I am an (applied) machine learning researcher. My current research interests lie in the development of artificial intelligence and representation learning in the context of business, specifically AI (LLMs) in marketing, finance (fintech), and healthcare. I received my PhD in computer science from Northwestern University (Advisor: Alok Choudhary). I am currently an Associate Professor of Information Systems, an Emerging Fellow in AI and Business Research at the Robert H. Smith School of Business, University of Maryland, College Park. I am affiliated with AMSC, MTI, and AIM. I was a (part time) research scientist at Meta from 2024 to 2026.
Teaching
Big Data and AI Infrastructure (Undergraduate)
Designing AI systems (FT-MBA)
AI Augmented Data Processing and Analysis (MSBA)
Machine Learning for Business Research (PhD)
GenAI Workshop (Exec)
Research Group
Lang Song, PhD in Applied Math (2020 - )
Frankie H. Shi, PhD in IS (2022 - )
Allen Li, PhD in IS (2025 - )
Shuxi Liang, visiting PhD (2025 - )
Efe Sertkaya (2021 - 2026)
Bingze Xu (2020 - 2026)
Yuchen Hao (2025-2026)
Wei Feng (2020 - 2025)
Feiyu E (Postdoc) (2023 - 2025)
Xuewen Han (2022 - 2025)
Mingwei Sun (2018 - 2024)
Dongcheng Zhang (2019 - 2024)
Active Research Grants
1. Smith Internal Research Grants x 2, $20,000
2. From Hotspots to Help-Spots: GEOAI Risk-resource Mapping Platform for Gun Violence Prevention in Maryland, U.S. Department of Justice (DOJ), 08/2026 – 06/2027, $42,500
3. A Novel Measure of Organizational Engagement with ESG Based on 2008-2023 Job Postings Data in the U.S., NSF, $300,000, 08/2024 – 08/2025
4. Understanding Electronic Health Records via Multimodal Learning Algorithms for Integrating Disparate Health Data and Building Analytical Data Models for Health Economics and Outcomes Research, PiHealth, $220,000, 06/2022 – 05/026
5. AI-Driven VC-Backed Startup Success Prediction Engine, Research Gift, $50,000 (under process)
Recent Publications (2026)
Journal
Consensus-Driven Distillation for Trustworthy Explanations in Self-Interpretable GNNs
Wenxin Tai, Fan Zhou, Steve Azzolin, Goce Trajcevski, Ting Zhong, Kunpeng Zhang, and Philip S. Yu
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Data and Methods for Identifying Artificial Intelligence-Related Patents
Tianjun Wu, Chao Min, Guolong Wang, Waverly W. Ding, and Kunpeng Zhang
Research Policy
Ulf Bockenholt and Kunpeng Zhang
International Journal of Research in Marketing (conditional accept)
Corrigendum: Score-based Graph Learning for Urban Flow Prediction
Pengyu Wang, Xucheng Luo, Wenxin Tai, Kunpeng Zhang, Goce Trajcevsky, and Fan Zhou
ACM Transactions on Intelligent Systems and Technology, 2026
GNN-Based Spatio-Temporal Manifold Learning: An Application of Landslide Prediction
Liu Yu, Rongfan Li, Kunpeng Zhang, Siyuan Liu, Goce Trajcevski, Jin Wu, and Fan Zhou
Machine Learning, Accepted
Understanding Interactive Stock Dynamics via Sensitivity-aware Dependency Learning
Li Huang, Yanzhe Xie, Zizheng Wang, Qiang Gao, Kunpeng Zhang, and Philip S. Yu
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2026
Pengfei Yang, Minyang Liu, Ting Zhong, Kunpeng Zhang, Philip S. Yu, and Fan Zhou
IEEE Transactions on Mobile Computing (TMC), 2026
Learning from Earnings Calls: Graph-Based Conversational Modeling for Financial Prediction
Yi Yang, Yixuan Tang, Yangyang Fan, and Kunpeng Zhang
Information Systems Research (ISR), 2026
Conference
Beyond Isolated Investor: Predicting Startup Success via Roleplay-Based Collective Agents
Zhongyang Liu, Haoyu Pei, Xiangyi Xiao, Xiaocong Du, Yihui Li, Suting Hong, Kunpeng Zhang, and Haipeng Zhang
IJCAI, 2026
Haoyu Pei, Zhongyang Liu, Xiangyi Xiao, Xiaocong Du, Suting Hong, Kunpeng Zhang, and Haipeng Zhang
ACL Findings, 2026
Frankie H. Shi, Jin Zhang, and Kunpeng Zhang
ICHI, 2026
Beyond Graph Priors: A Co-evolving Framework under Uncertainty for Enterprise Resilience Assessment
Yanzhe Xie, Li Huang, Qiang Gao, Xueqin Chen, Fan Zhou, and Kunpeng Zhang
AAAI, 2026