Dr. Samuel Kakraba’s research centers chiefly on the development and implementation of efficient computationally-driven pipelines aided by robust data science, statistical, mathematical and biostatistical predictive machine learning algorithms like neural networks, deep learning, support vector machines, k-nearest neighbors, random forests, Naïve Bayes, and others, in R statistical software, Python, SAS, and others, for estimation, prediction, and inferences into complex high-dimensional relationships in many fields like pharmaceutical sciences, public health, among others.

First year phd student in biostatistics, interested in censored data.

Dr. Lindsey Ho is an Assistant Professor at the Tulane University School of Public Health and Tropical Medicine in the Department of Biostatistics and Data Science. Dr. Ho is dedicated to ensuring academic success for Public Health professionals in training at the graduate level as teaching faculty (Biostatistics for Public Health), as an online technology expert, and a mentor in Biostatistics and Data Analytics education.

Ray's research is focused on data science and data mining, and he is particularly interested in biomedical informatics and genomics. His current dissertation research aims to develop advanced statistical learning methods to integrate high dimensional data, such as multi-omics datasets, and extract disease-relevant features in thousands of heterogeneous biological elements and predict disease status in a unified supervised multi-omics framework.