Yang Zhang, Ph.D.

Human-Centered Generative AI Lab

Our lab’s current research centers on designing collaborative frameworks that integrate Generative Artificial Intelligence (GenAI), particularly Large Language Models (LLMs), with human intelligence (HI) to tackle complex, real-world problems in social contexts. Human intelligence contributes unique capabilities such as reasoning, problem-solving, abstract thinking, and the ability to learn from experience. These attributes provide valuable context, domain expertise, and human-centered insights essential for understanding the intricate social and environmental factors that shape societies. Conversely, GenAI excels at processing large-scale data, identifying latent patterns, and making predictions, offering scalability and computational power for addressing complex issues.

Motivated by the complementary yet distinct strengths of GenAI and HI, our lab’s research is built upon three core thrusts: human-AI collaborative design, calibration, and interaction.

• The design thrust leverages human-in-the-loop mechanisms to optimize neural architectures and hyperparameters efficiently, focusing on adaptive solutions for resource-constrained scenarios such as disaster response and urban monitoring.
• The calibration thrust addresses adaptability, robustness, and cross-domain generalization by integrating GenAI and human intelligence through collective intelligence frameworks, ensuring alignment with diverse and dynamic domain requirements.
• The interaction thrust fosters seamless collaboration between humans and GenAI, integrating small and large language models with HI to maintain explainability, interpretability, and responsiveness in high-stakes applications.

New Book on Human-AI Collaboration in GenAI Era

Social Intelligence: The New Frontier of Integrating Human Intelligence and Artificial Intelligence in Social Space, 1st Edition, Springer Nature, 2025, ISBN: 978-3-031-90080-8

About this book: Given the rise of AI and the advent of online collaboration opportunities (e.g., social media, crowdsourcing), emerging research has started to investigate the integration of AI and human intelligence, especially in a collaborative social context. This creates unprecedented challenges and opportunities in the field of Social Intelligence (SI), where the goal is to explore the collective intelligence of both humans and machines by understanding their complementary strengths and interactions in the social space.

In this book, a set of novel human-centered AI techniques are presented to address the challenges of social intelligence applications, including multimodal approaches, robust and generalizable frameworks, and socially empowered explainable AI designs. The book then presents several human-AI collaborative learning frameworks that jointly integrate the strengths of crowd wisdom and AI to address the limitations inherent in standalone solutions. The book also emphasizes pressing societal issues in the realm of social intelligence, such as fairness, bias, and privacy. Real-world case studies from different applications in social intelligence are presented to demonstrate the effectiveness of the proposed solutions in achieving substantial performance gains in various aspects, such as prediction accuracy, model generalizability and explainability, algorithmic fairness, and system robustness.

Recent Publications

For a comprehensive list of the research papers, please visit my Google Scholar profile.

[IJCAI] R. Zong, Y. Zhang, L. Shang, F. Stinar, N. Bosch, D. Wang. Bidirectional Human–AI Collaboration for Equitable Student Performance Prediction via Deep Uncertainty Learning. International Joint Conference on Artificial Intelligence, Montreal, Canada, 2025.

[ICWSM] R. Zong, Y. Zhang, D. Wang. Impowering LLMs to Synthesize AI and Human Intelligence for Explainable Public Health Misinformation Detection on Social Media. International AAAI Conference on Web and Social Media, Copenhagen, Denmark, 2025.

[AAAI] L. Shang, B. Chen, S. Liu, Y. Zhang, R. Zong, A. Vora, X. Cai, N. Wei, D. Wang. SIDE: Socially Informed Drought Estimation Toward Understanding Societal Impact Dynamics of Environmental Crisis. AAAI Conference on Artificial Intelligence, Philadelphia, Pennsylvania, 2025.

[WWW] Y. Liu, Y. Liu, Z. Li, R. Yao, Y. Zhang, D. Wang. Modality Interactive Mixture-of-Experts for Fake News Detection. ACM Web Conference, Sydney, Australia, 2025.

[ACL] Z. Yue, H. Zeng, L. Shang, Y. Liu, Y. Zhang, D. Wang. Retrieval Augmented Fact Verification via Synthesis of Contrasting Arguments. Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand, 2024.

[ACL] H. Zeng, Z. Yue, Y. Zhang, L. Shang, D. Wang. Fair Federated Learning with Biased Vision-Language Model. Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand, 2024.

[WWW] Y. Zhang, R. Zong, L. Shang, H. Zeng, Z. Yue, D. Wang. SymLearn: A Symbiotic Crowd-AI Collective Learning Framework to Web-based Healthcare Policy Adherence Assessment. ACM Web Conference, Austin, Texas, 2024.

[WWW] L. Shang, Y. Zhang, B. Chen, R. Zong, Z. Yue, H. Zeng, B. Wei, D. Wang. MMAdapt: A Knowledge-guided Multi-source Multi-class Domain Adaptive Framework for Early Health Misinformation Detection. ACM Web Conference, Austin, Texas, 2024.

[ICDCS] H. Zeng, Z. Yue, Q. Jiang, Y. Zhang, L. Shang, R. Zong, D. Wang. Mitigating Demographic Bias of Federated Learning Models via Robust-Fair Domain Smoothing: A Domain-Shifting Approach. IEEE International Conference on Distributed Computing Systems, Jersey City, New Jersey, 2024.

[NAACL-HLT] Z. Yue, H. Zeng, Y. Lu, L. Shang, Y. Zhang, D. Wang. Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation. 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies, Mexico City, Mexico, 2024.

[ICWSM] L. Shang, B. Cheng, A. Vora, Y. Zhang, Z. Yue, X. Cai, D. Wang. A Social and News Media Driven Dataset and Analytical Platform Towards Understanding Societal Impact of Drought. International AAAI Conference on Web and Social Media, Buffalo, New York, 2024.

[ICWSM] L. Shang, Y. Zhang, Z. Yue, J. Choi, H. Zeng, D. Wang. Domain Adaptive Graph Learning Framework to Early Detection of Emergent Healthcare Misinformation on Social Media. International AAAI Conference on Web and Social Media, Buffalo, New York, 2024.

[SECON] Y. Zhang, R. Zong, L. Shang, H. Zeng, Z. Yue, and D. Wang. A Symbiotic Human-AI Co-Learning Framework for Healthcare Policy Adherence Assessment in Social Sensing. IEEE International Conference on Sensing, Communication and Networking, Madrid, Spain, 2023.

[IJCAI] Y. Zhang, R. Zong, L. Shang, H. Zeng, Z. Yue, and D. Wang. On Optimizing Model Generality in AI-based Disaster Damage Assessment: A Subjective Logic-driven Crowd-AI Hybrid Learning Approach. International Joint Conference on Artificial Intelligence, Macao, SAR, 2023.

[IJCAI] H. Zeng, Z. Yue, L. Shang, Y. Zhang, and D. Wang. Adversarial Robustness of Demographic Fairness in Face Attribute Recognition. International Joint Conference on Artificial Intelligence, Macao, SAR, 2023.

[WWW] Y. Zhang, L. Shang, R. Zong, H. Zeng, Z. Yue, and D. Wang. CollabEquality: A Crowd-AI Collaborative Learning Framework to Address Class-wise Inequality in Web-based Disaster Response. ACM Web Conference, Austin, Texas, 2023.

[WWW] R. Zong, Y. Zhang, L. Shang, and D. Wang. ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning. ACM Web Conference, Austin, Texas, 2023.

[AAAI] Y. Zhang, Z. Kou, L. Shang, H. Zhen, Z. Yue, and D. Wang. A Crowd-AI Duo Relational Graph Learning Framework Towards Social Impact Aware Photo Classification. Thirty-Seven AAAI Conference on Artificial Intelligence, Washington, DC, 2023.

[HCOMP] R. Zong, Y. Zhang, F. Stinar, L. Shang, H. Zeng, N. Bosch, and D. Wang. A Crowd-AI Collaborative Approach to Address Demographic Bias for Student Performance Prediction in Online Education. AAAI Conference on Human Computation and Crowdsourcing, Delft, Netherlands, 2023.

[CIKM] H. Zeng, Z. Yue, Y. Zhang, L. Shang, and D. Wang. Manipulating Out-Domain Uncertainty Estimation in Deep Neural Networks via Targeted Clean-Label Poisoning. ACM International Conference on Information and Knowledge Management, Birmingham, UK, 2023.

[ACL] Z. Yue, H. Zeng, Y. Zhang, L. Shang, and D. Wang. MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Similarity-Based Meta Learning. Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 2023.

[CSCW] Y. Zhang, R. Zong, L. Shang, Z. Kou, and D. Wang. CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage Assessment. ACM Conference on Computer-Supported Cooperative Work and Social Computing, Virtual Conference, 2022.

[CSCW] Y. Zhang, R. Zong, L. Shang, Z. Kou, H. Zeng, and D. Wang. CrowdOptim: A Crowd-driven Neural Network Hyperparameter Optimization Approach to AI-based Smart Urban Sensing. ACM Conference on Computer-Supported Cooperative Work and Social Computing, Virtual Conference, 2022.

[CSCW] Z. Kou, Y. Zhang, D. Y. Zhang, and D. Wang. CrowdGraph: A Crowdsourcing Multi-modal Knowledge Graph Approach to Explainable Fauxtography Detection. ACM Conference on Computer-Supported Cooperative Work and Social Computing, Virtual Conference, 2022.