Keynotes

Reimagining AI Systems for Science through the Lens of Data Systems

Arun Kumar, University of California, San Diego
Thursday, August 13, 2026

Abstract

When applying AI to real-world data, the bottleneck is rarely training a single model. It is about rapidly experimenting and iterating at scale, spanning data representations, ML hyperparameters, and nowadays, retrieval and agent configurations. I approach this challenge through a data systems lens, where we reimagine the notions of declarative abstractions, query optimization, and approximate query processing to help democratize AI systems. This lens has led to multiple productive collaborations with domain scientists for workloads spanning DL, polystores, and LLMs. This talk will give a tour of those projects and conclude with some open challenges I see in data management for AI in this era of agents.

First, Project Cerebro reimagined DL model selection as multi-query optimization to raise experimentation throughput while reducing runtimes and GPU costs. Via Project DeepPostures, Cerebro powered multiple public health studies on sedentary and sleep behaviors using large-scale accelerometer time series data. Second, Project AWESOME is building a declarative polystore system for unified analytics over graphs, relations, and text. By enabling novel cross-model query optimizations, it helps applications in social media analytics for political science and cybersecurity. Finally, at my startup RapidFire AI, we are reimagining such DB ideas for LLM applications by building a high-throughput experimentation engine for configuring RAG, agentic, and fine-tuning pipelines. It enables AI developers to reach better eval metrics more quickly while reducing token spend and/or GPU costs.

Speaker Bio

Arun Kumar is a Professor of Computer Science and Engineering and of the Halıcıoğlu Data Science Institute at UC San Diego. His research interests are in data management and systems for ML/AI engineering and analytics. His work has helped the research of domain scientists in public health, epidemiology, and political science, and it has been adopted by multiple companies, including Google, Meta, Oracle, and VMware. He has received three SIGMOD research paper awards and early career awards from the NSF, IEEE TCDE, and VLDB. His first PhD graduate received the ACM SIGMOD Jim Gray Doctoral Dissertation Award. He is a Cofounder and CTO of RapidFire AI, a software startup that helps enterprise teams boost their LLM applications while cutting costs.