Preface: In the new era of Artificial Intelligence, we are committed to cultivating students who possess critical thinking skills and can apply innovative thinking to solve both known and undiscovered problems. Our goal is to nurture students’ continuous curiosity about both the physical and digital worlds, encouraging them to ask new questions and become future talents in the fields of big data and artificial intelligence. This will help students better adapt to the digital and intelligent new era, driving the digital transformation and revolution of various industries. Currently, we focus on exploring General Artificial Intelligence (AGI), dedicated to enabling machine intelligence to possess human-like thinking, planning, reasoning, and action capabilities, making AI ubiquitous and deeply integrated into our daily lives and work.

Research Directions: Focus on cutting-edge technologies in Data Science and Artificial Intelligence.

Autonomous Language Agents: Focusing on AI agents' autonomous decision-making, continuous learning, and self-evolution, particularly in the area of complex task decomposition. The goal is to shift from “passive execution” to “active planning.” We conduct in-depth research on key technologies such as language-environment interaction and multimodal fusion to provide core algorithmic support for AGI, promoting autonomous adaptation and efficient execution of agents in diverse and complex scenarios.

AI for Science: Centered on the construction of national-level scientific discovery bases, we delve deep into empowering fundamental scientific research with artificial intelligence. A full-process AI auxiliary research system for scientists will be established to facilitate intelligent exploration and groundbreaking innovations in core disciplines including biomedicine, physics and materials science, as well as earth sciences. We leverage AI technologies to accelerate fundamental scientific discoveries and the transformation of research outcomes.

Large Language Models: Focused on the three core objectives of efficient training, lightweight deployment, and scenario adaptation of large models, aiming to solve current problems such as “too broad but not precise,” “high implementation costs,” and “poor scene adaptability.” We aim to drive the customized implementation of large models in vertical sectors across various industries, building a high-performance AI foundation to support the iterative upgrade of AGI and the digital empowerment of industries.

Large Model Data: Focused on core data support required for large model training, optimization, and iteration. Key research areas include data collection and cleaning, high-quality data filtering, standardized data annotation, data privacy protection, incremental data updates, and adaptation. We address challenges such as uneven data quality, high data security risks, and insufficient data-model compatibility, constructing a high-quality, secure, and reusable data resource system. This ensures core data support for efficient model training and precise optimization, promoting the steady development of AGI technology.

Intelligent Computing: Building an efficient and energy-saving intelligent computing system in line with the needs of AGI development, with a focus on innovations in computing architecture, power scheduling optimization, and energy efficiency enhancement. This aims to solve core bottlenecks such as insufficient computing power and low data processing efficiency. We strive to integrate intelligent computing with AI technologies, providing stable, efficient, and low-cost computing support for autonomous language agents, large models, and data processing, assisting in the large-scale development and industrialization of AGI.

Google Scholar: https://scholar.google.com/citations?user=N4rQYDAAAAAJ&hl=en

PhD, graduated from the University of Tokyo, Japan. He has focused on cutting-edge research in General Artificial Intelligence (AGI) and AI infrastructure for many years, aiming to equip machines with human-like thinking, planning, and autonomous decision-making abilities, using technological innovation to drive the intelligent upgrade of industries. With extensive experience in both academia and industry, he has worked at institutions such as the Institute of Software Research, Chinese Academy of Sciences, Qihoo 360 Search Division, Alibaba DAMO Academy, and Alibaba Cloud Intelligence Group, and has led product and R&D teams with over a hundred members.

In the academic field, he has published over a hundred papers in top conferences and journals in data science and artificial intelligence, including KDD, NeurIPS, SIGIR, WWW, CVPR, ICCV, ECCV, AAAI, ICDE, ACL, TPAMI, TOIS, TKDE, and others. He has led multiple projects funded by the National Natural Science Foundation of China (General/Young Scholars), the Ministry of Education's Research Start-up Fund for Overseas Returnees, and the Beijing Natural Science Foundation. His work has contributed to the deep integration of cutting-edge scientific research with industrial applications.

• Evo-Retriever: LLM-Guided Curriculum Evolution with Viewpoint-Pathway Collaboration for Multimodal Document Retrieval. CVPR 2026
• Co-EPG: A Framework for Co-Evolution of Planning and Grounding in Autonomous GUI Agents. AAAI 2026
• Importance-aware data selection for efficient llm instruction tuning. AAAI 2026
• RASD: Retrieval -Augmented Speculative Decoding, ACL 2025
• AirCache:  Activating Inter-modal Relevancy KV Cache Compression for Efficient Vision-Language Model Inference, ICCV 2025
• DBCopilot: Natural Language Querying over Massive Databases via Schema Routing, EBDT 2025 (Best Paper Runner-Up)
• Match, Compare, or  Select? An Investigation of Large Language Models for Entity Matching,  COLING 2025
• ChartM3: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension. EMNLP 2025
• Self-Paced  Unified Representation Learning for Hierarchical Multi-Label Classification. AAAI 2024
• Adaptive quantization error reconstruction for llms with mixed precision, COLM 2024
• Mixture-of-LoRAs:  An  Efficient Multitask Tuning for Large Language Models, COLING 2024
• Federated unlearning for on-device recommendation, ACM WSDM 2023
• UUKG: Unified  Urban  Knowledge  Graph Dataset  for  Urban Spatiotemporal Prediction. NeurIPS 2023
• A Good  Student is Cooperative and Reliable: CNN- Transformer Collaborative Learning for Semantic Segmentation. ICCV 2023
• A Knowledge-Enhanced Framework for Imitative Transportation Trajectory Generation. ICDM 2022
• Federated learning for personalized humor recognition, ACM Transactions on Intelligent Systems and Technology. ACM TIST, 2022
• Keyword-Guided Neural  Conversational Model,  The  Thirty- Fifth AAAI Conference on Artificial Intelligence. AAAI 2021
• CARE:  Commonsense-Aware Emotional Response Generation with Latent Concepts. AAAI 2021
• Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection. WWW 2021
• Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems. The 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. KDD 2021
• Learning to Ask Appropriate Questions in Conversational Recommendation. The 44th International ACM SIGIR Conference on Research and    Development in Information Retrieval. SIGIR 2021
• Relational  Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2021
• Uniting heterogeneity, inductiveness, and efficiency for graph representation learning. IEEE Transactions on Knowledge and Data Engineering. IEEE TKDE, 2021
• MusicBERT: A self-supervised learning of music representation. The 29th ACM International Conference on Multimedia (ACM MM 2021)
• Crsal: Conversational recommender systems with adversarial learning. ACM Transactions on Information Systems (ACM TOIS), 2020
• Towards Persona-Based Empathetic Conversational Models. EMNLP 2020
• Next point-of-interest recommendation on resource-constrained mobile devices. WWW 2020
• Online sales prediction via trend alignment-based multitask recurrent neural networks. Knowledge and Information Systems, 2020
• Machine Comprehension-Incorporated Relevance Matching. ICDM 2019
• Semi-supervised domain adaptation via Fredholm integral based kernel methods. Pattern Recognition, 2019
• Enhancing collaborative filtering with generative Augmentation.   The 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2019)
• Resumevis: A visual analytics system to discover semantic information in semi- structured resume data. ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2018
• Multi-view Adversarially Learned  Inference for Cross-domain Joint Distribution Matching. KDD 2018
• Redundancy-resistant Generative Hashing for Image Retrieval. IJCAI 2018
• Tada: trend alignment with dual- attention multi-task recurrent neural networks for sales prediction. ICDM 2018
• Emotion analysis for personality inference from EEG signals. IEEE transactions on affective computing (IEEE TAC), 2018
• PME: projected metric embedding on heterogeneous networks for link prediction. KDD 2018
• Stockassistant: a stock ai assistant for reliability modeling of stock comments. KDD 2018
• Semi-supervised deep generative modelling of incomplete   multi-modality emotional data. Proceedings of the 26th ACM international conference on Multimedia (ACM MM 2018)
• Neural memory streaming recommender networks with adversarial training. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2018)
• A machine reading comprehension-based approach for featured snippet extraction. ICDM 2018
• Information-theoretic domain adaptation under severe noise conditions. AAAI 2018
• Discriminative transformation for multi-dimensional temporal sequences. IEEE Transactions on Image Processing (TIP), 2017
• A Location- Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users. KDD2017
• Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2017
• Fredholm multiple kernel learning for semi-supervised domain adaptation. AAAI 2017
• An automatic approach for transit advertising in public transportation systems. ICDM 2017
• Discriminative dimensionality reduction for multi- dimensional sequences. IEEE transactions on pattern analysis and machine intelligence (TPAMI), 2017
• A hybrid term–term relations analysis approach for topic detection. Knowledge-Based Systems, 2016
• Discovering Interpretable Geo-Social Communities for User Behavior Prediction. The 32nd IEEE International Conference on Data Engineering, ICDE 16
• Hierarchical dynamic parsing and encoding for action recognition. ECCV 2016
• Learning graph-based poi embedding for location-based recommendation. Proceedings of the 25th ACM international on conference on information and knowledge management (CIKM 2016)
• Transfer Feature Representation via Multiple Kernel Learning. AAAI 2015
• Cross-Domain Metric Learning Based on Information Theory. AAAI 2014