My Research Focus
I am interested in the broad application of machine learning and deep learning to a wide spectrum of dataset from image, speech, and video to e-commerce click prediction and recommender system to healthcare, stock and so on. I am very interested and passionate about artificial intelligence and constantly seeking to push the boundary of this field and building more intelligent systems and models.
Competitions
- Rakuten-Viki TV recommendation competition 1st Prize (Sep 2015) out of 130 competitors lead by Tan Yong Kiam, Zhenzhou Wu, Bing Ru, Xulei Yang, Yong Liu
- One of 12 teams accepted for workshop presentation in Recsys Challenge 2015 (July 2015) out of 850 competitors lead by Zhenzhou Wu, Lucas Tan, Bing Ru, Yong Liu
- Unilever Overall Opinion Score Prediction Challenge 1st Position (Jan 2015) out of 300 competitors lead by Zhenzhou Wu and Lucas Tan
- Emotion Recognition In The Wild Challenge 1st Prize (2013) lead by MILA lab of Yoshua Bengio
Publications
- Zhenzhou Wu, Xin Zheng, Daniel Dahlmeier, From Character to Document Representation with Global Context Awareness International Conference on Communication and Information Processing (ICCIP 2017)
- Zhenzhou Wu, Xin Zheng, Character-Based Text Classification using Top Down Semantic Model for Sentence Representation arXiv:1705.10586, 2017 pdf
- Zhenzhou Wu, Sean Saito, HINET: Hierarchical Classification with Neural Network ICLR 2017 workshop pdf
- Zhenzhou Wu, Sunil Sivadas, Yong Kiam Tan and Bin Ma, Multi-Modal Hybrid Deep Neural Network for Speech Enhancement arXiv:1606.04750, 2017 pdf
- Xu Lei Yang, L. Gobeawan, Si-Yong Yeo, Wai Teng Tang, Zhenzhou Wu, Yi Su, Automatic Segmentation of Left Ventricular Myocardium by Deep Convolutional and De-convolutional Neural Networks, CINC Computing in Cardiology, 2016
- Xulei Yang, Si-Yong Yeo, Jia Mei Hong, Sum Thai Wong, Wai Teng Tang, Zhenzhou Wu, Gary Lee, Sulin Chen, Vanessa Ding, Brendan Pang, Andre Choo, and Yi Su, A Deep Learning Approach for Tumor Tissue Image Classification, IASTED International Conference on Biomedical Engineering, 2015
- Zhenzhou Wu, Shinji Takaki, Junichi Yamagishi, Deep Denoising Autoencoder for Statistical Speech Synthesis. arXiv:1506.05268, 2015 pdf
- Zhenzhou Wu, Lucas Tan, Bing Ru, Yong Liu, Rick Goh. Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns, RecSys '15 Challenge: Proceedings of the 2015 International ACM Recommender Systems Challenge pdf
- Shinji Takaki, Zhenzhou Wu, Junichi Yamagishi, Deep Denoising Autoencoder for Feature Extraction in Statistical Speech Synthesis System (in Japanese). The Acoustical Society of Japan, pp.263–264, March 2015
- Shinji Takaki, Zhenzhou Wu, Junichi Yamagishi, A Function-wise Pre-training Technique for Constructing a Deep Neural Network based Spectral Model in Statistical Speech Synthesis. Machine Learning in Speech and Language Processing, 2015
- Sunil Sivadas, Zhenzhou Wu and Bin Ma, Investigation of Parametric Rectified Linear Units for Noise Robust Speech Recognition. Interspeech, 2015 pdf
- Kahou, S. E. and Pal, C. and Bouthillier, X. and Froumenty, P. and Gulcehre, C. and Memisevic, R. and Vincent, P. and Courville, A. and Bengio, Y. and Ferrari, R. C. and Mirza, M. and Jean, S. and Carrier, P. and Dauphin, Y. and Boulanger-Lewandowski, N. and Aggarwal, A. and Zumer, J. and Lamblin, P. and Raymond, J. P. and Desjardins, G. and Pascanu, R. and Warde-Farley, D. and Torabi, A. and Sharma, A. and Bengio, E. and Cote, M. and Konda, K. R. and Zhenzhou Wu. Combining Modality Specific Deep Neural Networks for Emotion Recognition in Video. ICMI, 2013
Work Experience
Others
- Acted as an advisor to the national level AI.SG committee. AI.SG is Singapore's national AI initiative for directing Singapore's national policy in AI development.
Education
Master of Computer Science
McGill University 2014
Double Major in Physics and Mathematics
National University of Singapore 2012
For any collaborations, feel free to drop me an email:
hyciswu@gmail.com