Summary
Trevor Bonjour is an Assistant Teaching Professor and PhD candidate in Computer Science with a decade of experience bridging academia and industry in reinforcement learning, machine learning, and AI. He has built end-to-end ML systems across diverse, high-dimensional datasets—from clickstream and text to motion-capture and protein structures—combining feature engineering, visualization, and scalable model deployment. His background includes applied research roles at Purdue and HPE and prior software engineering experience that informs efficient, production-ready solutions. Trevor also has extensive teaching and mentorship experience, having led courses and workshop series that translate research advances into practical skills for students. Notably, his work spans causal inference and biomedical data pipelines, reflecting a taste for problems where rigorous modeling meets real-world impact. Based in San Diego, he blends strong research credentials with hands-on implementation expertise to move ML from concept to classroom and production.
10 years of coding experience
4 years of employment as a software developer
Johns Hopkins University
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Purdue University
Bachelor of Technology, Instrumentation and Control, Bachelor of Technology, Instrumentation and Control at Guru Gobind Singh Indraprastha University