AboutI'm currently an Applied Research Scientist and employee #7 at Ambient.ai. I work on building computer vision models that understand scenes from video in real-time.
Previously, I've worked as a Research Engineer at Google Brain, publishing research in large-scale distributed training and neural architecture search.
I completed my M.S. in Machine Learning at Carnegie Mellon University in 2017. Prior to that, I obtained my B.S. in Computer Science also at CMU in 2016.
My research interests are:
- Solving multi-task problems in computer vision using compositional data primitives.
- Using "outer-loop" optimization techniques like evolution to search for more accurate and efficient neural architectures.
- Combining the fields of deep learning and high-performance computing to train larger and faster neural networks.
- Applying machine learning techniques to solve the climate crisis.
Outside of work and academia, I'm an amateur photographer and a fan of anything outdoors (e.g. hiking, biking, camping). Fun fact: I really enjoy playing Dance Dance Revolution.