Jinwoo Jeong

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Education

Career

Research Experience

Crosswalk Task: A naturalistic reinforcement learning paradigm for characterizing real-world risky behavior

Jinwoo Jeong, Woo-Young Ahn

• Developed a custom GymnasiumTowers, M., Kwiatkowski, A., Terry, J., et al. (2024). Gymnasium: A standard interface for reinforcement learning environments. arXiv. environment and a new real-time decision-making task for cognitive research.
• Collected and analyzed behavioral data from 300+ participants in an web-based experiment using Prolific.
• Trained deep inverse reinforcement learningFu, J., Luo, K., & Levine, S. (2018). Learning robust rewards with adversarial inverse reinforcement learning. In Proceedings of the International Conference on Learning Representations. models to derive context-specific indicators of risk-taking.

Conferences: OHBM 2026Organization for Human Brain Mapping 2026 (Bordeaux, France) (posterOpen in another tab), CPC 2026Computational Psychiatry Conference 2026 (New Haven, USA) (poster), KSDT 2026Korean Society for Digital Therapeutics 2026 Spring Conference (Jeju, South Korea) (oral)

Hierarchical Bayesian Analysis on Hierarchical Gaussian Filter

Jinwoo Jeong, Juha Lee, Yusom Jo, Woo-Young Ahn

• Implemented Hierarchical Gaussian FilterMathys, C., Daunizeau, J., Friston, K. J., & Stephan, K. E. (2011). A Bayesian foundation for individual learning under uncertainty. Frontiers in human neuroscience, 5, 39. in the hBayesDM library to enable hierarchical bayesian analysis via MCMC.
• Validated parameter identifiability and estimation reliability via parameter recovery experiments.

Conferences: CPC 2025Computational Psychiatry Course 2025 (Zurich, Switzerland) (workshop, blog articleArticle at hBayesDM library website)

Overestimated Volatility and Inflexible Belief Updating Underpin Aberrant Decision Making in Type-I Bipolar Disorder

Juha Lee*, Jinwoo Jeong*, Jerome R. Busemeyer, Brian F. O'Donnell, Woo-Young Ahn

• Hierarchical Gaussian FilterMathys, Christoph and Daunizeau, Jean and Friston, Karl J and Stephan, Klaas Enno (2011). A Bayesian foundation for individual learning under uncertainty. Frontiers in human neuroscience 5, 39. model with volatility-dependent behavioral stochasticity was applied to probablisitic reversal learning tasks to analyze volatility perception.
• Individuals with type-I Bipolar Disorder overestimated environmental volatility and were more reluctant to update their beliefs based on the result of their decisions.

Conferences: CPC 2026Computational Psychiatry Conference 2026 (New Haven, USA) (poster)

Developing Rapid and Reliable Behavioral Paradigms Using Adaptive Design Optimization

Sangho Lee*, Jeongyeon Shin*, Jinwoo Jeong, Jooyeon Jamie Im, Elena Psederska, Jasmin Vassileva, Mark A. Pitt, Woo-Young Ahn

• Based on 200+ participant data from four sites, validated that Adaptive Design OptimizationMyung, J. I., Cavagnaro, D. R., & Pitt, M. A. (2013). A tutorial on adaptive design optimization. Journal of mathematical psychology, 57(3-4), 53-67.-based behavioral tasks can achieve consistent results in less time than traditional fixed-trial tasks.

Conferences: CPC 2025Computational Psychiatry Course 2025 (Zurich, Switzerland) (poster)

Investigating the effects of environmental stochasticity on real-time decision-making in a naturalistic driving task using inverse reinforcement learning

Chae-Youn Chung, Sang Ho Lee, Jinwoo Jeong, Min-Hwan Oh, Woo-Young Ahn

• Developed and validated a dynamic highway task in which players overtake other vehicles while responding to sudden cut-ins.

Conferences: SfN 2025Society for Neuroscience 2025 (San Diego, USA) (poster)

Industry Experience

Skills

• Programming Languages: Python, JavaScript, Java, Go, C++
• ML & RL: scikit-learn, PyTorch, GymnasiumTowers, M., Kwiatkowski, A., Balis, J., De Cola, G., Deleu, T., Goulão, M., ... & Younis, O. G. (2026). Gymnasium: A standard interface for reinforcement learning environments. Advances in Neural Information Processing Systems, 38., imitation
• Computational Modeling: Stan, R, MATLAB, hBayesDM
• Web & Backend: React, Spring, MySQL
• DevOps & Infrastructure: AWS, Terraform, Docker, Kubernetes