Andrew Holbrook's Pioneering Research at UCLA: From Viral Spread to Alzheimer's Disease
Introduction
Dr. Andrew Holbrook is an Associate Professor of Biostatistics at the University of California, Los Angeles (UCLA). His research interests span a wide range of topics, including Bayesian statistics and machine learning, dimension reduction, imaging statistics, and viral epidemiology. This article explores Dr. Holbrook's journey, research contributions, and the impact he is making in the fields of biostatistics and computational biology.
Early Life and Education: A Winding Road
Andrew Holbrook grew up in Orange County, California, with an older brother and a younger sister. From early childhood through his teenage years and beyond, he’s always been an intensely curious person. He loved all the subjects he studied at school, but was particularly drawn to math, science, and - his absolute favorite - history. Dr. Holbrook considers his career and intellectual path a “long and winding road.” As his education progressed, his love of history drove him to study Latin and ancient Greek. He wanted to develop a deeper understanding of how European and western civilizations developed, and how language influences the way that we think about the world. After high school, Dr. After graduation, Dr. Holbrook took a job in northeastern China teaching English and math, one of the few jobs he could find in the midst of the 2009 financial crisis. After working in China for a couple of years, Dr. Holbrook decided to make a career change. With all the financial uncertainty unfolding, he knew he wanted to pursue an intellectually engaging professional path that would leverage his longstanding interest in math and analysis while also providing him with stability. With those two goals in mind, he decided to attend graduate school at the University of California, Irvine, to earn a Ph.D.
Dr. Holbrook received his Ph.D. working with Professor Babak Shahbaba at University of California, Irvine, studying statistical methods for neural decoding and the analysis of longitudinal magnetic resonance imaging (MRI). He completed his postdoctoral training at the UCLA Department of Human Genetics, where he worked with Professor Marc Suchard to develop high-performance computing (HPC) tools for viral phylogeography.
Research Focus: HPC, Deep Learning, and Statistical Modeling
Dr. Holbrook’s research focuses on HPC strategies for big data statistical inference and the incorporation of deep learning into highly-structured statistical models. His work is characterized by the development of innovative computational approaches to analyze complex biological data, with applications ranging from viral epidemiology to neuroimaging.
His research interests include scalable Bayesian inference for applications in neural decoding and viral epidemiology.
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NSF CAREER Award: Modeling the Global Spread of Viruses
Dr. Andrew Holbrook, assistant professor in UCLA Fielding's Department of Biostatistics, received the National Science Foundation's (NSF) CAREER Award for his work building statistical models for the global spread of viruses. The NSF CAREER Award is considered a prestigious award for early career faculty "who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization," according to the NSF. As part of the award, Dr. Holbrook will receive more than $500,000 in funding to support his research that develops viral "evolutionary contagion" models parameterized by flexible neural networks. A significant component of Dr.
His work in this area involves developing viral "evolutionary contagion" models parameterized by flexible neural networks. These models aim to provide insights into the dispersal history of viruses during epidemics, a task known as phylogeographic inference.
As an example of how the techniques could be used, the researchers used historical data from the 2021-22 COVID-19 (SARS-CoV-2) outbreak in the United Kingdom and used the newly developed methods to check if the dispersal patterns could have been discerned earlier than they were, historically. “The usefulness of this sort of expedited analysis in improving a public health department’s reaction to an emerging public health crisis is obvious,” said UCLA Fielding’s Dr. Andrew Holbrook, an assistant professor in the Department of Biostatistics and a study co-author.
Applications in Public Health: Guiding Decisions in Infectious Disease Crises
Dr. Holbrook's research has direct implications for public health decision-making during emerging infectious disease crises. By developing methods to analyze viral sequences, his work helps to provide clear guidelines for the use of novel computational approaches.
“The results enable us to write clear guidelines for the use of novel computational approaches analyzing viral sequences to guide public health decisions in emerging infectious disease crises,” said Dr. Marc Suchard, a physician and professor in the UCLA Fielding School of Public Health's Department of Biostatistics. The studies focus on the use of viral disease genomes - the genetic material contained within a virus particle, which can be composed of either deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) - to estimate the dispersal history of the virus responsible for an epidemic, a task known as phylogeographic inference.
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AI and Alzheimer's Research: Revolutionizing Brain Science
In addition to his work on viral epidemiology, Dr. Holbrook is also involved in pioneering research at the intersection of artificial intelligence, advanced algorithms, and brain science, exploring how these tools are revolutionizing our understanding of neurological diseases.
Dr. Holbrook shares his fascinating journey from studying ancient languages and teaching in China to pioneering research in biostatistics and Alzheimer's disease. Together, they delve into the intersection of artificial intelligence, advanced algorithms, and brain science, exploring how these tools are revolutionizing our understanding of neurological diseases.
His research in this area focuses on using AI and advanced algorithms to understand the brain and Alzheimer's disease better. He emphasizes the importance of cortical thickness measurements and their role in predicting Alzheimer's.
Statistical Challenges and Innovations
Dr. Holbrook addresses the complexity of analyzing structured data with time and spatial dimensions, and the breakthroughs in AI that are making real-time brain analysis possible. He also discusses the role of deep learning in accelerating data analysis, making it feasible to process complex brain imaging data quickly.
Collaboration and Open Source
Collaboration and open source software play a critical role in advancing scientific research.
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Personal Connection to Alzheimer's
Dr. Holbrook's motivation is driven by personal experiences and the broader impact of neurological diseases on families.
Awards and Recognition
Dr. Holbrook is a recipient of both an NSF CAREER Award and an NIH K25 Award for quantitative research in biomedicine. He was also awarded the honorable mention for the 2019 Leonard J. Savage Award.
Future Directions: Precision Medicine and Early Detection of Dementia
Dr. Holbrook predicts the development of precision medicine tools and the use of functional brain data for early detection of dementia in the next 5-10 years.
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