Yoonsuck Choe

About

Yoonsuck Choe is a Professor in the Department of Computer Science & Engineering at Texas A&M University. He holds a Ph.D. and M.A. in Computer Sciences from the University of Texas at Austin, obtained in 2001 and 1995 respectively, and a B.S. in Computer Science from Yonsei University in 1993. His research interests include brain networks, neural intelligence, and multi-scale modeling of mouse brain networks. He is involved in projects such as the Brain Networks Lab, Neural Intelligence Lab, the Mouse Brain Networks project, and the Topographica cortical map simulator project. Dr. Choe has received numerous awards, including the 30th Anniversary Distinguished Alumni Award from Yonsei University, the College of Engineering Faculty Fellow at Texas A&M, and teaching excellence awards. His work focuses on understanding neural dynamics, cortical development, and brain-inspired computational models, contributing to the fields of neural networks, brain modeling, and artificial intelligence.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics
• Engineering
• Control engineering
• Human–computer interaction
• Simulation
• Algorithm
• Multimedia

Selected publications

• Advances in AI, neural networks, and brain computing: An introduction

  Elsevier eBooks · 2023-10-20 · 11 citations

  book-chapterSenior author
  PublisherDOI

• Utilizing Human Feedback in Autonomous Driving: Discrete vs. Continuous

  Machines · 2022 · 8 citations

  Senior authorCorresponding

  Deep reinforcement learning (Deep RL) algorithms are defined with fully continuous or discrete action spaces. Among DRL algorithms, soft actor–critic (SAC) is a powerful method capable of handling complex and continuous state–action spaces. However, a long training time and data efficiency are the main drawbacks of this algorithm, even though SAC is robust for complex and dynamic environments. One of the proposed solutions to overcome this issue is to utilize human feedback. In this paper, we in…

  PublisherOA PDFDOI

• Online Virtual Training in Soft Actor-Critic for Autonomous Driving

  2022 International Joint Conference on Neural Networks (IJCNN) · 2021 · 7 citations

  Senior authorCorresponding

  Deep Reinforcement Learning (RL) algorithms are widely being used in autonomous driving due to their ability to cope with unseen environments. However, in a complex domain like autonomous driving, these algorithms need to explore the environment enough to be able to converge. Therefore, these algorithms are faced with the problem of long training times and large amounts of data. In addition, using deep RL algorithms in areas that safety is an important factor such as autonomous driving can lead…

  DOI

• Reinforcement Learning May Demystify the Limited Human Motor Learning Efficacy Due to Visual-Proprioceptive Mismatch

  International Journal of Neural Systems · 2024-04-05 · 5 citations

  articleOpen access

  Vision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm and the simplified biomechanical elbow joint model were employed to mimic the motor learning…

  PublisherDOI

• AdjointBackMap: Reconstructing effective decision hypersurfaces from CNN layers using adjoint operators

  Neural Networks · 2022-07-03 · 3 citations

  articleSenior author
  PublisherDOI

Recent grants

• CRCNS: Data Sharing: Open Web Atlas for High-Resolution 3D Mouse Brain Data

  NSF · $207k · 2012–2015

• NIH Grant R01NS054252

  NIH · $962k · 2010

• Enhanced Knife-Edge Scanning Microscopy for Sub-micrometer Imaging of Whole Small Animal Organs

  NSF · $502k · 2013–2017

Frequent coauthors

• David Mayerich

  University of Houston

  31 shared

• John Keyser

  Texas A&M University

  29 shared

• Jaerock Kwon

  26 shared

• Louise C. Abbott

  23 shared

• Yingwei Yu

  Wuhan Textile University

  18 shared

• Heeyoul Choi

  15 shared

• Péter Érdi

  12 shared

• Huei‐Fang Yang

  11 shared

Education

• Ph.D., Computer Sciences

  The University of Texas at Austin

  2001

• M.A., Computer Sciences

  The University of Texas a Austin

  1995

• B.S., Computer Science

  Yonsei University

  1993

Awards & honors

• 30th Anniversary Distinguished Alumni Award (2013), Departme…
• Charles H. Barclay, Jr. ’45 Fellow (College of Engineering F…
• Graduate Faculty Teaching Excellence Award, Department of Co…
• Best Student Paper Award IEEE CIMSVP 2009
• Best Scientific Paper Award ICPR 2008

Similar researchers at Texas A&M University

• Thomas R. Ioergerh-60
• Shuiwang Jih-57
• Jianer Chenh-49