Yi Liu
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Yi Liu, Ph.D. Candidate |
Short Biography
I am currently a Ph.D. candidate in the Department of Computer Science & Engineering, Texas A&M University. My advisor is Dr. Shuiwang Ji. My research interests are graph structure learning using deep models, its methodologies, and real-world applications. Besides publishing top-tier papers, I am also active in open challenges and open-source communities. I am leading the champion team on Cleft Detection of MICCAI CREMI Open Challenge, a member of the #3 team on the Open Catalyst Challenge, and a contributor of the popular open-source library DIG:Dive Into Graphs.
Research Interests
Deep Learning
Graph Neural Networks
Graph Neural Networks for Science
Computational Biology
Education
Ph.D., Computer Science, Texas A&M University, Expected graduation: July 2022
M.S., Biomedical Engineering, University of Science and Technology of China, September 2012 - June 2015
B.S., Electronic Engineering, University of Science and Technology of China, September 2008 - June 2012
Ph.D. Students
Lu Wei, Ph.D. in Computer Science, Florida State University, Fall 2022 -
B.S., School of the Gifted Young, University of Science and Technology of China
Preprints
(* equal contribution)
- Limei Wang*, Yi Liu*, Yuchao Lin, Haoran Liu, and Shuiwang Ji
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs - Yi Liu and Shuiwang Ji
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis - Hongyang Gao, Yi Liu, Xuan Zhang, and Shuiwang Ji
Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence
Submitted, 2022
arXiv: 2101.05410, 2021
arXiv: 2103.08387, 2021
Publications
(* equal contribution)
- Yi Liu*, Limei Wang*, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji
Spherical Message Passing for 3D Molecular Graphs - Yi Liu and Shuiwang Ji
CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy - Hongyang Gao*, Yi Liu*, and Shuiwang Ji
Topology-Aware Graph Pooling Networks - Yi Liu, Hao Yuan, Lei Cai, and Shuiwang Ji
Deep Learning of High-Order Interactions for Protein Interface Prediction - Yi Liu, Hao Yuan, Zhengyang Wang, and Shuiwang Ji
Global Pixel Transformers for Virtual Staining of Microscopy Images - Yi Liu, Hao Yuan, and Shuiwang Ji
Learning Local and Global Multi-Context Representations for Document Classification - Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, Zhao Xu,
Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, and Shuiwang Ji
DIG: A Turnkey Library for Diving into Graph Deep Learning Research - Sina Mohseni, Fan Yang, Shiva Pentyala, Mengnan Du, Yi Liu, Nic Lupfer, Xia Hu, Shuiwang Ji, and
Eric Ragan
Machine Learning Explanations to Prevent Overtrust in Fake News Detection - Eric Ragan, Sina Mohseni, Fan Yang, Shiva Pentyala, Mengnan Du, Yi Liu, Nic Lupfer, Xia Hu, and Shuiwang Ji
Trust Evolution Over Time in Explainable AI for Fake News Detection
International Conference on Learning Representations (ICLR), 2022
IEEE Transactions on Medical Imaging (TMI, IF=13.94), 2021
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI, IF=16.39), 2021
The 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020
IEEE Transactions on Medical Imaging (TMI, IF=13.94), 2019
The 19th IEEE International Conference on Data Mining (ICDM), 2019
Journal of Machine Learning Research (JMLR), 2021
The 15th International AAAI Conference on Web and Social Media (ICWSM), 2021
CHI 2020 Workshop on Fair & Responsible AI (CHI 2020 Workshop), 2020
Awards and Honors
#1 on Cleft Detection, MICCAI CREMI Open Challenge, 2020
#3 on Open Catalyst Challenge, 2021
KDD Student Registration Award, 2020
ICDM Student Travel Award, 2019
Texas A&M Department of Computer Science and Engineering Travel Grants, 2019
Services
Program Committee Member
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020, 2021, 2022
International Conference on Machine Learning (ICML), 2022
Conference on Neural Information Processing Systems (NeurIPS), 2022
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
SIAM International Conference on Data Mining (SDM), 2022
AAAI Conference on Artificial Intelligence (AAAI), 2019
Journal Reviewer
Bioinformatics
IEEE Transactions on Medical Imaging (TMI)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Teaching and Mentoring
Teaching Assistant
CSCE 636: Deep Learning, Texas A&M University, Fall 2019
CSCE 611: Operating System and Applications, Texas A&M University, Spring 2020
Guest Lecturer
GNNs for Molecules (Material: Understand "GNNs for Molecules" in ONE Hour!), CSCE 636: Deep Learning, Texas A&M University, Fall 2021
GNNs for Molecules (Material: Understand "GNNs for Molecules" in ONE Hour!), BMI 6334: Deep Learning in Biomedical Informatics, UTHealth School of Biomedical Informatics, Fall 2021
Introduction to Deep Learning (Material: Know Deep Learning in ONE Hour!), Texas A&M Data Science Club, Spring 2022
Student Mentoring
Limei Wang, Ph.D. in Computer Science, Texas A&M University, January 2021 - present
Haoran Liu, Ph.D. in Computer Science, Texas A&M University, January 2022 - present
Keqiang Yan, Ph.D. in Computer Science, Texas A&M University, January 2022 - present
Yuchao Lin, Ph.D. in Computer Science, Texas A&M University, January 2022 - present
Jerry Kurtin, B.S. in Computer Science, Texas A&M University, Februray 2022 - present