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Hi there, 👋 this is Pawan Kumar Naveen

I am a highly skilled software engineer with expertise in full-stack web development and machine learning. With a 4.0 GPA in my MS in Computer Science from Northeastern University and 2 years of industry experience at Publicis Sapient, I have delivered high-quality solutions using technologies such as React, Node.js, Spring Boot, AWS, and Machine Learning.

Expertise 🚀:

Software Development (2 years) 💻

  • 2 years of software development experience
  • Skilled in building and revamping systems
  • Proficient in full-stack web development with a focus on back-end
  • Experienced in implementing DevOps practices
  • Strong background in continuous integration, deployment, and delivery

Machine Learning (2 years) 💡

  • Proficient in advanced model development, including neural nets, NLP, CNNs, collaborative filtering, and random forests.
  • Skilled in deploying and scaling machine learning models for seamless integration with real-world applications.
  • Expertise in deep learning, including backpropagation, MLP, autoencoders.
  • Familiarity with advanced techniques such as accelerated SGD, ResNets, DDPM, dropout, mixed precision training, attention mechanisms, transformers, super-resolution, and latent diffusion.

Technical Skills 🔧

  • Programming Languages: Javascript, TypeScript, Java, C++, Python, Kotlin
  • Web Development: React, NodeJS, REST API, GraphQL, JQuery, CSS,Spring Boot
  • Tools and Technologies: Numpy, Pandas, Keras, Fast AI, Hugging Face, Android App Development, Netflix OSS, Jenkins, Docker, Kubernetes, Bitbucket, Jira
  • Databases: MySql, Mongo DB, PostgreSQL
  • Cloud: Azure, Amazon Web Services !

Education 👨‍🎓

  • Master in Computer Science - Khoury College of Computer Sciences, Northeastern University, class of 2024
  • Bachelors Computer Science and Engineering - Vellore Institute of Technology, class of 2020

Reach out to me at: 📞:

Email 📧 | Phone 📱 | LinkedIn 🔗: |

Pawan Kumar Naveen's Projects

rice-quality-analysis icon rice-quality-analysis

Classify rice grains by calculating their average length/breadth ratio by using Image processing in python.

u.s.-patent-phrase-to-phrase-matching-nlp icon u.s.-patent-phrase-to-phrase-matching-nlp

This project utilizes natural language processing techniques for matching phrases in US patents. It involves data loading, preprocessing, and tokenization with the DeBERTa V3 tokenizer, along with finetuning using the Hugging Face Transformers library. The task's evaluation metric is the Pearson correlation coefficient.

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