Upcoming Events

Friday, January 24th, ARB Hess Commons, 11:45am Guest Lecture    Eric Sun PhD Student Stanford University Machine learning for aging and spatial omics Abstract:  Aging is a highly complex process and the greatest risk factor for many chronic diseases including cardiovascular disease, dementia, stroke, diabetes, and cancer. Recent spatial and single-cell omics technologies have enabled the high-dimensional profiling of complex biology including that underlying aging. As such, new machine learning and computational methods are needed to unlock important insights from spatial and single-cell omics datasets. First, I present the development of high-resolution machine learning models (‘spatial aging clocks’) that can measure the aging of individual cells in the brain. Using these spatial aging clocks, I discovered that some cell types can dramatically influence the aging of nearby cells. Next, I present new computational and statistical methods for overcoming the gene coverage limitations of existing spatial omics technologies, which have enabled the discovery of gene pathways underlying the spatial effects of brain aging. Finally, I introduce several methods for improving the reliability and robustness of high-dimensional data visualizations.   About the Biostatistics Departmental Seminars & Lectures for Spring 2025: During the Fall and Spring semesters, the Department of Biostatistics holds regular seminars on Thursdays, called the Levin Lecture Series, on a wide variety of topics which are of interest to both students and faculty. Over each semester, there are also often guest lectures outside the regular Thursday Levin Lecture Series, to provide a robust schedule the covers the wide range of topics in Biostatistics. The speakers are invited guests who spend the day of their seminar discussing their research with Biostatistics faculty and students. All members of the Columbia Community are welcome, though most lectures are held in the Medical Center Campus and require a CUIMC ID to enter the building. No registration is necessary to attend. **Lectures are in-person only unless marked otherwise.**  

Thursday, January 30th, ARB Hess Commons, 11:45am Levin Lecture    Paul Albert, PhD Senior Investigator, Biostatistics Branch NCI/DCEG Innovative Applications of Hidden Markov Models in Cancer, Epidemiology, and Genetics Abstract:  During the past 30 years, Hidden Markov modeling (HMM) has had a big impact in the analysis of biomedical data, with a few important application areas in genomics, natural history modeling, environmental monitoring, and the analysis of longitudinal data. In cancer genomics, for example, the use of HMM has played an important role in uncovering both susceptibility (germline) and tumor progression (somatic) of cancer. In this talk, I will present a series of novel applications of HMMs in cancer epidemiology and genetics. I will describe the use of HMM to identify multiple subclones in next-generation sequences of tumor samples (Choo-Wosoba et al., Biostatistics 2021). I will also discuss the application of HMMs for characterizing the natural history of natural history of human papillomavirus and cervical precancer (Aron et al., Statistics in Medicine, 2021). Further, I describe the use of HMMs for application of HMMs for investigating the effects of sleeping and activity on mortality. Last, I describe the use of HMMs for joinpoint analysis in cancer surveillance. All four examples required interesting adaptations of standard HMM estimation that will be highlighted.   About the Biostatistics Departmental Seminars & Lectures for Spring 2025: During the Fall and Spring semesters, the Department of Biostatistics holds regular seminars on Thursdays, called the Levin Lecture Series, on a wide variety of topics which are of interest to both students and faculty. Over each semester, there are also often guest lectures outside the regular Thursday Levin Lecture Series, to provide a robust schedule the covers the wide range of topics in Biostatistics. The speakers are invited guests who spend the day of their seminar discussing their research with Biostatistics faculty and students. All members of the Columbia Community are welcome, though most lectures are held in the Medical Center Campus and require a CUIMC ID to enter the building. No registration is necessary to attend. **Lectures are in-person only unless marked otherwise.**  

Tuessday, Februaruy 4th, ARB Hess Commons, 11:45am Guest Lecture    Ying Cui, PhD Postdoctoral Scholar, Department of Biomedical Data Science Stanford University Advancing Biomedical Data Science: From Population Insights to Personalized Decisions Abstract:  Rapid advances in biomedicine have enabled us to address important questions that were once intractable. There is a pressing need for analyzing massive data sets emerging from cutting-edge technologies, presenting challenges such as high-dimensionality and multi-modality. Additionally, there has been rising interests in personalized decision-making. Inspired by these challenges, my research aims to enhance the integration of statistical insights and data science innovations in biomedical research. In this talk, I will cover two projects. The first part of the talk explores key questions about identifying covariates relevant to clinical outcomes of interest. Addressing these questions, however, can be complicated due to the presence of complex covariate effects. To tackle this problem, I developed a new testing and screening framework by adopting a global view via the novel concept of interval quantile independence. I showed that this general testing framework can naturally yield both unconditional and conditional screening procedures for ultra-high dimensional settings and enjoy the sure screening property. In the second part of the talk, I address the feature selection problem from a personalized perspective. I designed a novel dynamic prediction rule to determine the optimal order of acquiring features in predicting clinical outcomes of interest for individual subject. The goal is to optimize model performance while reducing the costs associated with measuring features. To achieve this, I employed reinforcement learning, where the agent decides the best action at each step: either making a final decision or continuing to collect new predictors. The proposed approach mirrors and improves real life decision-making processes, employing a “learn-as-you-go” paradigm.   About the Biostatistics Departmental Seminars & Lectures for Spring 2025: During the Fall and Spring semesters, the Department of Biostatistics holds regular seminars on Thursdays, called the Levin Lecture Series, on a wide variety of topics which are of interest to both students and faculty. Over each semester, there are also often guest lectures outside the regular Thursday Levin Lecture Series, to provide a robust schedule the covers the wide range of topics in Biostatistics. The speakers are invited guests who spend the day of their seminar discussing their research with Biostatistics faculty and students. All members of the Columbia Community are welcome, though most lectures are held in the Medical Center Campus and require a CUIMC ID to enter the building. No registration is necessary to attend. **Lectures are in-person only unless marked otherwise.**