Design, imaging, tracking, control, testing, and applications of miniature medical robotic systems.
My interest is in miniature robotic systems for medicine. The creation of smaller devices and development of new surgical techniques have enabled a shift over the last decades towards minimally invasive diagnosis and surgery. Instead of open procedures requiring large incisions, these minimally invasive procedures can often be performed through millimeter-size incisions or in some cases even through natural body orifices. Miniature robotic systems promise to enable better patient outcomes through enhanced accuracy and control of tool motions, semi-automated navigation (GPS-like systems), and better imaging and information about the anatomy during surgery. Smaller and more flexible devices, in particular, can reach further into the body and enable new treatments for a wide variety of illnesses.
Ph.D. in Mechanical Engineering, Vanderbilt University, TN, USA 2016
B.S. in Mechanical Engineering, Rice University, TX, USA 2010
In the last decade, researchers and medical device companies have made major advances towards transforming passive capsule endoscopes into active medical robots. One of the major challenges is to endow capsule robots with accurate perception of the environment inside the human body, which will provide necessary information and enable improved medical procedures. We extend the success of deep learning approaches from various research fields to the problem of uncalibrated, asynchronous, and asymmetric sensor fusion for endoscopic capsule robots. The results performed on real pig stomach datasets show that our method achieves sub-millimeter precision for both translational and rotational movements and contains various advantages over traditional sensor fusion techniques.
Unser Ziel ist es, die Prinzipien von Wahrnehmen, Lernen und Handeln in autonomen Systemen zu verstehen, die mit komplexen Umgebungen interagieren. Das Verständnis wollen wir nutzen, um künstliche intelligente Systeme zu entwickeln.