Julian Asilis

About me

I’m a recent college grad working as a research associate in the computer science department at Boston College. Most of my current work is in machine learning, but I’m broadly interested in theoretical computer science.

Previously, I’ve worked briefly in industry as a quantitative researcher and studied math as an undergrad, with an interest in the topological and category-theoretic side of things. Always happy to talk about ideas in math and beyond!

Research

Computable PAC Learning of Continuous Features, with N. Ackerman, J. Di., C. Freer, and J. Tristan. In Logic in Computer Science (LICS), 2022. [pdf]

On the Computable Learning of Continuous Features, with N. Ackerman, J. Di., C. Freer, and J. Tristan. In Conference on Computability and Complexity in Analysis (CCA), 2021. [pdf]

Talks

• Boston College Computer Science Seminar, 2022.
[Slides].
• Boston College Math & Machine Learning Seminar, 2022.
[Slides].
• Conference on Computability and Complexity in Analysis, 2021. [Slides].

Thesis

Probability Monads, under the direction of Michael Hopkins.

Teaching

Computer Science I: CSCI 1101 @ BC, Spring 2022 Head TA. Notes here.

Applied Machine Learning: CSCI 3340.01 @ BC, Fall 2021 Teaching assistant. Notes here.

Sets, Groups, and Topology: Math 101 @ Harvard, Spring 2020 Course assistant. Partial notes here.

Real Analysis I: Math 112 @ Harvard, Spring 2019 Course assistant. Notes here.

Abstract Algebra I: Math 122 @ Harvard, Fall 2018 Course Assistant. Notes here, taken by Vaughan McDonald.

Course Notes

Algebraic geometry: Math 137 @ Harvard. Algebraic sets, Nullstellensatz (again), local rings, DVRs. Notes for first half of the course.

Commutative algebra: Math 221 @ Harvard. Localization, Nullstellensatz, Tor, Nakayama, dimension theory.

Category theory: Math 99r @ Harvard. Functors, natural transformations, Yoneda, (co)limits.

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