About Me

Who Am I?

Hi I'm Abhav Luthra

I have master's degree in computer science with 3 years of software development experience.

I have a go getter attitude and is passionate about uplifting the world with innovative technology & strong empathetic leadership. Along with my amazing team members, I have solved problems in the field of healthcare, Android, IoT, machine learning, linguistics, blockchain, data analytics, and data mining.

I deeply care about solving problems of communities around me and making them more diverse, inclusive and accessible because I believe, stronger uplifted communities helps one grow faster.

I am currently looking for volunteer opportunities.

My Specialty

My Skills

These skills have been acquired over a long period of time by investing huge my time, effort and energy from my end and a lot of love and dedication from friends, family, and colleagues.

Python

90%

C#

80%

Javascript(TypeScript)

75%

Communication and Team Building

95%

React

50%

Leadership

80%

Azure/ AWS

55%

Problem Solving

80%

Blockchain

50%

Agile

75%

Data Analytics

60%

Critical Thinking

80%

Data Mining

50%

Planning

80%
Education

Education

SUNY, University of Buffalo

GPA - 3.88

Courses Studied:

  • Machine Learning
  • Blockchain
  • Computer Security
  • Data Mining
  • Data Intensive Computing
  • Computational Linguistics
  • Distributed Systems

Bharati Vidyapeeth’s College Of Engineering (BVCOE) Delhi, INDIA

GPA - 7.3 / 10

Courses Studied:

  • Data Structure
  • Operating System
  • Algorithms Analysis and Design
  • Computer Networks
  • Database Mangement System
  • Computer Architecture
Experience

Work Experience

Technical Volunteer, Health4theWorld March 2020 - Present

  • Build web pages to spread Covid-19 best practices and connect with physicians battling covid across the world
  • Created machine learning models to predict the hot zones for covid cases and co-ordinated with hospital in rural areas in US
  • Help build database of PPE suppliers and manufactures across the South America, Africa and US for hospitals

Full Stack Developer and Project Manager, Xenon Health September 2020 - October 2021

  • Building an insurance claim filing tool from scratch which removes 3rd party dependency and decreases cost by 75%
  • Single-handedly, gather requirements from billers, develop technical requirements and deploy code in production
  • Research and Implement security features & protocols to protect critical patient data from data loss as per HIPAA compliance

Python Developer, Tek Ninjas April 2020 - July 2020

  • Built a web platform for home hair care service providers to connect with customers in flask from scratch
  • Wrote efficient SQL queries/procedure for performing various CRUD operations in MySQL according to business logic
  • Created unit and integration tests to maintain code quality and managed code using Docker and Kubernetes on AWS

Product Manager, University at Buffalo May 2019 - August 2019

  • Gathered requirements from executive management, students and faculty for IoT Lightening System tool being developed as part of their improving visual branding of the labs
  • Documented key user personas and their end-goals; defined user stories and corresponding acceptance criteria
  • Became the Voice of the Customer (VOC) by reviewing feedback and prioritizing issues in the product backlog
  • Created and maintained product roadmap along with required features and deadlines
  • Managed weekly sprint reviews & retrospectives during the development phase which identified and removed impediments
  • Educated the team with agile practices and coached them in self-organization and cross-functionality
  • Presented MVP to multiple stakeholders and ensured smooth handover with clean documentation and future roadmap

Software Engineer at GlobalLogic October 2016 - June 2018

Disa Global Solutions

  • Redesigned the database and created RESTful APIs to access and deliver the data coherently with request/response logging & global error handling under MVC design pattern
  • Wrote automation testing scripts using framework like Selenium for functional and NUnit for API testing
  • Deployed test environment (Jenkins) for maintenance & execution of the existing scripts, decreasing bug count by 20%

GlobalLogic Revenue Tracking Tool

  • Collaborated in prioritizing the product backlog for developing revenue tracking tool
  • Successfully created R scripts to extract, transform, and process revenue data and visualized through RShiny
  • Developed services to run automated scripts on the server, leading to a decreased manual effort by 90%

See

Recent Projects

Ben-Or Decentralized Consensus Algorithm (Github)

Nov - Dec, 2019 | Distributed System

Technical Stack: TLA, PlusCal

  • Implement a PlusCal program for Ben-Or Decentralized Consensus Algorithm using message board in TLA+

Classification Algorithms (Github)

Nov - Dec, 2019 | Data Mining

Technical Stack: Python, Jupyter Notebook

  • Implemented classification algorithms like K-Nearest Neighbor, Decision Tree, Random Forest, and Naïve Bayes from scratch in python for bioinformatics datasets
  • Implemented 10-fold Cross-Validation to evaluate performance measures like Precision, Recall, Accuracy and F-1 Measure for the datasets
  • Applied Ensemble Learning by Majority Voting on the results obtained from classification algorithms

Clustering on Gene Expression Datasets(Github)

Oct - Nov, 2019 | Data Mining

Technical Stack: Python, Jupyter Notebook

  • Implemented K-Means, Hierarchical Agglomerative Clustering with Min approach, Density-Based Clustering, Gaussian Mixture Model and Spectral Clustering to find clusters of genes that exhibit similar expression profiles.
  • Compared the results of clustering algorithms with the ground truth clusters using Rand and Jaccard Coefficients and visualized the clustering results by using PCA

Dimensionality Reduction and Association Analysis (Github)

Aug - Sep, 2019 | Data Mining

Technical Stack: Python, Jupyter Notebook

  • Implemented PCA (Principle Components Analysis) algorithm and used existing packages to run SVD and t-SNE algorithms; projected the high-dimensional data to 2 dimensions and plot the 2-dimensional data points
  • Implemented Apriori and association rule generation algorithms

Data Aggregation, Big Data Analysis and Visualization (Github)

Apr - May, 2019 | Data Analytics

Technical Stack: Python, R, Java, AWS-Elastic Map Reduce, Tableau

  • Used Twitter, Common Crawler and NYT Developer API's to collect data efficiently and performed data cleaning and preprocessing by stop word removal and stemming
  • Applied classical big data analytic method of Map Reduce - word count and word co-occurrence on each of the unstructured data sets
  • Presented the result obtained using the visualization tool Tableau as Word Cloud

Airline Consortium Using Ethereum Blockchain(Github)

Feb - May, 2019 | Blockchain

Technical Stack: JavaScript, Solidity, Ganache

  • Designed and implemented a full-blockchain stack(Backend and Frontend) using Javascript for airline consortium decentralized application
  • Designed and developed a smart contract for application for validation and verification of the participants and for keeping track of transactions among participants airlines using Solidity and deployed using Ganache

Data Collection And Exploratory Data Analysis(Github)

Feb - May, 2019 | Data Analytics

Technical Stack: Python, R, Shiny, TwitterR API

  • Used US government CDC data commonly available on flu.gov and replicated professional data analysis in R
  • Used Twitter Developer API's to collect tweets on flu efficiently and compared the tweet count normalized frequency with the CDC data
  • Presented the result obtained using the R Shiny framework

Classification using MNIST and USPS dataset (Digits)(Github)

Oct - Nov, 2018 | Machine Learning

Technical Stack: Python, Jupyter Notebook

  • Applied machine learning methods for classification - Logistic regression, Random Forest, Support Vector Machine and Neural Network (CNN & DNN) on MNIST dataset
  • Applied Ensemble Learning by Majority Voting on the results obtained from the five classifications on MNIST dataset
  • The model trained on MNIST dataset is run on USPS dataset and results were compared to see genericity of the model developed

Handwriting Detection(letters) using Linear Regression, Logistic Regression and Neural Network(Github)

Sep - Oct, 2018 | Machine Learning

Technical Stack: Python, Jupyter Notebook

  • Applied the machine learning concept of linear regression, Logistic regression and Neural Network on CEDAR Letter dataset
  • Compared the results obtained after model training on the two different sources: 1. Human Observed features: Features entered by human document examiners manually 2. GSC features: Features extracted using Gradient Structural Concavity (GSC) algorithm

Learning to Rank using Linear Regression(Github)

Aug - Sep, 2018 | Machine Learning

Technical Stack: Python, Jupyter Notebook

  • Used machine learning to solve a common Information Retrieval problem, known as the Learning to Rank (LeToR) problem
  • Mapped an input vector x to a real-valued scalar target y(x;w) using Linear Regression model - closed-form solution and stochastic gradient descent (SGD)
Get in Touch

Contact

abhav.usa@gmail.com
abhavlut@buffalo.edu

Richardson, Texas TX 75080

716 578 2343