I am a 3rd year PhD student in Electrical Engineering and Computer Science at the University of California, Berkeley. I work in the Center for Augmented Cognition, advised by Prof. Shankar Sastry. My primary research interest lies in advancing augmented reality technology through research in computer vision and human computer interaction
I graduated summa cum laude with a Bachelors of Science in Electrical Engineering from the University of California, Riverside. There I worked for the College of Engineering - Center for Environmental Research and Technology under Prof. Matthew Barth. I conducted research on identifying vehicles and other features in point cloud data and, as my senior design project, developed a fully autonomous electric utility truck along with two other students.
My LinkedIn page can be found here.
Advanced Place Recognition
One of the challenges to making augmented reality practical and ubiquitous is the ability to recognize previously visited locations and display virtual objects properly in the scene. In the short term this is typically taken care of by visual-inertial tracking algorithms, however for places the user may not have visited for a long time, a separate approach is needed. I am looking into ways to recognize these locations despite changes in the scene such as moved objects or altered lighting contions.
Occlusions in augmented reality
I am interested in developing new methods of determining occlusion of virtual objects by physical moving objects that cannot be mapped apriori. This is a fundamental problem to utlizing augmented reality in large areas. Incorrect occlusions will break the illusion that these objects are present in the real world and be confusing to the user. I am looking into applying generalized principal component analysis methods to solve for these occlusions.
Multi-Modal Indoor Device Positioning
Using WiFi, Image, and Magnetic measurements we are able to localize and track a smartphone in a large building such as a mall or conference center with a mean accuracy of 2.5m. To do this we first build a database that consists of fingerprints of each type of measurment. This database is collected with a single walkthrough of the building at normal walking pace. The maps have shown to remain accurate for months after data collection, though the ease of database generation makes it possible to rebuild maps whenever necessary.
Undergraduate Senior Design: Autonomous GEM Electric Truck
Modified a GEM electric utility truck to autonomously follow GPS waypoints and avoid obstacles. The project was carried out over a period of six months start to finish by a team consisting of two other undergraduate students and myself. For this we designed a PCB for connecting an ARM microcontroller to the various control interfaces of the truck. We used two SICK LIDAR modules for detecting obstacles. A Trimble GPS module used RTK GPS to provide position estimates accurate within 5cm. We added a computer running realtime linux for handling path following and interfacing with the LIDAR and GPS modules. A blog of our work can be found here.