Biography

I am a Ph.D. student in Intelligent Infrastructure Engineering and concurrent M.S. student in Computer Science at Iowa State University. My research focuses on integrating artificial intelligence with transportation and agricultural systems to improve real-time decision-making and sustainability. I have led several projects across AI for precision agriculture, document automation, and weather classification, developing transformer-based models and retrieval-augmented generation (RAG) systems. Through collaborations with industry partners such as Ag Leader Technology, Elanco, and IPERS, I have built end-to-end data pipelines that convert raw sensor, video, and document data into actionable insights for safety, productivity, and environmental stewardship. My technical expertise spans machine learning, deep learning, computer vision, and high-performance computing. I am passionate about advancing trustworthy, resource-efficient AI systems that benefit infrastructure resilience and food production. Participation in the SRP NAIRR program will allow me to collaborate with national laboratory researchers, explore scalable AI solutions using high-performance computing resources, and strengthen my ability to translate cutting-edge AI into sustainable real-world applications.

Academic Status

PhD Student - 5th

Research Area/Department

Computer Science; Data Science; Engineering; Machine Learning/AI

Major/Specialty

• Intelligent Infrastructure Engineering • Computer Science • Civil Engineering • Electrical/Electronic Engineering

Degrees Earned or in Progress

• Ph.D. in Intelligent Infrastructure Engineering, Iowa State University — In Progress (Expected Dec 2025) • M.S. in Computer Science, Iowa State University — In Progress (Expected Dec 2025) • M.S. in Civil Engineering, Iowa State University — 2023 • B.S. in Electrical/Electronic Engineering, Kwame Nkrumah University of Science and Technology (KNUST) — 2017

Academic Preparation

Answer: I have completed the following courses that have strengthened my technical and research foundation for a summer research or internship experience: • Deep Learning for Computer Vision – advanced CNN and transformer architectures for image and video understanding • Machine Learning – supervised, unsupervised, and semi-supervised learning algorithms • High Performance Computing – parallel and distributed computing using GPUs and clusters • Computational Methods II – numerical methods and scientific programming for engineering systems • Database Systems – relational models, SQL, and data management for large-scale systems • Intelligent Transportation Systems – applications of AI and sensing in infrastructure and mobility • Data Analytics for Infrastructure Management – predictive modeling and decision-making with sensor data • Sensors and IoT Systems – integration of sensor networks for real-time monitoring and automation

Research/Publications

1. Adu-Gyamfi, Kojo Konadu, et al. "MobiScout: A scalable cloud-based driving and activity monitoring platform featuring an IOS app and a WatchOS extension." SoftwareX 24 (2023): 101588. 2. Sivaraman, Anush Lakshman, et al. "Clearvision: Leveraging cyclegan and siglip-2 for robust all-weather classification in traffic camera imagery." arXiv preprint arXiv:2504.19684 (2025). 3. Owusu, Gideon Asare, et al. "Uncrewed Aerial Vehicle-Based Automatic System for Seat Belt Compliance Detection at Stop-Controlled Intersections." Remote Sensing 17.9 (2025): 1527.

Research/Academic Interests

My research integrates AI and civil infrastructure to improve transportation safety and agricultural sustainability. I focus on multimodal data fusion—combining imagery, sensor data, and textual information—to build interpretable models for event detection and decision support. I’m particularly interested in developing efficient, deployable AI architectures that can operate on edge devices and leverage cloud-scale HPC systems for training.

Computational and Data Science Areas

Agriculture, Forestry, and Fisheries; Artificial Intelligence and Intelligent Systems; Civil Engineering; Computer Science; Environmental Engineering; Health Sciences; Infrastructure and Instrumentation

Motivation

I am applying to the Sustainable Research Pathways – NAIRR program because it represents a unique opportunity to collaborate with leading researchers at U.S. national laboratories on projects that align with my mission to build sustainable, AI-driven systems for infrastructure and agriculture. My current work bridges computer vision, machine learning, and civil engineering to create intelligent tools that help transportation engineers and farmers make data-driven decisions in real time. Through my experience developing transformer-based weather-detection models, RAG-based document understanding pipelines, and Jetson-deployed trench-classification systems, I have gained deep appreciation for the computational demands of large-scale AI. The NAIRR ecosystem will enable me to explore distributed training, multimodal fusion, and resource-efficient inference using advanced HPC platforms—capabilities critical for scaling sustainable AI research. Beyond technical skills, I hope to contribute to a diverse and collaborative research environment that empowers under-represented voices in AI and data science. Participation in SRP will help me strengthen cross-disciplinary partnerships, refine my leadership in socially impactful AI, and prepare for a research career at the intersection of AI, infrastructure, and sustainability.

Lightning Talk Title

Scalable AI for Smart Infrastructure and Sustainable Systems

Keywords (Maximum 20 words)

Artificial Intelligence; High-Performance Computing; Intelligent Infrastructure; Sustainable Agriculture; Computer Vision; Edge AI; Multimodal Data Fusion; Transportation Systems; Document Automation