Human-AI Interfaces for the Future of Work (Research Track)
Park Sinchaisri, Professor
Business, Haas School
Applications for Fall 2024 are closed for this project.
[To be considered, please also complete this form: https://bit.ly/45sjUZw ]
Our lab is currently working on multiple research projects of various stages on human-AI interfaces for a variety of applications in the future of work. The overarching goals are to (i) understand how humans learn and make decisions in complex environments, (ii) design interpretable algorithms to help improve human decision-making, and (iii) apply our frameworks to real-world settings. In other words, we would like to design algorithms that are useful to humans not only in theory but also in practice, taking into account interpretability/explainability, human biases, and potential aversion to comply. See my working paper as an example: https://bit.ly/tipspaper
Example projects for the 2024-2025 academic year:
1. Optimal bias-aware recommendation system for EV charging/driving
2. Algorithmic tools to help improve on-demand workers' learning and performance
3. Dynamic incentives for on-demand platforms to combat multihoming among workers.
4. Algorithms to improve team collaboration
5. Algorithms to improve team formation
6. Modeling humans' biases when receiving machine-generated advice
7. Improving teachers' productivity with generative AI
8. Decision support tool for pricing managers
9. Understanding the impact of a side job on the main job
10. How human workers respond to an AI boss
11. How to improve the tipping recommendation
12. Recommendation system for a network of agents
13. Understanding and improving crowdsourced workers' learning
Role: You will gain exposure to research in human-computer interactions and behavioral operations management with real-world applications. The Research track will focus on model/theory development, econometric/data analysis, or algorithm design and development.
1. Model/Theory Development: Develop and refine theories or models that better human behaviors when interacting with algorithms. Models can be based on game theory, multi-armed bandits, Markov Decision Processes, behavioral economics, statistics, and reinforcement learning.
2. Econometric/Data Analysis: Work with existing data or put together public data. Test multiple model specifications to derive causal inference from the data.
3. Algorithm Design/Implementation: Design and implement state-of-the-art reinforcement learning and relevant algorithms to uncover human strategy from data.
Qualifications: Applicants majoring in EECS, Computer Science, IEOR, Statistics, Economics, Mathematics, or Data Science with an intention of pursuing graduate studies (especially PhD) and/or continuing in the Spring/Summer of 2025 will be given priority.
Useful courses/skills: CS188, CS189, machine learning (especially reinforcement learning), game theory, econometrics, industrial organization, behavioral economics.
Our most successful lab members and/or those who have had the best experience tend to be ones who are genuinely excited about the research questions, highly attentive to detail, enjoy thinking outside the box, and can be independent and self-initiative. Our lab alumni have gone to graduate programs at Harvard, MIT, Stanford, Berkeley, UPenn, CMU, Northwestern, and UMichigan.
Hours: 9-11 hrs
Off-Campus Research Site: Remote is possible.
Related website: https://parksinchaisri.github.io/
Related website: https://bit.ly/tipspaper