Start with the failure mode.
A model demo can hide what matters. I prefer to define evaluation, uncertainty, and operational constraints before optimizing the happy path.
ABOUT / AMAAN KHANCHAMPAIGN, IL
I care about the whole system.
I’m an engineer, ML researcher, and educator drawn to problems that demand both technical depth and practical judgment.
I’m currently pursuing a Master of Computer Science at the University of Illinois Urbana-Champaign, where I also support STAT 107 and CS 411 students across data science, statistical modeling, and databases.
My work has moved between backend services, applied ML, computer vision, market data, and educational tooling. The common thread is a desire to make complex systems more useful, legible, and reliable.
01 / HOW I THINK
Make the system observable.
Good infrastructure should explain itself: clear data lineage, measurable latency, useful traces, and interfaces that expose the right evidence.
Respect the person using it.
Whether I’m building a prediction interface or a student lab, clarity is part of correctness. The best technical choice still has to work for someone.
02 / EDUCATION
DEC 2027
MCSMaster of Computer Science
University of Illinois Urbana-Champaign
MAY 2025
BSB.S. Information Sciences
Minor in Computer Science · University of Illinois Urbana-Champaign
Artificial IntelligenceDeep LearningComputer VisionMachine LearningSoftware EngineeringDatabase SystemsData MiningMachine PerceptionData VisualizationAlgorithmic Market Microstructure
03 / WORKING TOOLKIT
Programming
Python · TypeScript · C++ · Java · SQL · R · Bash
ML / AI
PyTorch · TensorFlow · Scikit-learn · Transformers · XGBoost · OpenCV
Systems
FastAPI · Flask · Docker · Kubernetes · Airflow · Apache Spark
Cloud
AWS · SageMaker · Google Cloud · Vertex AI · BigQuery
LET’S TALK