I am an Assistant Professor in the Kahlert School of Computing at the University of Utah, where I co-lead the UtahDB research group. I received my Ph.D. in Computer Science from the University of California, Santa Barbara, where I was co-advised by Professor Divyakant Agrawal and Professor Amr El Abbadi.
Besides research, I enjoy trying new food, watching movies, and playing tennis and badminton. I'm also a trumpet player in which I played trumpet solo in Danzón No. 2 at the Disney Hall.
I am honored to have received the Microsoft Research PhD Fellowship from Microsoft Research, Charles Dana Fellowship, research gifts from Snowflake.
I am always looking for highly motivated students who are interested in data systems.
Check out my research interests and feel free to reach out.
Interested in collaboration? Drop me an email and let's chat!
News
- Oct 2026New preprint: JEVDB, scalable semantic query processing that prunes first and decides fast.
- Sep 2026Two papers at VLDB 2026: CrocSort, a resource-efficient parallel external merge sort, and the AtomSQL demo on interactive NL2SQL disambiguation.
- Jun 2026New preprint: Larch, learned query optimization for semantic predicates.
- May 2026PrvTel, lightweight models for private and accurate telemetry retention, appeared at NSDI 2026.
- Aug 2025New preprint: Access Paths for efficient ordering with large language models.
Open-Source Projects
A DuckDB extension and Python package that brings LLM-based semantic operators (ai_filter, ai_classify, ai_score, ai_complete, ai_agg, ai_embed, ai_image) into SQL, with an optimizer that deduplicates LLM calls, evaluates AI joins over distinct pair domains, and applies learned predicate ordering and early stopping.
GitHub · pip install swan-aisql
A benchmark of 120 "beyond-database" questions over BIRD databases that require combining LLM knowledge with SQL. SWAN 2.0 also measures how well systems plan their LLM calls, tracking quality, number of calls, tokens, cost, and latency, with a cache proxy for free, reproducible reruns.
GitHub
A scalable semantic database system for efficient and cost-effective semantic query processing over unstructured data. JEVDB answers most semantic questions with a low latency decision models and sends only the uncertain cases to large generative LLMs.
Website · GitHub · Paper
Redwood Storage Engine, FoundationDB
Redwood is led by Steve Atherton. I contributed on monitoring, caching, and scheduling disk I/O during my two summer internships at Snowflake.
Most of my contributions can be found here.
Selected Publications
Integrating LLMs into Data Systems
Fuheng Zhao, Pawel Liskowski, Zihan Li, Benjamin Han, Puxuan Yu, Varich Boonsanong, Dimitris Tsirogiannis, Anupam Datta
arXiv preprint, 2026
Fuheng Zhao, Shaleen Deep, Fotis Psallidas, Avrilia Floratou, Divyakant Agrawal, Amr El Abbadi
VLDB 2025
Fuheng Zhao, Divyakant Agrawal, Amr El Abbadi
CIDR 2025
Avrilia Floratou, Fotis Psallidas, Fuheng Zhao, Shaleen Deep, Gunther Hagleither, Wangda Tan, Joyce Cahoon, et al.
Work done while interning at Microsoft Gray Systems Lab
CIDR 2024
Streaming Algorithms
Fuheng Zhao, Punnal Ismail Khan, Divyakant Agrawal, Amr El Abbadi, Arpit Gupta, Zaoxing Liu
VLDB 2023
Fuheng Zhao*, Dan Qiao*, Rachel Redberg, Divyakant Agrawal, Amr El Abbadi, Yu-Xiang Wang
NeurIPS 2022
Fuheng Zhao, Divyakant Agrawal, Amr El Abbadi, Ahmed Metwally
VLDB 2022
Fuheng Zhao, Sujaya Maiyya, Ryan Wiener, Divyakant Agrawal, Amr El Abbadi
VLDB 2021
Storage Engines
Riki Otaki, Charles Benello, Fuheng Zhao, Aaron J. Elmore, Goetz Graefe
VLDB 2026
Fuheng Zhao, Zach Miller, Leron Reznikov, Divyakant Agrawal, Amr El Abbadi
Proposed a new compaction policy to gradually expand LSM horizontally and enhance the read performance.
ICDE 2025