Zhenhan Huang

Hi there! My name is Zhenhan Huang (黄臻瀚). I am a second year graduate student in the department of computer science, Rensselaer Polytechnic Institute, Troy, New York, United States. I am fortunate enough to be advised by Prof. Jianxi Gao.

My research interests align broadly with deep learning in computer vision and natural language processing. My current research focuses on meta learning and multimodal deep learning.

News

• [2024.07] Our work "Graph is all you need? Lightweight Data-Agnostic Neural Architecture Search Without Training" is accepted by AutoML 2024 at ICML
• [2024.04] Our work "Network Properties Determine Neural Network Performance" is accepted by Nature Communication.
• [2023.10] I am fortunate to become a Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC) scholar.
• [2023.05] I went to IBM Thomas J. Watson Research Center for 2023 summer intern. My research topic is to apply large language model in tabular dataset in pretraining + fine-tuning fashion.
• [2022.08] I was enrolled in the Ph.D. program in computer science of Rensselaer Polytechnic Institute.

Education

• Ph.D. in Computer Science, Rensselaer Polytechnic Institute
  2022 - Present, GPA: 4.0/4.0
• M.S. in Computer Science, Rensselaer Polytechnic Institute
  2021 - 2022, GPA: 4.0/4.0
• Ph.D. in Materials Engineering, Rensselaer Polytechnic Institute
  2017 - 2022, GPA: 3.8/4.0
• M.S. in Materials Engineering, Harbin Institute of Technology
  2015 - 2017, GPA: 3.5/4.0
• B.S. in Materials Engineering, Harbin Institute of Technology
  2011 - 2015, GPA: 3.7/4.0

Publication

1. Chunheng Jiang^*, Zhenhan Huang^*, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun and Jianxi Gao. Network Properties Determine Neural Network Performance. Nature Communication. (* Equal contribution). [PDF][Code].
2. Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Chunheng Jiang and Jianxi Gao. Graph is All You Need? Lightweight Data-Agnostic Neural Architecture Search Without Training. AutoML 2024 Workshop at International Conference on Machine Learning (ICML) [PDF] [Code].