Hi, I’m a undergrad senior in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, advised by Prof. Ju Sun and Prof. Shashi Shekhar. My research interests include federated learning, medical imaging analysis, deep learning, optimization, and causal AI. I have been the recipient of Google CS research mentorship, UMN undergrad research scholarship and the CS&E departmental scholarship. I serve as a Math and CS reviwer for the Minnesota Undergraduate Research & Academic Journal, and as a volunteer in ICLR 2021 and ICML 2021. You can also find an article about me on

Check my most recent CV here.


2018-2022 - Bachelor of Science in Computer Science with a Math minor, University of Minnesota, Twin Cities.

Advisors: Prof. Ju Sun & Prof. Shashi Shekhar

Published Papers (Ranked by contribution of mine)

[1] AAAI 2022 under review (challenge the conclusions of Google’s Transfusion; propose a novel transfer learning method in medical imaging classification)
[2] Submitted to SIGSPATIAL 2021

Selected Course Projects

CSCI 8980 Think Deep Learning (Fall 2020)

CSCI 5525 Machine Learning: Analysis and Methods (Spring 2021)

Other Papers

[1] G Luo. Application of Artificial Intelligence to Help Fight COVID-19, In Minnesota Undergraduate Research & Academic Journal (MURAJ), Vol.4 No.3, 2021,


[1] G Luo. Wetland Mapping Using Spatial Variability Aware Neural Networks (SVANN), In 2021 UMN Undergrad Research Symposium,


  • Google CSRMP Scholar
  • Undergraduate Research Scholarship (UMN) x2
  • Maximillian Lando Scholarship (CS&E Department)
  • CSE Dean’s List


Undergrad TA

  • CSCI 2011 Discrete Math (Fall 2020 & Spring 2021)
  • CSCI 2033 Computational Linear Algebra (Fall 2021)

Peer Tutor at UMN Library


  • Minnesota Undergraduate Research & Academic Journal, Math and Computer Science Reviewer, Oct. 2020 - present
  • ICLR 2021, May 2nd - 8th, 2021
  • ICML 2021, July 18th - 24th, 2021

Reading List

Fall 2021

The Modern Mathematics of Deep Learning
Deep learning: a statistical viewpoint

Spring 2021

Foundations of Machine Learning by by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David