Tinglin Huang

I am a third-year Ph.D. student at Yale University, advised by Prof. Rex Ying.

Previously, I worked with Prof. Jie Tang from Tsinghua University, and Prof. Tat-Seng Chua from National University of Singapore.

My research interests revolve around computational biology, including macromolecule modeling and geometric deep learning on 3D molecular structure.

Email: tinglin.huang[at]yale.edu

Publications (* denotes equal contribution)

Protein-Nucleic Acid Complex Modeling with Frame Averaging Transformer
Tinglin Huang, Zhenqiao Song, Rex Ying, Wengong Jin
NeurIPS, 2024
PDF / Code

Propose an equivariant transformer architecture based on frame averaging and an unsupervised aptamer screening approach.

SurfPro: Functional Protein Design Based on Continuous Surface
Zhenqiao Song, Tinglin Huang, Lei Li, Wengong Jin
ICML, 2024
PDF / Code

A surface-based functional protein designer.

Does Negative Sampling Matter? A Review with Insights into its Theory and Applications
Zhen Yang, Ming Ding, Tinglin Huang, Yukuo Cen, Junshuai Song, Bin Xu, Yuxiao Dong, Jie Tang
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
PDF

An extensive review of the landscape of negative sampling techniques across different domains.

Learning to Group Auxiliary Datasets for Molecule
Tinglin Huang, Ziniu Hu, Rex Ying
NeurIPS, 2023
PDF / Code

A routing-based molecule dataset grouping method.

BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
Zhen Yang*, Tinglin Huang*, Ming Ding*, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang
KDD, 2023
PDF / Code

A global negative sampling method for contrastive learning.

GRAND+: Scalable Graph Random Neural Networks
Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang
WWW, 2022
PDF / Code

A scalable GNN framework for semi-supervised graph learning.

MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems
Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, Jie Tang
KDD, 2021
PDF / Code

A general negative sampling plugin for GNN-based recommender systems.

Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang*, Tinglin Huang*, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua
WWW, 2021
PDF / Code

We proposed KGIN which (1) uncovers useritem relationships at the granularity of intents, and (2) applies relational path-aware aggregation.

Last updated February 2024.
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