Harvey Mudd College

A tool created by Harvey Mudd College students that helps developers consider the environmental impact of their code was the top pick of judges at the recent 2019 SD Hacks intercollegiate hackathon held at UC San Diego. EverGreen, created by juniors Alice Chi, Alfredo Gomez and Matthew Krager, is an eco-aware integrated development environment (IDE) inspired by the societal issues surrounding the environmental impact of CO2 emissions. The tool captures the environmental impact that a programmer's code will have by using various metrics, such as the carbon emitted in the average lifespan of a car. They calculated the CO2 emissions for a given piece of code and then generalized emissions for other hardware. During the three-day event, 143 student teams were tasked with creating a technical solution to an issue related to sustainability, education or health and wellness. The HMC team chose sustainability as their category because they wanted to develop a solution that would help programmers improve the way they code.

Harvey Mudd College senior and math-physics major Matthew Fox has published two single-author papers in high-impact journals. Physical Review D published "Palatini f(R,Lm,RT) gravity and its Born-Infeld semblance" in June, and the Journal of Mathematical Physics published "Multipole hair of Schwarzschild-Tangherlini black holes" in October. In December, in a seminar slot usually reserved for graduate students, postdocs and faculty, Fox gave a talk on his black hole research to the Particle Theory Group at University of California, Riverside.

Harvey Mudd College senior Mazda Moayeri co-authored a research paper on music informatics that was accepted to the 12th International Workshop on Machine Learning and Music held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases in Wurzburg, Germany Sept. 16. A joint major in computer science and mathematics, Moayeri conducted research on machine learning and music, a burgeoning interdisciplinary field that encompasses data-driven approaches to the production, analysis and retrieval of music. His paper, "Neural Symbolic Music Genre Transfer Insights," was presented by his supervisor and mentor Gino Brunner, as Moayeri wasn't able to attend the workshop. The paper was among many given by scholars on applications to music and machine learning. Accepted papers are published by Springer Verlag under their Lecture Notes in Computer Science series.

Harvey Mudd computer science students Jonathan Hayase '20, Julius Lauw '20, Dominique Macias '19, Akshay Trikha '21 and Julia Vendemiatti '21, together with Professor George Montanez, published a paper on learning algorithms titled "The Futility of Bias-Free Learning and Search." Learning algorithms are machines that turn data resources into predictions. Their paper showed that unless algorithms do this conversion in a biased way, predisposing their predictions toward predetermined outcomes, they cannot predict any more accurately than random guessing. The paper proved that finding a good bias for a given problem is difficult, when searching among any set of data resources that on average isn't itself positively biased. These results apply to machine learning algorithms, AI systems, genetic learning algorithms and many other forms of search and optimization. Harvey Mudd College's computer science department seeks to provide students with a strong foundational background blending experimentation, theory and design. CS graduates are equally well-prepared for work in industry and graduate school. The capstone Clinic Program provides students with a year-long software design project and an active Research Program involves approximately 50 students in research, funded by grants and gifts from the National Science Foundation, Howard Hughes Medical Institute, and the Rose-Hills and Baker Foundations.

Harvey Mudd computer science students Mara Downing, Chris Thompson and Professor Lucas Bang co-authored a research paper that was accepted at the Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence and Interactive Digital Entertainment Strategy Game Workshop. The paper is titled "Automatically Solving Deduction Games via Symbolic Execution, Model Counting, and Entropy Maximization." Downing and Thompson designed a DSL for expressing a class of puzzles called "deduction games," implemented a symbolic execution engine for it using an automated theorem prover, and then wrote an entropy maximizer that outputs the steps of game solution. A user can give their system the source code of a game and it will then automatically solve the game, playing it in real time.