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SUMMARY:Democratizing Data Science by Leveraging Structure - Amir Shaikhha
DTSTART:20240322T110000Z
DTEND:20240322T120000Z
UID:TALK213265@talks.cam.ac.uk
CONTACT:Luisa Cicolini
DESCRIPTION:Modern data science pipelines employ a variety of workloads\, 
 including tensor algebra\, graph processing algorithms\, and relational qu
 ery processing. This results in using a set of loosely coupled data proces
 sing frameworks that move the data across the analytics pipeline\, leading
  to unnecessary resource and energy consumption. This talk shows a compila
 tion-based approach to move the computation closer to the data. This is ac
 hieved by designing (domain-specific) languages that leverage the structur
 e of data with algebraic optimizations. We show that for a wide range of a
 pplications\, including database query processing and tensor processing\, 
 our proposed approach significantly outperforms the state-of-the-art frame
 works.\n\n*Speaker Bio*: Amir Shaikhha is an Assistant Professor (Lecturer
 ) in the School of Informatics at the University of Edinburgh. His researc
 h focuses on the design and implementation of data-analytics systems by us
 ing techniques from databases\, programming languages\, compilers\, and ma
 chine learning communities. Prior to that\, he was a Departmental Lecturer
  at Oxford. He earned his Ph.D. from EPFL in 2018\, for which he was award
 ed a Google Ph.D. Fellowship in structured data analysis\, as well as a Ph
 .D. thesis distinction award. He has won the Best Paper Award at GPCE 2017
  and the Most Reproducible Paper Award at SIGMOD 2017. He (co-)chaired the
  program committees of DBPL 2021\, Scala 2022\, and GPCE 2023.\n\nTo join 
 the video meeting\, click this link: https://meet.google.com/oms-poez-bas
LOCATION:Room SS03\, Computer Laboratory\, William Gates Building
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