{"id":22,"date":"2024-05-13T15:20:05","date_gmt":"2024-05-13T15:20:05","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2024team6\/?page_id=22"},"modified":"2024-12-17T01:00:37","modified_gmt":"2024-12-17T01:00:37","slug":"team","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2024team6\/team\/","title":{"rendered":"Team"},"content":{"rendered":"\n

Student<\/h2>\n\n\n\n
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Zihan Wang is presently a second-year Master’s student at the Robotics Institute, advised by Prof. Deva Ramanan. He obtained his bachelor’s degree in Electrical and Electronics Engineering at the University of Edinburgh. His research interests include Machine Learning, Computer Vision, and Robotics, especially understanding foundation models and how can vision enhance robotic decision-making. <\/p>\n<\/div><\/div>\n\n\n\n

Collaborators & Advisors<\/h2>\n\n\n\n
\n

Jeff Tan is a second year MSR student working on\u00a0Computer Vision<\/a>\u00a0at Carnegie Mellon University. He is interested in physical intelligence: building robots that can capture, reason about, and interact with our rich and dynamic 3D world.<\/p>\n<\/div>

\"\"<\/figure><\/div>\n\n\n\n
\"\"<\/figure>
\n

Neehar Peri is a third year PhD student working on Computer Vision<\/a> at Carnegie Mellon University. His work with the Center for Autonomous Vehicle Research<\/a> focuses on building robust 3D perception algorithms for detection, tracking, forecasting, planning, and simulation.<\/p>\n<\/div><\/div>\n\n\n\n

<\/p>\n\n\n\n

\n

Tarasha Khurana is a Ph.D. student at The Robotics Institute, Carnegie Mellon University advised by Prof. Deva Ramanan. Her research focus is broadly on spatiotemporal 3D scene understanding. She hold a B.E. degree in Computer Engineering from the University of Delhi.<\/p>\n<\/div>

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Deva Ramanan is a Professor in the Robotics Institute at Carnegie- Mellon University and the former director of the CMU Center for Autonomous Vehicle Research. His research interests span computer vision and machine learning, with a focus on visual recognition. <\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

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