Conference Tutorials

• Sheng Li, Liuyi Yao, Yaliang Li, Jing Gao and Aidong Zhang: " Representation Learning for Causal Inference”, The AAAI Conference on Artificial Intelligence (AAAI), 2020.
Sheng Li and Yun Fu: “ Low-Rank and Sparse Modeling for Data Analytics”, International Joint Conference on Artificial Intelligence (IJCAI), 2016.
• Rene Vidal, Ehsan Elhamifar, Zhouchen Lin, Jiashi Feng, Sheng Li, Yun Fu: “ Low-Rank and Sparse Modeling for Visual Analytics”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

Associate Editor / Editorial Board

• IEEE Computational Intelligence Magazine (2019 - )
• Journal of Electronic Imaging (2018 - )
• Neurocomputing (2017 - )
• IET Image Processing (2017 - 2020)
• Neural Computing and Applications (2017 - )

Program Chair

• Tusion (in conjunction with IJCAI), 2020
• Tusion (in conjunction with IJCAI), 2019
• IEEE AMFG (in conjunction with CVPR), 2019

Publicity Chair

• IEEE AMFG, 2015-2017
• ICMLA, 2016
• IEEE BigData-TCMFTL, 2016

Senior Program Committee / Area Chair

• IJCAI 2020
• AAAI 2019-2020
• VCIP 2017

Program Committee

• 2020: NeurIPS, ICML, CVPR, ECCV, KDD, ICLR, EMNLP, UAI, PAKDD, ECAI, IEEE BigData, AAAI-AISI, AAAI-AFFCON
• 2019: NeurIPS, ICML, IJCAI, CVPR, ICCV, KDD, ICLR, UAI, MIPR, IEEE BigData, BMVC
• 2018: NIPS, AAAI, IJCAI, KDD, ACM MM, MIPR, IEEE BigData, PAKDD, ICTAI, AAAI-AFFCON
• 2017: AAAI, IJCAI, IEEE FG, PAKDD, DSAA, ACII, NLPCC
• 2015: IJCAI

Reviewer

• IEEE TPAMI / TKDE / TIP / TNNLS / TMM / TBD / TC / TCSVT / TETCI / Access
• ACM CSUR / TKDD / TOMM / TOSN / TIST
• JMLR, Pattern Recognition, Neurocomputing, PLoS ONE, IJPRAI, JVCI, JEI, OE, NCAA, etc.

Professional Associations

• Senior Member, IEEE
• Member, ACM
• Member, AAAI
• Member, SIAM

Invited Talks

• Machine Learning Meets Causal Inference. Keynote Talk, NCAA 2020. Jul. 2020.
• Machine Learning Meets Causal Inference. IEEE Atlanta Section. Jul. 2020.
• Knowledge Guided Representation Learning on Graphs. Adobe Data Science Symposium. Jul. 2019.
• An Introduction to Machine Learning and Deep Learning. The University of Iowa, Iowa City, IA. Apr. 2019.
• Deep Representation Learning for Sequential Data. Kennesaw State University, Marietta, GA. Jan. 2019.
• An Overview of Deep Learning. Guest Lecture of CSCI 4550, University of Georgia, Athens, GA. Nov. 2018.
• An Overview of Machine Learning. Guest Lecture of CSCI 4530, University of Georgia, Athens, GA. Oct. 2018.
• Adversarial Training for Sequential Data. Institute for Artificial Intelligence, University of Georgia, Athens, GA. Oct. 2018.
• Adversarial Training for Sequential Data. DELUG Seminar, University of Georgia, Athens, GA. Oct. 2018.
• Representation Learning for Data Analytics and Knowledge Inference. ECE, University of Georgia, Athens, GA. Oct. 2018.
• Introduction to Artificial Intelligence and Machine Learning. Legal Group, Adobe Systems Inc., San Jose, CA. Mar. 2018.
• Learning Robust Representations for Data Analytics. ECE Seminar, University of Miami, FL. May 2017.
• Learning Robust Representations for Data Analytics. CIS Seminar, Temple University, PA. Nov. 2016.
• Robust Representations for Data Analytics and Knowledge Inference. Adobe Research, San Jose, CA. Nov. 2016.
• Learning Robust Representations for Data Analytics. CS Seminar, University of Minnesota Twin Cities, MN. Oct. 2016.
• Learning Robust Representations for Data Analytics. Yahoo!-DAIS Seminar, University of Illinois Urbana-Champaign, Champaign, IL. Sept. 2016.
• Low-Rank and Sparse Modeling. IFP, University of Illinois Urbana-Champaign, Champaign, IL. Sept. 2016.
• Temporal Subspace Clustering for Human Motion Segmentation. New England Computer Vision Workshop, Amherst, MA. Nov. 2015.
• Guest Lecture for EECE5642 Data Visualization: Dimensionality Reduction. Northeastern University, Boston, MA. Oct. 2015.
• Learning with Robust Data Representations: Methodologies and Applications. NEPSSS, Northeastern University, Boston, MA. Sept. 2015.
• Rethinking Campaign: A Causal Inference Approach. Adobe Research, San Jose, CA. Jul. 2015.
• Guest Lecture for EECE5698 Found of Visualization: Dimensionality Reduction. Northeastern University, Boston, MA. Sept. 2014.
• Predicting User Behaviors with Collaborative Filtering. Adobe Research, San Jose, CA. Jul. 2014.
• Introduction to Advanced Topics in Machine Learning. Northeastern IEEE, Boston, MA. Apr. 2014.
• Low-Rank Balanced Graphs for Semi-Supervised Learning. CDSP Workshop, Northeastern University, Boston, MA. Mar. 2014.
• Color image canonical correlation analysis for face recognition. Hohai University, Nanjing. Apr. 2012.
• Divide-and-Conquer based Discriminant Analysis. Harbin Institute of Technology Shenzhen Graduate School. Nov. 2011.