Team

Academic Members

Mark Silberstein, Associate Professor
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e-mailwebsitegoogle scholar
Research Interests:
Computer Systems, Operating Systems, Compute and I/O Accelerator, Hardware security and side channels, GPGPU and FPGA computing, Trusted Execution Environments, Programmable Networks

Staff

Pavel Lifshits, Leading Engineer
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e-mail
  • Systems Software and OS
  • GPUs
  • Trusted execution environment
  • Signal processing
  • Power side channels
Gabi Malka, Engineer
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  • FPGA development
  • FPGA-based SmartNICs
Bella Shavit, Research Coordinator
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Graduate Students

M.Sc

Lina Maudlej
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LinkedIn
  • SmartNIC-based access to devices in disaggregated systems
  • Accelerator-centric operating system
Menachem Adelman
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  • Accelerating DNN training via tensor product approximation
Lev Rosenblit
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  • Security analysis of GPUs

Ph.D

Alon Rashelbach
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LinkedIn
  • Computational approach to network packet classification
Meni Orenbach
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  • OS abstractions for trusted execution environments
  • Controlled side channel mitigations for SGX
Haggai Eran
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websitegoogle scholarLinkedIn
  • OS abstractions for programmable SmartNICs
  • Data center network congestion control
Shai Bergman
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google scholarLinkedIn
  • Architectural and OS support for disaggregated memory
  • OS Integration of peer-to-peer transfers between GPUs and SSDs
Lior Zeno
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google scholarLinkedIn
  • Programmable networks

Alumni

Lluis Vilanova
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2020, Post Doc
  • An OS for disaggregated data centers
Now at:
Imperial College London
Maroun Tork
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2019, MSc
  • SmartNIC-driven accelerator-centric network servers
Now at:
Facebook
Tanya Brokhman
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2019, MSc
  • A unified page cache in heterogeneous systems
Now at:
Network.IO Innovation Lab
Marina Minkin
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2019
  • Speculative execution attacks on SGX enclaves
Now at:
University of Michigan
Vasilis Dimitsas
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2018, MSc
  • I/O prefetcher for GPUs
Now at:
Graphcore
Shai Vaknin
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2018, M.Sc
  • Dynamic update of learning rate in DNN training
Now at:
Facebook
Amir Wated
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2018, MSc
  • Design of GPU-centric network servers
  • Native support for efficient GPU-side networking
Now at:
Cisco
Feras Daoud
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2017, M.Sc.
  • GPURDMA: high performance network I/O on GPUs
Now at:
Mellanox
Sagi Shahar
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e-mail
2016, MSc
  • Efficient file I/O from GPUs
  • Software-managed virtual memory in GPUs
Now at:
Google
Matan Hamilis
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2015, MSc
  • High performance additive FFT in finite fields
Now at:
IDF