Dr. Yuri Ivanov

The NewScientist article “Big brother” buildings offer less invasive security said

Tracking people’s every move using buildings packed with motion sensors is more effective than CCTV, and less invasive to privacy, say researchers who tried the technique on their own colleagues.
 
“We want to have a god’s eye view of the entire space,” says Yuri Ivanov of the Mitsubishi Electric Research Laboratories (MERL), who led the project with colleague Christopher Wren.
 
That may sound like the desire of George Orwell’s fictional “Big Brother” in 1984. But the MERL system should actually preserve people’s privacy better than CCTV and make buildings safer and more secure, says Ivanov.

Yuri Ivanov, Ph.D. is Research Scientist, Mitsubishi Electric Research Labs (MERL), and Visiting Scientist, MIT Center for Biological and Computational Learning. He is also a member of the Board of Immersion Music. Immersion Music is a nonprofit arts organization with origins at the MIT Media Lab that brings high-tech methods to the traditional performing arts.
 
Yuri’s research interests are: statistical models of visual and auditory perception, visual motion and speech processing, machine learning, reinforcement learning, computer vision, and online algorithms.
 
He coauthored Soda Pop Zombies: Soft Drink Consumption and Motion, Visualizing the History of Living Spaces, SocialMotion: Measuring the Hidden Social Life of a Building, Buzz: Measuring and Visualizing Conference Crowds, Tracking People in Mixed Modality Systems, Weighted Ensemble Boosting for Robust Activity Recognition in Video, Toward Spatial Queries for Spatial Surveillance Tasks, The MERL Motion Detector Dataset: 2007 Workshop on Massive Datasets, Robust Abandoned Object Detection Using Dual Foregrounds, and The KidsRoom: A perceptually-based interactive and immersive story environment.
 
His patents include Computer vision depth segmentation using virtual surface, Automatic mirror stabilization, Adaptive tracking for gesture interfaces, and Confidence weighted classifier combination for multi-modal identification.
 
Yuri earned his MS and BA in Electrical Engineering and Computer Science (EECS) at the State Academy of Air and Space, Leningrad (St. Petersburg), Russia in 1992. He earned his second MS in Media Arts and Sciences at MIT, USA in 1998. He earned his Ph.D. at MIT in 2001 with the thesis “State Discovery for Autonomous Learning.”