Summary
Ramiz Qudsi is a research scientist and data engineer with nearly a decade of experience building cloud-native Python systems that turn messy experimental telemetry into ML-ready datasets and deployed models. He has led NASA-funded efforts as a Principal Investigator to develop sequence-to-sequence deep learning models for solar wind forecasting—achieving ~25% MSE improvement—and has delivered public, interoperable datasets to NASA/SPDF. At Boston University he designed mission-critical flight and ground software for the LEXI telescope, authored and maintains the lexi-xray Python package, and implemented near-real-time ETL pipelines for spacecraft telemetry. Ramiz excels at translating between domain scientists, ML researchers, and engineers to operationalize reproducible science, and he champions high standards in code quality, CI/CD, and automation. An active open-source contributor to PlasmaPy and developer of numerical solvers, he combines rigorous physics training (PhD) with hands-on DevOps and production ML experience. Colleagues describe him as someone who turns “caffeination into citation,” pairing practical engineering with publication-grade research outputs.
9 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at University of Delaware
Bachelor of Engineering Physics, Bachelor of Engineering Physics at Indian Institute of Space Science and Technology
High School Diploma, High School Diploma at Chinmaya Vidyalaya, Bokaro Steel City
Indian Public School
English, Hindi, Urdu, Maithili, Bhojpuri