About Me
# recent visual computing graduate
# machine learning and deep learning enthusiast
# interested in computer vision, NLP and Recommendation Systems
# past freelance software and game developer
Intern Machine Learning Software Engineer – Flipboard | www.flipboard.com
[Internship | May 2019 -- Sept 2019]
Worked as a part of the recommendations team at Vancouver. Exposed topic extraction metrics to internal tools for topic curation team, integrated features in slack chatbot to increase productivity, developed a deep learning architecture for classifying documents based on user’s topic affinity to create responsive personalization filters for user feeds. Picked up technologies like React, GraphQL, Amazon Sagemaker, Amazon S3 and Amazon RDS
Game Developer – PixelNinja Games | www.pixelninjagames.in
[Individual freelancer | April 2016 -- Jan 2018]
Developed couple of game projects during this period. Helped me gain a good grip over many technologies related to design and programming games like Unity3d, Blender, MagicaVoxel, GIMP, Photoshop, Illustrator and Aesprite
Intern Game Developer – Pardy Panda Studios | www.pardypandastudios.com
[Internship | May 2017 -- September 2017]
Learned to design, develop and test mobile games. Helped broaden my knowledge of software development cycle and testing methodologies, even improving my programming and logical skills
Professional Master of Computer Science (Visual Computing Specialization)
[Simon Fraser University | September 2018 -- December 2019]
Relevant CourseWork:
Bachelor of Computer Science and Engineering
[Babaria Institute of Technology, Bits Edu Campus affiliated with Gujarat Technological University | July 2013 –– March 2017]
Relevant CourseWork:
Machine/Deep Learning
PyTorch, Tensorflow, Keras, Scikit-learn, Amazon Sagemaker, Jupyter
Programming Languages
Python, C, C++, Java, Microsoft .Net (C#)
Web Technologies
HTML5, CSS3, Javascript, jQuery, PHP, JSP, ASP, mySQL
Databases
Amazon RDS, Amazon S3, Microsoft SQL Server, MySQL, PostgreSQL, MongoDB
Development Tools
Unity3D, Eclipse, Visual Studio Code, Android Studio, GIT, PyCharm, JIRA, Jenkins
Art Tools
Photoshop, Illustrator, GIMP, Blender, MagicaVoxel
# Base Model - SigNet model with contrastive loss
# Model variation 1 - using triplet loss
# Model variation 2 - using binary cross entropy loss where the network outputs the L1 component wise distance between feature vectors outputted by each Siamese twin
# Datasets : CEDAR, GPDS300, GPDS Synthetic SignatureDatabase and BHSig260
Technologies used - PyTorch, TorchVision, Pillow, Matplot, Numpy
Check it out on GitHub
# 3D Generative Adversarial Network
# Generate 3D hand-written digits, represented in voxels
# Latent space Interpolation that allows interpolating between two voxelized digits
Technologies used - PyTorch, TorchVision, OpenCV, Matplot, Numpy
Check it out on GitHub
# Reconstructing lost or deteriorated parts of images using CNN
# Data Augmentation
# UNet and ResNet models
# MSE loss function
Technologies used - PyTorch, OpenCV, Matplot, Numpy
Check it out on GitHub
# Locate objects and boundaries in images
# Using UNet model
# Batch Normalization and Data Augmentation
# Implementation for Cross entropy loss function and softmax
# MSE loss function
Technologies used - PyTorch, OpenCV, Matplot, Numpy
Check it out on GitHub
# Implementation of Winged Edge Structure to save 3d model
# Calculating Flat and Smooth Normals
# Quadric Error Calculation
# Mesh Simplification with Edge Collapse
# Saving model from Winged Edge to .obj
Technologies used - C++, OpenGL, NanoGUI
Check it out on GitHub
# Vehicle detection and segmentation
# SSD (Single Shot MultiBox Detector), Mobilenet v2 as BaseNet
# Datasets: Cityscapes
Technologies used - PyTorch, OpenCV, Matplot, Numpy
# SSD Model with Alexnet as backbone network
# Data augmentation and transfer learning
# Dataset: Labeled Faces in the Wild(LFW)
Technologies used - PyTorch, OpenCV, Matplot, Numpy
# Localization and Mapping for a Turtlebot robot using LIDAR sensor
# ICP SLAM Algorithm
Technologies used - C++, ROS Framework
# 2D Generative Adversarial Network
# Generating hand-written digits
Technologies used - PyTorch, TorchVision, OpenCV, Matplot, Numpy
Check it out on GitHub
# Kaggle competition - Multi-label sentence classification
# Model 1: Logistic Regression using TF-IDF
# Model 2: Stacked Bidirectional LSTM
# Model 3: CNN by Yoon Kim
# Using pretrained word embeddings
Technologies used - PyTorch, Numpy, Keras, Seaborn, Matplotlib
# Finding non-recursive syntactic groups of words
# Using semi-characterRNN + LSTM to predict the phrasal chunking tags
# Concatenated semi-characterRNN's hidden state with word embeddings as input to LSTM
# Model learns word embeddings to minimize the loss on the phrasal chunking task
# Dataset : CoNLL 2000 shared task2
Technologies used - PyTorch, Numpy
# Finding a suitable substitution for a target word in a sentence
# Using WordNet ontology for semantic lexicon
# Retrofitting word2Vec word vectors with semantic lexicons
# Incorporating Context Words around the target word to find better substitute words
# Dataset : SemEval-2007 Task 10: English Lexical Substitution Task
Technologies used - Python, Numpy
Check it out on GitHub
# Segmenting a sequence of Chinese characters into the most likely word sequence
# Segmentation scored based on the probability of the words that occur in that segmentation
# Unigram, Bigram and Trigram Model
# Jelinek Mercer smoothing and Stupid Backoff smoothing
Technologies used - Python, Numpy
Check it out on GitHub
# Segmenting a sequence of English characters into the most likely word sequence
# Segmentation scored based on the probability of the words that occur in that segmentation
# Unigram model with smoothing for unknown unigrams
Technologies used - Python, Numpy
Check it out on GitHub
# Yoon Kim's Sentence classification CNN model
# Binary text classification with imbalanced classes
# Comparing CNN with Traditional Models (TFIDF + Logistic Regression and SVM)
# Predicting if a question on Quora is sincere or not
# Datasets : Dataset - Quora questions from a Kaggle competition
Technologies used - PyTorch, TorchText, ScikitLearn, Matplot, Numpy
# Kaggle competition - Time series problem
# Dataset with two years of item sales count for various stores across Russia
# Predicting item sales for the next month
# Exploratory Data Analysis, Clustering and Feature Extraction
# Using Stacked LSTM, XGBoost and LSTM Autoencoder
Technologies used - PyTorch, Numpy, Keras, Scikit-Learn, Plotly, Matplotlib
Find your way through a augmented reality maze.
Coming soon on Google Play.
Swipe your finger to guide the player to avoid the swinging rings.
Coming soon on Google Play.
It's pong but in a circular platform and played by a single player.
Coming soon on Google Play
Hop the character by tapping left or right part of the screen and avoid obstacles.
Get it on Google Play.