Buyun Liang

Buyun Liang

Computer and Information Science Ph.D. Student

University of Pennsylvania

Biography

I am a Computer and Information Science Ph.D. student at the University of Pennsylvania, advised by Prof. René Vidal. My research interests have spanned optimization for AI, trustworthy AI and AI for science. Before joining Penn, I obtained a M.S. degree in Computer Science at the University of Minnesota, Twin Cities, advised by Prof. Ju Sun, a M.S. degree in Materials Science at the University of Minnesota, Twin Cities, advised by Prof. J. Ilja Siepmann, and a B.S. degree in Physics at Nanjing University.

Download my Curriculum Vitae .

Interests
  • Optimization for AI
  • Trustworthy AI
  • AI for Science
Education
  • Ph.D. in Computer and Information Science, 2023 -

    University of Pennsylvania, Philadelphia, PA, USA 🇺🇸

  • M.S. in Computer Science, 2020 - 2023

    University of Minnesota, Minneapolis, MN, USA 🇺🇸

  • M.S. in Materials Science, 2018 - 2020

    University of Minnesota, Minneapolis, MN, USA 🇺🇸

  • B.S. in Physics, 2014 - 2018

    Nanjing University, Nanjing, Jiangsu, China 🇨🇳

Recent News

All news »

Poster on When Deep Learning Meets Nontrivial Constraints in the 3M Poster Session. (Jun 7, 2023)

Poster on When Deep Learning Meets Nontrivial Constraints in the Midwest ML Symposium (MMLS 2023). See our poster for more details! (May 16-17, 2023)

Tutorial on Deep Learning with Nontrivial Constraints in the SIAM International Conference on Data Mining (SDM23). See our slides for more details! (Apr 29, 2023)

M.S. final defense! Special thanks to my advisor Prof. Ju Sun! (Apr 7, 2023)

Paper release: Proud to release our paper Optimization and Optimizers for Adversarial Robustness on arXiv! We have created the first general-purpose method for evaluating adversarial robustness and challenged the reliability of the existing robustness evaluation and adversarial training frameworks. (Mar 24, 2023)

Paper acceptance: Our paper Implications of Solution Patterns on Adversarial Robustness has been accepted by Computer Vision and Pattern Recognition (CVPR) Workshop of Adversarial Machine Learning on Computer Vision: Art of Robustness! (Mar 22, 2023)

Publications

.js-id-selected
(2023). Implications of Solution Patterns on Adversarial Robustness. In Computer Vision and Pattern Recognition (CVPR) Workshop of Adversarial Machine Learning on Computer Vision (Art of Robustness).

Paper

(2022). NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning. In Neural Information Processing Systems (NeurIPS) Workshop on Optimization for Machine Learning (OPT 2022).

Paper Poster

(2022). Optimization for Robustness Evaluation beyond ℓp Metrics. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023) & Neural Information Processing Systems (NeurIPS) Workshop on Optimization for Machine Learning (OPT 2022).

Paper Poster

Software

NCVX PyGRANSO
NCVX (NonConVeX) is a user-friendly and scalable python software package targeting general nonsmooth NCVX problems with nonsmooth constraints.
NCVX PyGRANSO

Contact