• Federico B†, Chuanbo H†, Junyoung P†, Minsu K, Hyeonah K, Jiwoo S, Haeyeon K, Joungho K, Jinkyoo P
  RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark.
  arXiv preprints.
• Federico B†, Chuanbo H†, Laurin L†, Jiwoo S, Junyoung P, Kyuree A, Changhyun K, Lin X, Jinkyoo P
  PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization
  arXiv preprints.
• Haoran Y, Jiarui W, Zhiguang C, Federico B, Chuanbo H, Haeyeon K, Jinkyoo P, Guojie S
  Large Language Models as Hyper-Heuristics for Combinatorial Optimization
  arXiv preprints.
• Jiachen L†, Chuanbo H†, Jinkyoo P, Hengbo M, Victoria D, Mykel J K
  EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction.
  arXiv preprints.

• Federico B†, Chuanbo H†, Nayeli Gast Z, André H, Niels W, Leon L, Kevin T, Jinkyoo P
  RouteFinder: Towards foundation models for vehicle routing problems
  ICML 2024 Workshop (Oral).
• Jiwoong K, Chuanbo H, Subin L, Seoktae K, Joo-Hyon K, Mi-Hyun P
  Deep learning-based coagulant dosage prediction for extreme events leveraging large-scale data
  JWPE, doi: https://doi.org/10.1016/j.jwpe.2024.105934.
• Huijie T†, Federico B†, Zihan M, Chuanbo H, Kyuree A, Jinkyoo P
  HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
  AAMAS 2024 (Poster).
• Jiwoong K, Chuanbo H, Kyoungpil K, Subin L, Gunhak O, Mi-Kyun P, Seoktae K
  Optimizing coagulant dosage using deep learning models with large-scale data
  Chemosphere, doi: https://doi.org/10.1016/j.chemosphere.2023.140989.

• Federico B†, Chuanbo H†, Junyoung P†, Minsu K, Hyeonah K, Jiwoo S, Haeyeon K, Joungho K, Jinkyoo P
  RL4CO: a Unified Reinforcement Learning for Combinatorial Optimization Library.
  NeurIPS 2023 GLFrontiers Workshop (Oral).
• Chuanbo H†, Federico B†, Michael P, Stefano M, Jinkyoo P
  Learning Efficient Surrogate Dynamic Models with Graph Spline Networks.
  NeurIPS 2023 (Poster).
• Subin L, Jiwoong K, Chuanbo H, Seoktae K, Mi-Hyun P
  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.
• Subin L, Jiwoong K, Chuanbo H, Mi-Hyun P, Seoktae K
  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.

• Chuanbo H
  Efficient continuous spatio-temporal physics simulation with graph spline networks.
  KAIST Open Access (Master Dissertation).
• Chuanbo H†, Federico B†, Michael P, Stefano M, Jinkyoo P
  Efficient Continuous Spatio-Temporal Simulation with Graph Spline Networks.
  ICML 2022 AI4Science Workshop (Oral).

† = co-first author; * = co-corresponding author