Julian Asilis

About me

I am a third-year PhD student in the USC Theory Group, fortunate to be advised by Vatsal Sharan. My primary research focus is machine learning, and I have broad interests in theoretical computer science. This fall I am visiting the Simons Institute for the program on modern paradigms in generalization. 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!

Research

• Understanding Aggregations of Proper Learners in Multiclass Classification
  with Mikael Møller Høgsgaard and Grigoris Velegkas.
  Submitted, 2024.
  [arxiv] | [pdf]


• Proper Learnability and the Role of Unlabeled Data
  with Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, and Shang-Hua Teng.
  Submitted, 2024.
  (arXiv forthcoming)
  


• Transductive Learning Is Compact
  with Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, and Shang-Hua Teng.
  Neural Information Processing Systems (NeurIPS 2024).
  [arxiv] | [pdf] | [slides]


• Open Problem: Can Local Regularization Learn All Multiclass Problems?
  with Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, and Shang-Hua Teng.
  Open Problem @ Conference on Learning Theory (COLT 2024).
  [pdf] | [slides]


• Regularization and Optimal Multiclass Learning
  with Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, and Shang-Hua Teng.
  Conference on Learning Theory (COLT 2024).
  [arxiv] | [pdf] | [slides]


• Computable PAC Learning of Continuous Features
  with Nate Ackerman, Jieqi Di, Cameron Freer, and Jean-Baptiste Tristan.
  Logic in Computer Science (LICS 2022).
  [pdf]

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

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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