Research & Analysis

 As part of an interdisciplinary research effort focused on prehistoric settlement patterns in Kosovo, I created and analyzed spatial databases to track human movement across different time periods. Working alongside a chemical analysis team that processed artifacts from excavation sites, I was responsible for translating their data into spatial insights using ArcGIS and other geospatial tools.

 I built and organized databases that cataloged settlement locations based on chemical signatures and artifact distributions, then applied spatial analysis techniques—including kernel density estimation, nearest neighbor analysis, and K-function testing—to detect clustering, migration trends, and site significance across time. These statistical methods helped validate the accuracy of our spatial models and gave insight into how prehistoric populations moved in response to environmental and cultural factors.

 The research culminated in a poster selected as a finalist for the Deep Mapping Fellowship, and our findings were presented at the Society for American Archaeology Annual Meeting in Denver. This project strengthened my ability to work across disciplines, interpret complex datasets, and apply advanced geospatial tools to extract meaningful patterns from real-world archaeological data. Here is the poster that was used: View Poster

 As part of a team in ME235 (Thermodynamics) at the University of Michigan, I contributed to a detailed analysis of vapor-compression refrigeration and heat pump cycles using four different working fluids: R-134a, R-12, R-22, and NH₃. Each team member was responsible for evaluating the full thermodynamic behavior of one refrigerant. My assigned fluid was R-12.

 I conducted a complete thermodynamic analysis of the R-12 cycle, including calculating net work input, heat transfer in the evaporator and condenser, and coefficients of performance (COP) for both refrigeration and heat pump modes. Using provided inlet conditions and isentropic efficiency assumptions, I applied energy and entropy balance equations to determine key state properties and performance metrics. I then performed a sensitivity analysis by varying the maximum cycle temperature to examine its effect on entropy generation and cycle efficiency. Results showed a clear trade-off: as the maximum temperature increased, COP decreased while entropy generation rose—indicating greater irreversibility in the cycle.

 As a team, we compared the performance and feasibility of each fluid. While NH₃ exhibited the highest theoretical efficiency, safety concerns made R-22 the most balanced option in terms of performance and practicality. We also identified evaporator pressure as a critical lever for improving system performance without requiring changes to hardware.

 The project involved engineering communication, collaborative problem-solving, and technical analysis aligned with real-world refrigeration system design. Our final deliverable included a written technical report and a team presentation, supported by bar charts, p-v and T-s diagrams, and entropy generation comparisons. Through this work, I developed a stronger understanding of thermodynamic cycles, refrigerant behavior, and how to optimize system efficiency using both theoretical and applied methods.