Sadhana Pan

I’m a Python-centric Biomedical Informatics graduate with over 2 years of practical experience in Machine Learning, Computer Vision, and predictive modeling. I focus on turning high-dimensional, real-world medical and genetic data into reliable, end-to-end AI workflows.

As a Research Assistant at Arizona State University, I engineered end-to-end computer vision pipelines for DICOM medical imaging—handling data extraction, windowing, and high-precision visualization. I also developed ML models for anomaly detection and risk prediction using supervised and unsupervised approaches.

I take engineering best practices seriously: I managed collaborative workflows with Git in a Linux environment and used performance analysis to improve model interpretability and accuracy trends. In my Translational Genomics Research Institute internship, I leveraged Python and R to run logistic regression on genetic datasets and predict coronary artery disease risk using 13 unique genetic markers.

I’m excited to keep applying strong algorithmic thinking and automation to scalable AI applications—especially where accuracy, reproducibility, and actionable decision support matter. I also enjoy building educational, patient-facing resources that translate analytics into accessible guidance.