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UID:submissions.supercomputing.org_SC23_sess309@linklings.com
SUMMARY:Doctoral Showcase I Presentations
DESCRIPTION:Corralling the Computing Continuum:  Mobilizing Modern Distrib
 uted Resources for Machine Learning and Accessible Computing\n\nTo achieve
  the resource agnostic flexibility of compute described by the computing c
 ontinuum, we combined our work in workload profiling and cost estimation w
 ith task provisioning to present DELTA–a framework for serverless workload
  placement across a computing ecosystem. To address the dynami...\n\n\nMat
 t Baughman (University of Chicago)\n---------------------\nI/O Efficient M
 achine Learning\n\nMy research focuses on systems optimizations for machin
 e learning, specifically on I/O efficient model storage and retrieval.\n\n
 The first part of my work focuses on efficient inference serving of tree e
 nsemble models. Tree structures are inherently not cache friendly and thei
 r traversal incurs random...\n\n\nMeghana Madhyastha (Johns Hopkins Univer
 sity, Argonne National Laboratory (ANL))\n---------------------\nPreemptiv
 e Intrusion Detection:  Real-World Measurements, Bayesian-Based Detection,
  and AI-Driven Countermeasures\n\nThe problem of preempting attacks before
  damages remains the top security priority. The gap between alerts and ear
 ly detection remains wide open because noisy attack attempts and unreliabl
 e alerts mask real attacks from humans. This dissertation brings together:
  1) attack patterns mining driven by r...\n\n\nPhuong Cao (University of I
 llinois)\n---------------------\nDesign Automation Tools and Software for 
 Quantum Computing\n\nQuantum computing promises to solve problems beyond t
 he reach of today’s machines, but it requires efficient and reliable softw
 are tools to realize its potential. This poster gives an overview of vario
 us contributions towards design automation methods and software for quantu
 m computing that le...\n\n\nLukas Burgholzer (Technical University of Muni
 ch)\n---------------------\nInteractive In-Situ Visualization of Large Dis
 tributed Volume Data\n\nLarge distributed volume data are routinely produc
 ed in numerical simulations and experiments. In-situ visualization, the vi
 sualization of simulation or experiment data as it is generated, enables s
 imulation steering and experiment control, which helps scientists gain an 
 intuitive understanding of t...\n\n\nAryaman Gupta (Technical University D
 resden, Center for Systems Biology Dresden (CSBD))\n---------------------\
 nCharged Particle Track Reconstruction Algorithms for Massively Parallel S
 ystems\n\nThe reconstruction of the trajectories of charged particles thro
 ugh detector experiments is a core computational task in the domain of hig
 h-energy physics. Upcoming upgrades to accelerators such as the Large Hadr
 on Collider as well as to experiments like ATLAS threaten to render existi
 ng CPU-based a...\n\n\nStephen Nicholas Swatman (University of Amsterdam, 
 European Organization for Nuclear Research (CERN))\n\nTag: Accelerators, A
 rtificial Intelligence/Machine Learning, Applications, Cloud Computing, Di
 stributed Computing, Data Analysis, Visualization, and Storage, I/O and Fi
 le Systems, Quantum Computing, Security\n\nRegistration Category: Tech Pro
 gram Reg Pass\n\nSession Chairs: André Brinkmann (Johannes Gutenberg Unive
 rsity Mainz) and Xubin He (Temple University, Department of Computer and I
 nformation Sciences)
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