Bio

Joshua is a fourth-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 currently serving on Active Duty in the United States Air Force through the DAWN-ED PhD fellowship. Joshua 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)

Contact

  • Academic
    jott2@stanford.edu
    University - Student Department: Aeronautics and Astronautics Position: Graduate
    University - Student Department: Department of Geological Sciences Position: Graduate

Additional Info

Links

Lab Affiliations

All Publications

  • Safe and Efficient Navigation in Extreme Environments using Semantic Belief Graphs Ginting, M., Kim, S., Peltzer, O., Ott, J., Jung, S., Kochenderfer, M. J., Agha-mohammadi, A., IEEE IEEE. 2023: 5653-5658

    View details for DOI 10.1109/ICRA48891.2023.10161056

    View details for Web of Science ID 001036713004090

  • Fast and Scalable Signal Inference for Active Robotic Source Seeking Denniston, C. E., Peltzer, O., Ott, J., Moony, S., Kim, S., Sukhatme, G. S., Kochenderfer, M. J., Mac Schwager, Agha-mohammadi, A., IEEE IEEE. 2023: 7909-7915

    View details for DOI 10.1109/ICRA48891.2023.10161445

    View details for Web of Science ID 001048371101035

  • Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing Ott, J., Balaban, E., Kochenderfer, M. J., IEEE IEEE. 2023: 7894-7901

    View details for DOI 10.1109/ICRA48891.2023.10160859

    View details for Web of Science ID 001048371101033

  • Semantics-Aware Mission Adaptation for Autonomous Exploration in Urban Environments Moon, S., Peltzer, O., Ott, J., Kim, S., Agha-Mohammadi, A., IEEE IEEE. 2023: 2065-2070

    View details for DOI 10.1109/IROS55552.2023.10341632

    View details for Web of Science ID 001133658801079

  • FIG-OP: Exploring large-scale unknown environments on a fixed time budget IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Peltzer, O., Bouman, A., Kim, S., Senanayake, R., Ott, J., Delecki, H., Sobue, M., Kochenderfer, M. J., Schwager, M., Burdick, J., Agha-mohammadi, A. 2022

    View details for DOI 10.1109/IROS47612.2022.9981271

  • Adaptive coverage path planning for efficient exploration of unknown environments IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Bouman, A., Ott, J., Kim, S., Chen, K., Kochenderfer, M. J., Lopez, B., Agha-mohammadi, A., Burdick, J. 2022

    View details for DOI 10.1109/IROS47612.2022.9982287

  • 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

    View details for DOI 10.1016/j.addma.2020.101696

    View details for Web of Science ID 000609203100072

  • 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

    Abstract

    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

https://orcid.org/0000-0002-6754-8316