Download Chenyou Fan - Indiana University Bloomington

Chenyou Fan
Contact
Information
Phone: 812-606-2939
E-Mail: [email protected]
Address: 655 E Alpine Trl, Bloomington, IN 47401
Homepage: http://homes.soic.indiana.edu/fan6
Education
Indiana University, Bloomington, IN, USA
• Ph.D., Computer Science, minor in Statistics.
Aug. 2014 - May 2018 (expected)
• M.S., Computer Science.
Aug. 2012 - May 2014
• Cumulative GPA: 3.85/4.0
Nanjing University, Nanjing, China
Sept. 2007 - July 2011
• B.S., Computer Science.
• Cumulative GPA: 3.7/4.0, University Excellent Student Award
Research
Interests
Computer Vision, Machine Learning, Statistical Learning, Natural Language Processing.
Professional
Experience
University of Southern California, USA
Jun. 2017 - Aug. 2017
• Visiting Research Assistant of Information Sciences.
• Developing prototype algorithms and software in Python and C++ and evaluating the developed algorithms on large scale dataset.
Publications
1. Chenyou Fan, Jangwon Lee and Michael S. Ryoo. “Forecasting Hand and Object Locations in
Future Frames.” ArXiv submission.
2. Chenyou Fan, Jangwon Lee, Mingze Xu, Krishna Kumar Singh, Yong Jae Lee, David J. Crandall and Michael S. Ryoo. “Identifying First-person Camera Wearers in Third-person Videos.”
IEEE Conference on Computer Vision and Pattern Recognition 2017 (CVPR’17).
3. Chenyou Fan, Piergiovanni, A. J., and Michael S. Ryoo. “Temporal attention filters for human
activity recognition in videos.” AAAI Conference on Artificial Intelligence 2017 (AAAI’17).
4. Chenyou Fan, and David J. Crandall. “Deepdiary: Automatically captioning lifelogging image
streams.” Egocentric Perception, Interaction and Computing workshop in European Conference
on Computer Vision 2016 (EPIC ECCVW’16).
Research
Projects
Forecasting Hand and Object Locations in Future Frames
Mar. - May 2017
• Designed and implemented a new two-stream convolutional object detection network for video
frames based on current state-of-the-art object detector (SSD) for static images.
• Proposed to abstract object location information in scene with its intermediate representation
of our new detection network.
• Implemented a new object future location predictor which predicts (i.e., regresses) such representations corresponding to the future frames based on that of the current frame.
• Confirmed that combining the regressed future representation with our detection network
allows reliable estimation of future hands and objects in videos.
Identify First-person Camera Wearer in Third-person Video
July - Nov. 2016
• Collected and synchronized first- and third-person videos of everyday activities.
• Established person-level correspondences across first- and third-person videos.
• Proposed a new semi-Siamese Convolutional Neural Network architecture with a new triplet
loss function to learn a joint embedding space for first- and third-person videos.
• Implemented our new architecture and performed experiments to confirm our method exceeds
existing baseline methods.
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Recognize Human Activity by Using Temporal Attention Filters
Apr. - Aug. 2016
• Introduced temporal attention filters which can dynamically predict the location and duration
of the sub-events of videos.
• Mathematically formulated temporal filters and update rules during the training phase.
• Implemented temporal attention filters with CUDA C++ and performed experiments on highend parallel GPUs.
• Achieved state-of-the-art activity classification results on benchmark datasets.
Lifelogging Photo Captioning and Video Summarization
Oct. 2014 - Mar. 2015
• Created a website with graphical user interface to collect human descriptions as training corpus.
• Augmented deep-learning based image-captioning models with classical statistical methods to
generate multiple diverse captions that can describe photos from both first-person and thirdperson perspectives.
• Created video summaries by selecting an optimal subset of representative sentences from generated captions by adding temporal consistency constraints and solved with Viterbi algorithm.
• Showed our methods can produce correct and more accurate descriptions by evaluating with
both quantitative benchmark scores and qualitative human judges.
Academic Service Journal manuscript review
• Journal of Visual Communication and Image Representation.
Industrial
Experience
Software Development Engineer Intern at Amazon.com, Seattle
May - Aug. 2013
• Organized and formatted page review records from human review database as JSON data.
• Automated unqualified pages detection by rules mined from human records.
Awards
Paul Purdom Fellowship for Doctoral Studies in Informatics, Indiana University
2016
• Awarded to one PhD student per year.
• Cover full tuition and stipend.
University Excellent Student Award, Nanjing University
2008
• Awarded to top 5% students of school.
Key Courses
Machine Learning, Algorithm Design and Analysis, Computer Vision, Image Processing and Recognition, Advanced Databases, Data Mining, Distributed Systems, Scientific Computing, Operating
Systems
Teaching
Experience
Associate Instructor of School of Informatics and Computing, Indiana University
• Course B555 Machine Learning.
Fall 2015
• Course B503 Algorithm Design and Analysis.
Computer Skills
Fall 2014 - Spring 2015
• Languages: C/C++, CUDA C++, Python, Matlab, Java, PHP, HTML, SQL, LATEX.
• Platforms: Cray, Unix/Linux, MacOS, Windows.
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