My name is Handong Zhao. I am a senior research scientist at Adobe Research, San Jose, CA. I obtained the Ph.D. degree at Department of Electrical & Computer Engineering, Northeastern University, Boston, MA. Before joining Adobe, I have spent time at IBM Research. My current research interest includes machine learning and its applications to multimedia content analysis and marketing analytics. I was the recipient of the National Science Foundation’s Innovation Corps (I-Corps™) program award as an entrepreneurial lead.

Email  hazhao [at] adobe.com


Address  321 Park Avenue, San Jose, CA, 95112

[Internship] I am hiring self-motivated PhD student research interns, so please feel free to contact me if you are interested in working with me. My current research interests include (but not limited to) multimodal learning, domain adaptation, entity retrieval/recognition, text generation, etc.

News

• [05/22] One KDD-2022 on "KG Infusion for Tabular Pre-training Models" accepted.
• [04/22] One SIGIR-2022 on "Cross-domain Interaction Recommendation" accepted.
• [03/22] One CVPR-2022 on "Fashion CLIP" and one L4DC-2022 on "Neural Spatiotemporal Point Process" accepted.
• [02/22] Two ACL-2022 papers accepted on "Few-Shot Class-Incremental NER" and "Learning Adaptive Axis Attentions in Fine-tuning (Finding paper)".
• [01/22] One paper "Neural Contextual Bandits with Deep Representation and Shallow Exploration" accepted by ICLR-2022.
• [01/22] One paper on "Federated Learning for Multilingual NLU" accepted by WWW-2022.
• [12/21] One paper on "Cross-Domain Clustering" accepted by AAAI-2022 student abstract and poster program.
• [10/21] Two papers on "Continual NER" and "User-in-the-loop NER" accepted by NeurIPS ENLSP Workshop.
• [09/21] Two papers on "KD-based Graph Similarity Computation " and "Document Representation Learning" accepted by NeurIPS-2021.
• [08/21] One short paper on "Sequential Recommender System" accepted by CIKM-2021.
• [07/21] Two papers on "Source-Free DA" and "Semi-Supervised DA" accepted by ICCV-2021.
• [05/21] One paper on "Explainable Column Annotation" accepted by KDD-2021.
• [05/21] One paper on "KG-based Commonsense Reasoning" accepted by Findings of ACL-2021.
• [04/21] I'm co-organizing The 3rd Workshop on Continual and Multimodal Learning for Internet of Things (CML-IOT), at IJCAI-2021. The qualified papers are planned to invited as submissions to a journal special issue.
• [03/21] One CVPR-2021 on "Document Representation Learning" and one NAACL-2021 on "KG Enrichment" got accepted.
• [01/21] One ICLR-2021 on "Learning to Deceive KG" accepted.

• More ...

  • [12/20] One NeurIPS-KR2ML workshop paper on "KG-based Commonsense Reasoning" accepted.
  • [11/20] One paper on "Learnable Subspace Clustering" accepted by TNNLS. [arXiv].
  • [09/20] One NeurIPS-2020 on "Self-Supervised Relationship Probing" accepted.
  • [07/20] One ECCV-2020 on "Open Domain Image Manipulation" and one CIKM-2020 on "KG Reasoning for Recommendation" accepted.
  • [06/20] One paper on "Linear Quadratic Regulator" accepted by ICML-2020.
  • [05/20] One paper on "Personalized Image Retrieval" accepted by KDD-2020.
  • [04/20] One survey paper on "Representation Learning for User Modeling" was accepted by IJCAI-2020.
  • [03/20] Our IJCAI-2020 tutorial "Robust Multi-view Visual Learning: A Knowledge Flow Perspective" was accepted (with prof. Zhengming Ding and prof. Ming Shao).
  • [02/20] One paper on "Cross-domain Document Object Detection" was accepted by CVPR-2020.
  • [11/19] One SoCC-2019 poster and one BigData-2019 paper accepted.
  • [09/19] I gave a talk on "Multi-modal Representation Learning", invited by Prof. Hongfu Liu at Brandeis.
  • [07/19] One ICCV-2019 paper got accepted. Congratulations to Jiuxiang.
  • [04/19] Two KDD-2019 papers accepted on the topics of "Sequential Adversarial Learning" and "Interpretable User Modeling".
  • [02/19] My FIRST summer intern Jiuxiang's work on "Scene Graph Generation" got accepted by CVPR-2019.
  • [02/19] Co-host (with Myra and Subrata) session "AI For System" won Top Ten Session in Adobe-TechSummit-2019. Got my first trophy at Adobe.
  • [12/18] Accepted the invitation to serve as PC member for IJCAI-2019.
  • [11/18] Will host a mini-summit, "Analyze, Predict, and Visualize: when RNNs meet Adobe", at Adobe-TechSummit-2019 with Sungchul Kim.
  • [10/18] I have one paper accepted by WSDM-2019 (Acceptance Rate: 16.4%).
  • [10/18] AAAI-2019 Tutorial "Deep Multi-view Data Analytics" was accepted (with Allan Ding and Hongfu Liu).
  • [05/18] Accepted the invitations to serve as PC for NIPS-2018 and SPC for AAAI-2019.
  • [03/18] One book proposal (with Allan and Prof. Raymond Fu) is accepted by Springer.
  • [03/18] I accepted the invitation to serve as a member of the Program Committee for CVPR-2018 workshop on Analysis and Modeling of Faces and Gestures (AMFG).
  • [02/18] I accepted the invitation to serve as a member of the Program Committee for ICMLA-2018.
  • [01/18] I have one paper accepted by WACV-2018.

Publications

	Learning Representation for Multi-View Data Analysis Zhengming Ding, Handong Zhao and Yun Fu Springer, 2018, ISBN 978-3-030-00733-1 [book][Amazon]
	Multi-View Clustering via Deep Matrix Factorization Handong Zhao, Zhengming Ding and Yun Fu AAAI Conference on Artificial Intelligence (AAAI), 2017. [paper] [code]

Teaching/Tutorial

1. Tutorial: Robust Multi-view Visual Learning: A Knowledge Flow Perspective
   Jointly organized with Zhengming Ding and Ming Shao
   IJCAI-2020, virtual.
2. Tutorial: Analyze, Predict and Visualize: When RNNs Meet Adobe
   Jointly organized with Sungchul Kim, Fan Du and Sana Malik
   Adobe Tech Summit 2019, San Francisco, CA, February 2019.
3. Tutorial: Deep Multi-View Visual Data Analytics
   Jointly organized with Zhengming Ding and Hongfu Liu
   AAAI-2019, Honolulu, Hawaii, January 2019.
4. Tutorial: Multi-view Face Representation
   Jointly organized with Zhengming Ding and Yun Raymond Fu
   FG-2017, Washington, D.C., May 2017.