Developing algorithms for robotic systems which learn from past experiences to adapt to changing dynamics and unseen environments. This includes research in machine learning (ML), reinforcement learning (RL), and adaptation techniques which will enable effective real-world performance where only uncertain dynamics models of the system and environment are available.

Worked on a novel reinforcement learning algorithm which leverages a first-principles model to learn control policies with extremely limited data.

Managed two ISO 17025 accredited test labs (team of 6) which conduct research & development, qualification, and quality testing on aerospace hydraulic products and materials.

Designed, assembled, and developed adaptive test stands, sensing systems, and software to automate testing and the acquisition of data.

Within 5 months and with little experience, obtained ISO 17025 accreditation for two test labs by implementing completely revised lab operating procedures, drafted through research of industry standards and best practices.