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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. 1 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. 2