- Integrated multiple types of large-scale genetic data to visualize fusion breakpoints driving translocation renal cell carcinoma (tRCC) tumors, identifying the genetic mechanism responsible for the 2:1 female:male incidence of tRCC.- Utilized R and Python to create informative graphs and data visualizations for publication, enhancing the presentation of research findings.- Developed data pipelines to analyze the expression of hundreds of small molecules across 10-12 replications of 16 treatment conditions. Identified a novel metabolic pathway as a target for cancer therapeutics through comparative expression analyses.– Characterized physical mutations in prostate cancer by analyzing millions of rows of DNA contact data and devising intuitive visualizations to map unknown long-range enhancer-promoter interactions driving cancer tumor growth.Publications:Mingkee Achom*, Ananthan Sadagopan*, Chunyang Bao*, Fiona McBride, et al. A genetic basis for cancer sex differences revealed in Xp11 translocation renal cell carcinoma. Cell, no.187 (2024): 1-18. https://www.cell.com/cell/pdf/S0092-8674(24)00832-8.pdfJiao Li, Kaimeng Huang*, Fiona McBride*, et al. Activation of OXPHOS by oncogenic TFE3 fusions unveils novel vulnerabilities in translocation renal cell carcinoma. BioRxiv (2024). https://www.biorxiv.org/content/10.1101/2024.08.09.607311v1

- Developed a novel inverse reinforcement learning algorithm to identify sub-optimal clinical decisions.- Successfully identified improved treatment decisions for 3 target populations through iterative trajectory pruning.Presented research at the American Medical Informatics Association (AMIA) Annual Symposium 2024.Inko Bovenzi*, Adi Carmel*, Michael Hu*, Rebecca Hurwitz*, Fiona McBride*, et al. Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning. Published at AMIA 2024 (oral presentation).

- Processed and visualized demographic and goal achievement data in Excel to evaluate progress toward goal metrics set by programs at UC Santa Barbara and partnering institutions.- Wrote data analysis reports capturing data trends and summaries based on institutional needs and benchmarks. - Managed data collection from reporting programs using Qualtrics to ensure data accuracy.

- Investigated predictive coding responses to texture and motion in the primary visual cortex using microscopy to conduct comparative analyses between animal subjects and human brain function. - Used 2-photon microscopy to determine the roles of anterior and posterior visual area V1 in responding to natural- and noise-textured images in mice.- Trained mice in left/right decision-making mazes to investigate brain structures related to short-term memory.

- Created supplemental curricula for small group discussions, including midterm and final review sessions.- Advised students on upper-division courses and major declaration.

- Increased student test scores by 12%, as compared to students not in the mentoring program through structured academic coaching, advising, and mentoring. - Led weekly sections with 20+ students to review concepts from lectures and held midterm review sessions before each midterm examination.

- Invited and mediated discussions with guest speakers in medical and professional fields.- Led weekly meetings for 60-80 members; discussed medical school requirements, local involvement in healthcare and volunteer opportunities, and translational research advancing medical technology.- Oversaw all recruitment, programming, and fundraising events for the CA-Nu chapter.- Established Diversity, Equity, and Inclusion Committee and led meetings to discuss member education and involvement in DEI.

- Organized volunteer events to increase community and civic engagement and impact. - Created educational workshops for members on various topics including research, MCAT, post-baccalaureate programs, and healthcare inequality.

- Integrated Python toolkits (including Seaborn, Pandas, Scanpy, and Numpy) to construct a novel approach for spatial visualization of cellular signaling in tissue.- Created a database of cell-cell interactions using Python to understand the environment of complex tissue.- Trained a neural network designed to identify cell and fiber types in healthy, injured, and regenerating tissue.

- Assisted with data collection using wet lab techniques and analyzed data in Excel to investigate muscle stem cell function and regeneration in aging populations.