Thang Duc Chu

Hello world! Welcome to my personal webpage.

I am 1st year M.Sc. student @ University of Alberta. I am fortunately supervised by professor Csaba Szepesvari. I am also a RL Community Lead @ CohereAI

I am interested in understanding rigorously machine learning algorithms. Specifically, I care about the mathematical limits of current algorithms and want to design new ones with theoretical assurances. I also care about symbolic AI and reasoning.

A current list of my interests:

Reinforcement Learning theory
Learning theory
Online Learning
(Online) Optimization
Symbolic AI and reasoning

Sipping tea or coffee and watching Netflix are my hobby. Learning more about coffee is one of my side projects for this year. Additionally, I enjoy painting and fishing.

Finally, I am a huge fan of corgis, especially Brady and Graffiti.

news

Jan 8, 2024	I will be a TA for CMPUT 267: Basics of Machine Learning taught by Nidhi Hedge.
May 28, 2023	I will join the Department of Computing Science at University of Alberta to pursue my Msc degree in Winter 2024.
Jan 1, 2023	I will be a head TA for CMPUT 469: AI Capstone taught by Russ Greiner
Sep 15, 2022	Paper “Graph Transformer for drug response prediction” is accepted by IEEE/ACM Transactions on Computational Biology and Bioinformatics
May 1, 2022	I am doing summer research under the supervision of Martha White

latest posts

Feb 3, 2023	Reinforcement Learning
Feb 3, 2023	Resources
Jan 30, 2023	Statistical learning

selected publications

IEEE/ACM

Graph Transformer for drug response prediction

Thang Chu, Thuy Trang Nguyen, Bui Duong Hai, and 2 more authors

IEEE/ACM Transactions on Computational Biology and Bioinformatics, Sep 2022

Abs HTML PDF

Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations. Furthermore, integrating multi-omics data of cell lines increases the performance of drug response prediction. However, these models have shown drawbacks in extracting drug features from graph representation and incorporating redundancy information from multi-omics data. This paper proposes a deep learning model, GraTransDRP, to better drug representation and reduce information redundancy. First, the Graph transformer was utilized to extract the drug representation more efficiently. Next, Convolutional neural networks were used to learn the mutation, meth, and transcriptomics features. However, the dimension of transcriptomics features was up to 17737. Therefore, KernelPCA was applied to transcriptomics features to reduce the dimension and transform them into a dense presentation before putting them through the CNN model. Finally, drug and omics features were combined to predict a response value by a fully connected network. Experimental results show that our model outperforms some state-of-the-art methods, including GraphDRP and GraOmicDRP. Availability of data and materials : https://github.com/chuducthang77/GraTransDRP.