Immanuel Trummer
Immanuel Trummer is a computer scientist at Cornell whose research uses machine learning, and more recently large language models, to make data analysis more efficient and more user-friendly. He studied in Stuttgart and Nantes, earned a PhD at EPFL in 2016, and joined Cornell as an assistant professor in 2016. His SkinnerDB system, presented at SIGMOD in 2019, used reinforcement learning to choose join orders while queries ran, and he applied language models to database tuning with DB-BERT in 2021 and to SQL processing with CodexDB in 2022. He received an NSF CAREER Award in 2023 and published the book Data Analysis with LLMs in 2025. He is an associate professor of computer science at Cornell, where a 2026 LinkedIn grant supports his work on token-efficient agentic data analysis.
- Now at
- Cornell University
Timeline
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2010–2016
A PhD at EPFL
He earns a computer science diploma from the University of Stuttgart and an engineering diploma from the École Centrale de Nantes in 2010, then a computer science PhD from the École Polytechnique Fédérale de Lausanne in 2016.
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2016
Assistant professor at Cornell
He joins Cornell in 2016 as an assistant professor of computer science, with an academic focus on databases, data mining, data science and optimization.
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2019
SkinnerDB at SIGMOD
At SIGMOD 2019, he and coauthors including Junxiong Wang and Saehan Jo present SkinnerDB, a database system that uses reinforcement learning to learn join orders on the fly while a query runs.
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2021–2022
DB-BERT and CodexDB
In December 2021, he posts DB-BERT, a tuning tool that applies BERT language models to read database manuals and uses reinforcement learning to choose settings. In April 2022, he describes CodexDB, an SQL processing engine built on OpenAI's GPT-3 Codex whose internals can be customized through natural language instructions.
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March 2023
An NSF CAREER Award
In March 2023, the National Science Foundation names him one of five Cornell Bowers faculty to receive a CAREER Award, worth about $600,000 over five years. His project uses large language models to support automated database performance tuning.
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2024–2025
λ-Tune for database tuning
In November 2024, he and a coauthor post λ-Tune, a framework that has large language models write complete configuration scripts for database tuning. The paper is set for presentation at SIGMOD 2025.
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April 2025
A book on LLM data analysis
In April 2025, Manning publishes his book Data Analysis with LLMs, which covers text, tables, images and sound. It guides data scientists through projects that use OpenAI's Python library, LangChain, LlamaIndex and models from Anthropic, Cohere and Hugging Face.
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As of October 2026
Associate professor at Cornell
He is an associate professor of computer science at Cornell, where his research makes data analysis more efficient and more user-friendly, in particular through large language models. A 2026 Bowers-LinkedIn grant supports his project "Making Agentic Data Analysis Token-Efficient", which explores database systems that lower the costs of LLM-based agents performing iterative data analysis.
Career
- Since 2016Cornell UniversityAssistant Professor of Computer Science
Education
- 2016EPFLComputer science PhD
- 2010University of StuttgartComputer science diploma
- 2010École Centrale de NantesEngineering diploma
Sources and credits
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- Introducing new members of the faculty for 2016-17 | Cornell Chronicle
- Immanuel Trummer | Department of Computer Science | Cornell Bowers
- [1901.05152] SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning
- [2112.10925] DB-BERT: a Database Tuning Tool that "Reads the Manual"
- [2204.08941] CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex
- Basu, Bhattacharjee, Trummer, Kizilcec, and Zhang receive NSF CAREER Awards | Cornell Bowers
- [2411.03500] λ-Tune: Harnessing Large Language Models for Automated Database System Tuning
- Data Analysis with LLMs - Immanuel Trummer
- 2026 LinkedIn grant recipients to drive innovation in GenAI, LLMs | Cornell Bowers
- Immanuel Trummer


