CS Colloquium: Vasia Kalavri

Speaker:  Vasia Kalavri

Date:  Monday, March 9, 2026

Time:  3:30 PM - 4:30 PM

Location:  Harold Frank Hall 1132

Host:  Amr El Abbadi

Title:  Rethinking Streaming Dataflow Systems: A Case for Disaggregated, Adaptive, and Resource-Aware Engines

Abstract: Streaming dataflow systems, like Apache Flink, have recently gained widespread adoption in industry and academia, while all major cloud providers offer data stream analytics as managed services. However, the increasing diversity of streaming applications and the availability of compute-enabled edge devices, hardware accelerators, and new cloud computing paradigms, is pushing existing dataflow systems to their limits. In this talk, I will describe our ongoing research towards addressing three major shortcomings of the streaming platforms status quo: (i) rigid, monolithic designs, inherited from MapReduce-style predecessors, (ii) homogeneous resource assumptions that hinder the deployment of emerging use cases, like machine learning pipelines and edge analytics, and (iii) notoriously complex to configure infrastructure. I will present our recent results on adaptive reconfiguration and resource-conscious optimizations for stream processing, and I will share our vision towards developing a novel fully-disaggregated streaming runtime architecture.

Speaker Bio:  Vasiliki (Vasia) Kalavri is an Assistant Professor of Computer Science at Boston University, where she co-leads the Complex Analytics and Scalable Processing (CASP) Systems lab. Vasia and her team enjoy doing research on multiple aspects of (distributed) data-centric systems. Recently, they have been working on self-managed systems for data stream processing, systems for scalable graph Machine Learning, and systems for secure collaborative analytics. Before joining BU, Vasia was a postdoctoral researcher at ETH Zurich and received a joint PhD from KTH (Sweden) and UCLouvain (Belgium). Vasia has received various awards for her research, including the IBM Innovation Award for her PhD dissertation, the ETH Zurich Postdoctoral Fellowship, the ACM SIGMOD’23 Systems Award, and the NSF CAREER Award.