Winner of SPEC Kaivalya Dixit Distinguished Dissertation Award 2021
Tuesday, March 15, 2022 00:00
André Bauer from University of Würzburg: Automated Hybrid Time Series Forecasting: Design, Benchmarking, and Use Cases
The SPEC Kaivalya Dixit Distinguished Dissertation Award is an annual award that aims to recognize outstanding doctoral dissertations within the scope of the SPEC Research Group in terms of scientific originality, scientific significance, practical relevance, impact, and presentation.
The winning dissertation “Automated Hybrid Time Series Forecasting: Design, Benchmarking, and Use Cases” was authored by André Bauer from University of Würzburg, under the supervision of Professor Samuel Kounev. The selection committee was impressed with the importance of forecasting of hybrid time series and the application to autoscaling along with the comprehensive coverage including design, benchmark platform and use cases.
The award is to be presented at the 13th ACM/SPEC International Conference on Performance Engineering (ICPE) scheduled to be held virtually in April 2022.
Given the high quality of dissertations nominated for this award, the committee decided to publicly recognize another dissertation as Runner-Up, “Enabling High-Performance Large-Scale Irregular Computations” authored by Dr. Maciej Besta, of ETH Zürich, under the supervision of Dr. Torsten Hoefler.
The award selection committee for 2021 was chaired by Prof. John Murphy of University College Dublin, Ireland and consisted of the following members:
- Mathew Colgrove (NVIDIA, USA)
- David Daly (MongoDB, USA)
- William Knottenbelt (Imperial College London, UK)
- Andrea Marin (Università Ca' Foscari di Venezia, Italy)
- Rekha Singhal (Tata Consultancy Services, India)
- Evgenia Smirni (College of William and Mary, USA)
The SPEC Kaivalya Dixit Distinguished Dissertation Award was established in 2011 to recognize outstanding dissertations within the scope of the SPEC Research Group. Contributions of interest span the design of metrics for system evaluation as well as the development of methodologies, techniques and tools for measurement, load testing, profiling, workload characterization, dependability and efficiency evaluation of computing systems. Dissertations defended between October 2021 and September 2022 will be eligible to be nominated for the 2022 award.