Lulu Ricketts

Lulu is a graduate student at studying Computer Vision at CMU’s Robotics Institute (MSCV). Prior to attending CMU, she completed her bachelors degree at the University of California, San Diego in Cognitive Science and Machine Learning. For the past two summers, she has interned at Aurora Innovation developing data-driven methods to improve processes on their simulation scenarios team and used computer vision models for the perception evaluation team. Her current interests lie in autonomous vehicle research and finding innovative ways to solve the long tail perception problem.

Roshini Rajesh Kannan

Roshini is a graduate student at Carnegie Mellon University currently pursuing her Master of Science in Computer Vision. She completed her undergraduate degree from Vellore Institute of Technology, India. During her graduate studies, she interned at L5 Automation where she worked on the perception pipeline for detecting and tracking strawberries. Prior to that she has worked on projects including 3D medical image segmentation and defect detection in steel plates.

Srinivasa Narasimhan (advisor)

Srinivasa Narasimhan is a Professor of the Robotics Institute at Carnegie Mellon University. He served as Interim Director of the RI from Aug 2019 to Dec 2021. He obtained his PhD from Columbia University in Dec 2003. His group focuses on novel techniques for imaging and illumination to enable applications in vision, graphics, robotics, agriculture, intelligent transportation and medical imaging. His works have received a dozen Best Paper or Best Demo or Honorable mention awards at major conferences [IV (2021), ICCV (2013), CVPR (2019, 2015, 2000), ICCP (2020, 2015, 2012), I3D (2013), CVPR/ICCV Workshops (2007, 2009)]

Project Responsibilities

Lulu and Roshini worked together on formulating the problem statement and methodology under the guidance of Prof. Srinivasa Narasimhan. Lulu worked on the weather classification model and feature extraction. Roshini focussed on synthetic data generation and restoration model.