<p>During my Post‑Doctoral Research Associate tenure at The University of Texas at Arlington, I am currently leading the development of the manuscript titled <em>&ldquo; <strong style="text-align: center;"><span style="font-family: 'Times New Roman', serif;">Integrated Machine Learning&ndash;Life-Cycle Cost Analysis Models for Comparative Prediction of Sewer Rehabilitation Methods for Resilient Underground </span></strong><strong><span style="font-family: 'Times New Roman', serif;">Infrastructure Planning: Construction, Environmental, </span></strong><strong style="text-align: center;"><span style="font-family: 'Times New Roman', serif;">and Social Cost Implications</span></strong>.&rdquo;</em> As the corresponding author, I was responsible for conceptualization, data curation, formal analysis, investigation, methodology development, supervision, validation, visualization, and preparation of the original draft (&ldquo;Roles: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Supervision; Validation; Visualization; Roles/Writing &ndash; original draft.&rdquo;).</p>
<p>This work involved integrating machine learning with life‑cycle cost analysis to evaluate construction, environmental, and social cost implications of sewer rehabilitation methods, contributing to resilient underground infrastructure planning. The project was conducted in collaboration with faculty and research leadership at UTA&rsquo;s Department of Civil Engineering and CUIRE, reflecting strong interdisciplinary and applied research engagement</p>