Darnesha Collier

As a quantitative researcher, I present data analytics, data visualizations and predictive modeling to forecast the most viable property investments. With my role, I have launched productionalized software application using a Power BI Tenant and Python backtesting, manage Third-Party data vendor contracts, and have have generated 10 different dashboards that have been used throughout our firm. As of today, I have managed and onboarded 13 different data vendor contracts successfully in the Data Governance Risk Framework for the UBS Asset Management division and Human Resources team as well as created 20 data dashboards that display key performance indicators, macro-economic metrics, and geospatial mapping features to our clients. I have used R programming for backtesting real estate market research and forecasting models. I have also used Qualtrics to conduct surveys on the insights of our clients and stakeholders. 

In this lab, I use R programming for mathematical analysis to examine the nonlinear dynamic of neurons and small networks, synchronization, oscillation, central patter generation, bursting neurons, midbrain dopaminergic neurons, and the regulation of the firing pattern of neurons. I also use computational techniques, including high-performance computing, Linux(Ubuntu), and C++, to simulate neuronal activity. Programming languages: R, Linux(Ubuntu), C++, HPC (high-performance computing)

As a data visualization assistant in the Data Visualization Lab at Xavier University of Louisiana, I help organize and teach workshops on applying basic Python and R programming skills to research projects. I also do consultation with students and faculty on how data visualization can become more efficient in their analysis using collaboration and analysis tools including Visio, Lucid charts, Microsoft Office 365, SharePoint, and Teams. Programming languages used R, Python, and also the use of Tableau

I have done participant recruitment, manipulated fMRI brain scan coding data using Python primarily to focus on analyzing part of the insula functional parcellation. This research has allowed me to shadow fMRI brain scanning and calculating multi-dimensional analysis of neural network algorithms, unsupervised learning, and high-dimensional clustering in Python. In this lab, I also read and discuss psychological studies of concept neurofeedback. Programming languages used R, Linux (Ubuntu), Python, MATLAB

As a research assistant in the Neurocognitive Representation and Learning Lab at Louisiana State University. In this lab I perform the following tasks include reading and writing to develop insight into an area of study, data collection using Qualtrics by administering tests, proctoring cognitive tasks in a lab setting, deploying and monitoring online experiments, data cleaning using Excel and Python scripts,and data management using Python and SQL server.

Here I have done high performance computing, intensive R and Python programming, machine learning, and data mining. I have done most of coding in Google Analytic suites. I have working knowledge in using SQL server and OpenRefine. I gained my knowledge at the University of Wisconsin in the Psychology Research Experience Program, where I have obtained hands on learning about expanding my capabilities in data science that can also be incorporated in developing technology for the clinical psychology and education field.