Summary
John Helmsen is a research team lead with 11+ years of experience specializing in machine learning, distributed graph algorithms, and big data systems, currently based in Rockville, MD. He has a strong track record transitioning organizations into advanced technology areas, delivering patented threat-scoring and online distributed graph processing techniques used in operational security products. His background spans applied physics and numerical methods—ranging from radar and sensor simulation to computational fluid dynamics and semiconductor process modeling—rooted in a PhD-level research career at UC Berkeley. John combines hands-on engineering (Spark, JanusGraph/HBase, Ambari, GPU computing) with technical leadership, mentoring teams and guiding government and industry projects to completion. Notably, he is a recognized authority on front-tracking methods for the inverse Eikonal problem and has repeatedly demonstrated methods for eventual consistency and online parameter/model updates in distributed inference systems.
11 years of coding experience
20 years of employment as a software developer
Master of Science (M.S.), Electrical Engineering, Master of Science (M.S.), Electrical Engineering at University of California, Berkeley
Bachelor of Science (B.S.), Electrical Engineering, Computer Engineering, Mathematics, Bachelor of Science (B.S.), Electrical Engineering, Computer Engineering, Mathematics at Carnegie Mellon University
Japanese