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UID:34@escience.washington.edu
DTSTART;TZID=America/Los_Angeles:20230531T043000
DTEND;TZID=America/Los_Angeles:20230531T052000
DTSTAMP:20230525T210230Z
URL:https://escience.washington.edu/events/uwdss-khoda/
SUMMARY:UW Data Science Seminar: Elham Khoda
DESCRIPTION:\n\nPlease join us for a UW Data Science Seminar event on Wedn
 esday\, May 31st from 4:30 to 5:20 p.m. PST. The seminar will feature Elha
 m E. Khoda\, Postdoctoral Scholar at the UW Department of Physics.\n\n\nUs
 e this zoom link to join\n&nbsp\;\n"Fast Machine Learning on FPGAs for par
 ticle physics applications"\n\nAbstract: In particle physics\, we are expe
 riencing a very high raw data rate at the Large Hadron Collider (LHC)\, wh
 ere the protons collide at a 40 MHZ rate. It is impossible to read out and
  store all the data at this high rate. So\, the particle detectors around 
 the LHC ring use an electronic hardware "trigger" system to select potenti
 ally interesting particle collisions for further analysis. Currently\, one
  out of 400 proton-proton collision events passes the hardware trigger. As
  the collision rate will increase by 5-7 times in the future alternative a
 lgorithms\, such as ML\, can be used for fast and accurate decisions.\nIn 
 this talk\, I will highlight the potential applications of ML for hardware
  (ASIC or FPGA) triggers. I will discuss a method to implement the ML algo
 rithms on an FPGA using the hls4ml software package. hls4ml is a user-frie
 ndly software based on High-Level Synthesis (HLS) designed to deploy neura
 l network architectures on FPGAs. I will highlight my recent work on recur
 sive neural networks (RNN)-based and Transformer-based algorithms for trig
 ger applications.\nBiography: Elham E Khoda is a UW particle physics postd
 oc in the EPE group working with Prof. Shih-Chieh Hsu on new particle sear
 ches and Machine Learning algorithms for particle physics. He completed hi
 s Ph.D. in particle physics at the University of British Columbia\, Vancou
 ver\, Canada\, on “Searches for new high-mass resonances in top-antitop 
 and di-electron final states using the ATLAS detector”. He is interested
  in developing ML algorithms to solve particle physics challenges. He is a
  major contributor to EPE's activities toward data-driven discovery with a
 ccelerated AI algorithms. He is working on accelerating ML inference with 
 coprocessors like GPUs and FPGAs.\nThe UW Data Science Seminar is an annu
 al lecture series at the University of Washington that hosts scholars work
 ing across applied areas of data science\, such as the sciences\, engineer
 ing\, humanities and arts along with methodological areas in data science\
 , such as computer science\, applied math and statistics. Our presenters c
 ome from all domain fields and include occasional external speakers from r
 egional partners\, governmental agencies and industry.\nThe 2022-2023 semi
 nars will be virtual\, and are free and open to the public.
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