Martha Asare
she/her/hers
University of Texas Rio Grande Valley
Computer and Electrical Engineering
Biography
Martha Asare, a PhD student in Computer Science at the University of Texas Rio Grande Valley, possesses a robust background in statistics and data science. Her research endeavors encompass machine learning, computer vision, and intelligent systems, with a specific focus on additive manufacturing and extensive data analytics. She develops machine vision systems for real-time quality control in 3D metal printing, employing deep learning methodologies such as YOLO, NLP and CNNs to identify anomalies and enhance operational efficiency. During the summer of 2024, Martha served as a Research Data Scientist at Lawrence Berkeley National Laboratory. In this capacity, she constructed Python-based pipelines to analyze over 6,000 NERSC support tickets and usage logs, resulting in a 25% reduction in resolution time and a 15% increase in team productivity. Her contributions have been recognized through publications in the FAIM 2025 Springer LNME series on AI-enhanced defect detection in manufacturing and the accepted lecture at IEEE Sensors 2025 on digital twin-driven multi-camera edge computing sensing for additive manufacturing. Martha is dedicated to harnessing the potential of AI for sustainable innovation and fostering equitable participation in STEM fields.
Academic Information
Status: PhD Student
Year in Program: 2nd
Major/Specialty: - Computer Science (PhD) – Specialization in Computer Vision, Artificial Intelligence, and Additive Manufacturing. - Applied Statistics and Data Science (Master’s) – Specialization in Machine Learning, Big Data Analytics, and Predictive Modeling. - Statistics (Bachelor’s) – Focus on Biostatistics, Probability Theory, and Statistical Modeling.
Degrees: • PhD in Computer Science (Interdisciplinary Applications) – University of Texas Rio Grande Valley, Aug 2024 – Present (in progress) • MS in Applied Statistics and Data Science – University of Texas Rio Grande Valley, Aug 2022 – Aug 2024 • BS in Statistics – Kwame Nkrumah University of Science and Technology, Ghana, Sep 2013 – Jun 2017
Research Areas
Applied Mathematics; Computer Science; Data Science; Engineering; Machine Learning/AI
Research Interests
My research interests lie at the intersection of artificial intelligence, computer vision, and intelligent sensing systems. I focus on developing AI-driven methods for real-time quality control in additive manufacturing, including anomaly detection, digital twins, and trustworthy AI approaches that improve efficiency and sustainability in production systems. I am also interested in advancing applied machine learning for science and engineering, including large-scale data analytics, predictive modeling, and AI-enabled automation. My work bridges theory and practice, combining deep learning (NLP, YOLO, CNNs), stereo vision, and 3D reconstruction to tackle real-world problems in manufacturing and beyond. More broadly, I am motivated by how AI for science and society can be applied to address challenges in sustainability, healthcare, and equitable education. I aim to continue building research that contributes not only to technical innovation but also to societal impact.
Topical Areas
Applied Computer Science; Artificial Intelligence and Intelligent Systems; Computer Science; Educational Sciences; Electrical, Electronic, and Information Engineering; Informatics, Analytics and Information Science; Materials Engineering; Performance Evaluation and Benchmarking; Statistics and Probability; Training; Visualization and Human-Computer Systems
Relevant Coursework
I have completed a comprehensive range of advanced coursework in both computer science and statistics, which directly equip me for research internships. These include: • Computer Science/Engineering: Machine Learning, Image Processing, Smart Sensors, Swarm Robotics, Computer Vision, Big Data Analytics. • Statistics/Data Science: Probability Theory, Biostatistics, Logistic Regression, Structural Equation Modeling, Advanced Data Analysis. Combined with my national lab experience at Lawrence Berkeley Lab and ongoing PhD research in additive manufacturing anomaly detection, these courses provide me with strong computational, analytical, and applied research skills.
Publications & Research Projects
FAIM 2025 (Springer LNME) – “AI-Enhanced Real-Time Additive Manufacturing Defect Detection” (Asare, Garcia, & Yang). Published. • FAIM 2025 (Springer LNME) – “AI-Enhanced Real-Time AM Defect Detection Method Using Large Language Models (LLM)” (Garcia, Asare, & Yang). Published. • IEEE Sensors 2025 (Vancouver) – “Digital Twin Driven Multi-Camera Edge Computing Sensing System for AM.” Accepted Lecture. • Asare, M. (2024). Evaluating Feature Selection Methods in Machine Learning With Class Imbalance (Master's thesis, The University of Texas Rio Grande Valley). • Fernandez, L. M., Villalobos, C., Ortiz, M. L., & Asare, M. Preliminary Results of Specificationis Grading in Calculus 1. In 2024 Fall Central Sectional Meeting. AMS. • Villalobos, C., Fernandez, L. M., Ortiz, M. L., & Asare, M. Student Attitudes in Specifications Grading Calculus 1 classes. In 2024 Joint Mathematics Meetings (JMM 2024). AMS. • Bilingual Research Journal – “Intersecting Beliefs on Mathematics and Emergent Bilingual Mathematics Education” (Ortiz Galarza, Nguyen, & Asare). Under review. • Research conducted at Lawrence Berkeley National Laboratory (NERSC data pipeline, 2024). • Ongoing PhD research at the IMVSS Laboratory, UTRGV (real-time defect detection in 3D printing).