• F Berto*, C Hua*, J Park*, M Kim, H Kim, J Son, H Kim, J Kim, and J Park
  RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark.
  arXiv preprints.
• C Hua*, F Berto*, Z Zhao, J Son, C Kwon, and J Park
  A Unified Learning Model for the Profiled Vehicle Routing Problem
  arXiv preprint.
• F Berto*, C Hua*, L Lautmann, J Son, J Park, K Ahn, C Kwon, L Xie, and J Park
  PARCO: Learning parallel autoregressive policies for efficient multi-agent combinatorial optimization
  arXiv preprint.
• F Berto*, C Hua*, NG Zepeda*, A Hottung, N Wouda, L Lan, K Tierney, and J Park
  Routefinder: Towards foundation models for vehicle routing problems
  arXiv preprint.
• J Son, Z Zhao, F Berto, C Hua, C Kwon, and J Park
  Neural Combinatorial Optimization for Real-World Routing
  arXiv preprint.
• J Li*, C Hua*, H Ma, J Park, V Dax, and MJ Kochenderfer
  Multi-agent dynamic relational reasoning for social robot navigation
  arXiv preprint.
• J Li*, C Hua*, J Park, H Ma, V Dax, and MJ Kochenderfer
  Evolvehypergraph: Group-aware dynamic relational reasoning for trajectory prediction
  arXiv preprint.

• C Hua*, F Berto*, J Son, S Kang, C Kwon, and J Park
  CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
  AAMAS 2025 (Poster).
• Y Chen, M Son, C Hua, and JY Kim
  AoP-SAM: Automation of Prompts for Efficient Segmentation
  AAAI 2025 (Poster).
• H Kim, F Berto, J Lee, H An, T Shin, C Hua, J Park, Y Kim, and J Kim
  Accelerating Chiplet Placement & Routing Optimization with Machine Learning
  DesignCon 2025 (Best Paper).

• H Ye, J Wang, Z Cao, F Berto, C Hua, H Kim, and J Park
  Large Language Models as Hyper-Heuristics for Combinatorial Optimization
  NeurIPS 2024 (Poster).
• F Berto*, C Hua*, NG Zepeda, A Hottung, N Wouda, L Lan, K Tierney, and J Park
  RouteFinder: Towards foundation models for vehicle routing problems
  ICML 2024 Workshop (Oral).
• J Kim, C Hua, S Lee, S Kim, JH Kim, MH Park
  Deep learning-based coagulant dosage prediction for extreme events leveraging large-scale data
  JWPE, doi: https://doi.org/10.1016/j.jwpe.2024.105934.
• H Tang*, F Berto*, Z Ma, C Hua, K Ahn, J Park
  HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
  AAMAS 2024 (Poster).
• J Kim, C Hua, K Kim, S Lee, G Oh, MK Park, S Kim
  Optimizing coagulant dosage using deep learning models with large-scale data
  Chemosphere, doi: https://doi.org/10.1016/j.chemosphere.2023.140989.

• F Berto*, C Hua*, J Park*, M Kim, H Kim, J Son, H Kim, J Kim, J Park
  RL4CO: a Unified Reinforcement Learning for Combinatorial Optimization Library.
  NeurIPS 2023 Workshop (Oral).
• C Hua*, F Berto*, M Poli, S Massaroli, J Park
  Learning Efficient Surrogate Dynamic Models with Graph Spline Networks.
  NeurIPS 2023 (Poster).
• S Lee, J Kim, C Hua, S Kim, MH Park
  Comparing artificial and deep neural network models for prediction of coagulant amount and settled water turbidity: Lessons learned from big data in water treatment operations.
  JWPE, doi: https://doi.org/10.1016/j.jwpe.2023.103949.
• S Lee, J Kim, C Hua, MH Park, S Kim
  Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model
  Water Research, doi: https://doi.org/10.1016/j.watres.2023.119665.

• C Hua
  Efficient continuous spatio-temporal physics simulation with graph spline networks.
  KAIST Open Access (Master Dissertation).
• C Hua*, F Berto*, M Poli, S Massaroli, J Park
  Efficient Continuous Spatio-Temporal Simulation with Graph Spline Networks.
  ICML 2022 Workshop (Oral).

* = equal contribution