Graduate Education: Ph.D. & M.Sc. in Electrical Engineering
Dissertation (2017-2022): Surgical robots are becoming increasingly common in operating rooms, which provides the opportunity to deploy automation algorithms for surgery. Surgical task automation aims to improve patient throughput, reduce quality-of-care variance among surgeries, and potentially deliver complete automated surgery in the future. While progress in developing autonomous surgical tasks has leaped forward, reactive maneuvers to traumatic events, such as hemostasis, represent a critical area that has attracted little attention. Hemostasis describes a state of the surgical field that is achieved when there is no site of active bleeding and the tissues are unobstructed by blood. Unlike previously automated tasks that occur in a more predictable cadence within a procedure, bleeding can be unpredictable, which necessitates hemostatic maneuvers at any time during surgery.
In my dissertation, all the necessary perception, motion planning, and control strategies are presented to autonomously control a robotic suction tool to clear the surgical field from blood. First, a surgical tool tracking technique is proposed that localizes the robotic agent, which will clear the surgical field, in the endoscopic camera frame. The surgical tool tracking is combined with a deformable tissue tracker to completely track a surgical scene before a vessel rupture occurs. The combination of the two trackers is coined SuPer, the Surgical Perception framework. Next, the blood from a vessel rupture scenario is perceived by detecting and reconstructing the flowing blood from the endoscopic camera data. Finally, a controller and a motion planner for the robotic suction tool to clear the surgical field of blood are presented.
Coursework at University of California San Diego (2017-2019): Covered mathematical foundations in Sensing and Estimation in Robotics, Control and Learning in Robotics, Reinforcement Learning, and Statistical Learning. The theoretical frameworks covered include Hidden Markov Models, Markov Decision Processes, Bayesian Decision Theory, and Lagrangian and Gradient based Optimization
Undergraduate Education: B.Sc. in Electrical Engineering & Internships
Coursework at University of Illinois Chicago (2013-2017): Advanced coursework provided fundamentals in robotic kinematic and dynamic modeling, control and sensing on embedded systems, and random processes through linear and non-linear systems. Senior design project was a custom PCB design for a high performing, audio Digital to Analog Converter, and Class AB amplifier which included over 10 active and 100 passive components. Developed a custom line-following car for Mechatronic System Design course which won first place by utilizing 3 embedded, micro-controllerers communicating through I2C for parallelized sensing from 2 cameras and velocity control from wheel-encoders and 2 full bridge motor drivers.
Internship at Apple’s Special Project Group (May to Dec 2016 & May to Aug 2017): Developed a tracking system using an Extended Kalman Filter to help visualize and measure performance of noisy sensors. Quantified and validated camera performances by developing a measurement system to timestamp the exposure of a camera frame within 50nS of accuracy, created test procedures for full sensor characteristic collection including MTF, photon transfer curve, and dark & and bright fields.
Internship at Apple’s Audio Electrical Engineering Team (Jan to Aug 2015): Wrote automation code to characterize and validate electrical audio systems for MacBook, MacBook Pro, iMac, and Apple Watch product lines through standard metrics including SNR, THD, PSRR, frequency response, and efficiency. The audio electrical systems used a variety of communication systems including I2C, I2S, TDM, and PDM.
Internship at Knowle’s ASIC/Systems Research and Design Internship (May to Aug 2014): Evaluated low current analog and digital silicon MEMS microphones intended for the mobile market using high precision electronic equipment such as Audio Precision and best practices for low power measurements. Designed and manufactured PCB’s for custom test procedures and automation code for acoustical and electrical charactereization.