Jung-Hoon Cho

I am a Ph.D. candidate in the Department of Civil and Environmental Engineering and Laboratory for Information and Decision Systems (LIDS) at Massachusetts Institute of Technology (MIT), advised by Prof. Cathy Wu.

My research lies at the intersection of machine learning and smart transportation. I develop reinforcement learning methods that make transportation systems work in the real world, where human behavior and environmental variability make reliable decision-making challenging. Applications include shared autonomy, advisory autonomy, eco-driving, incentive design, mixed-autonomy traffic, and agentic modeling.

I received my M.S. and B.S. in Civil and Environmental Engineering from Seoul National University, where I worked with Prof. Dong-Kyu Kim and Prof. Seung-Young Kho.

Email / CV / Google Scholar / GitHub / LinkedIn / ORCID

News

Upcoming	I will give a talk at Vanderbilt Mobility Accelerator (Sep 2026, Remote). I will be attending: ITSC (Sep 2026, Naples, Italy) · INFORMS Annual Meeting (Nov 2026, San Francisco, USA); TRB Annual Meeting (Jan 2027, Washington DC, USA); ASCE 2027 (Mar 2027, Philadelphia, USA). If you'll be there, I'd love to grab a coffee ☕ — please reach out!
Aug 2026	I gave a talk at RTRI on "Data Science for Transportation Systems" and at JR East on "Schedule-Responsive Energy-Efficient Train Control".
Aug 2026	I presented our paper "AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?" at BTR 2026.
Aug 2026	I gave a talk at KAIST CEE on "How Much Intelligence Does a Transportation System Actually Need?".
Jul 2026	Our paper on "A Roadmap for Climate-Relevant Robotics Research" was accepted in Foundations and Trends in Robotics.
Jun 2026	Our paper on "Large Language Models for Travel Behavior Prediction" was accepted in TRIP.
Jun 2026	I presented our paper "Formalizing Task-Space Complexity for Zero-Shot Generalization" at L4DC 2026.
Jun 2026	I presented our T-RO paper "Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy" at ICRA 2026.
May 2026	I presented our paper "Route Recommendations for Traffic Management Under Learned Partial Driver Compliance" at ACC 2026.
May 2026	Our abstract on "Auditable Engagement Design for Participation-Sensitive Transportation Planning" was accepted at ASCE 2027.
May 2026	I gave a talk at Seoul National University TRAIL Forum.
May 2026	It is a great honor to be selected as SCIPE Chishiki AI Graduate Student Fellow.
May 2026	Our paper on AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning? was accepted at ITSC 2026. See you in Naples, Italy!
Jan 2026	We presented our poster "Structure Detection for Contextual Reinforcement Learning" at AAAI 2026.
Jan 2026	Our paper on "Formalizing Task-Space Complexity for Zero-shot Generalization" was accepted at L4DC 2026.
Oct 2025	Our paper on Eco-driving Incentive Mechanisms was accepted in IEEE TCNS.
Oct 2025	Our paper on Temporal Transfer Learning for Advisory Autonomy was accepted in IEEE T-RO.
Aug 2025	I gave a talk at Cornell IDS Lab on Model-Based Transfer Learning for Contextual Dynamical Systems.
Jun 2025	Our paper on Nah Bandit for User Non-compliance was accepted in IEEE TCNS.
Jan 2025	Our paper on RL for Robust Advisories was accepted in IEEE T-ITS.
Dec 2024	We presented our poster on Model-Based Transfer Learning for Contextual RL at NeurIPS 2024.
Nov 2024	Our paper featured by MIT News and SciTechDaily!
Apr 2024	Passed General Exam at MIT CEE.
Mar 2024	Our paper on Expert with Clustering was accepted at L4DC 2024.
Feb 2024	Our paper on Incentive Design for Eco-driving was accepted at ECC 2024.
Feb 2024	It is a great honor to receive the fellowship to join the 2024 IEEE ITSS WiE/YP Workshop and Research Forum. See you in Seoul!
Jan 2024	I gave a talk at University of Seoul on Learning for Traffic Flow Optimization.
Aug 2022	Joined Wu Lab at MIT!
May 2022	Received Kwanjeong Scholarship from the Kwanjeong Educational Foundation.

Research

Representative papers are highlighted. Papers in reverse chronological order. († equal contribution, * corresponding author)

Filter:

Area:

	Where to Deploy Eco-Driving: Recasting a City-Scale Benchmark as Evidence for Agency Investment Decisions Jung-Hoon Cho, Junyi Ji, Cathy Wu Under review, 2026 Transportation Machine Learning
	Schedule-Responsive Energy-Efficient Train Control: Quantifying the Value of Online Information Jung-Hoon Cho, Anson Stewart, Yasuyori Sato, Cathy Wu Under review, 2026 Transportation Control / Robotics
	Task Specialization Fine-Tuning for Contextual Reinforcement Learning Jianan Zhou, Jung-Hoon Cho, Tianyue Zhou, Han Zheng, Jie Zheng, Roy Dong, Yining Ma, Cathy Wu Under review, 2026 arXiv Machine Learning
	AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning? Jung-Hoon Cho, Cathy Wu [ITSC 2026] IEEE International Conference on Intelligent Transportation Systems (Oral) arXiv [BTR 2026] Bridging Transportation Researchers (Oral) Machine Learning Transportation
	A Roadmap for Climate-Relevant Robotics Research Alan Papalia^†, Charles Dawson^†, Laurentiu L. Anton, Norhan Magdy Bayomi, Bianca Champenois, Jung-Hoon Cho, Levi Cai, Joseph DelPreto, Kristen Edwards, Bilha-Catherine Githinji, Cameron Hickert, Vindula Jayawardana, Matthew Kramer, Shreyaa Raghavan, David Russell, Shide Salimi, Jingnan Shi, Soumya Sudhakar, Yanwei Wang, Shouyi Wang, Luca Carlone, Vijay Kumar, Daniela Rus, John E. Fernandez, Cathy Wu, George Kantor, Derek Young, Hanumant Singh To appear in Foundations and Trends in Robotics, 2026 arXiv Transportation Control / Robotics
	Large Language Models for Travel Behavior Prediction Baichuan Mo, Hanyoung Xu^, Ruoyun Ma, Jung-Hoon Cho, Dingyi Zhuang, Xiaotong Guo, Jinhua Zhao [TRIP 2026]* Transportation Research Interdisciplinary Perspectives paper [TRC-30 2024] Conference in Emerging Technologies in Transportation Systems (Poster) abstract Machine Learning Transportation
	Formalizing and Estimating Task-Space Complexity for Zero-shot Generalization Jung-Hoon Cho, Heling Zhang, Siqi Du, Roy Dong, Cathy Wu Under review, 2026 [L4DC 2026] Learning for Dynamics and Control Conference (Poster) paper Machine Learning Control / Robotics
	Route Recommendations for Traffic Management Under Learned Partial Driver Compliance Heeseung Bang^†, Jung-Hoon Cho^†, Cathy Wu, Andreas A. Malikopoulos [ACC 2026] American Control Conference (Oral) paper / arXiv Transportation Control / Robotics
	DeciGen: Generative Multi-Criteria Decision Making in Transportation Planning Eun Hak Lee, Jung-Hoon Cho, Zhengzhong Tu, Hyunsoo Yun Under review, 2026 Machine Learning Transportation
	Structure Detection for Contextual Reinforcement Learning Tianyue Zhou^†, Jung-Hoon Cho^†, Cathy Wu [AAAI 2026] AAAI Conference on Artificial Intelligence (Poster) (acceptance rate: 17.6%) paper / arXiv / webpage Machine Learning
	Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy Jung-Hoon Cho, Sirui Li, Jeongyun Kim, Cathy Wu [IEEE T-RO 2026] IEEE Transactions on Robotics paper Machine Learning Transportation
	Eco-driving Incentive Mechanisms for Mitigating Emissions in Urban Transportation M. Umar B. Niazi, Jung-Hoon Cho, Munther A. Dahleh, Roy Dong, Cathy Wu [IEEE TCNS 2026] IEEE Transactions on Control of Network Systems arXiv [ECC 2024] European Control Conference (Oral) arXiv Transportation Control / Robotics
	Learning-Based Incentive Design for Promoting Eco-Driving in Urban Transportation Jung-Hoon Cho, M. Umar B. Niazi, Siqi Du, Roy Dong, Cathy Wu In preparation, 2025 [TRC-30 2024] Conference in Emerging Technologies in Transportation Systems (Podium) abstract Machine Learning Transportation
	Nah Bandit for Modeling User Non-compliance in Recommendation Systems Tianyue Zhou, Jung-Hoon Cho, Cathy Wu [IEEE TCNS 2025] IEEE Transactions on Control of Network Systems paper / webpage Machine Learning Control / Robotics
	Reinforcement Learning for Robust Advisories under Driving Compliance Errors Jeongyun Kim, Jung-Hoon Cho, Cathy Wu [IEEE T-ITS 2025] IEEE Transactions on Intelligent Transportation Systems paper Machine Learning Transportation
	Model-Based Transfer Learning for Contextual Reinforcement Learning Jung-Hoon Cho, Vindula Jayawardana, Sirui Li, Cathy Wu [NeurIPS 2024] Conference on Neural Information Processing Systems (Poster) (acceptance rate: 25.8%) arXiv / webpage [EWRL 2024] European Workshop on Reinforcement Learning (Poster) OpenReview Media: MIT News \| SciTechDaily Machine Learning Transportation
	Cooperative Advisory Residual Policies for Congestion Mitigation Aamir Hasan, Neeloy Chakraborty, Haonan Chen, Jung-Hoon Cho, Cathy Wu, Katherine Driggs-Campbell [ACM JATS 2024] ACM Journal on Autonomous Transportation Systems paper [IEEE ITSC 2023] IEEE International Conference on Intelligent Transportation Systems (Oral) paper Machine Learning Transportation Control / Robotics
	Expert with Clustering: Hierarchical Online Preference Learning Framework Tianyue Zhou, Jung-Hoon Cho, Babak Rahimi Ardabili, Hamed Tabkhi, Cathy Wu [L4DC 2024] Learning for Dynamics and Control Conference (Poster) arXiv Machine Learning Transportation Control / Robotics
	Multi-scale Causality Analysis between COVID-19 Cases and Mobility Level Using Ensemble Empirical Mode Decomposition and Causal Decomposition Jung-Hoon Cho, Dong-Kyu Kim, Eui-Jin Kim^* [Physica A 2022] Physica A: Statistical Mechanics and its Applications paper Transportation
	Developing Variable Speed Limit Control and Ramp Metering Strategy for Freeways Using Deep Reinforcement Learning Jung-Hoon Cho [M.S. Thesis 2022] Seoul National University Machine Learning Transportation Control / Robotics
	A Comparative Analysis of Usage Patterns of Bike-sharing and E-scooter-sharing in Seoul Jung-Hoon Cho, Seung Woo Ham, Eui-Jin Kim, Dong-Kyu Kim^* [TRBAM 2022] Transportation Research Board 101st Annual Meeting (Poster) Transportation
	Efficiency Comparison of Public Bike-sharing Repositioning Strategies Based on Predicted Demand Patterns Jung-Hoon Cho, Young-Hyun Seo, Dong-Kyu Kim^* [TRR 2021] Transportation Research Record paper [B.S. Thesis 2020] Seoul National University Transportation
	Enhancing the Accuracy of Peak Hourly Demand in Bike-Sharing Systems Using a Graph Convolutional Network with Public Transit Usage Data Jung-Hoon Cho, Seung Woo Ham, Dong-Kyu Kim^* [TRR 2021] Transportation Research Record paper [TRBAM 2021] Transportation Research Board 100th Annual Meeting (Poster) Machine Learning Transportation
	Spatiotemporal Demand Prediction Model for E-scooter Sharing Services with Latent Feature and Deep Learning Seung Woo Ham, Jung-Hoon Cho, Sangwoo Park, Dong-Kyu Kim^* [TRR 2021] Transportation Research Record paper Machine Learning Transportation

Education

Massachusetts Institute of Technology Ph.D. in Civil and Environmental Engineering, Sep 2022 - present Advisor: Prof. Cathy Wu	Cambridge, MA
Seoul National University M.S. in Civil and Environmental Engineering, Mar 2020 - Feb 2022 Advisor: Prof. Dong-Kyu Kim and Prof. Seung-Young Kho	Seoul, Korea
Seoul National University B.S. in Civil and Environmental Engineering, Mar 2014 - Feb 2020 Advisor: Prof. Dong-Kyu Kim Cum laude, Best Thesis Award (2nd place)	Seoul, Korea

Experience

Massachusetts Institute of Technology Graduate Research Assistant (Laboratory for Information & Decision Systems (LIDS)) Aug 2022 - present PI: Prof. Cathy Wu	Cambridge, MA
Seoul National University Researcher (Institute of Engineering Research) Apr 2022 - Jul 2022 PI: Prof. Dong-Kyu Kim	Seoul, Korea
Seoul National University Graduate Researcher (Transportation Research Laboratory) Feb 2020 - Feb 2022 PI: Prof. Dong-Kyu Kim and Prof. Seung-Young Kho	Seoul, Korea
VCNC Inc. Value Innovator (Intern) (TADA Business Development Team) Jun 2019 - Aug 2019	Seoul, Korea

Honors & Awards

SCIPE Chishiki AI Fellowship, NSF & UT Austin	2026 - 2027
Kwanjeong Scholarship, Kwanjeong Educational Foundation	2022 - 2026
Fellowship Program for Promoting Diversity and Leadership in ITS, IEEE ITSS	2024
Speedwell Foundation and the Robert E. Thurber Fellowship, MIT CEE	2022 - 2023
KOTAA TRB Annual Meeting Travel Grant, KOTAA	2022, 2021
Best B.S. Thesis Paper Award, SNU CEE	2019
Best Portfolio Award, SNU CEE	2019
Best Undergraduate Paper Award, Korean Institute of Intelligent Transportation Systems	2019
Academic Excellence Award (GPA 4.30/4.30), SNU CEE	2018

Invited Talks

East Japan Railway Company (JR East) Seminar: "Schedule-Responsive Energy-Efficient Train Control"	Aug 2026
Railway Technical Research Institute (RTRI) Seminar: "Data Science for Transportation Systems"	Aug 2026
KAIST CEE Seminar: "How Much Intelligence Does a Transportation System Actually Need?"	Aug 2026
Seoul National University School of Transdisciplinary Innovations Seminar: "Resonance: Language and Communication for Convergence"	May 2026
MIT LIDS Student Conference Seminar: "Formalizing and Estimating Task-Space Complexity for Zero-shot Generalization"	Jan 2026
Cornell IDS Lab Seminar: "Model-Based Transfer Learning for Contextual Dynamical System"	Aug 2025
MIT LIDS Autonomy Tea Talk Seminar: "Model-Based Transfer Learning for Contextual Reinforcement Learning"	Nov 2024
University of Seoul Department of Transportation Engineering Seminar: "Learning for Traffic Flow Optimization"	Jan 2024
KOSEN Seminar: "Studying in a U.S. Graduate School: Preparing for a New Start"	Jun 2023
Unjung High School Seminar: "Smart Mobility System Using Machine Learning and Data Science"	Jul 2022, Jul 2021

Teaching

1.041/1.200: Transportation: Foundations and Methods – Teaching Assistant, MIT	Spring 2026
Introduction to Traffic Operation – Teaching Assistant, SNU	Spring 2021, 2020
Public Transportation Engineering – Teaching Assistant, SNU	Spring 2021
Integrated Design of Civil Engineering Systems – Teaching Assistant, SNU	Fall 2020
Traffic Engineering and Laboratory – Teaching Assistant, SNU	Fall 2020

Academic Service

Journal Reviewer:
Transportation: Transportation Research Part B: Methodological (TR-B), Transportation Research Part C: Emerging Technologies (TR-C), Transportation Research Part E: Logistics and Transportation Review (TR-E), IEEE Transactions on Intelligent Transportation Systems (T-ITS), Transportation Research Records (TRR), Transportation, Transport Policy, Data Science for Transportation, Discover Cities, Journal of Korean Society of Transportation.
Others: IEEE Transactions on Control Systems Technology (TCST), IEEE Transactions on Cognitive and Developmental Systems (TCDS), Scientific Report, Physica A, Knowledge and Information Systems, Discover Computing, Discover Artificial Intelligence, Journal of Big Data, Journal of Geovisualization and Spatial Analysis, International Journal of Data Science and Analytics, Expert Systems With Applications (ESWA).

Conference Reviewer:
  AI/ML: AAAI (2027, 2026), RLC (2025), ICML (2025), AISTATS (2025), NeurIPS (2025, 2024), EWRL (2026).
  Transportation: TRBAM (2027, 2026, 2025), ITS WC (2026, 2025, 2024), IEEE ITSC (2026, 2025, 2024), IEEE IV (2026, 2024), BTR (2026).
  Control/Robotics: ACC (2026, 2025), ECC (2024), ICRA (2026, 2025).

Mentoring: Mentor for MIT-UF-NEU Joint Summer Research Camp (2026 - Present), Mentor for MIT CEE Graduate Application Assistance Program (GAAP) (2024 - Present), Mentor for MIT CEE Peer Mentorship Program (2024 - Present), Mentor for MIT CEE Mini-UROP (2026), Organizing Committee and Mentor for ITSS Incubator (2024).

Others: Student Volunteer for INFORMS TSL Conference (2026), Student Volunteer for ACC (2026), Judge for UPenn Ecoventure Challenge (2026), Organizing Committee for MIT LIDS & Stats Tea Talks (2023 - 2024).

Students Mentored

Graduate Students

Joshua Bartnicki (UT Austin, MIT-UF-NEU Summer Camp), Sanjula Jayawardana (ITSS Incubator, currently La Trobe University PhD student), George Cao (MIT MEng)

Undergraduate Students

Haoyang (Mike) Liu (Washington University in St. Louis, MIT-UF-NEU Summer Camp), George Cao (MIT UROP & 6.7920, currently MIT MEng student), Andrew Zheng (MIT 6.7920), Ruth Lu (MIT UROP, incoming University of Cambridge PhD student), Anniston Pierce (MIT Mini UROP), Tan Le (MIT Mini UROP), Stephen Andrews (MIT UROP), Tianyue Zhou (ShanghaiTech, currently MIT PhD student), Rajeev Datta (Caltech, currently Cornell PhD student)

Volunteering & Service

Teach For Korea, Seoul, Korea

Planning and Operation Manager (HQ)	2019 - 2020
Principal Teacher (Seongbuk School)	2016
Head of Financial Administration (HQ)	2015 - 2016
Led classes in Mathematics, Physics, and Chemistry (13 months, 550+ hours) (Seongbuk School)	2015 - 2016

Republic of Korea Army, Yeoncheon, Korea

Mandatory Military Service

2016 - 2018

Design adapted from Dingyi Zhuang, Vindula Jayawardana, and Jon Barron. Last updated: September 2026.