People

Dr. Jinghao Yang earned his Ph.D. in Electrical Engineering from Texas A&M University in 2022, following a Ph.D. in Mechatronics Engineering from Dalian University of Technology in 2018. His academic journey began with a B.S. in Mechanical Manufacturing and Automation from Northeastern University in 2012. Currently, he holds the position of Assistant Professor in UTRGV. Before joining the university, he was a Senior Vision Systems Engineer at Tesla, Inc. His research is primarily focused on development of precision, intelligent and flexible multi-scale sensors leveraging computer vision and AI, as well as real-time, non-destructive, and label-free multifunctional measuring and sensing technologies for onsite and remote monitoring and controlling.  Dr. Yang published articles in the journals of optics, sensing, and instrument area, such as Optics Express and IEEE Sensors Journal, as well as in Sensors and Actuators A: Physical.  He also holds over 20 Chinese patents.  

Martha Asare is a PhD student in Computer Science at the University of Texas Rio Grande Valley (UTRGV), supervised by Dr. Jinghao Yang. Her research is focused on machine vision systems for metal 3D printing utilizing advanced machine learning algorithms to innovate additive manufacturing. Martha holds a bachelor's degree in Statistics from Kwame Nkrumah University of Science and Technology, Ghana, and a master’s degree in Applied Statistics and Data Science from UTRGV. Her expertise includes handling large datasets and improving predictive modeling. Martha's academic excellence is evidenced by the Best Master’s Research Student award at UTRGV and other honors like the Outstanding Poster Award at Florence Nightingale Day 2024. She has significant experience in data analytics, as evidenced by her role at Lawrence Berkeley National Laboratory, where she worked with the Perlmutter supercomputer. Martha is proficient in technical skills such as R, Python, NLP, MATLAB, and SPSS, and she is a recognized leader and innovator in her field.

Zhugang (Tony) Liu is a Ph.D. student in Computer Science at The University of Texas Rio Grande Valley (UTRGV). He received his B.S. in Applied Mathematics from the University of California, Davis. His research interests center on artificial intelligence and robotics, with a focus on intelligent perception and decision-making. He is currently working on robotic manipulation and robotic arm systems in Dr. Jinghao Yang’s lab. He has prior experience in frontend development, data processing, and multimodal AI applications, including projects in image processing and automation. He is also interested in exploring cross-disciplinary applications of AI in design and healthcare.

Jingyi Li earned her B.S. in Informatics with minors in Computer Science and Business from the University of Massachusetts Amherst in 2024, where she was named to the Dean’s List for multiple semesters. Her academic interests include decision-making in harsh environments through Edge AI, multi-modal machine learning, and emerging applications of human-AI collaboration. She is proficient in Python, R, and Java and currently working on multi-vision 3D reconstruction under Dr. Yang’s supervision. She has also served as a teaching assistant for data structures and algorithms, supporting students in mastering advanced programming concepts. 

Elian Cantu is a master’s student in Electrical Engineering at the University of Texas Rio Grande Valley (UTRGV), where he also earned his B.S. in Electrical Engineering in 2024. His research focuses on applying machine learning (ML) to predict the remaining lifespan of railcar bearings. As part of this work, he collaborates with researchers at the University Transportation Center for Railway Safety (UTCRS), processing accelerometer data from their bearing experiments. He uses MATLAB to prepare datasets and Python ML models to study bearing health trends. Elian has shared his research at the NSF CREST/HBCU-RISE/PRP PI Meeting in 2024 and the 8th STEM ED Annual Conference in 2025. He is interested in embedded systems and microcontrollers, having previously developed a C++ touchscreen GUI for an STM32 microcontroller as his senior project. 

Graduate Research Assistant

Graduate Research Assistant

Graduate Research Assistant

Graduate Research Assistant

Miguel Garcia is an undergraduate student pursuing a Bachelor of Science in Computer Science at the University of Texas Rio Grande Valley (UTRGV), expected to graduate in December 2025. His academic interests span computer science, physics, and machine learning, with particular expertise in computational simulations and autonomous systems. Miguel has been involved in two major research projects: a Physics project focused on the electronic properties of MoS2 and Graphene absorbed over gold, and a Computer Engineering project where he explored the behavior of autonomous agents in multi-agent reinforcement learning. His proficiency in programming languages such as Python and C++ is demonstrated through his work on computational simulations involving Density Functional Theory calculations and machine learning algorithms. His research on the electronic properties of MoS2 and Graphene has been published in Applied Surface Science, showcasing his contributions to innovation in the fields of engineering and science. 

Efren Saenz is an undergraduate student pursuing a Bachelor of Science in Computer Science at the University of Texas Rio Grande Valley (UTRGV) and is to graduate in Spring 2026. He has particular interests such as theoretical computer science and AI/ML, emphasized by a strong math background. Efren has broken into the field with a winning project at Frontera Devs 24-Hour Hackathon (UTRGV), where he collaborated with a team to integrate classroom knowledge and real-world realization, resulting in a successful deployment of a budgeting application. Recently, he presented the convergence of math and computer science at UTRGV's Stem Research Conference, where he combined his strong foundation in math and programmed linear algebra matrix factorization algorithms in C++. He is currently researching integrating machine vision with machine action, to further finetune to enhance additive manufacturing purposes and processes.