Joshua is a second-year Ph.D. Candidate in Aeronautics & Astronautics at Stanford University and is a recipient of the Stanford Graduate Fellowship (SGF) in Science & Engineering. He is a researcher in the Stanford Intelligent Systems Lab (SISL) where his research focuses on decision making under uncertainty for autonomous systems. Joshua has also conducted research in collaboration with SISL and NASA JPL related to the DARPA Subterranean Challenge.

Joshua earned his Bachelor of Science in Mechanical Engineering from the University of California, Berkeley in 2020. During his time at UC Berkeley, Joshua's work focused on optimization methods for bioinspired design, machine learning for real time manufacturing control, and experimental multi-phase flow analysis. Joshua has also interned at Lawrence Livermore National Laboratory and the NASA Jet Propulsion Laboratory.

Honors & Awards

  • Stanford Graduate Fellowship (SGF) in Science & Engineering, Stanford University (03/2020)

Education & Certifications

  • M.S., Stanford University, Aeronautics & Astronautics (2021)
  • B.S., University of California, Berkeley, Mechanical Engineering (2020)

All Publications

  • Precise localization and semantic segmentation detection of printing conditions in fused filament fabrication technologies using machine learning ADDITIVE MANUFACTURING Jin, Z., Zhang, Z., Ott, J., Gu, G. X. 2021; 37
  • Algorithmic-driven design of shark denticle bioinspired structures for superior aerodynamic properties BIOINSPIRATION & BIOMIMETICS Ott, J., Lazalde, M., Gu, G. X. 2020; 15 (2): 026001


    All engineering systems that move through fluids can benefit from a reduction in opposing forces, or drag. As a result, there is a significant focus on finding new ways to improve the lift-to-drag ratios of systems that move through fluids. Nature has proven to be an extremely beneficial source of inspiration to overcome current technical endeavors. Shark skin, with its low-drag riblet structure, is a prime example of an evolutionary design that has inspired new implementations of drag reducing technologies. Previously, it has been shown that denticles have drag reducing properties when applied to airfoils and other surfaces moving through fluids. Researchers have been able to mimic the structure of shark skin, but minimal work has been done in terms of optimizing the design of the denticles due to the large number of parameters involved. In this work, we use a combination of computational fluid dynamics simulations and optimization methods to optimize the size and shape of shark skin denticles in order to decrease drag. Results show that by changing the size, shape, and orientation of the denticles, the boundary layer can be altered, and thereby reduce drag. This research demonstrates that denticles play a similar role as vortex generators in energizing the boundary layer to decrease drag. These mechanisms, along with the fundamental knowledge gained through the study of these drag reducing structures can be applied to a vast number of fields including aeronautical, oceanic, and automotive engineering.

    View details for DOI 10.1088/1748-3190/ab5c85

    View details for Web of Science ID 000508176300001

    View details for PubMedID 31775125