Gloria Quiceno

- Collaborated with other Volunteer data specialist to delivery an advanced dashboard for data collected on Nature-Based Solutions (NbS) for the World Resources Institute (WRI). - This product enabled the WRI to measure NbSs efficacies and make informed future decisions.

3 month intensive course to learn the latest technical skills used in the Data Science field

- Characterized Dopamine in memory formation and successfully established a novel technique in the institute - Successfully created script protocols for signal processing (IgorPro) and statistical analysis (R).

- Successfully characterized and analyzed neuronal development in mutated mice - Effectively formulated sound interpretations and successfully conveyed them to peers

- Conclusively demonstrated capacity of a quantitative model to map the visual cortex for various modes of ocular stimulation via fMRI processing with Unix, SPM, Matlab and SamSurf - Effectively conveyed results to peers

- Used python/pandas/numpy to collect/analyze/visualize data - Explored machine learning (supervised and unsupervised) with scikit-learn and stat models - Built dashboards based on Postgres database and deployed them online via AWS. - Built a complete ETL using Postgres, MongoDB and Docker. - Emplo

Thesis: Alterations of the Prefrontal Cortex circuit in GluD1 deficient mice

Thesis: Optimization of monocular visual fMRI Population receptive field mapping

Double Major in Biology and Psychology (Research concentration) with minor in Neuroscience