Tajana Šimunić Rosing
University of California, San Diego
Accelerating bioinformatics workloads
Personalized medicine is driven by deluge of genomics, transcriptomics, proteomics, and metabolomics data. In this talk I discuss how we accelerated a number of key bioinformatics tools using novel machine learning techniques, such as Hyperdimensional computing, in & near memory and storage acceleration, resulting in multiple orders of magnitude speed up as compared to the state of the art. Our accelerated microbiome and COVID-19 pipelines are currently used by the UCSD medical center. Such results go a long way toward ushering a new age of portable tools that can be used for personalized medicine.
Tajana Šimunić Rosing is a Distinguished Professor and Fratamico Endowed Chair of CSE & ECE, a director of SRC and DARPA funded $56.5M PRISM Center and the System Energy Efficiency Lab at UCSD. She is also an ACM & IEEE Fellow, and was selected as Semiconductor Industry Association’s University Research Award winner for Design in 2022. Her research interests are in energy efficient computing, computer architecture, neuromorphic computing, distributed and embedded systems. She has been involved as a PI and Theme lead in GSRC, MuSyC, TerraSwarm, CRISP, and is currently a PI in Cognitive Computing Center. She is also leading DARPA, NSF and SRC funded projects on Hyperdimensional Computing, SRC funded project acceleration of Fully Homomorphic Encryption, and NSF AI TILOS Research Institute projects. From 1998 until 2005 she was a full time research scientist at HP Labs while also leading research efforts at Stanford University. She finished her PhD in EE in 2001 at Stanford, concurrently with finishing her Masters in Engineering Management. Prior to pursuing the PhD, she worked as a senior design engineer at Intel for 4 years.
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