Dr. Guo Cheng
Assistant Professor
Department of Computer Science
Texas A&M University–Corpus Christi
· Starting August 2026
Ph.D. in Computer Science, Purdue University, 2022
Previously: Machine Learning / Software Engineer at Bloomberg L.P., New York
Research Interests: Computer Vision · Deep Learning · Artificial Intelligence · Autonomous Driving
I am hiring Ph.D. students starting Fall 2026 / Spring 2027 / Fall 2027. If you are interested in computer vision, AI/ML, autonomous driving or robotics, please read the prospective students page and reach out.
About me
I am an incoming tenure-track Assistant Professor of Computer Science at Texas A&M University–Corpus Christi (TAMU-CC), starting in August 2026. My research is in computer vision, deep learning, and autonomous driving, with a particular focus on perception and scene understanding for safety-critical systems.
I received my Ph.D. in Computer Science from Purdue University in 2022, advised by Professor Jiangyu Zheng, with a dissertation titled "Sequential Semantic Segmentation of Streaming Scenes for Autonomous Driving." My work has been published in IEEE T-ITS, IEEE T-IV, ICPR, IV, and ITSC. I earned an M.S. in Statistics from Stony Brook University and a B.S. in Information and Computer Science from Wuhan Textile University. Prior to joining TAMU-CC, I was a Software Engineer at Bloomberg L.P. in New York.
At TAMU-CC, I am building a research group at the intersection of computer vision and autonomous systems. I am actively recruiting Ph.D. students. Please see the prospective students page.
Research interests
- Computer Vision — vision-language models (VLMs), image/video processing, scene understanding, semantic segmentation, 2D/3D object detection and tracking, image/video synthesis.
- Deep Learning — multimodal learning, efficient inference, CNN/Transformer models, diffusion models, reinforcement learning, transfer and few-shot learning.
- Artificial Intelligence — Generative AI, domain adaptation, RAG systems, fine-tuning and evaluation for applied NLP, deploying scalable and reliable ML/AI systems in safety-critical domains.
- Autonomous Driving & Intelligent Transportation — perception, sensor fusion, path and speed planning, edge deployment, robust vision under adverse conditions, vision-language-action.
News
- 2026 / 08Joining Texas A&M University–Corpus Christi as a tenure-track Assistant Professor of Computer Science. Recruiting Ph.D. students — details here.
- 2026 / 01New preprint: Semantic Misalignment in Vision-Language Models under Perceptual Degradation (arXiv:2601.08355).
- 2022 / 09Defended my Ph.D. dissertation at Purdue University: Sequential Semantic Segmentation of Streaming Scenes for Autonomous Driving.
- 2022 / 07Paper accepted at IEEE T-ITS: Sequential Semantic Segmentation of Road Profiles for Path and Speed Planning.
- 2022 / 05Paper accepted at ICPR 2022: SE3: Sequential Semantic Segmentation of Large Images with Minimum Memory.
Selected projects
Sequential Semantic Segmentation for Streaming Driving Scenes
Ph.D. dissertation work on memory-efficient, sequential semantic segmentation of high-resolution driving video, with applications to path and speed planning for autonomous vehicles. Published in IEEE T-ITS and ICPR.
Vision-Language Models under Perceptual Degradation
Recent preprint studying when and how VLMs lose semantic alignment under noise, blur, weather, and other perceptual perturbations — with implications for safety-critical perception pipelines.
Big-Video Mining of Driving Appearances
Mining large-scale driving footage to model weather and illumination, supporting all-weather road-edge detection and robust perception. Published in T-IV and ITSC.
Teaching
- COSC-4346: Introduction to Deep Learning
Services
Conference reviewer: ICPR (2020–2022), IEEE IV (2018–2022), IEEE ITSC (2018–2022).
Journal reviewer: The Journal of Supercomputing (2026), The Visual Computer (2025), Multimedia Tools and Applications (2025), IEEE TPAMI (2021), IEEE T-ITS (2020–2021), IEEE T-IV (2019–2021), IEEE TVT (2019).