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X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
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DTSTART;TZID=America/Los_Angeles:20260731T160000
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SUMMARY:Aaron Meyer\, Associate Professor\, Department of Bioengineering - UCLA
DESCRIPTION:“Building an Integrative View of Immunity with Data Tensors” \nHigh-throughput technologies now allow us to characterize immune responses with comprehensive scale and detail. While profiling cells across parameters like time\, perturbations\, and genetics yields rich data\, these multidimensional datasets challenge conventional analysis. Standard workflows typically flatten data into two-dimensional matrices\, sacrificing the essential context provided by the experimental structure. Instead\, I will show how tensor decompositions can preserve the structure of multidimensional studies. Choosing the right mathematical representation allows us to derive robust insights—from utilizing in vitro characterization to optimize engineered cytokines\, to identifying single-cell features that define autoimmunity. Furthermore\, these approaches enable integrative analysis that links cellular states to alterations at the tissue\, organ\, and individual scales.
URL:https://qcb.ucla.edu/event/aaron-meyer-associate-professor-department-of-bioengineering-ucla/
LOCATION:Boyer Hall 159
CATEGORIES:QCBio Seminar Series,Research Seminars
ATTACH;FMTTYPE=image/png:https://qcb.ucla.edu/wp-content/uploads/sites/14/2026/07/Meyer-8.19.53-AM.png
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DTSTART;TZID=America/Los_Angeles:20260808T160000
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DTSTAMP:20260724T143901Z
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SUMMARY:Sriram Sankararaman\, Professor of Computer Science\, Human Genetics and Computational Medicine - UCLA
DESCRIPTION:“Insights into the evolutionary and genetic architecture of human complex traits from biobank-scale data” \nThe quest to understand the interplay between evolution\, genes and traits has  been revolutionized by the collection of rich phenotypic and genetic data across millions of individuals in diverse populations.  However analyses of these Biobank-scale datasets present substantial statistical and computational challenges. I will present new statistical and computational techniques that aim to provide a deeper understanding of the role of archaic admixture in human evolution and the genetic architecture of complex traits. While multiple instances of interbreeding between modern and archaic humans (Neanderthals and Denisovans) have been documented\, our understanding of the structure and functional impact of archaic introgression remains limited. I will discuss how we combine maps of introgressed archaic DNA with biobank-scale data to assess the contribution of introgressed alleles to complex traits. In the second part of my talk\, I will describe our efforts to understand the genetic architecture underlying complex traits. Our current picture of genetic architecture is largely one of additive models due\, in part\, to the computational challenges involved in fitting even the simplest models at scale. I will describe a new class of algorithms that can learn non-additive(context-dependent) models of genetic architecture while scaling to biobanks with millions of individuals. By applying these methods to data from the UK Biobank\, we obtain novel insights into the contributions of additive\, dominance\, gene-environment\, and gene-gene interaction effects to complex trait variation.
URL:https://qcb.ucla.edu/event/sriram-sankararaman-professor-of-computer-science-human-genetics-and-computational-medicine-ucla/
LOCATION:Boyer Hall 159
CATEGORIES:QCBio Seminar Series,Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://qcb.ucla.edu/wp-content/uploads/sites/14/2026/07/Sriram.jpeg
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