About

I am an Assistant Professor in the Web Information Systems (WIS) group at TU Delft, and the lead of the InfiniData Team. Before joining TU Delft, I received my Ph.D. degree from RWTH Aachen University, and my Master’s and Bachelor’s degrees from Tsinghua University. My research focuses on data lakes, data management for machine learning, and quantum data management.

My goal is to build intelligent, efficient, and future-ready data systems that seamlessly integrate classical and quantum computing. My work bridges data management, machine learning, and quantum technologies to advance scalable AI, sustainable infrastructures, and novel computing paradigms.

For more information, please check my personal webpage and my team’s website.

Selected Publications

Quantum Data Management

1. Rihan Hai, Shih-Han Hung, Tim Coopmans, Tim Littau, and Floris Geerts. Quantum data management in the NISQ era. PVLDB, 18(6):1720–1729, 2025.
2. Tim Littau and Rihan Hai. Qymera: Simulating quantum circuits using RDBMS. In SIGMOD, pp. 179–182, 2025.
3. Rihan Hai, Shih-Han Hung, and Sebastian Feld. Quantum data management: From theory to opportunities. In ICDE, pp. 5376–5381, 2024.

AI in Data Lakes (Funded by NWO Talent Programme Veni)

2026

1. Wenbo Sun, Qiming Guo, Wenlu Wang, and Rihan Hai. Transql+: Serving large language models with SQL on low-resource hardware. SIGMOD, 2026. To appear.
2. Aditya Shankar, Lydia Chen, Arie van Deursen, and Rihan Hai. Wavestitch: Flexible and fast conditional time series generation with diffusion models. SIGMOD, 2026. To appear.

2025

1. Wenbo Sun, Ziyu Li, and Rihan Hai. Database as runtime: Compiling LLMs to SQL for in-database model serving. In SIGMOD, pp. 231–234, 2025. (Best demo runner-up)

2024

1. Ziyu Li, Wenbo Sun, Danning Zhan, Yan Kang, Lydia Chen, Alessandro Bozzon, and Rihan Hai. Amalur: The convergence of data integration and machine learning. TKDE, pp. 1–14, 2024.
2. Ziyu Li, Hilco Van Der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, and Rihan Hai. Model selection with model zoo via graph learning. In ICDE, pp. 1296–1309, 2024.
3. Andra Ionescu, Kiril Vasilev, Florena Buse, Rihan Hai, and Asterios Katsifodimos. Autofeat: Transitive feature discovery over join paths. In ICDE, pp. 1861–1873. IEEE, 2024.
4. Aditya Shankar, Hans Brouwer, Rihan Hai, and Lydia Chen. Silofuse: Cross-silo synthetic data generation with latent tabular diffusion models. In ICDE, pp. 110–123, 2024.
5. Ziyu Li, Wenjie Zhao, Asterios Katsifodimos, and Rihan Hai. LLM-PQA: LLM-enhanced prediction query answering. In CIKM, pp. 5239–5243, 2024.
6. Andra Ionescu, Zeger Mouw, Efthimia Aivaloglou, Rihan Hai, and Asterios Katsifodimos. Human-in-the-loop feature discovery for tabular data. In CIKM, pp. 5215–5219, 2024.

2023

1. Rihan Hai, Christos Koutras, Christoph Quix, and Matthias Jarke. Data lakes: A survey of functions and systems. TKDE, 35(12):12571–12590, 2023.
2. Rihan Hai, Christos Koutras, Andra Ionescu, Ziyu Li, Wenbo Sun, Jessie van Schijndel, Yan Kang, and Asterios Katsifodimos. Amalur: Data integration meets machine learning. In ICDE, pp. 3729–3739, 2023.

Publications