Useful Theory Innovations Lab (UTIL)
The UTIL Lab@UMSI, directed by Professor Grant Schoenebeck, develops theoretical frameworks with real-world impact. We bridge mechanism design, machine learning, and social choice theory to solve pressing problems in crowdsourcing, peer review, social networks, and online platforms.
Our research transforms abstract theory into practical tools: from designing better peer review systems to creating recommendation algorithms that serve users’ long-term interests.
Our research (briefly):
- Aligned Information Evaluation: Peer Prediction Mechanisms, LLM-based Quality Scoring, Mutual Information Frameworks, Strategic Robustness
- Recommendation and Temptation: Dual-Self User Models, Enrichment vs. Gratification, Algorithmic Filtering Agents
- Making Decisions with Diverse Preferences and Information: Information Aggregation, Bridging ML and Social Choice, Wisdom of the Crowd Voting
- AI and Social Decision Making: Pluralistic Alignment, LLM Evaluation without Ground Truth, Platform vs. User Objectives
More details in Research and Publications