Data Science Machine Learning

SIMON H CHAISOUANG

Springdale, AR ************@*****.*** 501-***-**** LinkedIn GitHub Website PROFESSIONAL SUMMARY

As a dedicated student focusing on Data Science, I am deeply committed to applying my academic knowledge in practical and diverse settings. My educational journey has sharpened my attention to detail and has equipped me with proficiency in various data science tools, laying a strong foundation for my analytical skills. I am eager to leverage my undergraduate studies to explore data analysis's practical applications across multiple sectors. I approach each new experience as an opportunity for growth and reflection, constantly aiming to broaden my understanding and enhance my capabilities. My objective is to contribute significantly to projects and roles that capitalize on my unique blend of data science expertise, aspiring to create a positive impact in various fields even before graduation. EDUCATION

Master of Science: Statistics and Analytics University of Arkansas - Fayetteville, Fayetteville, AR May 2023 - May 2025

• GPA: 3.60 Major: Statistics and Analytics, Concentration: Computational Analysis Bachelor of Science: Computer Science Arkansas Tech University, Russellville, AR August 2019 - May 2023

• GPA: 3.93 Major: Computer Science; Minor: Business Data Analytics, Mathematics SKILLS & QUALIFICATIONS

• Programming Languages: C#, C++, Java, JavaScript (with Angular and TypeScript), Python, R, SQL, SAS

• Web Development Technologies: CSS, HTML, Docker, Drupal, .NET Framework, Node.js, PHP, Vue, Vuetify

• Data Analysis & Machine Learning Tools: Pandas, NumPy, Scikit-Learn, TensorFlow, Data Mining, Predictive Analysis, Machine Learning

• Database & Data Warehousing: SQL-based Technologies, Data Warehousing Techniques

• Reporting & Visualization Tools: MS Power BI

• Development Tools: Visual Studio 2019, VS Code, Git

• Other Skills: Business Requirements Analysis, Logical & Analytical Thinking, Attention to Detail PROFESSIONAL EXPERIENCE

Industrial Engineering Graduate Assistant University of Arkansas - Fayetteville, Fayetteville, AR May 2024 – May 2025

• Developed advanced methodologies for hybrid computer/physical testing of engineered systems, enhancing system reliability under a multi-year grant-funded project focused on improving decision-making processes in engineering contexts.

• Applied machine learning and statistical techniques to optimize sequential learning processes, focusing on functions with high evaluation costs and varying error sources, driving more efficient and accurate system assessments.

• Designed and implemented optimization models that balance trade-offs between multiple function types and evaluation costs, contributing to innovative solutions in engineering testing protocols.

• Collaborated with a multidisciplinary team to advance research in statistical optimization and machine learning, presenting findings that inform best practices in planning complex engineering tests with diverse data sources. Data Science Graduate Assistant University of Arkansas - Fayetteville, Fayetteville, AR August 2023 – May 2024

• Independently conducted twice-weekly laboratory sessions, effectively supervising and mentoring over 30 students per semester, fostering an engaging and productive learning environment for data science concepts.

• Provided daily technical support to students, addressing queries related to course material, thereby enhancing their understanding and application of data science principles.

• Organized and led weekly meetings with the course professor, contributing to the development and refinement of course material, and playing a pivotal role in student progress tracking and academic support.

• Demonstrated exceptional leadership in facilitating lab sessions and offering technical guidance, contributing significantly to the academic success and practical skills enhancement of data science students.

Software Developer Intern ArcBest Technologies, Fort Smith, AR August 2022 – December 2022

• Actively identified, troubleshooted, and documented bug fixes for the official ArcBest website, ensuring optimal functionality and user experience as part of the Marketing department's technical team.

• Worked closely with the lead-funnel/U-Pack team at ArcBest, playing a key role in the development of a new website, contributing to both front-end and back- end elements to enhance user interaction and functionality.

• Underwent comprehensive training in utilizing Lando environment, PHP, and APEX, significantly improving proficiency in managing and updating the ArcBest website, demonstrating a commitment to continuous learning and skill development. Software Developer Intern ArcBest Technologies, Fort Smith, AR May 2022 - August 2022

• Utilized C#, .NET Frameworks, and Git to contribute significantly to the development of the 'Group Activity Planner' application, focusing on enhancing organized event planning functionalities.

• Collaborated effectively in a 3-member team to design and develop a new, modern application for the Sales Department, replacing an outdated system and optimizing sales processes.

PROJECTS

CSCE 57003 Computer Vision Final Project Fall 2024

• Modular Benchmark Development: Designed and implemented a flexible benchmarking pipeline to evaluate both traditional and deep learning-based image matching algorithms across wide baselines, ensuring standardized performance comparisons.

• Feature Matching Analysis: Conducted comparative analysis of feature detection techniques, including AKAZE, ORB, and SURF, assessing their effectiveness in handling varying lighting, occlusion, and viewpoint changes in large-scale image datasets.

• Fundamental Matrix Estimation: Applied RANSAC-based outlier rejection and eight-point algorithm methods to estimate the fundamental matrix, enabling accurate camera pose determination for 3D reconstruction tasks.

• Dataset Curation & Processing: Processed and curated the PhotoTourism dataset, consisting of 25 landmark scenes with over 30,000 images, to provide a robust evaluation framework for real-world image matching challenges.

• Experimental Validation & Insights: Evaluated algorithm performance based on downstream metrics such as camera pose accuracy, demonstrating that well- tuned classical methods can sometimes rival or outperform deep learning-based approaches. Machine Learning Exploration on Cirrhosis Patient Data Fall 2023

• Advanced Model Implementation: Implemented and optimized advanced machine learning models, including logistic regression and neural networks, to accurately predict survival outcomes in liver cirrhosis patients, enhancing medical prognosis capabilities.

• Model Performance Optimization: Conducted rigorous fine-tuning and cross-validation of models to ensure optimal performance and generalizability, ensuring robust predictive reliability in diverse clinical scenarios.

• Data Analysis and Modeling: Engaged in detailed exploratory data analysis using Python, developing sophisticated statistical models to forecast patient survivability based on critical factors such as health conditions and chronic alcohol consumption. Data Analysis of Death Rate by Air Pollution Fall 2023

• Dataset Assessment and Analysis: Conducted a comprehensive assessment of a real-world dataset focusing on death rates due to air pollution using Python, extracting critical insights into environmental health impacts.

• Variable Significance Determination: Identified and isolated significant variables impacting death rates while filtering out non-contributory factors, enhancing the accuracy and relevance of the analytical model.

• Insightful Data Visualization: Employed Matplotlib to create compelling data visualizations, succinctly summarizing and communicating key findings and insights derived from the air pollution dataset.

Predicting University Admission Rates Fall 2022

• Predictive Model Development: Developed a sophisticated predictive model using multiple linear regression in Python to estimate university admission rates, integrating various influencing factors.

• Factor Analysis and Evaluation: Performed an in-depth analysis of multiple variables to identify key aspects influencing college admission rates, utilizing statistical analysis to validate hypotheses.

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