• Conducted research at the crossroads of topological data analysis, data visualization, and parallel computing.• Developed an interactive interface for analyzing and tracking topological features, aimed at facilitating seamless exploration and analysis of discrete Morse theory concepts.• Implemented dynamic plot generation functionality and integration with Free OpenGL Utility Toolkit, enhancing user accessibility and ensuring compatibility with diverse datasets.

• Supervised the safety and well-being of over 53 on-campus residents, adeptly resolving interpersonal conflicts.• Assisted and supported numerous international and transfer students, addressing their inquiries and concerns to ease their transition into the university setting.

• Led two weekly campus tours, guiding over 50 prospective students, parents, and guests through an engaging exploration of campus life, history, traditions, and achievements, resulting in an annual surge in applications.• Organized “Discover/Choose the U" events, drawing over 600 prospective students to interact with faculty and current students, promoting academic programs and enhancing engagement.

• Applied Bayesian Dynamic Borrowing techniques to leverage historical data in randomized controlled trials, aiming to minimize control group sample sizes while maintaining statistical integrity.• Implemented Bayesian hierarchical models alongside methodologies such as power priors and commensurate priors to enhance trial efficiency and decision-making processes.• Conducted simulations to assess and refine the performance of Bayesian methods in terms of bias, variance, and statistical… Show more • Applied Bayesian Dynamic Borrowing techniques to leverage historical data in randomized controlled trials, aiming to minimize control group sample sizes while maintaining statistical integrity.• Implemented Bayesian hierarchical models alongside methodologies such as power priors and commensurate priors to enhance trial efficiency and decision-making processes.• Conducted simulations to assess and refine the performance of Bayesian methods in terms of bias, variance, and statistical power, ensuring robust and efficient designs for clinical trials. Show less

• Led interactive weekly tours for groups exceeding 20 students and families, showcasing facilities and academic opportunities.• Organized Engineering Day, an annual half-day event attracting over 200 high school students, undecided majors, parents, transfer students, and engineering enthusiasts annually.

• Coordinated events like "Tabling Tuesdays" to showcase the diversity and significance of humanities disciplines.• Collaborated with faculty and students to cultivate a welcoming and inclusive community within the College of Humanities.• Facilitated enhanced accessibility for Spanish-speaking individuals, fostering more inclusive interactions.

• Visited K-12 students to enhance science education through interactive activities and demonstrations, aiming to promote interest in STEM disciplines among local schools.• Organized events to help students discover resources and support within the university community.

Project: "Optimizing Gravitational Wave Detection Using Topological Data Analysis and Machine Learning”• Integrated topological data analysis with machine learning methodologies to improve gravitational wave signal detection accuracy.• Implemented sliding window embeddings for time series analysis to facilitate visualization and comprehensive data assessment.• Employed Principal Component Analysis (PCA) for dimensionality reduction, effectively streamlining the training time of… Show more Project: "Optimizing Gravitational Wave Detection Using Topological Data Analysis and Machine Learning”• Integrated topological data analysis with machine learning methodologies to improve gravitational wave signal detection accuracy.• Implemented sliding window embeddings for time series analysis to facilitate visualization and comprehensive data assessment.• Employed Principal Component Analysis (PCA) for dimensionality reduction, effectively streamlining the training time of machine learning models. Show less