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Learning & Improving
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Learning & Improving

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animikhaich/README.md

Typing SVG

🧐 About Me

πŸ‘‹ Hi, I'm Animikh! I specialize in building and scaling end-to-end Machine Learning Systems.

  • πŸ”­ I’m currently working as a Computer Vision and Machine Learning Engineer at Moultrie, developing next-gen CV algorithms for wildlife monitoring.
  • πŸŽ“ I hold an MS in AI from Boston University, where my research focused on end-to-end Autonomous Driving at the H2X Lab. We recently published at IROS '25.
  • πŸ‘¨β€πŸ’» Previously, I was the Computer Vision Engineer and Lead at Wobot.ai, where I built a real-time video analytics from the ground up and scaled globally.
  • 🌱 My core interests lie in Computer Vision, Multi-Modal AI, and Software Engineering.
  • πŸ’¬ Let's connect! I'm up for a good chat. Find me on X and LinkedIn.

πŸ“Š Some Stats

πŸ’» Tech Stack

Tools

Visual Studio Code Sublime Text Linux macOS Windows Google Chrome LaTeX Jupyter ChatGPT Gemini GitHub Copilot

Languages++

Python C++ TypeScript

Machine Learning & AI

PyTorch TensorFlow Keras OpenCV NumPy scikit-learn mlflow Matplotlib

Web Development

React ShadcnUI Streamlit Flask FastAPI Nginx Replicate MongoDB PostgreSQL Supabase

Cloud & CI

Hetzner AWS Azure Git GitHub Actions Docker Docker Compose

πŸ”— Connect With Me

X LinkedIn Gmail Google Scholar ResearchGate

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  1. VidTune VidTune Public

    Forked from tensorsofthewall/VidTune

    VidTune: Tailored Music For Your Videos

    Python 1

  2. No-Code-Classification-Toolkit No-Code-Classification-Toolkit Public

    Containerized image classification training utility with Streamlit-based interface designed to choose between common architectures and optimizers for quick hyperparameter tuning.

    Python 8 3

  3. 3D-Text2LIVE 3D-Text2LIVE Public

    Zero-shot, text-driven appearance manipulation on multiple views of an object to generate 3D renderings.

    Python 3 2

  4. Semantic-Segmentation-using-AutoEncoders Semantic-Segmentation-using-AutoEncoders Public

    Lightweight and Fast Person Segmentation using Autoencoders (Trained Weights Included)

    Jupyter Notebook 21 7

  5. ECG-Atrial-Fibrillation-Classification-Using-CNN ECG-Atrial-Fibrillation-Classification-Using-CNN Public

    This is a CNN based model which aims to automatically classify the ECG signals of a normal patient vs. a patient with AF and has been trained to achieve up to 93.33% validation accuracy.

    Jupyter Notebook 53 19

  6. Deep-Convolutional-Background-Subtractor Deep-Convolutional-Background-Subtractor Public

    End-to-end CNN-based Autoencoder that can segment any objects even if it is out of the classes present in the training set.

    Jupyter Notebook 4