Biography :

Dr. Cong Leng is currently an associate professor at the Institute of Automation, Chinese Academy of Sciences. His research areas include machine learning, deep learning, AI chip design, large vision model, model quantization and acceleration. He received his bachelor's degree from Central South University in 2011 and his doctorate from the Institute of Automation, Chinese Academy of Sciences in 2016. He is an Associate Researcher at the National Laboratory for Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, and a recipient of the President's Award of the Chinese Academy of Sciences.

• In 2015, he served as a Big Data Algorithm Consultant at Baidu.
• From 2016 to 2018, he was a Senior Algorithm Expert at Alibaba's DAMO Academy.
• Since 2018, he has been an associate professor at the National Laboratory for Pattern Recognition, Institute of Automation, Chinese Academy of Sciences.
• In August 2020, he became the vice president of the Zhongke Nanjing Artificial Intelligence Innovation Research Institute, focusing on algorithmic innovation research in the field of AI applications and core AI platform research.
• Since July 2021, he has been appointed as the General Manager of Zhongke Fangcun Zhiwei (Nanjing) Technology Co., Ltd.

Publications :

[1] C Leng, J Wu, J Cheng, X Zhang, H Lu. Hashing for distributed data. International Conference on Machine Learning (ICML), 2015.

[2] Cong Leng, Jiaxiang Wu, Jian Cheng, Xiao Bai, Hanqing Lu. Online Sketching Hashing. CVPR 2015.

[3] Qiang Song, Sixie Yu, Cong Leng, Jiaxiang Wu, Qinghao Hu, Jian Cheng. Learning Deep Features for MSR-Bing Information Retrieval Challenge. ACM Multimedia 2015. (Ranked the 1st place on Visual Recognition task and 3rd place on Image Retrieval task)

[4] J Wu, C Leng, Y Wang, Q Hu, J Cheng. Quantized convolutional neural networks for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[5] Jiaxiang Wu, Qinghao Hu, Cong Leng, Jian Cheng. Shoot to Know What: An Application of Deep Networks on Mobile Devices. AAAI 2016 (Demo track).

[6] Jian Cheng, Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu. Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Vol.29, No.10, pp.4730-4743, 2018.

[7] C Leng, Z Dou, H Li, S Zhu, R Jin. Extremely low bit neural network: Squeeze the last bit out with admm Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2018

[8] Fanrong Li, Zitao Mo, Peisong Wang, Zejian Liu, Jiayun Zhang, Gang Li, Qinghao Hu, Xiangyu He, Cong Leng, Yang Zhang, Jian Cheng. A System-Level Solution for Low-Power Object Detection. ICCV 2019 Workshop on Low-Power Computer Vision.

[9] Gang Li, Peisong Wang, Zejian Liu, Cong Leng, Jian Cheng. Hardware Acceleration of CNN with One-Hot Quantization of Weights and Activations. DATE 2020.

[10] Tianli Zhao, Qinghao Hu, Xiangyu He, Weixiang Xu, Jiaxing Wang, Cong Leng, Jian Cheng. ECBC: Efficient Convolution via Blocked Columnizing. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), in press,2022.