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Chuanbo Hua

Ph.D. Candidate
Systems Intelligence Lab (SILab)
Industrial & Systems Engineering
Korea Advanced Institute of Science & Technology (KAIST)

GitHub: cbhua
Email: cbhua [at] kaist.ac.kr
Phone: (+86) 010-6608-6548
Address: 4217, E2-1, 291 Daehak-ro, Yuseong-gu, Daejeon, South Korea

"Education does for the mind, experience does for the soul."


💡  Research Interests: reinforcement learning, combinatorial optimization, scientific computing.


I'm a second year Ph.D. in the Industrial & Systems Engineering department (ISysE) at Korea Advanced Institute of Science & Technology (KAIST). I'm doing research with my colleagues at the Systems Intelligence Laboratory (SILab) suprevised by Prof. Jinkyoo Park. I am also working with great peers at the AI4CO open source group and the DiffEqML open source group.

The primary focus of my ongoing research is the development of reinforcement learning (RL) algorithms for the study of combinatorial optimization (CO) problems. We create an extensive open-source benchmark RL4CO to provide a unified framework for RL-based CO algorithms, and to facilitate reproducible research in this field, decoupling the science from the engineering. I also explore machine learning methods for dynamical physical system simulation, scientific computing, and trajectory prediction. Recent interests include reinforcement learning for continuous dynamical system control, robot navigation and high efficient complex physical system simulation.

Find my works:  Publications  ∣  Research  ∣  Projects
Recent News
07/15/2024.
Paper "RouteFinder: Towards Foundation Models for Vehicle Routing Problems" got accepted at the ICML 2024 Workshop (Oral).
06/01/2024.
Paper "Deep learning-based coagulant dosage prediction for extreme events leveraging large-scale data" got accepted at the JWPE (IF: 7).
02/13/2024.
Paper "HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding" got accepted at the AAMAS 2024 (Poster).
02/01/2024.
Paper "Optimizing coagulant dosage using deep learning models with large-scale data" got accepted at the Chemosphere (IF: 8.1).
10/31/2023.
Paper "RL4CO: a Unified Reinforcement Learning for Combinatorial Optimization Library" got accepted at the NeurIPS 2023 Workshop (Oral).
09/30/2023.
Paper "Learning Efficient Surrogate Dynamic Models with Graph Spline Networks" got accepted at the NeurIPS 2023 (Poster).
08/01/2023.
Paper "Comparing artificial and deep neural network models for prediction of coagulant amount and settled water turbidity" got accepted at the JWPE (IF: 7).
04/01/2023.
Paper "Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model" got accepted at the Water Research (IF: 12.8).
09/01/2022.
I started my Ph.D. in KAIST advised by Prof. Jinkyoo Park.
06/15/2022.
Paper "Efficient Continuous Spatio-Temporal Simulation with Graph Spline Networks" got accepted at the ICML 2022 Workshop (Oral).

Mailing Address
Industrial & Systems Engineering, Korea Advanced Institute of Science & Technology (KAIST)
4217, E2-1, 291 Daehak-ro, Yuseong-gu, Daejeon, South Korea