I am currently on the job market!
I am seeking industry positions where I can apply my technical background in AI to contribute to data-driven innovation. I'm particularly interested in positions that blend technical depth with strategic thinking such as data science, applied research, or AI strategy.
About Me
Hello! I am passionate about leveraging AI and data to generate actionable insights and inform strategic decision-making. I have experience applying methods from machine learning, simulation, and game theory to analyze complex domains such as finance and healthcare. I currently work as a postdoctoral researcher developing data and AI tools to facilitate cancer research and support enhanced clinical decision-making.
I graduated from the University of Michigan with a PhD in computer science & engineering advised by Michael Wellman. My dissertation research lay at the intersection of computer science and economics. I developed computational methods using agent-based modeling and empirical game-theoretic analysis to analyze strategic risk mitigation decisions and their consequences in financial networks. My work has led to findings such as a greater understanding of the importance of asset recovery rates in canceling cycles of debt and identifying the potential for strategic restrictions of real-time payments to lead to a suboptimal outcome for all participants.
Prior to graduate school, I applied data analysis and machine learning methods to predict ER visits of cancer patients and performed event detection using social media data. I hold undergraduate degrees from the University of Massachusetts Amherst in computer science and economics, as well as a minor in Chinese. My honors thesis, advised by Brendan O'Connor, applied time series forcasting to Google trends data for predicting state unemployment rates.
Research Interests
Areas: computational economics, multi-agent systems, empirical game theory, applications of machine learning
Applications: real-time payments, fraud, systemic risk, healthcare
Publications
Conference Publications
- Fraud Risk Mitigation in Real-Time Payments: A Strategic Agent-Based Analysis
Katherine Mayo, Nicholas Grabill, and Michael P. Wellman
IJCAI '24: 33rd International Joint Conference on Artifical Intelligence, August 2024
Acceptance rate: 14%; 12 min presentation (alloted to 16% of main track papers)
[paper][slides][poster] - A Strategic Analysis of Portfolio Compression
Katherine Mayo and Michael P. Wellman
ICAIF '21: 2nd ACM International Conference on AI in Finance, November 2021
[paper][slides]
Previous version presented as a poster at AAMAS 2021: [paper][poster] - An Agent-Based Model of Strategic Adoption of Real-Time Payments
Katherine Mayo, Shaily Fozdar, Michael P. Wellman
ICAIF '21: 2nd ACM International Conference on AI in Finance, November 2021
[paper][slides]
Journal Publications
- Navigating in a Space of Game Views
Michael P. Wellman and Katherine Mayo
Journal of Autonomous Agents and Multi-Agent Systems, July 2024
[paper] - Machine Learning Model of Emergency Department Use for Patients Undergoing Treatment for Head and Neck Cancer Using Comprehensive Multifactor Electronic Health Records
Michelle Mierzwa MD, Charles Mayo PhD, Pratyusha Yalamanchi MD, Joseph Evans MD, Francis Worden MD, Richard Medlin MD, Matt Schipper PhD, Caitlin Schonewolf MD, Jennifer Shah MD, Matthew Spector MD, Paul Swiecicki MD, Katherine Mayo MS, and Keith Casper MD
JCO Clinical Cancer Informatics, January 2023
[paper]
Workshop Papers
- Flagging Payments for Fraud Detection: A Strategic Agent-Based Model
Katherine Mayo, Shaily Fozdar, Michael P. Wellman
MUFin '23: 3rd International Workshop on Modeling Uncertainty in the Financial World (@ 37th AAAI Conference on Artificial Intelligence), February 2023
[paper][slides]
Coursework
Advanced Data Mining, Electronic Commerce (now called Incentives and Strategic Behavior in Computational Systems), International Finance, Reinforcement Learning Theory, Advanced Artificial Intelligence