Descent solution for linear regression on the LeToR Dataset released by Microsoft Asia.
Python
Implemented linear regression, logistic regression and Neural Network on Human Observed Features Dataset and GSC Features Dataset extracted from CEDAR Letter Dataset which consists the image snippets of the word “AND” to compare handwriting.
Python
Using Sobel Operator to detect horizontal and vertical edges in an image.
Python
Implemented first three steps of SIFT in order to detect keypoints in an image.
Python
Warping two images using the Homography matrix computed with RANSAC.
Python OpenCV
Using thresholding to separate object in foreground from the background.
Python
Detecting porosity in an image using point detection algorithm.
Python
Android application which enables two android devices to send messages to each other.
JAVA Android Socket Programming
To understand the concept of epipolar geometry which includes computing the Fundamental matrix with RANSAC, selecting inlier match pair and for each keypoint in the left image draw epilines on the right image and vice-versa and then computing disparity map of two images.
Python OpenCV