2019 Research Projects


Confidence in STEM: Foe to scientific and social progress?

Faculty Mentor: Rafael Irizarry
Graduate Student Mentor: Christina Howe
Program Participants: Kimberlyn Bailey

A wealth of research suggests that women are less confident than men and that high confidence is often not reflective of high competence, yet nevertheless wins higher social esteem. If confidence plays the same role in STEM, confidence could exacerbate gender inequality, as well as favor the most confident researchers rather than the best thinkers/hypotheses. We propose a method to investigate whether such is the case at a crucial juncture in science – grant funding decisions – and present preliminary results.

Tuning the Epigenetic Clock: Exploring how Analyzing Regional Methylation Can Improve Age Prediction

Faculty Mentor: Rafael Irizarry
Graduate Student Mentors: Christina Howe and Rolando Acosta Nuñez
Program Participants: Quentin Bethune

Over the last decade DNA methylation has emerged as a highly-effective epigenetic predictor of biological age. Even the most effective predictor utilizes methylation data from a small set of disparate methylation sites. We use an exploratory approach to examine whether a region-based approach to analyzing methylation could improve age predictions.

Digitization, Wrangling, and Visualization of Published Data on how Race and Gender Influence Professors’ Perceptions of Post-Doctoral Candidates

Faculty Mentor: Rafael Irizarry
Graduate Student Mentor: Isabella Grabski
Program Participants: Orandy Forth, Claidys Lanzot Cruz, Sabrina Trombetta

Eaton and Saunders (2019) studied the influence of race and gender on professors’ perceptions of hypothetical post-doctoral candidates. They found physics and biology professors to be biased towards certain groups in terms of rated competence, likeability, and hireability composites. We developed improved graphical representations in R to portray the researchers’ findings based on tables and box plots provided in the original paper. Students gained extensive experience in data digitization, wrangling, and visualization.

Application of Classification Methods on Neurodevelopmental Outcomes after Infant Cardiac Surgery

Faculty Mentor: David Wypij
Graduate Student Mentor: Lara Maleyeff
Program Participants: Alexa Gonzalez-Figueroa, Ian Reyes, Carissa Villanueva

In this study we investigate previous work conducted by Dr. Wypij, focused on neurodevelopmental outcomes after infant cardiac surgery. A fresh application of classification methods is implemented in this project to observe variability in outcomes and prediction behavior.

Determination of causal relationships between the gut microbiome and complex traits using Mendelian randomization methods

Faculty Mentor: Liming Liang
Graduate Student Mentor: Luli Zou
Program Participants: Daisy Hernandez, Quoc Nguyen, Ana Stevens

We investigated the extent of the causal relationship between the gut microbiome and the risks of coronary artery disease (CAD) and type 2 diabetes (T2D). We used the Mendelian randomization method mr_presso in R to quantify the relationship between the variables. Application of similar methods may be helpful in providing more accurate results.

A New Kawasaki Ninja- The Covert Effect of Neighborhood Socioeconomic Status on Hospital Outcomes of Kawasaki Disease

Faculty Mentor: Kimberlee Gauvreau
Graduate Student Mentor: Patrick Emedom-Nnamdi
Program Participants: Jeffrey Okewunmi and Abigail Derton

Kawasaki Disease is the leading cause of acquired heart disease in children in developed countries. In this research project, we use data on neighborhood socioeconomic status to investigate whether neighborhood differences have a significant impact on outcomes following the diagnosis of Kawasaki disease.