i am a software engineer and mathematician. my primary objective is to understand why there is something it is like to be me. i do not believe this objective can be reached from an armchair, so i am seeking potential employers working on interesting kinds of machine learning and machine understanding.

Current Reading

  • Deep Learning. Adaptive computation and machine learning.
  • The Character of Consciousness.
  • The Go Programming Language.
  • The Age of Em: Work, Love, and Life when Robots Rule the Earth.
  • The LION way. Machine Learning plus Intelligent Optimization.
  • The Concious Mind.
  • Quantum Computing Since Democritus.



  • Planar graphs are 9/2-colorable. (with Dan Cranston) J. Combin. Theory Ser. B, Accepted. (pdf)
  • A simple solution to the hardest logic puzzle ever. (with Brian Rabern), Analysis 68(298), 105-112, 2008. (doi)
  • The Hilton--Zhao Conjecture is True for Graphs with Maximum Degree 4 (with Dan Cranston) (pdf)
  • A better lower bound on average degree of k-list-critical graphs. (pdf)
  • Improved lower bounds on the number of edges in list critical and online list critical graphs. (with Hal Kierstead) (pdf)
  • Short fans and the 5/6 bound for line graphs. (with Dan Cranston) SIAM J. Discrete Math., Accepted. (pdf)
  • Extracting list colorings from large independent sets. (with Hal Kierstead) J. Graph Theory, Accepted. (pdf)
  • Subcubic edge chromatic critical graphs have many edges (with Dan Cranston) J. Graph Theory, Accepted. (pdf)
  • List-coloring claw-free graphs with Δ - 1 colors. SIAM J. Discrete Math., Accepted. (with Dan Cranston) (pdf)
  • Edge Lower Bounds for List Critical Graphs, via Discharging. Combinatorica, Accepted. (with Dan Cranston) (pdf)
  • A better lower bound on average degree of 4-list-critical graphs. Electron. J. Combin., Accepted. (pdf) (ejc)
  • Planar graphs have independence ratio at least 3/13 (with Dan Cranston) Electron. J. Combin., Accepted. (pdf)
  • Painting squares in Δ^2 - 1 shades. (with Dan Cranston) Electron. J. Combin., Volume 23(2), 2016. (pdf)
  • Graphs with χ = Δ have big cliques. (with Dan Cranston) SIAM J. Discrete Math., 29(4), 1792–1814. (pdf) (doi)
  • The fractional chromatic number of the plane. (with Dan Cranston) Combinatorica, Accepted. (pdf)
  • A note on coloring vertex-transitive graphs. (with Dan Cranston) Electron. J. Combin., Volume 22(2), 2015. (pdf)
  • Conjectures equivalent to the Borodin-Kostochka conjecture that appear weaker. (with Dan Cranston) European J. Combinatorics., Volume 44, Part A, February 2015, Pages 23–42 (pdf) (doi)
  • The list-chromatic index of K_8 and K_10. (pdf)
  • Yet another proof of Brooks' theorem. (pdf)
  • A game generalizing Hall's Theorem. Discrete Math., 320(6):87-91, 2014. (pdf) (doi)
  • Coloring graphs with dense neighborhoods. J. Graph Theory, 76(4):323-340, 2014. (pdf) (doi)
  • Coloring graphs from almost maximum degree sized
  • palettes.
  • Dissertation. (pdf)
  • Partitioning and coloring graphs with degree constraints. Discrete Math., 313(9):1028-1034, 2013. (pdf) (doi)
  • Coloring claw-free graphs with Δ - 1 colors. (with Dan Cranston) SIAM J. Discrete Math., 27(1):534-549, 2013. (pdf) (doi)
  • A note on vertex partitions. (pdf)
  • Destroying non-complete regular components in graph partitions. J. Graph Theory, 72(2):123-127, 2013. (pdf) (doi)
  • A strengthening of Brooks' Theorem for line graphs. Electron. J. Combin., N145, Volume 18(1), 2011. (pdf)
  • Δ-Critical graphs with small high vertex cliques. J. Combin. Theory Ser. B, 102(1):126-130, 2012. (pdf) (doi)

  • On hitting all maximum cliques with an independent set. J. Graph Theory, 66(1):32-37, 2011. (pdf) (doi)

  • The Borodin-Kostochka conjecture for graphs containing a doubly critical edge. Electron. J. Combin., N22, Volume 14(1), 2007. (pdf)

  • A note on Reed’s conjecture. SIAM J. Discrete Math., 22(2):820-827, 2008. (pdf) (doi)

  • On graph associations. SIAM J. Discrete Math., 20(2):529–535, 2006. (pdf) (doi)

  • Dangerous reference graphs and semantic paradoxes. (with Brian Rabern and Matthew Macauley), Journal of Philosophical Logic, 42(5), 727-765, 2013. (doi)

  • Applying groebner basis techniques to group theory. Journal of Pure and Applied Algebra, 210(1):137-140, 2007. (doi)

  • A common generalization of Hall's theorem and Vizing's
  • edge-coloring theorem
  • (Miami University Colloquium) (slides)
  • Extending Alon-Tarsi Orientations (AMS Special Session on Structural and Extremal Problems) (slides)
  • List coloring with large maximum degree. (poster)
  • An improvement on Brooks' theorem (CU-Denver Discrete Math Seminar). (slides)
  • Improving Brooks' theorem (The 26th Clemson Conference on Discrete Math and Algorithms, 2011) (slides)


when i was a child, i was obsessed with machine intelligence, i coded a strong chess AI (codenamed Betsy) and experimented with using neural networks in Betsy, both for the static evaluation at leaf nodes and within the tree for pruning. the networks learned from self-play to get about as good as my hand-tuned functions (discounting the slowdown incurred by sigmoid evaluation). i concluded that to do better, i would need to use raw game state data instead of the set of features i preselected as network inputs; unfortunately, this was 2000 and i did not have nearly enough processing power to do so.

to avoid spinning my wheels while waiting for faster hardware, i decided to put my machine intelligence ambitions on hold and study mathematics instead. my first year as a math major was spent abroad at utrecht university in the netherlands. my dutch was pretty poor, but luckily all the math textbooks were in english. learning to learn from just a textbook was painful at first, but this method of learning has been indispensable ever since.

in 2005 i felt the pull of making real things and left my math phd program (where i was researching fun things like quantum groups) to work for a defense contractor. we simulated nuclear weapons effects like damage caused to hardware by electromagnetic pulse. this work was interesting, but for family reasons, after about a year, i moved to colorado and took a job for a subsidiary of goldman sachs.

the goldman sachs job was not particularly exciting and i had lots of downtime. i had just read the book the man who loved only numbers about paul erdős; a homeless math genius that traveled around for decades to work on hard problems with other discrete mathematicians. i decided to use my work downtime to teach myself graph theory and see if i was smart enough to play with these discrete mathematicians. this was in 2007, it took three years and a lot more than just my work downtime, but in 2010 i made a breakthrough on a conjecture of two prominent graph theorists (alexandr kostochka and hal kierstead). i made arrangements with kierstead to come out to arizona state and finish my phd.

now it is 2017, i have fully satisfied my curiosity about playing with erdős's friends and achieved the best possible erdős number of 2 (since erdős no longer lives). neural networks are all over the news, now called deep learning, researchers are having great success; hardware is now fast enough and there are massive amounts of data to play with. it is past time for me to get back to my childhood obsession.