Nitheesh is an MSCV student at the Robotics Institute, Carnegie Mellon University. A Roboticist passionate about computer vision and open-source software. His primary areas of interest are multi-modal 3D perception, sensor fusion, and online learning methods for machine perception. Nitheesh has 9 years of professional experience spanning a wide variety of technologies from consumer-based IoT devices to vision-based autonomous systems.
David Held is an assistant professor at Carnegie Mellon University in the Robotics Institute and is the director of the RPAD lab: Robots Perceiving And Doing. His research focuses on perceptual robot learning, i.e. developing new methods at the intersection of robot perception and planning for robots to learn to interact with novel, perceptually challenging, and deformable objects. David has applied these ideas to robot manipulation and autonomous driving. Prior to coming to CMU, David was a post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in Computer Science at Stanford University. David also has a B.S. and M.S. in Mechanical Engineering at MIT. David is a recipient of the Google Faculty Research Award in 2017 and the NSF CAREER Award in 2021.
Floris is an experienced Perception Engineer at MUJIN with a demonstrated history of working in various fields of medical imaging, robotics, and automated driving. He is highly skilled in programming and machine learning as well as skilled in test engineering, project management, system, and product design. He completed his Ph.D. in Computer Vision at Technische Universiteit Delft.
Jose Jeronimo Moreira Rodrigues
Jeronimo is a Computer Vision technical lead automating the industrial automation at MUJIN. His previous industry experience includes Google, Qualcomm, and Honda, among others, and holds several industrial and academic patents. He completed his Ph.D. in Computer Science at CMU and has been a researcher in several top Robotics Institutions. His main areas of interest are in computer vision and machine learning – especially on geometric approaches scalable to large datasets. Broader areas of interest include convex optimization, advanced linear algebra, randomized algorithms, computational geometry, structured learning, computational photography, distributed algorithms, signal processing, differential geometry, sensing technologies, sensor design, and technology in general.