Biography

As a PhD student in Industrial and Systems Engineering with strong interests in artificial intelligence (AI), data analytics, machine learning (ML), and sustainable systems, I have pursued advanced training in AI through professional certifications and coursework that emphasize machine learning, natural language processing, and responsible AI practices. My hands-on experience includes applying statistical modeling, optimization, and human-centered design principles to projects that integrate AI tools into engineering and decision-making contexts. I am committed to expanding my expertise in ML and AI applications across domains such as sustainable manufacturing, systems resilience, and equitable technology adoption. I have experience with programming, data-driven modeling, and experimentation, which I use to explore how AI can improve efficiency, support human decision-making, and address societal challenges. I am seeking opportunities that would connect me with other professionals and allow me to gain more experience in the field. I continue to teach and mentor undergraduate students in programming, design, and systems engineering, where I integrate AI-supported approaches to enhance student learning. With a strong foundation in both technical and applied dimensions of AI, I am motivated to collaborate with interdisciplinary teams and contribute to advancing sustainable, equitable solutions through the National AI Research Resource Pilot.

Academic Status

PhD Student - 2nd

Research Area/Department

Data Science; Engineering; Machine Learning/AI

Major/Specialty

Industrial and Systems Engineering

Degrees Earned or in Progress

Bachelors of Enfineering in Industrial and Production Engineering 2016 Master of Science in Industrial and Systems Engineering 2023 Doctor of Philosophy in Industrial and Systems Engineering (in progress)

Academic Preparation

Big Data Analytics and Visualization Multivariate Statistics Machine Learning Cloud Infrastructure and Generative artificial Intelligence

Research/Publications

Under review

Research/Academic Interests

My research interests focus on the application of artificial intelligence, machine learning, and data science to advance sustainable and equitable solutions in engineering, engineering education, and society. Artificial intelligence holds the potential to accelerate discovery and innovation while changing the way of work. However, researchers and educators lack access to the AI resources necessary to fully conduct their research activities and to train the next generation. I am particularly motivated by how artificial intelligence (AI) and machine learning (ML) methods can be scaled to address complex, data-intensive challenges. My research explores how Generative Artificial Intelligence (GenAI) can be responsibly integrated into engineering education to enhance learning and decision-making. Through a mixed-methods study with first-year engineering students, I examine how collaboration with AI affects problem-solving, cognitive load, and reasoning during complex design tasks. Using quantitative analyses and qualitative insights from think-aloud protocols and interaction logs, I identify patterns of productive and unproductive AI use. The goal of my dissertation is to develop a validated model of student-AI collaboration that guides educators in balancing technological support with authentic learning, ensuring that AI enhances, rather than replaces, the critical thinking and creativity essential to engineering practice. I have developed experience in applying AI to human-centered problems, including analyzing large transcript datasets with natural language processing, using machine learning models to classify interaction patterns, and applying multivariate statistical methods to evaluate decision-making and performance. I have also pursued professional certifications in AI and data science, strengthening my expertise in supervised and unsupervised learning, model evaluation, and responsible AI practices. My long-term goal is to gain experience that expands my expertise to research that contributes to sustainability and resilience. This includes applying AI and advanced analytics to sustainable manufacturing, energy efficiency, equitable technology adoption, and educational systems that prepare future engineers for global challenges.

Computational and Data Science Areas

Artificial Intelligence and Intelligent Systems; Educational Sciences; Other Engineering and Technologies; Statistics and Probability; Visualization and Human-Computer Systems

Motivation

As a PhD student in Industrial and Systems Engineering, I am eager to expand my research experience and build meaningful collaborations that broaden my expertise. I want to join the Sustainable Research Pathways program because it aligns closely with my interests in artificial intelligence, data science, and sustainable systems. I look forward to learning from and working alongside faculty whose research integrates computation, sustainability, and societal impact. I am particularly drawn to research that uses computation and data analysis to promote sustainability and equity. My curiosity about how AI systems improve human life has evolved into a deeper interest in how these systems can be designed to be fair, transparent, and sustainable. In my course projects, I have applied AI and machine learning to analyze complex datasets in education and healthcare, developing algorithms that support decision-making and systems optimization. I have also earned professional certifications in AI to strengthen my technical foundation and prepare for interdisciplinary collaboration. Beyond coursework, I actively engage with professional communities. I have presented my research at the Institute of Industrial and Systems Engineers (IISE) conference in the Data Analytics track and continue to attend workshops on sustainability and AI to stay connected to emerging research. These experiences have expanded my network and deepened my commitment to bridging technical innovation with real-world relevance. Through the Sustainable Research Pathways program, I hope to apply my skills to projects that explore the intersection of technology, equity, and resilience. I am especially interested in how AI and high-performance computing can advance sustainable manufacturing, energy efficiency, and equitable technology adoption. Collaborating with researchers at national laboratories will help me strengthen my ability to scale AI models for large, data-intensive problems and contribute to solutions that create lasting social and environmental impact. Finally, I see SRP as an opportunity to learn, contribute, and grow as a scholar who advances sustainability through innovative, inclusive, and human-centered applications of AI.

Lightning Talk Title

Human-Centered Design, AI, and Data Science for Sustainable Engineering Systems

Keywords (Maximum 20 words)

Artificial Intelligence Machine Learning Natural Language Processing Human-Centered Design Sustainability Data Analytics Data Science Learning Analytics Cognitive Modelling Engineering Education