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Zhiding Yu (禹之鼎) |
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About Me |
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I am a senior research scientist at the Machine Learning Research Group, NVIDIA Research. Before joining NVIDIA in 2018, I obtained Ph.D. in ECE from Carnegie Mellon University in 2017, and M.Phil. in ECE from The Hong Kong University of Science and Technology in 2012. I graduated with a bachelor's degree from the Union Class of Electrical Engineering (FENG Bingquan Pilot Class), South China University of Technology in 2008. My current research interests mainly focus on deep representation learning, weakly/semi-supervised learning, transfer learning and deep structured prediction, with their applications to vision and robotics problems. I am broadly interested in: (1) general scene and video understanding, (2) bottom-up grouping and mid-level representation, (3) robust representation for cross-domain/cross-task/open set generalization and adaptation, (4) lifelong learning with interactive/weak/self-supervisions. My research goal is to is to leverage the abundant domain knowledge, priors and structured information to eliminate uncertainties with minimum human supervision and uncover the secret towards true visual intelligence. I am a winner of the Domain Adaptation for Semantic Segmentation Track, WAD Challenge@CVPR18. I received the Best Paper Award at WACV15, and was twice awarded the HKTIIT Post-Graduate Excellence Scholarships in 2010 and 2012. |
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Education Background |
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Ph.D., Carnegie Mellon University |
2012 - Present |
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M.Phil., The Hong Kong University of Science and Technology |
2009 - 2012 |
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B.Eng., South China University of Technology |
2005 - 2008 |
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Intern Experience |
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Research Intern, Mitsubishi Electric Research Laboratories (MERL) |
2016.07 - 2016.10 |
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Research Intern, Microsoft Research (Redmond, WA) |
2015.06 - 2015.08 |
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Research Intern, Adobe Research (San Jose, CA) |
2013.06 - 2013.08 |
