Anshul Gandhi Named to Leadership Position for Top Performance Modeling Conference: ACM SIGMETRICS 2021

Assistant Professor Anshul Gandhi of the Department of Computer Science in the College of Engineering and Applied Sciences has been named as the Technical Program Committee (TPC) co-chair for the Association for Computing Machinery (ACM) SIGMETRICS 2021 conference. SIGMETRICS is the ACM's flagship conference for the computer systems performance evaluation community special interest group.

In addition to its conference, SIGMETRICS also publishes a newsletter and runs a mailing list to promote research in performance analysis techniques and the innovative use of current methods and tools. The group's target areas include performance modeling and evaluation of file and memory systems, database systems, computer networks, operating systems, architecture, distributed systems, fault tolerant systems and real-time systems.

Along with two other co-chairs, Gandhi will organize the paper selection and program committee selection process for the conference throughout the 3-deadline yearly cycle for 2021. He will also run the associated TPC meetings. "Sigmetrics is the premier venue for performance modeling and evaluation research, and I consider Sigmetrics as my 'home community,'" he said. "As such, I am very honored and excited to be serving as PC Co-Chair for Sigmetrics, especially at this stage of my academic career."

Gandhi has previously been honored by ACM SIGMETRICS with their 2019 Rising Star Research Award. In addition, he was presented the NSF CAREER Award in 2018 for his cloud computing research.

About the Researcher
Anshul Gandhi earned his PhD in computer science at Carnegie Mellon University. After graduating, he spent a year as a post-doctoral researcher at the IBM T.J. Watson Research Center. He leads Stony Brook's PACE Lab and is affiliated with the Smart Energy Technologies Cluster. His current research projects include building performance models for analytics workloads, applying queueing theory and control theory to provide performance guarantees for cloud-deployed applications, scaling multi-tier applications in response to unpredictable workload demand, dynamically provisioning the caching tier, analysis of multi-server systems with setup costs and the impact of scaling on performance and power consumption.