I'm Ryan Marcus, an assistant professor of computer science at the University of Pennsylvania. I'm using machine learning to build the next generation of data management tools that automatically adapt to new hardware and user workloads, invent novel processing strategies, and understand user intention.

I am especially interested in query optimization, index structures, intelligent clouds, programming language runtimes, program synthesis for data processing, and applications of reinforcement learning to systems problems. Check out my list of current projects for more, especially if you are interested in working with us!

Email: rcmarcus@seas.upenn.edu
Office: AGH 407

News

19 Oct 2026Our 📄 data-oriented debugging paper, which considers debugging workflows as queries over execution traces, will be presented at UIST '26.
17 Sep 2026Our work on 📄 Data Canvas, a provenance-guided harness for agentic data engineering, in collaboration with URI, will be presented at COLM '26.
07 Aug 2026Our 📄 Ultron paper investigating history-based query optimization in collaboration with Databricks, was published at VLDB '26.
12 Jul 2026Our 🤝 NSF grant for combining online and offline query optimization for scientific discovery data systems, with co-PIs Jacob R. Gardner and Zachary G. Ives, was funded!
22 Apr 2026The 📰 Databricks blog and the 📰 UC Berkeley ADRS blog highlighted our work on offline query optimization with LLMs.

Previous news items ...

19 Mar 2026Our 📊 paper analyzing GROUP BY implementation strategies was published at VLDB '26. This was Daniel Xue's 🎓 bachelor's thesis!
20 Jan 2026Our work on 📄 survivorship bias in industrial database workloads won the 🏆 best paper award at CIDR '26!
16 Jan 2026I gave a 🗣️ keynote talk at North East Database Day '26 about the future of reinforcement learning for query optimization.
28 Oct 2025Our paper on 📄 Marlin, a system for adaptive sharding in Byzantine environments, and 📄 SEFRQO, an LLM-powered self-evolving query optimizer led by USC, will appear at SIGMOD '26!
01 Oct 2025Our 🤝 NSF TIP grant for building a trusted integration data exchange system, led by Sebastian Angel, was funded!
01 Sep 2025Our workshop paper on using 📄 wavelet trees for learned secondary indexes, led by BU, won a 🏆 best paper honorable mention at the AIDB@VLDB2025 workshop.
01 Jul 2025I received the 🎉 2025 Google ML and Systems Junior Faculty Award!
30 Jun 2025Our 📄 theory of generalization in learned cardinality estimation, along with our paper on 📄 learning cardinality estimates from incomplete data, will appear at VLDB '25! Both papers are from 🎓 final-year PhD student Peizhi Wu.
05 May 2025We'll present two papers on learned offline query optimization at SIGMOD '25: Jeff Tao's 📄 BayesQO work on using Bayesian optimization to find "super-optimized" query plans, and Zixuan Yi's 📄 LimeQO work on optimizing entire query workloads at once.
15 Apr 2025Our demonstration of 🛠️ ScaleLLM, which combines embeddings and small models to emulate using an LLM on each row of a large database, will be presented at SIGMOD '25.
01 Mar 2025The 📄 BFTBrain paper, which uses reinforcement learning to maximize the performance of adversary-tolerant distributed systems, will be presented at NSDI '25.
06 Dec 2024Our work on 📄 LLMSteer, a system for steering query optimizers with large language models, will be presented during a 🔦 spotlight talk at the NeurIPS ML4Sys workshop!
20 Jul 2024We'll be presenting our 📄 vision for full stack adaptivity via machine learning for blockchain systems at VLDB '24, along with a 🛠️ demo of BFTGym, our environment for performance testing BFT protocols under various fault conditions.
01 Jun 2024Two fresh takes on query planning presented at SIGMOD '24: first, 📄 Stage, the cache-based multistage query latency predictor used in Redshift, and second, 📄 LimeQO (aiDM workshop), a workload-level query steering technique using linear methods.
20 May 2024I appeared on the 🎙️ Disseminate podcast.
06 Dec 2023I gave a 🗣️ talk at PrestoCon about learned query optimization and 📄 AutoSteer (abstract).
16 Aug 2023Our 📄 AutoSteer paper, an extensible learned query optimizer for any SQL database, was published in VLDB '23. We're also presenting a demo of 🛠️ QO-Insight, our tool for exploring and understanding learned query optimizers.
19 Jun 2023Our 📄 Kepler (robust learned parametric query optimization) and 📄 Auto-WLM (learning enhanced workload management) papers were published at SIGMOD '23.
07 Apr 2023Our 📄 AdaChain paper, the first adaptive blockchain that switches architectures in order to optimize throughput for dynamic workloads, was published at VLDB '23.
20 Feb 2023Our 📄 paper on robust cardinality estimation under dynamic workloads was published at VLDB '23.
15 Sep 2022Our 📄 SageDB paper, the first complete data system built with instance optimization as a foundational design principle, was published at VLDB '22.
30 Apr 2022I will be 👋 joining the CIS faculty at the University of Pennsylvania in Fall 2023!
15 Jun 2021Our 📄 Bao paper, a practical approach to learned query optimization, 🏆 wins the Best Paper Award at SIGMOD '21.
18 Mar 2021Our 📊 experiments and analysis paper presenting the first 🛠️ benchmark of learned indexes has been accepted to VLDB '21.

Blog Posts

The Weierstrass Transform An interactive demo of the Weierstrass transform, a data smoothing technique found in image processing, signal processing, physics, and statistics (09 Sep 2016).
Vulcan: A JavaScript Automated Proof System An interactive demo of resolution-based theorem proving, and an explaination of how it works (02 Sep 2015).
A JavaScript Connect Four AI A demo of a Connect Four AI, and an explanation of the minimax search technique + heuristic used (23 Dec 2014).
A Dirty JSON Parser A JSON parser that works on malformed JSON… or least does its best (05 Oct 2014).
rdtheory.js: relational database algorithms in JavaScript A tool to compute database normalizations in your browser (09 May 2014).

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