👤Mohammed Abdul Kalam Khan

Hi! I'm a passionate developer and researcher with a focus on machine learning, computer vision, and MLOps. I enjoy building intelligent systems that enhance decision-making, especially in healthcare and video analysis domains.

🎓 Education & Learning

I'm actively studying and deepening my knowledge in:

• Artificial Intelligence
  • Unification
  • Lifting Forward Chaining
  • Resolution
• Probability & Statistics
  • Expectation
  • Discrete & Continuous Distributions
  • Sampling, Estimation & Hypothesis Testing
  • Stochastic Processes
• Deep Learning with PyTorch
  • Tensors and Operations
  • GPU Acceleration
  • Autograd & Computational Graphs
• MLOps & DevOps
  • Docker & Kubernetes
  • FastAPI & Uvicorn deployment on K8s
• Data Structures & Algorithms (DSA)

💻 Projects

🔬 Hematopoietic Cell Transplantation (HCT) Prediction System

• Developed an ensemble-based predictive system using XGBoost and CatBoost.
• Predicts post-transplant survival outcomes with personalized risk scores.
• Integrates HLA feature engineering and clinical data.
• Focuses on accuracy, fairness, and clinical decision-making.
• Built a web interface for clinician access.

🎥 Video Classification using MViT_V2_S

• Built a video classification system using PyTorch.
• Employed MViT_V2_S_Weights.KINETICS400_V1 from

  torchvision

  .
• Implemented a custom Dataset and DataLoader with OpenCV support for video processing.

📱 IdleTimer App (Kotlin + Jetpack Compose)

• Created a Kotlin app that:
  • Sets idle timers and triggers Always-On Display (AOD).
  • Shows a countdown on lock screen when idle.
• Features:
  • Room database
  • Foreground services
  • AlarmManager for precise scheduling
• Also implemented:
  • Color picker with

    colorpicker-compose

  • Analytics screen to track user idle time

🤖 Research Assistant Bot (Manim + NLP)

• Built a bot that:
  • Helps users understand papers and complex concepts.
  • Generates summaries, scripts, and Manim visualizations.
  • Follows a structured pipeline for clarity and educational value.

🔧 Tech Stack

• Languages: Python, Kotlin, JavaScript
• Frameworks: FastAPI, Flask, Jetpack Compose, Next.js
• ML/DL: PyTorch, scikit-learn, XGBoost, CatBoost
• DevOps: Docker, Kubernetes (local with Docker Desktop)
• Frontend: React (Next.js), Tailwind CSS
• Tools: OpenCV, Manim, Uvicorn, Gunicorn, joblib

🚀 Interests

• Building scalable AI systems
• Developing educational tools using visualizations
• Applying ML in healthcare for better outcomes
• Improving ML model deployment and infrastructure