Project Overview
Computer Science and Mathematics Meet Biology
Department(s)
Computer Science
Mathematics
Biology
Abstract
My lab's research focuses on understanding biological systems and human disorders through mathematical modeling, machine learning, network analysis, and computer programming. Since 2010, I've supervised over 80 research students from a variety of disciplines (including computer science, mathematics, and biology). Over 30 research students helped to publish more than 20 articles. My students also gave research presentations at national and international conferences. Over 20 past research students are now employed by firms such as Google, Microsoft, Facebook, Amazon, Square, and Illumina. More than 20 students are pursuing Ph.D., M.D., or M.S. degrees at Columbia, Harvard, Yale, UCLA, UCSD, and the University of Chicago.
This summer, we will tackle several exciting projects using an extensive array of computational techniques, including machine learning, mathematical modeling, statistical analysis, and network science. Please keep in mind that I pair students with different backgrounds. Thus, knowing the biology underlying these problems is neither expected nor required for CS/Math students. Similarly, knowing how to code is neither expected nor required for biology students.
Project 1. Predicting Human Mood Disorders with Machine Learning
Human mood disorders such as addiction are a significant issue impacting a large segment of the population. Recent research has identified a connection between mood disorders and disruptions in human circadian rhythms, which are often due to variations in circadian clock genes. In this project, we plan to employ machine learning and statistical analysis techniques to identify key circadian clock gene variations and clinical factors that contribute to human mood disorders. Based on these identified risk factors, we aim to develop a predictive model for these disorders, enhancing our understanding and ability to predict them.
Project 2. Predicting Wild Seal Population Dynamics with Artificial Intelligence and Genomics
In this project, we aim to leverage artificial intelligence and facial recognition technology, combined with environmental DNA-based population genomics, to assess key population metrics for harbor seals. Our primary objective is to monitor the population size and structure, genetic diversity, and the patterns of habitat loyalty and migration in wild seal populations. This will be achieved in a non-intrusive manner, ensuring minimal disturbance to both the seals and their natural ecosystem.
Project 3. Identifying microRNA Biomarkers for Cancer with Machine Learning and Network Science
We aim to utilize machine learning and network science to discover potential microRNA (miRNA) biomarkers for major types of cancer. Our goal is to identify specific miRNAs associated with each type of cancer, understand the biological pathways they influence, and determine their roles in either promoting or inhibiting cancer growth. This study represents a non-invasive, data-centric approach to advancing our understanding of cancer and developing new strategies for its diagnosis and treatment.
Project 4. Creating a Computer Algorithm to Accurately Diagnose Viral Infections
This study is dedicated to enhancing the bait enrichment process, a crucial technique in genomics involving synthetic probes, or "baits," to identify and amplify specific DNA fragments. This approach is critical in disease research, including viral infections. Traditional methods focus on selecting baits that target specific DNA sequences. However, our research aims to innovate by designing baits that specifically target a particular organism's DNA sequences while avoiding others' DNA sequences. We will evaluate our approach using metrics such as the number of baits required, algorithm runtime, and redundancy in covered regions. Our goal is to significantly improve the bait design process, contributing to advancing genomic research.
Student Qualifications
Computer science students must have taken COSC 102 and/or COSC 202. Math students must have taken MATH 260 and/or MATH 376. Biology students must have strong literature search and writing skills.
Number of Student Researchers
4 students
Project Length
10 weeks