I am a Ph.D. Candidate in the University of Michigan's Computer Science and Engineering Division. My research advisor is Professor Grant Schoenebeck from Michigan's School of Information.
Email: burrelln [at] umich [dot] edu
Currently, my primary research is focused on using empirical methods to complement theoretical work in:
Information elicitation, i.e. the design of economic mechanisms to reward people for reporting information in a way that incentivizes effort in gathering it and honesty in reporting it, and
Information aggregation, i.e. the design of algorithms to combine information elicited from multiple sources, so that the combined information is more useful than the information from any single source.
I have also worked on questions related to the game-theoretic notion of common knowledge, particularly in network settings.
Keywords: Algorithmic Game Theory - Complex Systems - Computational Social Science - Network Science.
Dirty Faces, Clean Model: Formalizing Theory-of-Mind Reasoning in a Classic Model of Common Knowledge. J. Zhang, N. Burrell. [MathPsych/ICCM 2021 Video]
University of Michigan, Graduate Student Instructor.
EECS 598-013: Randomness and Computation; Fall 2020.
EECS 586: Design and Analysis of Algorithms; Winter 2019.
Duke University, Undergraduate Teaching Assistant.
CS 590.01: Advanced Computer Security; Spring 2018, Spring 2017.
CS 290: Computer Security; Fall 2017.
Program Committees:
Complex Networks: 2022, 2021.
I am in my 5th year at the University of Michigan.
Previously, I studied Computer Science, Math, and Russian at Duke University, where I also performed in the Marching Band and Jazz Ensemble.