Woojoo Na

I am a PhD student in Electrical and Computer Engineering at Northeastern University, advised by Professor Jennifer Dy. I study the mathematical foundations of machine learning, with a focus on optimal transport. My recent work, ITSPACE (ICML 2026), gives monotone, descent-guaranteed updates for Gaussian optimal transport between covariances.

Before Northeastern, I received my M.S. in Computer Science from Tufts University, where I worked on weak supervision with Professor Abiy Tasissa, and my B.A. in Mathematics from the University of Oxford, where I did undergraduate research with Professor Christophe Petit, Professor Thomas Lukasiewicz, and Dr. Andrey Kormilitzin.

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• 07/2026: Presented ITSPACE as a poster at ICML 2026 in Seoul. [arXiv]
• 05/2026: ITSPACE was accepted at ICML 2026! [OpenReview]
• 09/2024: Joined Northeastern University as a PhD student.
• 12/2023: Graduated from Tufts University with an M.S. in Computer Science.
• 07/2023: My paper, "RACH-Space: Reconstructing Adaptive Convex Hull Space with Applications in Weak Supervision", is now available.
• 09/2021: Joined Tufts University as a PhD student.
• 08/2021: Discharged from the Korean Army as Sergeant after completing 18 months of service.
• 02/2020: Joined the Korean Army to serve as FDC in artillery.
• 01/2020: I presented our work on cryptography at CMS-Ewha Womans University.
• 12/2019: We presented at 17th IMA International Conference on Cryptography and Coding.
• 07/2019: Graduated from the University of Oxford with a B.A. (Hons) in Mathematics.

A proximal majorization–minimization method for the exact Bures–Wasserstein (Gaussian W₂) objective on covariances, with closed-form updates in a square-root factorization. Each iteration is guaranteed to decrease the objective in exact arithmetic, with a certificate-gap bound under inexact polar computations; the updates preserve positive semidefiniteness and support rank-restricted factors. On real-world covariance-alignment benchmarks, ITSPACE reaches low-BW-gap solutions substantially faster than BW gradient descent and sample-OT baselines.

An algorithm for labeling unlabeled data in weakly supervised learning from incomplete, noisy information about the labels, based on reconstructing an adaptive convex hull. It reported state-of-the-art performance on the weak-supervision benchmark tasks considered at the time.

Undergraduate and master's-level research and course projects, kept here for the record.

Using group-theoretic properties of Cayley hash functions to construct trapdoor attacks on their parameters. Undergraduate research at the Oxford Mathematical Institute.

Studied the framework of group-equivariant CNNs, its applications and limitations, with experiments on MNIST.

An online platform for fake-news detection with neural models, aimed at small investors exposed to fake news.

Explored the relationship between transformer encoder word embeddings and conceptual-space geometry.

CNN and character-level word-embedding models for concept and relation extraction from electronic health records.

Knowledge graph completion for ontology reasoning.

Source code credit to Dr. Barron