Algorithm Design & Analysis (CSCI311)• Facilitated the understanding of complex algorithmic concepts for a class of 21 students during TA office hours, fosteringa strong foundation in algorithm design and analysis.• Guided students through problem sets, emphasizing mathematical and algorithmic thinking, aiding in the development ofskills necessary for software engineering.• Graded and provided detailed, individualized feedback on homework problem sets, using a data-driven approach todemonstrate a significant correlation between homework scores and exam performance.

• Modeling, simulation, & analysis in support of United States Space Force acquisition decision process.Integrated space mission simulation tools into an automated system-level trade study execution architectureutilizing Ansys/Phoenix ModelCenter• Employed Multi-Disciplinary Analysis Optimization (MDAO) methodology to define and execute an orbitaldebris remediation trade study. Utilized ModelCenter to implement a weighted objective function to find asystem configuration optimized for cost and maneuvering performance.• Simulated launch, mission maneuvering, and debris remediation techniques with MATLAB, Python, andAnsys/AGI Systems Toolkit (STK)• Provided weekly written status reports and weekly demonstrations of progress to leadership team. Presentedfinal report to company leadership and USSF customer• Utilized Agile Kanban process in Atlassian Jira for all task assignments.• Utilized Atlassian Confluence and Microsoft Teams for collaboration and documentation

I enhanced a data pipeline for post-processing deep learning model outputs, successfully calculating ejection fraction (EF) and global longitudinal strain (GLS) from echocardiograms in a Pytorch multi-GPU Linux environment. Analyzing echocardiographic data from 1065 patients, I achieved a correlation of r=0.54 for EF estimation, demonstrating the convolutional model's effectiveness in large-scale clinical applications. Under the mentorship of a professor, I utilized Pytorch, NumPy, Matplotlib, and other Python libraries to investigate the applicability of motion tracking vector fields on AP4 LV image segmentations. I calculated global and regional longitudinal strain metrics using these motion tracking vector fields. Additionally, I created data visualizations, including histograms and linear regression plots, to summarize my summer work, culminating in a poster presentation: [Poster Link](http://eg.bucknell.edu/~jvs008/research/cardiac/posters/2022_Wang_Motion_Tracking_Poster.pdf). My work is also available on GitHub: [GitHub Link](https://github.com/warrenmwang/echo_22).

• Encouraged autodidacticism and inquisitiveness of students to approach problems from different angles• Helped students to understand course material at their own individual conceptual level

• Developed students’ mastery of introductory physics concepts to help them succeed in their course• Fostered students’ ability to ask questions and collaborate amongst peers effectively