Shashikant Ilager
Postdoc Researcher, Vienna University Of Technology

Room HE0214
Favoritenstrasse 11
Vienna, Austria, 1040
Hello World!
I am a Postdoctoral Researcher at the High Performance Computing (HPC) Group, Vienna University of Technology (TU Wien), Austria. I work at the intersection of distributed systems, energy efficiency, and machine learning. In my research, I study how to design, optimize, and manage large-scale computing systems, such as cloud and edge platforms, that can support the growing demand for data-intensive and AI applications while minimizing environmental and economic costs. I ground my work in the characterization of distributed systems and optimization using learning-centric approaches. Recently, I have been exploring the energy efficiency of distributed AI applications to evaluate and improve their performance and sustainability.
Previously, I obtained my Ph.D. in Computer Science and Engineering at the CLOUDS Lab, University of Melbourne, Australia.
news
Oct 15, 2023 | I will be working as a visiting researcher (Oct 2023- Dec 2023) at INRIA, STACK team, hosted by Daniel Balouek @IMT Atalantique, Nantes, France. |
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Sep 26, 2023 | Our paper on A Self-adaptive Energy-aware Appraoch for Edge-AI Application Management has been accepted @ASE 23. Please find the paper here |
May 15, 2023 | I visited CLOUDS lab @UniMelb and DisNet lab @Monash Univeristy, Australia and presented our recent work on edge monitoring and symbolic representation of data. |
Dec 8, 2022 | I presented our work on “Decentralized Edge Monitroing” at UCC conference, Vancouver, Washington, USA. Please check out the paper here. |
Sep 26, 2022 | I attended IC2E 2022 conference, Pacific Grove, California, USA. |
Oct 20, 2021 | My PhD thesis awarded Outstanding PhD Thesis Award by IEEE Technical Committee on Cloud Computing (IEEE TCCLD). Citation reads as “For outstanding research on machine learning-based energy and thermal efficient management of Cloud Data Centres”. For more details, please browse here. |
May 20, 2020 | Our paper on data-driven GPU frequency scaling has recieved Best paper award @ ACM/IEEE CCGRID 20. |