Samuel Kakraba, PhD

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. Through his collaborative research, he has designed and implemented data science-driven machine learning algorithms in quantitative structure-activity relationship (QSAR) modeling for screening, identification, and characterization of novel small NSAID (non-steroidal anti-inflammatory drug) molecules for the treatment of neurodegenerative diseases like Alzheimer’s, Parkinson’s, Huntington’s, and others toward designing better drugs to target diseases. Additionally, part of his research involves the application of mathematical graph-theoretic modeling (network graphs) interfaced with statistical and mathematical machine learning, molecular modeling, docking, simulations, cheminformatics, and other bioinformatics techniques to address critical questions in complex network, systems biology, and protein science. Dr. Kakraba is a co-inventor on international and US drug patents for the treatment of neurodegenerative diseases and cancer (international patent publication number W02021163572A1). Furthermore, he has co-authored many peer-reviewed journals, presented his research at countless scientific and academic meetings, and is very passionate about teaching and student mentorship. Most recently, Kakraba has joined the Tulane Center for Aging faculty. 

Prior to joining Tulane University, Dr. Kakraba was an assistant professor of statistics and data science at the department of mathematics and statistics at Eastern Kentucky University from Fall 2021 to Fall 2023.