苏勤亮

所属研究所、院系: 
大数据与计算智能研究所
职称: 
副教授
E-mail: 
suqliang@mail.sysu.edu.cn
办公地点: 
广州大学城外环东路132号中山大学超算中心503M
教师简介: 

中山大学数据科学与计算机学院副教授,博士生导师。2014年12月于香港大学取得博士学位,之后前往美国杜克大学电子与计算机工程系从事博士后研究工作,2018年1月回国加入中山大学至今。当前主要从事机器学习、深度神经网络、贝叶斯分析、统计推理、概率图模型、自然语言处理等方面的基础研究及具体应用的工作。近年来在国内外刊物/会议发表论文20多篇,包括中国科学院推荐的Top期刊和CCF推荐的A类会议,如:IEEE Trans. on Signal. Process, ICML, NIPS, AAAI, ACL等。

研究领域: 
  • 机器学习、深度学习、人工智能
  • 贝叶斯分析、统计推理、概率图模型
  • 自然语言处理
教育背景: 
  • 2010.9 — 2014.12       香港大学               博士
  • 2007.9 — 2010.3         浙江大学               硕士
  • 2003.9 — 2007.6         重庆大学               本科
工作经历: 
  • 2018.1 — 至今           中山大学               副教授
  • 2015.3 — 2017.12       美国杜克大学       博士后
  • 2015.1 — 2015.2         香港大学               高级研究助理
获奖及荣誉: 
  • 美国数学建模竞赛一等奖
  • 因特尔杯——全国大学生嵌入式电子设计竞赛三等奖
  • 重庆市优秀毕业生
  • 浙江大学优秀毕业生
主要学术兼职: 

审稿人:JLMR, TIT, TSP, TCom, AAAI, UAI, AISTATS, ICC, Globecom

代表性论著: 
  • Liqun Chen, Shuyang Dai, Yunchen Pu, Chunyuan Li, Qinliang Su, Erjin Zhou, Lawrence Carin, Symmetric Variational Auto-encoder and Connections to Adversarial Learning, 21th Inter. Conf. Artificial Intelligence and Statistics, AISTATS2018, Canary Islands, Spain, 2018.4.9-4.11
  • Dinghan Shen, Yizhe Zhang, Ricardo Henao, Qinliang Su, Lawrence Carin, Deconvolutional Latent-Variable Model for Text Sequence Matching, 32th Proc. American Association of Artificial Intelligence, AAAI2018, New Orleans, USA, 2018.2.2-2.7
  • Qinliang Su, Xuejun Liao, Lawrence Carin, A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks, 31th Annual Conference on Neural Information Processing Systems, NIPS2017, Long Beach, USA, 2017.12.4-12.9
  • Zhe Gan, Chunyuan Li, Changyou Chen, Yunchen Pu, Qinliang Su, Lawrence Carin, Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling, 55th Annual Meeting of the Association for Computational Linguistics, ACL2017, Vancouver, CA, 2017.7.30-8.4
  • Qinliang Su, Xuejun Liao, Chunyuan Li, Zhe Gan, Lawrence Carin, Unsupervised Learning with Truncated Gaussian Graphical Models, 31th Proc. American Association of Artificial Intelligence, AAAI2017, San Francisco, USA, 2017.2.4-2.9
  • Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin, Nonlinear Statistical Learning with Truncated Gaussian Graphical Model, 33th International Conference on Machine Learning, ICML2016, New York, USA, 2016.6.24-6.26
  • Qinliang Su, Yik-Chung Wu, Distributed estimation of variance in Gaussian graphical model via belief propagation: accuracy analysis and improvement, IEEE Trans. Signal Process., 2015, 63(23): 6258-6271
  • Qinliang Su, Yik-Chung Wu, On convergence of Gaussian belief propagation, IEEE Trans. Signal Process., 2015, 63(5): 1144-1155
  • Qinliang Su, Yik-Chung Wu, Convergence analysis of the variance in Gaussian belief propagation, IEEE Trans. Signal Process., 2014, 62(19): 5119-5131
  • Qinliang Su, Yik-chung Wu, Determine the Convergence of Variance in Gaussian Belief Propagation via Semi-definite Programming, International Symposium on Information Theory, ISIT2014, Honolulu, USA, 2014.6.29-2014.7.4
  • Qinliang Su, Aiping Huang, Zhaoyang Zhang, Kai Xu, Jian Yang, A Non-Cooperative Method for Path Loss Estimation in Femtocell Networks, IEEE Globecom Workshop on Femtocell Networks, Globecom2010, Miami, USA, 2010.12.6-2010.12.10
  • Qinliang Su, Aiping Huang, Zhouyun Wu, Guanding Yu, Zhaoyang Zhang, A Distributed Dynamics Spectrum Access and Power Allocation Algorithm for Femtocell Networks, International Conference on Wireless Communication and Signal Processing, Nanjing, China, 2009.11.13-2009.11.15
  • Qinliang Su, Aiping Huang, Jing Li and Hsiao-Hwa Chen, Complexity reduction of signal detection by exploiting correlation characteristics of spreading sequences, Int. J. Communication Systems, 2009, 22(11): 1427-1443