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Tanay Nayak

👋 About Me:

Hi there! I'm Tanay,

  • 🎓 MSE in CS @ Johns Hopkins University, specializing in areas such as Deep Learning, Natural Language Processing (NLP), and Human Computer Interaction (HCI).
  • 🔬 Student Researcher @ Laboratory of Computational Intensive Care Medicine, JHMI.
  • 📚 BTech in CSE from Manipal Institute of Technology, with a minor in Computational Math.
  • 👨‍💻 Previously, Software Engineer 2 @ Cisco, working in Backend Development and DevOps.
  • 💡 Passionate about pioneering ML and AI advancements while embracing the foundational practices of software development to drive innovation and solve complex challenges.
  • 🎨 Outside of technology, I enjoy Cooking, Gaming, and Graphic Design.

💻 Tech Stack:

Python C C++ Java JavaScript HTML5 CSS3 Node.js MongoDB React PyTorch TensorFlow Keras Pandas NumPy Scikit-learn Matplotlib Seaborn SciPy Linux Docker Kubernetes Jenkins ArgoCD Prometheus Elasticsearch Grafana Adobe Photoshop Adobe Illustrator

📊 Statistics

GitHub Streak Top Langs

Note: Top languages is only a metric of the languages my public code consists of and doesn't reflect experience or skill level.

🙋🏻‍♂️ Let's Connect!

Tanay Nayak's Projects

clrs icon clrs

:notebook:Solutions to Introduction to Algorithms

cs671-nlpssm icon cs671-nlpssm

Repo of the programming homework for the course "CS 601.471/671 NLP: Self-supervised Models" - Spring 2024

cs792-cui icon cs792-cui

Resources for 601.792 Advanced Topics in Conversational User Interfaces course taught at JHU

ece638-dl icon ece638-dl

Homework assignments for Deep Learning (520.638) taught at JHU

mdt icon mdt

Masked Diffusion Transformer is the SOTA for image synthesis. (ICCV 2023)

neetcode icon neetcode

My attempt at leetcode problems in the neetcode algorithms roadmap

nlpssm-final-project icon nlpssm-final-project

Codebase for CS 671 (NLP: Self Supervised Models) Final Project - Enhancing Text-to-Image Models through Direct Preference Optimization

tbi-subphenotypes icon tbi-subphenotypes

Utilizing unsupervised machine learning techniques to identify and categorize distinct sub-phenotypes in patients with Traumatic Brain Injury (TBI), enabling targeted therapeutic approaches and contributing to a deeper understanding of Traumatic Brain Injury(TBI) diversity and complexity.

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