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Conferences

  • SIGMETRICS is the flagship conference of the SIGMETRICS community.
  • Performance is the flagship conference of IFIP WG 7.3 and occurs jointly with SIGMETRICS once every three years.
  • IMC is at the intersection between SIGCOMM and SIGMETRICS, focusing on Internet measurement.
  • SenSys focuses on the design, implementation, and application of sensor networks.
  • ICPE (formerly WOSP/SIPEW) focuses on the intersection of software design and performance evaluation.
  • MAMA is a high profile workshop that focuses on mathematical performance modeling tools.
  • NetEcon focuses on the Economics of Networks, Systems and Computation
  • ValueTools bridges the study of performance methodologies and the development of tools for performance evaluation.

R. Srikant 2021 ACM SIGMETRICS Achievement Award

ACM SIGMETRICS is pleased to announce the selection of Prof. R. Srikant of the University of Illinois at Urbana-Champaign (UIUC) as the recipient of the 2021 ACM SIGMETRICS Achievement Award for outstanding contributions towards unifying optimization, control and stochastic networks for the design and analysis of computer systems and communication networks.

Prof. R. Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering and the Coordinated Science Lab at the University of Illinois at Urbana-Champaign (UIUC). He is also one of two Co-Directors of the C3.ai Digital Transformation Institute, a consortium of universities and industries jointly headquartered at UIUC and Berkeley, dedicated to the promotion of the digital transformation of society using machine learning, artificial intelligence, cloud computing and IoT. He received his B. Tech from the Indian Institute of Technology, Madras in 1985 and his M.S. and Ph. D. from UIUC in 1988 and 1991, respectively. Prior to returning to UIUC as a faculty member, he spent four years as a Member of Technical Staff at AT&T Bell Laboratories.

His research interests are in the areas of design and performance analysis of algorithms for machine learning, and protocols and architectures for communication networks and cloud computing. The common theme in his research is the application of optimization, control theory and stochastic networks to study complex networks and algorithms. His early work focused on heavy-traffic analysis and fast simulation techniques to study loss networks. Later, he studied the design of architectures and algorithms for the Internet and wireless networks. He pioneered the use of the Lyapunov drift method for the performance analysis of Internet switches, wireless networks, and load balancing algorithms for cloud computing systems and data centers. The drift method resulted in the resolution of a long-standing open conjecture on the heavy-traffic delay performance of switches. More recently, he has been interested in understanding the fundamental limits of performance of neural networks and reinforcement learning algorithms.

Prof. Srikant has received several awards for his research including the IEEE Koji Kobayashi Computers and Communications Award in 2019, the IEEE INFOCOM Achievement Award in 2015, and several best paper awards including the 2015 IEEE INFOCOM Best Paper Award and the 2017 INFORMS Applied Probability Society’s Best Publication Award. Seventeen of his former PhD and post-doctoral advisees are currently on the faculty of many top departments and universities in the US and elsewhere. More information is available on his website at https://sites.google.com/a/illinois.edu/srikant/

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