Lingling
Tao (陶凌玲）
Email: ltao4
at jhu dot edu
Address:
322 Clark
Hall,
3400 N.
Charles Street,
Baltimore,
21218, USA
About Me:
I am a Ph.D.
student in the department of Electrical
and Computer Engineering, Johns Hopkins University. I'm working in the
Vision, Dynamics and Learning lab under
the supervision of Dr. René
Vidal. Before coming to Hopkins, I
obtained my bachelor degree from Department
of Electronic Engineering, Tsinghua
University in 2010. My
research interests are in computer vision and machine learning, and in
general developing algorithms for visual understanding.
Work
Experience:
Publications:
- L. Tao and
R. Vidal, Moving Poselets: A Discriminative and Interpretable Skeletal
Motion Representation for Action Recognition, In ChaLearn
Looking at People Workshop (held in conjunction with CVPR),
2015. Download: [pdf]
- L. Tao, F.
Porikli, and R. Vidal. , Sparse Dictionaries for Semantic
Segmentation, In European Conference on
Computer Vision, 2014. Download: [pdf] [HTML]
- L. Tao,
L. Zappella, G. Hager, and R. Vidal, Segmentation and Recognition of
Surgical Gestures from Kinematic and Video Data, In Medical
Image Computing and Computer Assisted Intervention,
2013. Download: [pdf] [HTML]
- L. Tao,
E. Elhamifar, S. Khudanpur, G. Hager, and R. Vidal, Sparse Hidden
Markov Models for Surgical Gesture Classification and Skill
Evaluation, In Information Processing in
Computed Assisted Interventions, 2012.
Download: [pdf]
- S. Swaroop Vedula, Anand O.
Malpani, Lingling Tao,
George Chen, Yixin Gao, Piyush Poddar, Narges Ahmidi, Christopher
Paxton, Rene Vidal, Sanjeev Khudanpur, Gregory D. Hager, Chi Chiung
Grace Chen, Analysis
of the Structure of Surgical Activity for a Suturing and Knot-Tying
Task, PLoS ONE, 2016. Download:[HTML]
Professional
Activities:
- Organizer of Women
in Computer Vision 2016 Workshop (WiCV
2016), which will be held in conjunction with CVPR 2016.
- Reviewer of MICCAI 2014, ACM
Computing Surveys, Image and Vision Computing Journal.
Selected
Graduate Course:
- Random Signal Analysis
- Image Processing and Analysis
- Compressed Sensing
- Statistical Methods in Imaging
- Computer Vision
- Machine Learning in Complex Domain