Guillermo Angeris (Guille)

Stanford BS/MS/PhD in Electrical Engineering


BS/MS/PhD in Electrical Engineering interested in optimization, physics, and puppies; I also sometimes pretend to do research and things.

Currently working on inverse design in the Nanoscale and Quantum Photonics lab with Prof. Jelena Vučković and Prof. Stephen Boyd. I also sometimes work with Prof. Tatiana Engel.


A note on privacy in constant function market makers (Feb. 2021)
Guillermo Angeris, Alex Evans, Tarun Chitra. (arXiv)
When does the tail wag the dog? Curvature and market making (Dec. 2020)
Guillermo Angeris, Alex Evans, Tarun Chitra. (arXiv)
LinRegOutliers: a Julia package for detecting outliers in linear regression (Dec. 2020)
Mehmet Satman, Shreesh Adiga, Guillermo Angeris, Emre Akadal. (code)
Published in the Journal of Open Source Software.
Heuristic methods and performance bounds for photonic design (Nov. 2020)
Guillermo Angeris, Jelena Vučković, Stephen Boyd. (arXiv, code)
Published in Optics Express.
Improved price oracles: constant function market makers (rewritten Jun. 2020)
Guillermo Angeris, Tarun Chitra. (arXiv, original version from Mar. 2020)
Presented in ACM's Advances in Financial Technologies 2020.
Optimal representative sample weighting (May 2020)
Shane Barratt, Guillermo Angeris, Stephen Boyd. (arXiv, code)
Accepted in Statistics and Computing.
Bounds for scattering from absorptionless electromagnetic structures (Mar. 2020)
Rahul Trivedi, Guillermo Angeris, Logan Su, Stephen Boyd, Shanhui Fan, Jelena Vučković. (arXiv, code)
Published in Physical Review Applied.
A new heuristic for physical design (Feb. 2020)
Guillermo Angeris, Jelena Vučković, Stephen Boyd. (arXiv, code)
Automatic repair of convex optimization problems (Jan. 2020)
Shane Barratt, Guillermo Angeris, Stephen Boyd. (arXiv, code)
Published in Optimization and Engineering.
An analysis of Uniswap markets (Nov. 2019)
Guillermo Angeris, Hsien-Tang Kao, Rei Chiang, Charlie Noyes, Tarun Chitra. (arXiv)
Accepted in MIT's CES 2020. (reviews)
Minimizing a sum of clipped convex functions (Oct. 2019)
Shane Barratt, Guillermo Angeris, Stephen Boyd. (arXiv, code)
Published in Optimization Letters.
Fast reciprocal collision avoidance under measurement uncertainty (May 2019)
Guillermo Angeris*, Kunal Shah*, Mac Schwager. (arXiv, code)
Presented at ISRR 2019.
Computational bounds for photonic design (Nov. 2018, edited Dec. 2018)
Guillermo Angeris, Jelena Vučković, Stephen Boyd. (arXiv, code)
Published in ACS Photonics.

* Indicates equal authorship.

Workshop papers and other writings

Optimal fees for geometric mean market makers (Jan. 2021)
Alex Evans, Guillermo Angeris, Tarun Chitra.
Accepted in the DeFi workshop at FC 2021.


Bounds on achievable performance via Lagrange duality (July 2021)
Guillermo Angeris, Rahul Trivedi, Logan Su, Jelena Vučković, Stephen Boyd. (summary)
Invited talk. META 2020, University of Warsaw.
Constant function market makers: Pushing Uniswap and friends to do more with lower fees (May 2020)
Guillermo Angeris, Tarun Chitra
DeFi Discussions. (recorded talk)
An analysis of Uniswap markets (Mar. 2020)
Guillermo Angeris, Hsien-Tang Kao, Rei Chiang, Charlie Noyes, Tarun Chitra.
CES 2020, MIT. (recorded talk)


For my work and more info, my blog can be found here which I (sometimes) update. Additionally, most of my code can be found on my GitHub profile.

I love having coffee chats! If you'd like to talk about something interesting, send me an email at

[my last name]

so we can schedule something.


CME/EE103: Introduction to Matrix Methods
TA'd on: Autumn 2015, Autumn 2016, Autumn 2017.

EE364A: Convex Optimization
TA'd on: Spring 2017, Winter 2018.

EE104: Introduction to Machine Learning
TA'd on: Spring 2018.

Class projects

LEigOpt.jl: A fast, first-order interior-point optimizer for eigenvalue optimization
We propose a new first-order interior-point method for eigenvalue optimization of matrices with common sparsity structure, which, in the current testing, outperforms SCS and CVXOPT by at least an order of magnitude of runtime. (pdf, code)

Electrical network approximation to constrained minimum k-cut (multiway cut)
We propose a novel, fast method for approximating the minimum k-Cut problem by using electrical networks. Our approach has similar performance to the current best LP approximation but much faster empirical runtime. (pdf, code)

DominAI: An AI for the imperfect information game of Dominoes
We develop an algorithm for approximating good play in imperfect information games and show an application of the algorithm to the team-based game of Venezuelan dominoes. (pdf, poster, code)

GameBoy emulator on bare metal Raspberri Pi
We developed a complete (though not cycle-accurate) CPU/GPU emulator for the original GameBoy, built on top of the Raspberry Pi without an operating system. (code)

Unsupervised learning for brain tumor categorization
We correctly infer tumour regions without reference to masked examples by using unsupervised learning, showing that a simple approach for unknown or little-known classes of tumors can yield possibly useful insights into these classes. (pdf, poster, code)

Other Projects

Autonomous, multi-objective path planner for the AUVSI–SUAS competition
Currently working on a path-planning algorithm for fixed-wing search-and-rescue UAVs with real-time objective creation, modification, and updating. No code will be available until the end of the competition, but progress and mathematical descriptions of the solution can be found on my blog. (blog, competition)

Chocolate-Arch: A tiny 8-bit processor
I designed an 8-bit architecture made to fit on a Lattice iCEStick (with 1K LUTs). A small team of us began a complete implementation on an FPGA along with an external Arduino-based debugger during a hackathon. (code)

Frequently asked questions

Wait, so how do I pronounce your nickname, Guille?

Gee (like geese) - ye (like yet, but without the t).

Have you ever not been in school or writing papers?

Temporarily, I guess.

I spent my 2016 and 2017 summers at D.E. Shaw Research (DESRES) doing work on fast algorithms for protein folding detection ('16) and some other work on information-limited labelling schemes ('17). I spent my summer of 2018 at Facebook doing work in scam detection. Currently, I consult for Gauntlet.

Do you do anything other than research or program? E.g., anything useful?

Probably not, to be honest.

I do like to make pizza (picture credits to Katie and Kaitlyn!), listen to music, and hike, though.