Jongeun Choi

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Yonsei University

South Korea

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About

Professor Jongeun Choi is a faculty member at Yonsei University in South Korea. His research focuses on areas such as robot learning and control, rehabilitation robotics, and human motion prediction, as evidenced by his recent publications. He has contributed significantly to the development of socially integrated wearable robots and innovative approaches in physical therapy. His work also includes exploring explainable deep learning techniques for healthcare applications.

Recent Grants

Grant: Close

Student Travel Support for 2010 Inverse Problems Symposium, June 6-8, at Michigan State University

Open Date: 2010-05-01

Close Date: 2011-04-30

Grant: Close

CAREER: Multi-Agent Systems and Gaussian Processes: Applications in Environmental Sciences

Open Date: 2009-01-15

Close Date: 2015-12-31

Articles (21)

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Collaborators (6)

Roberto Horowitz

Professor

University of California, Berkeley

UNITED STATES

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Jacek Cholewicki

Professor

Michigan State University

UNITED STATES

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Jin-Won Noh

Yonsei University

SOUTH KOREA

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John Popovich

Michigan State University

UNITED STATES

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Masayoshi Tomizuka

Professor

University of California

UNITED STATES

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Jin-Woo Kim

Associate Professor

Ewha Womans University

SOUTH KOREA

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Jongeun Choi

Research Interests

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About

Recent Grants

Articles (21)

Explainable Deep Learning Approaches for Risk Screening of Periodontitis

ENHANCEMENT OF SOCIAL INTEGRATION IN 192 INDIVIDUALS WITH WALKING DISABILITIES BY REHABILITATION ROBOTS

SE(3)-equivariant Robot Learning and Control: A Tutorial Survey

SURVEY ON SOCIALLY INTEGRATED WEARABLE GAIT ROBOTS IN 192 CHILDREN AND ADULTS WITH GAIT DYSFUNCTION: A NEW ROBOT STRATEGY AND DESIGN

Data-Driven Driver Training via Counterfactual and Language-Based Guidance in Racing Scenarios

Contact-Rich SE(3)-Equivariant Robot Manipulation Task Learning via Geometric Impedance Control

Human Motion Prediction: Assessing Direct and Geometry-Aware Approaches in 3D Space

Scale-Equivariant Object Perception for Autonomous Driving

Identifying optimal candidates and interventions in physical therapy and exoskeletal and end-effector robot-assisted gait training for balance, gait, and cognition: A longitudinal study of 190 patients with stroke

Learning Expert-Level Racing Strategies Via Scheduled Cost Functions in Model Predictive Control

Distributional and hierarchical reinforcement learning for physical systems with noisy state observations and exogenous perturbations

Output feedback control design for quadrotor using recursive least square dynamic inversion

Explainable deep learning-based clinical decision support engine for MRI-based automated diagnosis of temporomandibular joint anterior disk displacement

Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation

Suspension Control Strategies Using Switched Soft Actor-Critic Models for Real Roads

Integration and Evaluation of an Immersive Virtual Platform

Differentiable Moving Horizon Estimation for Vehicle Kinematics via Learning Covariance Matrices

Safety-Critical Control With Nonaffine Control Inputs Via a Relaxed Control Barrier Function for an Autonomous Vehicle

Layered-Cost-Map-Based Traffic Management for Multiple AMRs via a DDS

Regularized nonlinear regression for simultaneously selecting and estimating key model parameters: Application to head-neck position tracking

Hierarchical Primitive Composition: Simultaneous Activation of Skills With Inconsistent Action Dimensions in Multiple Hierarchies

Collaborators (6)

Roberto Horowitz

Jacek Cholewicki

Jin-Won Noh

John Popovich

Masayoshi Tomizuka

Jin-Woo Kim

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