Neil Agarwal will present his FPO, "Offline Processing for Machine Learning-Based Video Systems," on Monday, May 5, 2025, at 9 AM in COS 302. 

Please see below for details.

Committee:

Adviser/Examiner: Professor Ravi Netravali

Examiner: Professor Wyatt Lloyd

Examiner: Professor Kai Li

Reader: Professor Maria Apostolaki

Reader: Professor Amit Levy

Title: Offline Processing for Machine Learning-Based Video Systems

Abstract: Video-based applications proliferate our society, from sophisticated video analysis pipelines to large-scale video content and delivery platforms. Machine learning algorithms are instrumental in supporting these applications, enabling fine-grained, high-quality analysis of video data and supporting highly efficient and dynamic control of underlying system processes in video applications. However, integrating machine learning techniques into existing video pipelines brings significant overheads that hinder usability and deployability. This dissertation examines how we can leverage offline processing techniques to mitigate these potential overheads. By carefully shifting computational tasks from application runtime into an offline phase, it is possible to reduce runtime latency, computational cost, and disruptive behaviors experienced by end users. We present two systems that demonstrate the potential benefits of this approach in real-world video applications. Boggart builds a conservative, model-agnostic video index ahead of time to accelerate retrospective video analytics querying pipelines. Mowgli enables practical, offline-trained learning for real-time video rate control by leveraging historical telemetry logs to learn high-quality bitrate adaptation policies without disrupting user experience during deployment.