Yichuan Zhang

Research

Human–AI Collaboration in Forecasting Systems

Frameworks that enhance expert decision-making through synergistic human–AI interaction in time-series forecasting.

Status
Active
Period
2023 – Present
Multimodal data collection setup

Challenge

AI-only forecasting systems struggle under sparse data, unexpected events, and shifting regimes. Expert forecasters routinely adjust model outputs using domain knowledge, yet current systems rarely support structured collaboration between humans and AI.

Approach

I design interactive decision-support interfaces implementing an AI-first paradigm, where ML models produce initial forecasts that experts refine. Cognitive state transitions and interaction strategies are modelled with sequence models over multimodal features (gaze, mouse trajectories, screen interaction).

Results

Human–AI collaboration improved forecast accuracy under sparse and volatile conditions compared to AI-only baselines. Findings consolidated into a human-centered collaboration framework; results disseminated via IJHCS submission and TE2025/INCOSE(CAS) acceptances.