ETAIC Research Lab Members

Hongtei Eric Tseng, Ph.D.
Principal Investigator

Director of ETAIC Lab
National Academy of Engineering Member
Distinguished University Professor
hongtei.tseng[at]uta[dot]edu
Profile
0000-0001-5087-9592
Hongtei's Citations

Prof. Tseng is a Distinguished University Professor at the University of Texas at Arlington, and established the ETAIC (Embodied Technology for Autonomy, Intelligence, and Control) Research Lab. He is a member of the U.S. National Academy of Engineering, recognized for his contributions to intelligent vehicle control and automotive systems integration.

He received his B.S. in Mechanical Engineering from National Taiwan University in 1986, and his M.S. and Ph.D. from the University of California, Berkeley in 1991 and 1994, respectively.

After joining Ford Motor Company in 1994 as a product design engineer in chassis engineering, he transitioned in 1998 to Ford Research and Advanced Engineering, where he focused on vehicle system control. Over his 28-year career at Ford, he led the development of key technologies that were deployed across production vehicles. These include vehicle state estimation for roll stability control, fast skip-downshift logic for F-150’s 10-speed transmission, lateral control algorithms for BlueCruise lane centering, and trailer angle estimation for Pro Trailer Backup Assist. From 2017 to 2022, he served as Senior Technical Leader of Controls and Automated Systems, one of Ford’s highest technical positions.

He received seven Henry Ford Technology Awards, the company’s top technical honor, for innovations ranging from traction control to driveline torque management and trailer assist systems. In 2013, he was awarded the Control Engineering Practice Award by the American Automatic Control Council for impactful real-world control applications.
He joined UTA as the first faculty under the RISE 100 initiative, bringing decades of industrial expertise to academia and continuing his research at the intersection of control theory, robotics, and transportation systems.

Prof. Tseng holds over 100 U.S. patents, with around one-third implemented in production vehicles, and has published more than 150 technical papers, including several book chapters. His work has significantly advanced vehicle safety, automation, and intelligent mobility technologies.

Hao Zhang, Ph.D.

Associate Director of ETAIC Lab
Research Associate
zhang-h19@tsinghua.org.cn
haoz4@andrew.cmu.edu
Profile
0000-0001-5395-4017
Hao's Citations

Dr. Zhang serves as the Associate Director of the ETAIC Lab (Embodied Technology for Autonomy, Intelligence, and Control) at the University of Texas at Arlington, working with Prof. Eric Tseng, a member of the U.S. National Academy of Engineering. He is also a Research Associate in the Safe AI Lab at Carnegie Mellon University, working with Prof. Ding Zhao. Prior to this, he worked as a Research Fellow at Tsinghua University and a Visiting Researcher at University College London. He received his Ph.D. from the School of Vehicle and Mobility at Tsinghua University, co-advised by Prof. Zhi Wang and Prof. Shengbo Eben Li.

He was the recipient of the Outstanding Doctoral Dissertation Award, the Outstanding Ph.D. Graduate Award, and the “Shuimu Scholar” talent program at Tsinghua University in 2024. His doctoral research directly contributed to the successful industrial deployment of reinforcement learning methods in advanced driver-assistance systems and energy management systems, significantly improving safety, drivability, energy efficiency, and driving comfort, thereby enhancing the overall driving experience of connected and automated vehicles. Notably, the control systems he developed have been implemented in leading automotive companies such as BYD Auto, Dongfeng Motor, SAIC Motor, as well as in start-up automotive companies such as Hybot.

He has authored over 50 peer-reviewed SCI journal and conference papers and is a co-inventor on more than 20 patents. He serves as Guest Editor for several journals and as a member of the International Program Committee and Associate Editor for several conferences, including IEEE ITSC and IEEE IV. His current research focuses on multi-agent reinforcement learning theory, the integration of vision-language models with closed-loop control, and human–robot collaboration using game theory. He aims to advance human-centric, trustworthy AI agents for real-world deployment in autonomous systems.

John Chenxi Song

Graduate Student
Research Assistant at ETAIC
chs342@pitt.edu

Mr. John Song is a PhD student in Computer Science at the University of Texas at Arlington. He received his Master’s degree in Information Science from University of Pittsburgh, and has over 1.5 years of research experience in bioinformatics and applied artificial intelligence. His research interests lie at the intersection of machine learning, large language models, and AI-driven scientific discovery.

John has worked on several interdisciplinary projects involving artificial intelligence in healthcare and biomedical data analysis. He implemented a Java-based Bayesian Network model for influenza diagnosis, designed to assist healthcare professionals in making accurate clinical decisions by analyzing complex probabilistic relationships. In addition, he has contributed to projects leveraging large language models (LLMs) for medical applications, including prompt design for AI-assisted disease diagnosis.

His research experience also includes pathology image analysis using large-scale pre-trained models. He contributed to code development for analyzing whole-slide pathology images with the Prov-Gigapath model, enabling downstream tasks such as anomaly detection in medical imaging. As a research assistant in bioinformatics, he also worked extensively on data preprocessing and management, handling unstructured data such as medical text and whole-slide images as well as structured tabular data. John is broadly interested in AI for Science (AI4Science). He aims to investigate the foundational principles underlying modern AI systems while developing intelligent methods that can accelerate scientific discovery and improve real-world applications.

Rikkin Panchal

Graduate Student
Research Assistant at ETAIC
rxp6313@mavs.uta.edu

Mr. Panchal is a Graduate Research Assistant and M.S. thesis student in Electrical Engineering, specializing in autonomous vehicle research. His work focuses on artificial intelligence, machine learning, deep learning, reinforcement learning, computer vision, object detection, lane detection, semantic segmentation, SLAM, and real-time perception for self-driving systems. He develops intelligent navigation and control frameworks that combine data-driven learning with robust system behaviour.

His engineering experience includes embedded systems, bare-metal programming, real-time operating environments, robotic middleware, and sensor fusion for perception and control. He designs and implements full-stack autonomous systems that integrate AI algorithms with reliable low-level execution.

Previously, he contributed to research at the Indian Space Research Organisation (ISRO), where he worked on embedded software and Linux-based systems for mission-critical applications.

Aashi Ashokbhai Goyani

Graduate Student
Research Assistant at ETAIC
axg5247@mavs.uta.edu

Ms. Aashi Goyani is a Graduate Research Assistant and M.S. thesis student in the Department of Computer Science and Engineering at the University of Texas at Arlington. Her research focuses on deep learning and computer vision for autonomous systems, with particular emphasis on camera calibration, 3D reconstruction, and visual perception under real-world conditions.

She has hands-on experience in developing and deploying deep neural networks for object detection, semantic segmentation, and visual tracking. Her work integrates advanced calibration techniques with learning-based perception models to improve spatial understanding and system accuracy in robotics and autonomous platforms. Aashi is also experienced in multi-sensor data processing, including LiDAR-camera fusion and visual-inertial odometry, enabling precise localization and mapping in dynamic environments.

Her goal is to bridge the gap between perception algorithms and practical deployment, building intelligent systems that are both accurate and reliable for real-time applications.