Introduction

Jeongwhan Choi is a Ph.D. candidate advised by Prof. Noseong Park in the Dep. of Artificial Intelligence at Yonsei University.  His research interests span a variety of areas including graph neural networks, recommender systems, spatio-temporal forecasting, and differential equations. Recently, his focus has been on the development of graph-based deep learning methods inspired by differential equations in natural science, such as heat diffusion equations.

Previously, he completed his undergraduate studies at Jeonbuk National University (2016-2020), where he majored in Software Engineering. During this time, he had the opportunity to be mentored by Prof. Suntae Kim and Prof. Duksan Ryu.

My Erdős number is 4: Noseong Park → Sushil Jajodia → Yechezkel Zalcstein → Paul Erdős.

News

• 📈 Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations was accepted in  IEEE International Conference on Big Data (Big Data 2023).

• ⏲️ HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts was accepted in NeurIPS 2023 Workshop on Distribution Shifts (DistShift).

• 🕸️ QoS-Aware Graph Contrastive Learning for Web Service Recommendation was accepted in APSEC 2023.

• 📨 Blurring-Sharpening Process Models for Collaborative Filtering was invited to the "Top Conference Session" at KCC 2023.

• 🛣️ Graph Neural Rough Differential Equations for Traffic Forecasting was accepted in ACM Transactions on Intelligent Systems and Technology (TIST) (IF=10.489).

• 🕸️ I've joined as a contributor to the Graph User Group (GUG) community.

• 📨 GREAD was invited for SEA-CROGS, where Sandia National Labs and Pacific Northwest National Labs are collaborating to solve scientific problems with deep learning. Prof. Noseong Park, who is my research advisor, will present the paper in the 27th Jun via weekly webinars. 

• 🔝 My Research Work (LT-OCF) is featured in the "Top 23 Python recommender-system Projects" and "Top 14 collaborative-filtering Open-Source Projects" on LibHunt.

• 🍞 GREAD: Graph Neural Reaction-Diffusion Networks was accepted in ICML 2023.

• 🪓 Blurring-Sharpening Process Models for Collaborative Filtering was accepted in SIGIR 2023.

• 🌏 Climate Modeling with Neural Advection-Diffusion Equation was accepted in Knowledge and Information Systems (IF=2.531). 

• 🅿️ Prediction-based One-shot Dynamic Parking Pricing was accepted in CIKM 2022.

• 🏅 Graph Neural Controlled Differential Equations for Traffic Forecasting was selected for Innovation Awards at Yonsei University.

• 📨 Graph Neural Controlled Differential Equations for Traffic Forecasting was invited to the "Top Conference Session" at KCC 2022.

• 🔝 Graph Neural Controlled Differential Equations for Traffic Forecasting was selected for oral presentation in AAAI 2022. The top 4.21% (380/9,020) of papers were selected for oral presentation in AAAI 2022.

• 🚘 Graph Neural Controlled Differential Equations for Traffic Forecasting was accepted in AAAI 2022.

• 🤔  Linear, or Non-Linear, That is the Question! was accepted in WSDM 2022.

• 🌏  Climate Modeling with Neural Diffusion Equations was accepted in ICDM 2021.

• 🔖  LT-OCF: Learnable-Time ODE-based Collaborative Filtering was accepted in CIKM 2021.

Selected Publications (see all)

• Jeongwhan Choi and Noseong Park, "Graph Neural Rough Differential Equations for Traffic Forecasting", ACM Transactions on Intelligent Systems and Technology (TIST), 2023.

• Jeongwhan Choi, Seoyoung Hong, Noseong Park, and Sung-Bae Cho, "GREAD: Graph Reaction-Diffusion Networks," ICML 2023.

• Jeongwhan Choi, Seoyoung Hong, Noseong Park, and Sung-Bae Cho, "Blurring-Sharpening Process Models for Collaborative Filtering," SIGIR 2023.

• Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, and Noseong Park, "Graph Neural Controlled Differential Equations for Traffic Forecasting," AAAI 2022. 

• Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, Kookjin Lee, Dongeun Lee, and Noseong Park, "Climate Modeling with Neural Diffusion Equations", ICDM 2021.

• Jeongwhan Choi, Jinsung Jeon, and Noseong Park,  "LT-OCF: Learnable-Time ODE-based Collaborative Filtering", CIKM 2021.

Research Interests

• Artificial Intelligence

  • Graph Neural Networks

  • Recommender Systems

  • Spatiotemporal Forecasting

  • Differential Equations-based Deep Learning

• Software Engineering

  • Software Analytics & Software Defect Prediction

Research Experience

• Sep 2020 - Present, Graduate Student Researcher, BigDyL(Big Data Analytics Lab) (Advisor: Prof. Noseong Park)

• Jan 2020 – Sep 2020, Undergraduate Student Researcher, AI & SE Lab (Advisor: Prof. Duksan Ryu)

• Nov 2018 - Dec 2019, Undergraduate Student Researcher, Systems & Software Engineering Lab (Advisor: Prof. Suntae Kim)

Awards & Scholarships

• Jul 2022, Innovation Award, Yonsei University (Best paper in Dept. of Artificial Intelligence) [link]

• Feb 2021, Best Paper Awards, the 23rd Korea Conference on Software Engineering (KCSE 2021)

• Jun 2019, Best Paper Awards, Korean Institute of Information Technology

• 2018, The National Scholarship for Science and Engineering, KOSAF(Korea Student Aid Foundation)

  • This scholarship supports undergraduates with strong academic performance in science and engineering, with the purpose of developing future leaders in those fields.

Talks

• Jun 2023, Talk on 1st Seminar held by Graph User Group (GUG).

• Jun 2023, Invited talk on Top-conference session,  Korea Computer Congress (KCC 2023)

• Feb 2023, Talk on 2023 KSIAM AI Winter School held by Korean Society for Industrial and Applied Mathematics (KSIAM). [Slides]

• Oct 2022, Talk on 1st Workshop on AI held by Yonsei Univ. [Slides][Poster]

• Aug 2022, Poster presentation for AIGS Symposium 2022 held at the COEX Grand Ballroom. [Poster]

• Jul 2022, Invited talk on Top-conference session,  Korea Computer Congress (KCC 2022) [Slides]

• Nov 2021, Graph-based Collaborative Filtering and Neural ODEs, 한국인공지능학회(KAIA) | LG AI Research, (This talk is part of a tutorial called "Deep Learning Inspired by Differential Equation". ) [Slides]

Academic Services

• Reviewer in IRonGraphs 2024: First International Workshop on Graph-Based Approaches in Information Retrieval, co-located with ECIR 2024

• Reviewer in 4th Workshop on Graphs and More Complex Structures for Learning and Reasoning (GCLR) colocated with AAAI 2024

• Reviewer in SDM 2024

• Reviewer in AAAI 2024

• Reviewer in Applied Artificial Intelligence 2023 (2 times)

• Reviewer in KDD 2023

• Reviewer in Learning on Graph Conference 2022, 2023

• Reviewer in IEEE Transactions on Intelligent Transportation Systems 2022

• Reviewer in ICDM 2021, 2022