Tim Kraska
Employers on the program
- 2026Massachusetts Institute of Technology1 talk
- 2024–2025Amazon Web Services2 talks
- 2019–2023MIT3 talks
Events6
Conferences5
Talks62 keynotes
Years active2019–2026
Miles between events13,4626 in-person events · 3 countriesAppearances by year
- AWS re:Invent
- ACM SIGMOD/PODS (International Conference on Management of Data)
- CIDR (Conference on Innovative Data Systems Research)
- IEEE ICDE (International Conference on Data Engineering)
- VLDB (International Conference on Very Large Data Bases)
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| May 4, 2026 | ICDE 2026 (42nd IEEE International Conference on Data Engineering) | IEEE ICDE (International Conference on Data Engineering) | keynote | The End of Data System Engineering As We Know It as Massachusetts Institute of Technology | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | chalk talk | The MIT Everest Lab: ML and Generative AI for Data Systems as AWS Machine Learning (ML) and Generative AI (GenAI) are transforming the way we build, operate, and interact with data systems. For instance, ML-enhanced…Show lessMachine Learning (ML) and Generative AI (GenAI) are transforming the way we build, operate, and interact with data systems. For instance, ML-enhanced algorithms—such as learned scheduling methods and adaptive index/storage layouts—are now deployed in commercial data services, such as AWS data warehouse system Redshift. | |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | breakout | Data engineering for ML and AI with AWS analytics as AWS The performance and accuracy of AI and ML systems directly depend on the quality, relevance, and integrity of the data, which are used to either train the model…Show lessThe performance and accuracy of AI and ML systems directly depend on the quality, relevance, and integrity of the data, which are used to either train the model or accessed as part of Retrieval Augmented Generation (RAG) systems for generative AI assistants. | Video |
| Jun 18, 2023 | SIGMOD/PODS 2023 | ACM SIGMOD/PODS (International Conference on Management of Data) | industry paper | Auto-WLM: ML-enhanced workload management in Amazon Redshift as MIT / AWS | |
| Aug 16, 2021 | VLDB 2021 (47th) | VLDB (International Conference on Very Large Data Bases) | keynote | Towards instance-optimized data systems Database internals as MIT | |
| Jan 13, 2019 | CIDR 2019 | CIDR (Conference on Innovative Data Systems Research) | paper | SageDB: A Learned Database System as MIT | Paper |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Aaron Chen | 2 | — | 2025 |
| Aaron Dailey | 2 | — | 2025 |
| Aaron Sempf | 2 | — | 2025 |
| Aarthi Raju | 2 | — | 2025 |
| Abbas Mooraj | 2 | — | 2025 |
| Abhi Khanna | 2 | — | 2025 |
| Abhi Shivaditya | 2 | — | 2025 |
| Abhijeet Jangam | 2 | — | 2025 |
| Abhilash Nagilla | 2 | — | 2025 |
| Abhishek Pandey | 2 | — | 2025 |
| Abhishek Sharma | 2 | — | 2025 |
| Abhishek Srivastav | 2 | — | 2025 |
| Abilashkumar P C | 2 | — | 2025 |
| Adam Hensley | 2 | — | 2025 |
| Adam Hill | 2 | — | 2025 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.