Ryan Johnson

About

Ryan F. Johnson is an associate professor in the Department of Mechanical Engineering at Carnegie Mellon University. His research focuses on predictive computational modeling of propulsion and reacting-flow systems, with interests spanning computational fluid dynamics, chemical kinetics, GPU-enabled high-performance computing, and embedded machine learning. He develops scalable prediction capabilities for complex, multiscale problems, with the goal of enabling accurate simulation of real propulsion and energy devices. Before joining Carnegie Mellon, he was a scientist and aerospace engineer at the U.S. Naval Research Laboratory in Washington, DC, where he led efforts in high-speed propulsion modeling. He also held a visiting scholar appointment at Stanford University, working on problems at the intersection of high-performance computing, CFD, and chemical kinetics. Johnson received his Ph.D. from the University of Virginia in 2014 under the supervision of Professor Harsha Chelliah. He is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE). His educational background includes a BS in Aerospace Engineering from the University of Virginia.

Research topics

• Computer Science
• Computer Security
• Operating system
• Information Retrieval
• Parallel computing
• Database
• World Wide Web
• Embedded system
• Programming language

Selected publications

• Processing Analytical Workloads Incrementally

  arXiv (Cornell University) · 2015-09-16 · 2 citations

  preprintOpen access

  Analysis of large data collections using popular machine learning and statistical algorithms has been a topic of increasing research interest. A typical analysis workload consists of applying an algorithm to build a model on a data collection and subsequently refining it based on the results. In this paper we introduce model materialization and incremental model reuse as first class citizens in the execution of analysis workloads. We materialize built models instead of discarding them in a way t…

  PublisherOA PDFDOI

Frequent coauthors

• Ippokratis Pandis

  Amazon (United States)

  29 shared

• Anastasia Ailamaki

  27 shared

• Angelos Stavrou

  14 shared

• Manos Athanassoulis

  7 shared

• Tianzheng Wang

  Simon Fraser University

  7 shared

• Mohamed Elsabagh

  7 shared

• Radu Stoica

  IBM Research - Zurich

  7 shared

• Nikos Hardavellas

  5 shared

Education

• B.S.

  University of Virginia

  2010

• Ph.D., Mechanical and Aerospace Engineering

  University of Virginia

  2014

Awards & honors

• Presidential Early Career Award for Scientists and Engineers…

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