Assoc. Prof. Dr. Dehong Gao | Artificial Intelligence | Outstanding Scientist Award
Associate Professor at Northwestern Polytechnical University, China
Dehong Gao is a distinguished expert in the fields of natural language processing, machine learning, and large language models. With over a decade of research and practical experience, he has made significant contributions to advertising technologies, multimodal AI models, and AI-driven e-commerce solutions. Gao currently leads groundbreaking work on multimodal pre-training models, including the award-winning 70B-FashionGPT model. As an associate professor at Northwestern Polytechnical University and a distinguished researcher at Zhejiang University of Technology, Gao continues to advance AI research, particularly in the areas of large language models, multimodal learning, and cross-lingual search. 🚀📚💡
Publication Profile :
Educational Background 🎓
- Ph.D. in Computer Science from Hong Kong Polytechnic University (2014-2022), under the supervision of Li Wenjie.
- Master’s in Automation from Northwestern Polytechnical University (2010).
- Bachelor’s in Automation from Northwestern Polytechnical University (2007).
Professional Experience 💼
Dehong Gao’s career spans both academia and industry. He is currently an Associate Professor at the School of Cyberspace Security at Northwestern Polytechnical University. He also serves as a Distinguished Researcher at the Zhejiang University of Technology Artificial Intelligence Innovation Institute. Gao previously held the position of Senior Algorithm Expert (P8) at Alibaba Group, where he led a team of over 20 full-time algorithm engineers and contributed to the development of large-scale machine translation and AI-driven e-commerce solutions. As an expert in Alibaba AIR Project, Gao has been instrumental in technical breakthroughs related to large model technologies, fine-tuning multimodal models, and advancing AI-based search and advertising systems. 💻📈
Research Interests 🔬
Gao’s research interests are focused on:
- Large Language Models (LLMs) and Multimodal Learning 🌐🤖
- Natural Language Processing: Information retrieval, recommendation systems, sentiment analysis, and automated summarization 📑🔍
- E-commerce AI: Developing search algorithms and multilingual representation learning for cross-border e-commerce applications 🌏🛒
- Federated Learning and AI-driven personalization in business settings 🔒🤖
He has authored and co-authored several influential papers and has been a leading figure in the development of multimodal AI models for industries such as fashion, e-commerce, and healthcare. His work continues to push the boundaries of AI application in real-world environments. 🏆📚
Publications 📚
- Gao, D., Chen, K., Chen, B., et al. (2024). LLMs-based Machine Translation for E-commerce. Expert Systems with Applications, Volume 258 (SCI Zone 1, Top Journal).
- Chen, K., Chen, B., Gao, D., Dai, H., et al. (2024). General2Specialized LLMs Translation for E-commerce. The Web Conference (WWW), short paper (CCF-A).
- Shen, G., Sun, S., Gao, D., Yang, L., et al. (2023). EdgeNet: Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising. The 32nd ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
- Gao, D., Ma, Y., Liu, S., Song, M., Jin, L., et al. (2024). FashionGPT: LLM Instruction Fine-tuning with Multiple LoRA-adapter Fusion. Knowledge-Based Systems, Volume 299 (SCI, Top Journal).
- Chen, B., Jin, L., Wang, X., Gao, D., et al. (2023). Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval. Industry Track of The Web Conference (WWW), (CCF-A).
- Mei, X., Yang, L., Jiang, Z., Cai, X., Gao, D., et al. (2024). An Inductive Reasoning Model Based on Interpretable Logical Rules Over Temporal Knowledge Graphs. Neural Networks, Volume 174, Pages (SCI Zone 1, Top Journal).
- Liang, Z., Chen, B., Ran, Z., Wang, Z., Gao, D., et al. (2024). Self-Renewal Prompt Optimizing with Implicit Reasoning. The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) findings (CCF-A).
- Yang, Z., Gao, H., Gao, D., Yang, L., et al. (2024). MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction. The 18th ACM Conference on Recommender Systems (RecSys), (CCF-B).
- Zhang, X., Wang, D., Gao, D., Jiang, W., et al. (2022). Revisiting Cold-Start Problem in CTR Prediction: Augmenting Embedding via GAN. The 31st ACM International Conference on Information & Knowledge Management (CIKM), (CCF-B).
- Zhang, F., Zhang, Z., Gao, D., Zhuang, F., et al. (2022). Mind the Gap: Cross-lingual Information Retrieval with Hierarchical Knowledge Enhancement. The 36th AAAI Conference on Artificial Intelligence (AAAI), (CCF-A).