I'm a Computer Science major at the University of Cincinnati, passionate about building intelligent systems at the intersection of software engineering and Computer-Vision research. I thrive on writing clean, maintainable code and design with users in mind.

• Languages: Python, C++, Java, JavaScript, Dart
• Frameworks: Flask, Node.js, Express.js, Flutter, React
• AI & CV Libraries: scikit-learn, TensorFlow, Keras, MONAI, auto-sklearn, UNET, RESNET
• Tools & Platforms: Docker, Firebase, GitHub Actions, Postman, WSL, Home Assistant
• Databases: MongoDB, MySQL, PostgreSQL, SQLite

• 🧠 Full-Stack Research Developer
  I'm working on full-stack systems that power internal research platforms. My current focus is on building efficient REST APIs in Node.js, writing scalable React interfaces, and embedding user analytics via Matomo. I also optimize SQL queries to enhance database performance and enforce clean, testable code through CI pipelines and unit testing.

• 📍 Biomedical Motion Capture Analysis
  I'm also part of an exciting research initiative using Qualisys motion capture systems and Python to analyze biomechanical data (marker points, joint angles, etc.) for medical applications. This role involves integrating sensor data, exploring signal processing, and potentially applying computer vision techniques on recorded motion data for enhanced insight into human movement.

• 🩺 AI Researcher – Medical Image Segmentation
  As part of a medical imaging research team, I developed deep learning models (UNET) using the MONAI framework to segment heart substructures in 3D MRI data. I explored SAM 2 (Segment Anything Model) from Meta (LLaMA) to test its utility in diagnostics, annotated data using ITK-Snap, and trained models with high Dice coefficient performance. This project was a deep dive into applied computer vision in healthcare.

📬 Feel free to reach out!

• 📧 Email: abhishek.23.01.2005@gmail.com
• 💼 LinkedIn: linkedin.com/in/abhishekchandra123