Tim Kraska
Professor
Builds data systems that use large language models and AI, including systems for ML, ML for systems, and interactive data exploration.
Tim Kraska's lab at MIT works on large language models and data management & big data. 40 papers since 2023, 19 at top venues, mostly VLDB and ICDE. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 48%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 6 papers2026 · T1 4 · collecting
- 12 papers2025 · T1 5
- 12 papers2024 · T1 4
- 10 papers2023 · T1 6
T1 venuesOtherVenues: VLDB, ICDE, ICLR, KDD, NeurIPS
Research focuspaper titles that use each topic's words
- Data management & big data13 papers
- Large language models2 papers
- interactive data0 papers
- data systems0 papers
- exploration0 papers
Paper list
From DBLP.
20266 papers
- VLDB (T1)Abacus: A Cost-Based Optimizer for Semantic Operator Systems (opens in a new tab)
- VLDB (T1)Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries (opens in a new tab)
- CIDRDeep Research is the New Analytics System: Towards Building the Runtime for AI-Driven Analytics (opens in a new tab)
- VLDB (T1)Incremental Query Optimizer Statistics in Amazon Redshift (opens in a new tab)
- ICLR (T1)KRAMABENCH: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes (opens in a new tab)
- CAISTressoir: Unifying Online, Offline, and HIL Design and Evolution of Multi-Agent Systems via Interpretable Blueprints (opens in a new tab)
202512 papers
- Commun. ACMA Write-Optimized Distributed B+Tree Index on Disaggregated Memory (opens in a new tab)
- VLDB (T1)Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries (opens in a new tab)
- DaMoNInsert-Optimized Implementation of Streaming Data Sketches (opens in a new tab)
- CIDRPalimpzest: Optimizing AI-Powered Analytics with Declarative Query Processing (opens in a new tab)
- VLDB (T1)Parachute: Single-Pass Bi-Directional Information Passing (opens in a new tab)
- VLDB (T1)PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking (opens in a new tab)
- KDD (T1)PipeRAG: Fast Retrieval-Augmented Generation via Adaptive Pipeline Parallelism (opens in a new tab)
- NeurIPS (T1)SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL (opens in a new tab)
- VLDB WorkshopTailorSQL: A NL2SQL System Tailored for Your Query Workload (opens in a new tab)
- IEEE Data Eng. Bull.Towards AI-Enabled Data-to-Insights Systems (opens in a new tab)
- HILDA@SIGMODUtilizing Past User Feedback for More Accurate Text-to-SQL (opens in a new tab)
- SIGMOD Conference CompanionVirtualizing Cloud Data Infrastructures with BRAD (opens in a new tab)
202412 papers
- SIGMOD Conference CompanionAutomated Multidimensional Data Layouts in Amazon Redshift (opens in a new tab)
- VLDB (T1)Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD (opens in a new tab)
- VLDB (T1)Databases Unbound: Querying All of the World's Bytes with AI (opens in a new tab)
- SoCCForecasting Algorithms for Intelligent Resource Scaling: An Experimental Analysis (opens in a new tab)
- SIGMOD Conference CompanionIntelligent Scaling in Amazon Redshift (opens in a new tab)
- aiDM@SIGMODMallet: SQL Dialect Translation with LLM Rule Generation (opens in a new tab)
- CIDRPanda: Performance Debugging for Databases using LLM Agents (opens in a new tab)
- SIGMOD Conference CompanionPredicate Caching: Query-Driven Secondary Indexing for Cloud Data Warehouses (opens in a new tab)
- VLDB (T1)Resource Management in Aurora Serverless (opens in a new tab)
- SIGMOD Conference CompanionStage: Query Execution Time Prediction in Amazon Redshift (opens in a new tab)
- SoCCVista: Machine Learning based Database Performance Troubleshooting Framework in Amazon RDS (opens in a new tab)
- VLDB (T1)Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet (opens in a new tab)
202310 papers
- SIGMOD Conference CompanionAuto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift (opens in a new tab)
- VLDB (T1)Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes (opens in a new tab)
- VLDB WorkshopCorBit: Leveraging Correlations for Compressing Bitmap Indexes (opens in a new tab)
- VLDB (T1)Extract-Transform-Load for Video Streams (opens in a new tab)
- SIGMOD (T1)FactorJoin: A New Cardinality Estimation Framework for Join Queries (opens in a new tab)
- VLDB WorkshopHyperspecialized Compilation for Serverless Data Analytics (opens in a new tab)
- VLDB (T1)Robust Query Driven Cardinality Estimation under Changing Workloads (opens in a new tab)
- SIGMOD Rec.Technical Perspective for Sherman: A Write-Optimized Distributed B+Tree Index on Disaggregated Memory (opens in a new tab)
- VLDB (T1)The Case for Learned In-Memory Joins (opens in a new tab)
- ICDE (T1)Unshackling Database Benchmarking from Synthetic Workloads (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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Data
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