picture of me :)

Julian Asilis

Ph.D. Student
Department of Computer Science
University of Southern California
asilis at usc.edu

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About me

I am a second-year PhD student in the USC Theory Group, fortunate to be advised by Vatsal Sharan. My primary research focus is machine learning, but I have broad interest in theoretical computer science. I am grateful to be supported by an NSF Graduate Research Fellowship.

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 at Harvard.

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



"The Computability of PAC Learning"


Probability Monads, under the direction of Michael Hopkins.


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

Advanced algorithms: CS 670 @ USC. Shortest paths, spanning trees, matroids, Fibonacci heaps, dynamic programming, max-flow, hardness.

Combinatorial analysis: Math 532 @ USC. (Exponential) generating functions, inclusion and exclusion, Mobius inversion, set and number partitions.

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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