Quick Bio
I love solving complex problems for customers by pushing systems to physics-based limits. I have experience driving technical progress as an individual contributor as well as securing funding as a Principal Investigator (PI), working with DARPA, IARPA, and other IC customers.
- Senior Research Engineer at SRI International
- Ph.D. in Physics from Stanford
- B.S. in Physics from Caltech
- Thesis: Testing gravity at micron scales
Connect with me on LinkedIn!
Extended Bio
Directions for future research
- Leveraging deep learning algorithms and computing frameworks:
- Explore using language/sequence models to remove interference from data.
- Potential applications in radio astronomy interference suppression.
- Applying reinforcement learning (RL) to RF systems:
- Develop adaptive RF systems to avoid detection or jamming.
I have a variety of technical skills that many STEM Ph.D. students possess, including:
- deep learning
- computer programming
- data science
- nano-fabrication
- electrical and mechanical engineering
What I feel I can uniquely contribute to an organization is a physicist’s perspective for distilling complex systems down to their most essential components.
What does this mean?
Without comparing myself too closely to Isaac Newton, let me give an example of how Newton used this way of thinking to revolutionize our understanding of the solar system.
Before Newtonian mechanics and Newton’s law of gravity, predicting how the planets move was extremely complicated.
At best, the empirical relationships had no justification. At worst, there were empirical tables that spanned several books to characterize the position of the planets in the night sky.
As the apocryphal story goes, one day, Newton was sitting under an apple tree. An apple falling from the tree hit his head and gave him an epiphany. He realized that, assuming the same force that pulled the apple to his head was holding the planets in orbit, he could describe the motion of the planets with a simple equation.
As such, Newton became famous for the inch-long equation: f = -GmM / r².
This one simple equation was able to predict what people saw in the night sky.
This drive to describe complex systems and ideas by distilling them down to the simplest possible model is what physicists do best.
Why should you care? Innovation often occurs when connections are made across disparate fields. Without condensing ideas down to the simplest model, connections are often hard to make. This perspective allows us to see existing information in a different light.
It is this penchant for distilling down complexity that I believe I can bring to any technology organization.