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    <title>Dominic Varghese - Resume</title>
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            <h1>Dominic Varghese</h1>

              <address>
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                    <li>
                      <a href="mailto:vargh085@umn.edu">vargh085@umn.edu</a>
                    </li>


                    <li>
                      <a href="tel:++19522459931">+1-9522459931</a>
                    </li>

                  <li>Minneapolis, MN</li>
                </ul>
              </address>

            <p class="header-summary">PhD candidate in Materials Science with a proven track record of leveraging machine learning and first-principles calculations to solve complex materials engineering problems. Seeking to apply expertise in developing predictive models and computational simulations to drive new technology development in a corporate R&amp;D environment.</p>
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            <section>
              <h2 class="section-heading">Work Experience</h2>

                <section>
                  <h3>

                      Graduate Research Assistant at <a href="https://birol.cems.umn.edu/">CEMS, University of Minnesota</a>

                  </h3>

                    <p class="section-label">12/2023 - Present</p>

                  <p>Research on the flexoelectric effect in binary metal oxides under the guidance of Prof. Turan Birol.</p>

                    <ul>
                      <li>Discovered that epitaxial strain significantly enhances the flexoelectric coefficient in alkaline-earth-metal oxides by lowering optical phonon mode frequencies.</li><li>Quantified the relationship between strain and flexoelectric tensor components using first-principles calculations (ABINIT), Landau theory, and Group theory.</li><li>Fine-tuning foundational Machine Learning Interatomic Potentials (MLIPs) to enable accurate and efficient modeling of flexoelectricity in larger, more complex crystal structures.</li>
                    </ul>

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                  <h3>

                      Undergraduate Researcher at <a href="https://sites.google.com/site/awadheshnarayan00/Home">SSCU, Indian Institute of Science</a>

                  </h3>

                    <p class="section-label">08/2022 - 06/2023</p>

                  <p>Research on the non-linear Hall effect in Ferroelectrics under the guidance of Prof. Awadhesh Narayan.</p>

                    <ul>
                      <li>Identified optimal doping concentrations in ferroelectric perovskites that maximize the Berry Curvature Dipole (BCD), leading to an enhancement of the Non-Linear Hall effect.</li><li>Systematically varied doping concentrations and computed BCD using Quantum Espresso and Wannier90, successfully demonstrating a tunable electronic response.</li>
                    </ul>

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            <section>
              <h2 class="section-heading">Projects</h2>

                <section>
                  <h3>

                      Fraud Detection in Financial Transactions Using Machine Learning

                  </h3>
                  <p>Course project focusing on applying various machine learning classifiers to a large, imbalanced financial dataset to detect fraudulent transactions.</p>

                    <ul>
                      <li>Engineered and evaluated multiple classifiers (K-Means Clustering, SVM, Naive Bayes) to detect fraud within a highly imbalanced dataset of 1 million transactions.</li><li>Optimized a SVM with an RBF kernel, achieving a robust AUC of 0.93 and capturing 69% of fraudulent cases at a practical 5% False Positive Rate.</li><li>Developed a K-Means clustering pipeline using Random Under-Sampling and PCA to overcome class imbalance, boosting model performance to a peak F1-score of 0.80.</li>
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                  <h3>

                      Piezoelectric Modulus Prediction Using Machine Learning

                  </h3>
                  <p>Developed a complete machine learning pipeline to classify and predict the piezoelectric modulus for a large set of inorganic materials from the JARVIS database.</p>

                    <ul>
                      <li>Constructed a dual classification and regression pipeline to predict the piezoelectric modulus for 1,354 unique inorganic materials.</li><li>Achieved 78% accuracy with an XGBoost classifier to identify piezoelectric materials, utilizing SHAP analysis to discover that crystal symmetry was a critical predictive feature.</li><li>Engineered a LightGBM regression model that accurately predicted piezoelectric constants, achieving a final Root Mean Squared Error of 0.49 by integrating a pre-trained model for band gap prediction.</li>
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                  <h3>

                      Predicting Band Gap in Inorganic Materials Using Machine Learning

                  </h3>
                  <p>Course project exploring various machine learning algorithms to predict band gaps and classify materials as conductors or insulators.</p>

                    <ul>
                      <li>Explored multiple ML algorithms - regression, SVM, decision trees and neural networks - to create a predictive framework for inorganic material band gaps.</li><li>Developed a classification pipeline that successfully categorized materials as conductors or insulators, identifying XGBoost as the most accurate and efficient model for the task.</li><li>Demonstrated that Artificial Neural Networks outperformed all other tested models for the regression task, establishing them as the superior method for predicting precise band gap values.</li>
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              <h2 class="section-heading">Languages</h2>
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                    <h3>English</h3>
                    <p class="section-label">Full Professional Proficiency</p>
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