Exploring My Research Journey
Published 14 scientific papers and delivered impactful presentations as a Qualitative and Quantitative Researcher at UCLA.
Enhanced fraud-detection algorithms by improving performance by 10% as a Data Scientist and Researcher at Mavenir, Inc.
In this section, I proudly present my accomplishments as a Researcher and Data Scientist at the University of California, Los Angeles (UCLA) and Mavenir, Inc., reframing them within the context of UX. Here, I :
outline my roles and highlight the skills that are relevant to UX.
emphasize the quantitative methods that have been instrumental in my work.
provide valuable insights into my decision-making process through the presentation of one of my Ph.D. projects.
present the impact generated through my contributions and collaborations.
My professional experience timeline from 2012-2019 as a Researcher showcasing my roles, accomplishments, skills and technical tools.
Project: Drug Interactions
Research Question: How do multiple drugs work together to fight off bacteria: synergistically, diminishingly, or independently?
Schematic of three-drug interactions measured through bacterial growth in a petri dish under single-drug, two-drug, and three-drug treatments.
Research Process: Methodology, Ideation, Data Collection, Analysis, Iteration and Publication
End-to-end representation of the research process, encompassing background research, publication phase, and future directions.
Quick Overview of Research, Analysis and Results
Investigated interaction effects of multiple drugs.
Developed a mathematical model for quantitative and statistical analysis and inference of drug combinations.
Schematics of 3-drug interactions via comparison with two-drug interaction effects.
Conducted data analysis on a comprehensive dataset comprising 300 pages of CSV files, encompassing approximately 500K data points using Python and MATLAB.
Distribution of 3-drug interaction results
Refined the three-drug interaction metric and repeated the analysis.
Key findings:
Interaction types are clearly distinguished based on the model's expected behavior and empirical data.
Three-drug emergent interactions are more prevalent, indicating their substantial impact beyond pairwise effects.
Example dataset for analyzing interactions among three drugs.
Leveraged data analysis and visualization to enhance our understanding.
Research Problem: The bar graph illustrates a uniform distribution, indicating an absence of specific patterns or biases. This suggests each possibility is equally likely, potentially missing significant interactions.
Research Solution: Employ an iterative approach to enhance the mathematical model based on previous research and to capture a precise representation of complex drug interactions.
Distribution of 3-drug interaction results based on the new model.
Impact and Publications
Clinical interventions can greatly benefit from three-drug interactions to improve treatment efficacies or to slow the evolution of resistance.
We can now accurately evaluate the therapeutic benefits of incorporating an additional third drug into the treatment regimen.
My collaborative work with teammates and collaborators from various departments has resulted in publications in high-impact journals. All of these publications are accessible through my Google Scholar profile.