Skip to content
View animikhaich's full-sized avatar
🎯
Learning & Improving
🎯
Learning & Improving
58 followers · 49 following

Achievements

Achievement: YOLOAchievement: StarstruckAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Achievements

Achievement: YOLOAchievement: StarstruckAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Block or report animikhaich

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
animikhaich/README.md

Typing SVG

🧐 About Me

👋 Hi there! I'm Animikh, a Machine Learning Engineer with a passion for Anime and Video Games. I'm currently working as a Computer Vision and Machine Learning Engineer at Moultrie - An EBSCO Company. Here, we're developing next-generation Computer Vision algorithms for Cellular Trail Cameras, aimed at enhancing wildlife monitoring.

I graduated with an MS in AI from Boston University, where I worked under Prof. Eshed Ohn-Bar at the H2X Lab. My research focused on end-to-end Autonomous Driving, specifically on closing the Sim2Real gap and developing offline and online driving evaluation metrics for my thesis.

Previously, I was the Computer Vision Engineer and Lead at Wobot.ai, where I spearheaded the development of a robust deep learning tech stack that powers real-time video analytics across hundreds of cameras worldwide.

I ❤️ building things and strongly believe that Multi-Modal Self-Supervised Learning is key to AGI 🤫. My areas of focus include Generative AI, Multi-Modal Learning, and more.

I'm always open to new opportunities and a good chat ☕. Feel free to connect with me on LinkedIn or reach out at animikhaich@gmail.com.

💻 Tech Stack

Tools

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

Languages++

Python C++ JavaScript Dart Flutter Markdown HTML5 CSS3

Machine Learning & AI

PyTorch TensorFlow Keras OpenCV NumPy scikit-learn mlflow OpenAI Matplotlib

Web Development

Streamlit Flask FastAPI Nginx Replicate MongoDB

Cloud

AWS AWS S3 Azure Git Docker

📊 Some Stats

trophy GitHub Streak Logo Logo

🔗 Connect With Me

X LinkedIn Gmail Instagram Google Scholar ResearchGate

Pinned Loading

  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 Tensorflow-based 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 20 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 47 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 1