Hi, I’m Sadia Afrin Purba, a PhD student in Electrical and Computer Engineering at Temple University, based in Philadelphia and advised by Dr. Joseph Picone. My research sits at the intersection of quantum computing and machine learning, with a focus on how quantum structure, especially entanglement, can improve correlation modeling. I build reproducible benchmarking pipelines and implement quantum and hybrid machine learning algorithms (for example QSVM, QNN, and QRBM) using Python with Qiskit and PennyLane, working with real datasets such as EEG and digital pathology through the Institute for Signal and Information Processing (ISIP).

Before my PhD, I spent five years as a Senior Machine Learning Engineer delivering production systems, including LLM-enabled ICD-10 medical coding, large-scale data quality monitoring, and retail inventory monitoring.

Core skills: quantum ML experimentation, benchmarking and evaluation, Python, Qiskit, PennyLane, research software development, and clear technical communication.