Taijing Chen ⚡️
Hey there 👋
Taijing Chen
I'm a Computer Science PhD student at the University of Texas-Austin. I'm fortunately working with Dr. Joydeep Biswas, Dr. Roberto Martin-Martin, and Dr. Georgios Pavlakos. Before that, I obtained my B.S. in Computer Science at the University of Wisconsin-Madison. I worked in the the Speech Motor Action + Control Lab, part of the Speech Motor Neuroscience Group at UW-Madison. I was honored to work with Dr. Ben Parrell and Dr. Adam Lammert.
Currently, my research focuses on mobile manipulation, with a particular interest in long-term household robots. I develop my research on my earlier works in long-term SLAM and human motor control modeling. I have a broad interest in robotics, machine learning, and computational neuroscience and cognitive science. I'm interested in how humans/machines perceive the world, make decisions under uncertainty, and leverage on the acquired information to adapt to the dynamic, uncertain environments.
In my spare time, I love reading about history, psychology, physics and technologies. I also greatly enjoy music, arts, and anime.
Let's connect!
Publications
Sensorimotor Adaptation to a Nonuniform Formant Perturbation Generalizes to Untrained Vowels

Sensorimotor Adaptation to a Nonuniform Formant Perturbation Generalizes to Untrained Vowels
NEW

B. Parrell, C. Niziolek, T. Chen | J. Neurophysiol. 2024

Demonstrate that adaptation to nonuniform auditory perturbations spreads to untrained vowels, revealing local generalization of speech sensorimotor learning.
ObVi-SLAM: Long-Term Object Visual SLAM

ObVi-SLAM: Long-Term Object Visual SLAM
NEW

A. Adkins, T. Chen, and J. Biswa | RA-L 2024

Introduced ObVi-SLAM to overcome Visual SLAM challenges by leveraging the best of feature-based and object-based V-SLAM.
Multi-Factor Visual SLAM Failures

Multi-Factor Visual SLAM Failures

A. Adkins*, T. Chen*, and S. Modak*, and J. Biswas | RSS Workshop 2023

Examined multi-factor Visual SLAM failures in everday mobile service robots.
Probabilistic Object Maps for Long-Term Robot Localization

Probabilistic Object Maps for Long-Term Robot Localization

A. Adkins, T. Chen, and J. Biswas | IROS 2022

Introduced probabilistic object maps (POMs) and its localization module for globally consistent localization challenging, highly-dynamic real-world environments such as parking lots
Modeling Sensorimotor Adaptation in Speech Through Alterations to Forward and Inverse Models

Modeling Sensorimotor Adaptation in Speech Through Alterations to Forward and Inverse Models

T. Chen, A. Lammert, and B. Parrell | INTERSPEECH 2021

Introduced a neural-network based model to simulate speech sensorimotor adaptation whose results replicated the ones from behavioral experiments
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