Navneeth Krishna is a Web, Android, & Full-Stack Software Engineer, a Machine Learning Researcher, and an Adventurer.

He is a M.S. in Computer Engineering graduate from NYU Tandon School of Engineering and a B.E. in Computer Science & Engineering graduate from DSCE, Bengaluru, India. As a Full-Stack Software Engineer at One Community Global, Navneeth utilizes his expertise in React.js, Node.js, Express.js, and MongoDB for the development & enhancement of an open-source team management software. Navneeth is a globe-trotter and enjoys blogging, stargazing, and catching Pokémon on his journey. He actively volunteers and manages leadership roles in several domains in his free time.


Dev Projects

Video Subtitle Generator.AI (A GPT-based app)

Developed and documented a video subtitle generator application to enable file upload of video, generate subtitles based on real-time audio transcription with Deepgram, and burn subtitles into the video. Created a live subtitle generator for a live video stream using OpenAI’s Whisper API and GPT-3.5 for translation. Utilized standard software engineering practices & creativity in documentation.

HTML CSS JS Python Flask OpenAI GPT-3.5 OpenAI Whisper API Heroku REST API Deepgram open-cv
FinGPT (A GPT-based financial assistant)

Implemented a full-stack application built with React.js and Python Flask + MongoDB/PostgreSQL. It is an educational PoC finance app in development that provides various tools & analysis for managing investment portfolios with collaborative effort using HTTP methods like GET, POST, PUT, and DELETE. The app authenticates OpenAI API keys with principles of RESTful APIs.

React.js CSS Node.js Python Flask GPT-3.5 LLM MongoDB Heroku REST API
YouTube Video Summarizer (A GPT-based app)

Developed a YouTube Video Summarizer based on GPT-3.5-turbo and Whisper API by OpenAI. Validates YouTube link and OpenAI API Key, downloads the video, transcribes the video's audio, and summarizes the content. Frontend on HTML, CSS, and JS. Backend uses Firebase NoSQL Cloud Firestore database, Python Flask, Whisper API, gpt-3.5-turbo, pytube, etc. Interaction uses RESTful APIs.

HTML CSS JS Python Flask OpenAI GPT-3.5 OpenAI Whisper API Heroku REST API Firebase NoSQL
Wordle Solver

Developed a website to solve the trending ‘Wordle’ game with a team of 2 based on iterative feedback & text analytics. Deployed an AWS EC2 instance to host a front-end built on HTML, CSS, & JS to feed user input for processing. Set up a backend server to service front-end requests and securely transfer data through REST APIs.

HTML CSS JS Python Flask Text Analytics AWS EC2 Heroku REST API
Say Flight -- Travel Insurance Booking with Authentication and CRUD operations

Developed a full-stack application with user authentication and CRUD functionalities as the captain of a team. Designed & developed a relational schema following database design conventions for a graduate-level course requirement. Utilized Python Flask for the web server and deployed the front- and back-end on Heroku.

HTML CSS JS Python Flask Heroku Relational DB REST API
Cloud Service Optimizer

Created a back-end system with Python & Flask to service REST API calls from the front-end and the research hosts. Designed and developed an impactful relational database by leading a team of 3 and meeting 100% research goals. Created a front-end web application using React to service user requests and maintained a robust architecture. Optimized regression models by training & testing research data with 2.5 M entries in order to reduce empirical loss.

JavaScript SQL JS (React) Python Flask Machine Learning AWS EC2 Heroku REST API AWS Research Machine Learning Linear Regression
Residual Neural Networks for classifying Artworks

Utilized Residual Neural Networks on classifying 220k+ classes of artworks from ‘The Met’ dataset. Developed and trained 10+ varieties of CNN backbones in a parametric approach alongside Contrastive Learning. Derived and documented insights obtained through KNN hyperparameter tuning and performance evaluation metrics.

Python CUDA PyTorch Deep Learning Residual Neural Networks K-nearest Neighbors Deep Learning Research
Residual Neural Networks for the CIFAR-10 Dataset

Utilized Residual Neural Networks to solve the CIFAR-10 Classification problem with 94.3% test accuracy. Developed and trained batches using Stochastic Gradient Descent, CNNs, Skip Connections, and Cross-Entropy Loss. Derived and documented insights and hyperparameter tuning outputs throughout the process.

Python Hyperparameter Tuning PyTorch Deep Learning Residual Neural Networks CIFAR-10 Dataset Deep Learning Research
License Plate Character Detection and Recognition

Developed a model that performs character recognition from images of license plates captured from CCTV footage. Transformed Support Vector Machines and employed Connected Component Analysis. Implemented a Python script that reads image input of license plates, detects characters, and prints the characters.

Python Machine Learning Support Vector Machine Connected Component Analysis Computer Vision Video Frame Extraction Character Recognition
Mamma Mia -- A restaurant management application

A restaurant management Android application built with Android Studio and SQLite. Mamma Mia employs a modern-day intuition of managing a restaurant's diners at ease. Features include User Checkin, Table Allocation, Order Placement from Menu, and Bill Calculation.

Android SQLite User Checkin CRUD Operations Concurrency Management Java XML


questions about projects? coffee? hiring? anything else?

reach me at navneeth dot padaki15 at gmail dot com

  • have a nice day :)