Selected to lead a team of five engineers on a oneâyear,$500K project funded by the National Advanced Mobility Consortium (NAMC), with a technical focus on improving KEFâs monocular depth model and multi-class object detection.
Gave a talk with my coworker Paul Frivold in front of more than 150 people at the XchangeIdeas Pittsburgh event. My section of the talk revolved around the value of deep learning in solving complex computer vision tasks.
Published a first-author article in Accounts of Chemical Research (impact factor of 24) where I explain how I improved upon the bond-centric model leading to a 71% reduction in the RMSE for Au,Pd,Pt NP systems. Also, our art (made in Blender) was selected for the cover of the journal (click the image to view the full cover art)!
Started at KEF Robotics as a computer vision engineer, initially working on improving the image segmentation model for their tethered UAV hazard detection/avoidance system.
Won 2nd place out of 29 teams in the pitt challenge with a $2,000 cash prize. The project applied computer vision for malignant/benign skin mole classification with the goal of decreasing healthcare barriers and improving early detection!