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Yi Liu, Ph.D. Candidate |
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.
Deep Learning
Graph Neural Networks
Graph Neural Networks for Science
Computational Biology
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
Lu Wei, Ph.D. in Computer Science, Florida State University,   Fall 2022 -
(* equal contribution)
(* equal contribution)
#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
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
Bioinformatics
IEEE Transactions on Medical Imaging (TMI)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
CSCE 636: Deep Learning, Texas A&M University, Fall 2019
CSCE 611: Operating System and Applications, Texas A&M University, Spring 2020
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
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