Computer Vision and Machine Learning
Department of Electrical and Computer Engineering, Whiting School of Engineering
Department of Biomedical Engineering, School of Medicine
Rama Chellappa is an expert in computer vision, pattern recognition, image and signal processing, machine learning, and biometrics who uses data, geometry, and physics to help computer systems interpret the visual world. Chellappa’s work has impacted smart cars, forensics, and 2D and 3D modeling of faces, humans, objects, and terrain, and has the potential to significantly improve diagnosis and treatment for patients spanning a wide range of diseases.
Chellappa’s research has shaped the field of facial recognition technology—developing detailed face models based on shape, appearance, texture, and bone and muscle structure. Under a recent program, Chellappa and his team developed a high-accuracy face recognition system that serves critical needs for federal and commercial sectors. The team has also worked on modeling facial expressions, with potential for a variety of medical applications. Some of Chellappa’s current projects focus on designing robust machine learning systems that can nimbly adapt to new environments and tasks, as well as on collaborating with mathematicians to build new models for deep learning, a subset of machine learning that maps data to decisions.
Chellappa is the author of Can We Trust AI? which recounts the evolution of AI from its post-World War II origins, celebrates its advances in medical care, transportation, and disaster relief, and offers a pioneering inventor’s view on how it must evolve. It includes a balanced account of the benefits and hazards of AI and how researchers and governments can lead the way toward more convenient, safer, and more equitable uses. The book is part of the Johns Hopkins Wavelengths series.
Chellappa joined Johns Hopkins University as a Bloomberg Distinguished Professor in 2020 from the University of Maryland.