Bo Waggoner


Maxwell-Dworkin 219
33 Oxford Street
Cambridge, MA 02138

I am a Ph.D. candidate at Harvard University in Computer Science.
Page last modified: 2014-02-26.

Contents: Bio, Research Interests, Teaching, Publications, Talks, Writeups, Links

See also: My misc page


Bio

I grew up in Maumee, Ohio. I graduated from Duke University in 2011 with an Interdepartmental Major in Math and Computer Science. I am currently studying with Professor
Yiling Chen and the EconCS group at Harvard. My interests include mathematics, economics, and computer science. I am a runner.



Research Interests

Mostly in theoretical CS and economics, especially mechanism design (from an algorithmic perspective).

Specific topics: algorithmic auction design, information elicitation (particularly in crowdsourcing settings, from both game-theoretic and machine-learning perspectives), online algorithms (particularly online bipartite matching problems), differential privacy, computational social choice (e.g. fair division, voting), computational learning theory (e.g. testing of discrete distributions).

Looking forward: I'd love to work on anything that seems interesting, exciting, and mathematically beautiful!


Teaching

Fall 2013:
CS 284r, Incentives and Information in Networks. with Professor Yaron Singer.
Teaching Fellow.

Fall 2012: CS 121, Intro to Theory of Computation. with Professor Salil Vadhan.
Teaching Fellow.



Publications

Designing Markets for Daily Deals.
Yang Cai, Mohammad Mahdian, Aranyak Mehta, and Bo Waggoner.
Proceedings of the Ninth Conference on Web and Internet Economics (WINE-13).
    Also available on arxiv. Both links are the "full" version.

Evaluating Resistance to False-Name Manipulations in Elections.
Bo Waggoner, Lirong Xia, and Vincent Conitzer.
Proceedings of the Twenty-Sixth AAAI Conference on Artifical Intelligence (AAAI-12).
    Supplementary: simulation code.


Working Papers

Information Elicitation Sans Verification.
Bo Waggoner and Yiling Chen.
Appeared at the 3rd Workshop on Social Computing and User-Generated Content, at EC-13.



Talks

2014-01-10. Toward Buying Labels from the Crowd.
Indo-US Lectures Week in Machine Learning, Game Theory, and Optimization, Bangalore.
    slides (pdf), slides and notes (pdf).

2013-12-13. Designing Markets for Daily Deals.
The 9th Conference on Web and Internet Economics (WINE-13), Cambridge, MA.
    slides (Google presentation, with notes), slides (pdf), slides and notes (pdf).

2013-06-16. Information Elicitation Sans Verification.
The 3rd Workshop on Social Computing and User-Generated Content (SCUGC), at EC-13, Philadelphia, PA.
    slides (pdf).

2012-03-26. Evaluating Resistance to False-Name Manipulations in Elections.
Harvard EconCS Group.
    slides (pptx), slides (pdf), slides and notes (pdf).



Writeups

Useful Tips, Tricks, and Techniques. A list-in-progress I am compiling of some of the most common and useful approximations, inequalities, bounds, proof techniques, etc. for theoretical computer science.

Jumpstart GLPK. Intro by examples to the GNU Linear Programming Kit's stand-alone LP solver, glpsol.
    Supplementary: example0.mod, example1.mod.

Jumpstart Flex/Bison. Basic examples and how-to for flex and bison, the lexing and parsing tools for writing compilers using C or C++ (the free versions of lex and yacc).
    Supplementary: all example files (zip), example0.l, example0.y, example1.l, example1.y.

Jumpstart LaTeX. Intro by examples to LaTeX.
    Supplementary: all example files (zip), example0.tex, example1.tex, example2.tex, bibexample.tex, bibexample.bib.

Jumpstart Linux. Quick intro to Linux and the command line for the absolute beginner.



Links


Possible misspellings of my name include "Bo Wagner" and "Bo Wagoner".