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Omkar Karnik

Hello 👋 I'm Omkar Karnik! I like building Web Applications !

Who am I ?

I am a driven individual with a passion for technology and innovation. I'm a recent graduate with a Master's in Computer Science from the University of Dayton and I'm actively looking for software engineering roles. I am eager to apply my knowledge, skills, and experience to new challenges and opportunities, and I am confident that I would make a valuable contribution to any team. I absolutely love talking about astronomy because the sheer vastness of space never fails to excite me.

My Skills

  • Html
  • Css
  • Javascript
  • React
  • Node
  • git
  • Express
  • Docker
  • Jquery
  • Ajax
  • Agile/Scrum
  • MongoDB
  • SQL
  • Python
  • JSON
  • REST

Projects 🛠️ My academic and personal projects so far.

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Secure Password Manager Using MERN

A password management portal which securely allows users to store, edit and share passwords among their team members. Encrypted the passwords using private and public key pair and created the chrome extension to store the private key on the browser to decrypt the password. Implemented RESTful APIs using Node.js and Express.js and authenticated the user using JWT token.

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Web Chat Application and basic Chatbot using Node.js, Express.js, HTML, CSS, and socket.io

Created an application for a graduate course, allowing users to communicate with multiple users through chat messages. Built three distinct microservices for login, signup, and storing chat history and implemented real-time communication functionality in the web chat application using Socket.io programming. Hosted the microservices on Microsoft Azure, Bitbucket Cloud, Heroku and programmed the CI/CD setup for automatic deployments.

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Audio Classification Android Application Using TensorFlow Lite.

Built an Audio Classification Application using Python that listens to the user’s voice and predicts the alphabet spoken. Collaborated with other teams to record and acquire a dataset of the voices of 10,000+ people of different cultures. Trained the neural network model using the dataset and achieved an accuracy of 65%.

Let's Connect !