- Research Program Mentor
PhD candidate at Stanford University
Mechanical engineering, dynamics, physics, biomechanics, assistive devices, device design, machine learning, deep learning, movement disorders, promoting healthy movement
BioI am an avid mover and am interested in developing and exploring ways in which to help others move. I am currently developing computational tools that can explore the metabolic energy cost of motion at the muscle level. This level of detail can be used to better understand the influence assistive devices have on their users. I am interested in using analyses such as these to better understand and design assistive devices. In my research, I have experience using lots of physics and multibody dynamics based simulations, statistical and machine learning tools, as well as mechanical design. Outside of research, I enjoy lots of different activities like rock climbing, basketball, hiking, photography, working on cars, checking out all the best food spots, and exploring the city. I look forward to how I might be able to use my experience to make your research project a reality!
Create musculoskeletal model and use it in a physics based platform to simulate a motion you care about. You might think about simulating your favorite sport to see if you can learn about the forces involved, or perhaps you've always wondered how cats seem to land on their feet.
Create a machine learning model
Use statistics and machine learning to build a model that can estimate predict, or compute something you are interested in. It could be a model that detects your favorite animal in a picture or one that identifies when someone is dancing in your favorite style. Identify something that you want to build and this would be a great way to practice coding and using statistical methods.
Biomechanics in the wild
Find something in your community that causes people trouble. It could be a set of stairs that is steeper than usual, a sidewalk that is inclined, or a door that is too heavy. Do an analysis of what you find to quantify and recommend different parameters that would be more favorable.