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2024-04-29 13:00:00
2024-04-29 14:00:00
America/New_York
ML-enabled Genetic Analysis of High-Content Phenotypes
Abstract: In my talk, I will discuss new machine learning (ML) approaches for human genetics. First, I will present ML-enhanced genetic analysis of histological traits, where we leverage a novel semantic autoencoder to compress histological images into trait embeddings for GWAS. In an application to multiple tissues from the GTEx dataset, we discover 4 genome-wide significant loci associated with histological changes, which we can visualise and interpret for each of the discovered variants thanks to our decoder.Second, I will introduce a new method combining machine learning and genetic causal inference for risk predictions. A key advantage of this method is that it doesn't require longitudinal data. This allows for risk prediction of late-onset diseases in large biobanks, where follow-up cases are often limited.Overall, these contributions demonstrate the transformative power of ML in human genetics. Our approaches enable more nuanced analyses of high-dimensional traits and facilitate biomarker discovery.Bio: Francesco Paolo Casale studied physics at the University of Naples Federico II, Italy. He received his PhD in statistical genetics at the University of Cambridge and the European Bioinformatics Institute in 2016, where he developed new computational methods for genetic association studies and contributed to landmark international projects such as the last phase of the 1000 Genomes Project and the Blueprint initiative. He conducted his postdoctoral studies at the Microsoft Research New England lab in Boston, working on deep generative models for imaging genetics and automated machine learning. In 2019, he joined insitro, a drug discovery and development company located in the bay area. There, he led the statistical genetics team, working at the intersection of human genetics, machine learning and functional genomics to enable target identification and characterization. Since January 2022, he is a Principal Investigator in Machine Learning in Biomedicine at the Helmholtz Munich Institute of AI for Health.
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Events
April 26, 2024
No events scheduled
April 29, 2024
May 07, 2024
Quest | CBMM Seminar Series: Invariance and equivariance in brains and machines
Bruno Olshausen
UC Berkeley
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2024-05-07 16:00:00
2024-05-07 17:30:00
America/New_York
Quest | CBMM Seminar Series: Invariance and equivariance in brains and machines
Abstract: The goal of building machines that can perceive and act in the world as humans and other animals do has been a focus of AI research efforts for over half a century. Over this same period, neuroscience has sought to achieve a mechanistic understanding of the brain processes underlying perception and action. It stands to reason that these parallel efforts could inform one another. However recent advances in deep learning and transformers have, for the most part, not translated into new neuroscientific insights; and other than deriving loose inspiration from neuroscience, AI has mostly pursued its own course which now deviates strongly from the brain. Here I propose an approach to building both invariant and equivariant representations in vision that is rooted in observations of animal behavior and informed by both neurobiological mechanisms (recurrence, dendritic nonlinearities, phase coding) and mathematical principles (group theory, residue numbers). What emerges from this approach is a neural circuit for factorization that can learn about shapes and their transformations from image data, and a model of the grid-cell system based on high-dimensional encodings of residue numbers. These models provide efficient solutions to long-studied problems that are well-suited for implementation in neuromorphic hardware or as a basis for forming hypotheses about visual cortex and entorhinal cortex.Bio: Professor Bruno Olshausen is a Professor in the Helen Wills Neuroscience Institute, the School of Optometry, and has a below-the-line affiliated appointment in EECS. He holds B.S. and M.S. degrees in Electrical Engineering from Stanford University, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology. He did his postdoctoral work in the Department of Psychology at Cornell University and at the Center for Biological and Computational Learning at the Massachusetts Institute of Technology. From 1996-2005 he was on the faculty in the Center for Neuroscience at UC Davis, and in 2005 he moved to UC Berkeley. He also directs the Redwood Center for Theoretical Neuroscience, a multidisciplinary research group focusing on building mathematical and computational models of brain function (see http://redwood.berkeley.edu).Olshausen's research focuses on understanding the information processing strategies employed by the visual system for tasks such as object recognition and scene analysis. Computer scientists have long sought to emulate the abilities of the visual system in digital computers, but achieving performance anywhere close to that exhibited by biological vision systems has proven elusive. Dr. Olshausen's approach is based on studying the response properties of neurons in the brain and attempting to construct mathematical models that can describe what neurons are doing in terms of a functional theory of vision. The aim of this work is not only to advance our understanding of the brain but also to devise new algorithms for image analysis and recognition based on how brains work.
Singleton Auditorium (46-3002)
May 24, 2024
Dynamic Adaptive Optimization: Recovering from Hardware Errors and Software Crashes in a Distributed Virtual Machine
University of California, Santa Cruz
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2024-05-24 14:00:00
2024-05-24 15:00:00
America/New_York
Dynamic Adaptive Optimization: Recovering from Hardware Errors and Software Crashes in a Distributed Virtual Machine
Abstract: TidalScale was a startup aquired by HPE in December 2022. TidalSale developed a software architecture called distributed virtual machines. Today's virtual machines in widespread use today allows multiple operating systems to share a server. TidalScale inverts this paradigm. A single virtual machine running on TidalScale runs a single operating system instance across a cluster of standard servers. This virtual machine sits between an operating system and a cluster of servers. It runs on premise or in the cloud. Because they are virtual, resources like processors and memory can migrate among nodes in the cluster. The virtual machine dynamically self-optimizes resource placement in real time under contol of a set of machine learning algorithms. Servers can automatically and dynamically be added and removed depending on fluctuationg workloads, allowing for dynamic hardware scalability, but also increasing reliability and resiliency. In this talk, we specifically show how these servers automatically, without any human intervention, recover from most hardware failures, and and provide excellent restart performance should OS failures occur.Bio: Ike Nassi is a consultant and an Adjunct Professor of Computer Science at UC Santa Cruz, a Founding Trustee at the Computer History Museum and an advisory board member of TTI/Vanguard. Ike was the founder of TidalScale, sold to HPE Dec. 2022. Previously, he was an Executive Vice President and Chief Scientist at SAP. Ike started or helped to start four companies: Encore Computer Corporation building hierarchical strongly coherent shared memory symmetric multiprocessors (1984); InfoGear Technology, which developed both Internet appliances (including the first iPhone) (1996); Firetide, an early wireless mesh networking company (2000), and TidalScale (2012). He was SVP for Software at Apple Computer and a Corporate Officer. He worked at Visual Technology, and Digital Equipment Corporation. In the past, Dr. Nassi was a Visiting Scholar at Stanford University, twice a Research Scientist at MIT, and a Visiting Scholar at University of California, Berkeley. He has served on the board of the Anita Borg Institute for Women and Technology, and the IEEE Computer Society Industry Advisory Board. He holds a PhD in Computer Science from Stony Brook University.He was awarded two certificates for Distinguished Service from the Department of Defense, one for his work on the design of the programming language Ada and one for his work on DARPA ISAT. He is a Life Fellow of IEEE and a Life member of ACM. He is named on over 35 patents.
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