Julian Asilis

About me

Hi, I’m a first year PhD student in the computer science department at USC. Most of my current work is in machine learning, but I’m broadly interested in theoretical computer science.

Previously, I’ve worked at Boston College as a research associate and in industry as a quantitative researcher. Before then, I studied math as an undergrad, with an interest in the topological and category-theoretic side of things.

Always happy to connect and talk about ideas in computer science, math, and beyond!

Research

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


• On the Computable Learning of Continuous Features
  with N. Ackerman, J. Di., C. Freer, and J. Tristan.
  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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