Making Language Models Easier to Understand
Work-in-Progress
Finding Hidden Patterns in Groups Without Making Too Many Mistakes
This research introduces a breakthrough method that can automatically discover important association patterns among different population subgroups in complex data, helping researchers more accurately analyze characteristics across different populations and providing a scientific basis for personalized decision-making.
Manufacturing & Service Operations Management
Scalable Causal Analysis: Estimating Treatment Effects Using AI-Driven Models
This work advances causal inference by introducing a doubly robust estimator for ATE (average treatment effect) that ensures consistency, dimension-free scalability, and valid statistical inference, validated through real-world applications.
Working Paper
Humans and AI Working Together: Human Involvement Boosts AI Results
AI-personalized services combined with consumer participation nudges boost purchases by 22% through effort-driven preference alignment. AI-consumer co-creation maximizes outcomes, outperforming solo AI or nudging strategies.
Production and Operations Management（Under Review）
How Consumers Search and Their Preferences Evolve
The Consumer Preference Transformer (CPT) integrates deep learning with sequential search theory to predict dynamic preferences while explaining decision-making processes, outperforming existing models in accuracy and interpretability.
Management Science（Major Revision）
Tailoring Large Language Models for Business Use
Customizing LLMs via domain-specific theory and supervised fine-tuning (SFT) bridges gaps in expertise, trust, and satisfaction between AI and human doctors in medical consultation.
Information Systems Research（Major Revision）
Exploring How Human Intelligence Shapes Our Understanding of Generative AI: Findings from Experiments
Human-aligned framework evaluates LLMs: GPT-4 exceeds humans, reveals intelligence tradeoffs, RLHF hinders creativity, predicts labor needs, guides AI adoption.
Information Systems Research（Major Revision）
How Humans and AI Work Together to Generate Product Ideas: Balancing Quality and Creativity
Combining human ideas with AI refinement optimizes creative output in product ideation. We integrate human creativity with AI refinement (human draft + AI polish) offers firms a strategic hybrid workflow to maximize innovation efficiency.
Information Systems Research（Major Revision）
Use reinforcement learning to help enterprises optimize consumer shopping experience
Integrating reinforcement learning (RL) with historical randomized experiments via a Bayesian recurrent Q-network enables holistic optimization of intervention sequences along customer journeys while balancing exploration-exploitation tradeoffs.
Management Science
Making Language Models Easier to Understand
Work-in-Progress
Finding Hidden Patterns in Groups Without Making Too Many Mistakes
This research introduces a breakthrough method that can automatically discover important association patterns among different population subgroups in complex data, helping researchers more accurately analyze characteristics across different populations and providing a scientific basis for personalized decision-making.
Manufacturing & Service Operations Management
Scalable Causal Analysis: Estimating Treatment Effects Using AI-Driven Models
This work advances causal inference by introducing a doubly robust estimator for ATE (average treatment effect) that ensures consistency, dimension-free scalability, and valid statistical inference, validated through real-world applications.
Working Paper
Humans and AI Working Together: Human Involvement Boosts AI Results
AI-personalized services combined with consumer participation nudges boost purchases by 22% through effort-driven preference alignment. AI-consumer co-creation maximizes outcomes, outperforming solo AI or nudging strategies.
Production and Operations Management（Under Review）
How Consumers Search and Their Preferences Evolve
The Consumer Preference Transformer (CPT) integrates deep learning with sequential search theory to predict dynamic preferences while explaining decision-making processes, outperforming existing models in accuracy and interpretability.
Management Science（Major Revision）
Tailoring Large Language Models for Business Use
Customizing LLMs via domain-specific theory and supervised fine-tuning (SFT) bridges gaps in expertise, trust, and satisfaction between AI and human doctors in medical consultation.
Information Systems Research（Major Revision）
Exploring How Human Intelligence Shapes Our Understanding of Generative AI: Findings from Experiments
Human-aligned framework evaluates LLMs: GPT-4 exceeds humans, reveals intelligence tradeoffs, RLHF hinders creativity, predicts labor needs, guides AI adoption.
Information Systems Research（Major Revision）
How Humans and AI Work Together to Generate Product Ideas: Balancing Quality and Creativity
Combining human ideas with AI refinement optimizes creative output in product ideation. We integrate human creativity with AI refinement (human draft + AI polish) offers firms a strategic hybrid workflow to maximize innovation efficiency.
Information Systems Research（Major Revision）
Use reinforcement learning to help enterprises optimize consumer shopping experience
Integrating reinforcement learning (RL) with historical randomized experiments via a Bayesian recurrent Q-network enables holistic optimization of intervention sequences along customer journeys while balancing exploration-exploitation tradeoffs.
Management Science