Location: De Anza

Barbara Engelhardt, PhD Princeton University Barbara E. Engelhardt, an associate professor, joined the Princeton Computer Science Department in 2014 from Duke University, where she had been an assistant professor in Biostatistics and Bioinformatics and Statistical Sciences. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.

Spatial single-cell technologies have enabled the comprehensive study of complex tissues and organs at a single-cell level. However, the statistical methods to perform analyses have not kept pace with the technologies for measuring spatially-resolved genes and proteins in tissue samples. In this talk, I will present two methods that allow the study of spatially-resolved proteomics data. First, nonnegative spatial factorization develops a low-dimensional representation of the spatially-resolved samples to identify variation in protein expression across space. Second, Gaussian process spatial alignment allows multiple slices from different technologies to be aligned to a known or unknown common coordinate system, enabling the construction of a tissue atlas from disparate samples of that tissue type.

Location: De Anza

Meena Choi, PhD Genentech Extensive experience for the development of statistical methods, tools, and software for the analysis of mass spectrometry-based proteomics and metabolomics data. Highly effective statisticians with over eight years of experience communicating statistical and technical concepts to researchers and clinicians. 5+ years’ experience creating and implementing training programs for chemists, biologists, clinicians and bioinformaticians in Mass Spectrometry-based proteomics community.

Quantitative mass spectrometry-based proteomics is a technology of growing importance in biological and clinical research. As modern quantitative mass spectrometry-based proteomics workflows become complex and diverse, it requires statistical considerations, methods, and computational tools for experimental planning and data analysis to reduce bias and inefficiencies and maximize the reproducibility of results. This talk will highlight statistics essential for proteomics experiments and considerations for designing a proteomics experiment for biological research biomarker discovery and issues related to large quantitative proteomic datasets generated by diverse types of acquisition of proteomics. Also, this talk will present the comprehensive mass-spectrometry proteomics-based strategy and case studies for the clinical protein biomarker development, from a model organism to large-scale clinical samples, with rigorous experimental design and statistical analysis.

Location: De Anza

Patrick Mathias, MD, PhD University of Washington Patrick Mathias, M.D., Ph.D., is a board-certified clinical pathologist and Associate Director of Informatics for UW Laboratory Medicine. Lab medicine has large impact on the general practice of medicine. It is key to correctly diagnosing diseases and selecting the right treatments for patients. Dr. Mathias's goal is to combine technical and medical knowledge to fulfill the triple aim--reduce the per capita cost of health care, improve the health of populations and most importantly improve the patient experience of care. Dr. Mathias earned his M.D. and Ph.D. from the University of Illinois. His clinical and research interests include clinical informatics, clinical chemistry and molecular diagnostics.

The COVID-19 pandemic has been a challenging time for clinical laboratories, but it has also improved awareness of the important role labs play across health care and public health domains. The University of Washington Department of Laboratory Medicine and Pathology has worked to establish diagnostic SARS-CoV-2 testing as a widely available resource over a large footprint of our state by combining expertise in laboratory testing and informatics with community partnerships to expand testing access and convenience. Over the course of the pandemic the needs of our laboratories as well as of the public and of public health authorities have evolved. Staffing and supply chain challenges have placed a greater emphasis on efficiency and productivity. The need for genomic surveillance of the evolving virus has required a close integration of clinical workflows with research-focused genomics workflows. Increased demand for services coupled with record high positivity rates during the omicron surge has challenged our model for expanding capacity using sample pooling. This presentation will describe the evolution of our testing program and infrastructure amidst these challenges, highlighting the informatics capabilities we needed to adapt. Finally, we will discuss what needs and capabilities are likely to persist beyond the pandemic and how the lessons we have learned can better equip laboratories to improve the health of the public.

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Location: Bonsai