Hongbin Zhong(钟宏斌)

CS PhD Student

Georgia Institute of Technology

Email: hzhong81 [at] gatech [dot] edu


Biography

I am a second-year PhD student in Computer Science at Georgia Tech, fortunate to be advised by the kind and supportive Prof. Kexin Rong.

I also closely collaborate with Dr. Adriana Szekeres from Microsoft Research and Dr. Nina Narodytska from VMware Research, as well as Dr. Matthew Lentz from Duke University.

Previously, I was a research intern at Microsoft Research Redmond during the summer of first year. Before my PhD, I was also an intern at Columbia University, working with Prof. Eugene Wu, where I contributed to the publication of multiple DB top-tier conference papers.

Additionally, I have extensive industry research/engineering experience, including but not limited to LLM agents and planning, core database systems, and server development, which you can learn more about on my LinkedIn.

My research interest lies in AI agents, where I focus on the entire stack, from the underlying computer systems to the algorithmic and application layers. I am deeply interested in both low-level systems and pure AI research. Coming from the systems community, I firmly believe that true growth comes from stepping beyond one’s comfort zone. Therefore, I do not want to confine myself solely to systems research; I aspire to explore all layers of the AI stack, so that I can develop a more comprehensive perspective and capability to solve complex problems.

Key themes I have led as the first and only student author, which equipped me with rich hands-on experience:

News

Preprint and Draft

Beyond Screenshots: An Dynamic State-Machine Memory and Global Programmatic Planner for WebAgents

Hongbin Zhong* with Microsoft Research People

HoneyBee: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning

Hongbin Zhong*, Matthew Lentz, Nina Narodytska, Adriana Szekeres, Kexin Rong

arxiv 2025(Under Revision in SIGMOD 2026)

[paper]

Selected Publications

Fast Hypothetical Updates Evaluation

Haneen Mohammed*, Alexander Yao*, Charlie Summers*, Hongbin Zhong, Gromit Yeuk-Yin Chan, Sub- rata Mitra, Lampros Flokas, Eugene Wu

SIGMOD 2025 DEMO

FaDE: More Than a Million What-ifs Per Second

Haneen Mohammed*, Alexander Yao*, Charlie Summers*, Hongbin Zhong, Gromit Yeuk-Yin Chan, Sub- rata Mitra, Lampros Flokas, Eugene Wu

VLDB 2025

[code]

Accelerating Deletion Interventions on OLAP Workload

Haneen Mohammed, Alexander Yao, Lampros Flokas,Hongbin Zhong, Charlie Summers, Eugene Wu

ICDE 2024

PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation

Xianzhi Zeng*, Shuhao Zhang, Hongbin Zhong, Hao Zhang, Mian Lu, Zhao Zheng, Yuqiang Chen

SIGMOD 2024

[paper] [code]

Experience

Microsoft Research

May. 2025 - Aug. 2025, Redmond, Seattle, US

closely worked with Adriana Szekeres, Suman Nath

Focus: Architected the Beyond Screenshots WebAgent planner/memory stack, raising WebArena success to ~90%.

Georgia Institute of Technology

Aug. 2024 - Present, Atlanta, US

Advisor: Kexin Rong; Collaboration with VMware Systems Group

Focus: Built RBAC-aware vector database partitioning (13.5x faster, 90% less memory) and joint batching for RAG pipelines.

InfiniFlow

Apr. 2024 - Jun. 2024, Shanghai, China

Vector Databases Contributor

Focus: Reworked timestamp persistence and bulk-deletion cleanup to cut vector storage latency and I/O.

4Paradigm

Feb. 2024 - Jun. 2024

Part-time Full-stack Engineer Intern

Focus: Tuned AI assistant caching for lower latency and shipped async community features with scheduled refresh.

Columbia University

Jul. 2023 - Dec. 2023, New York, US

closely worked with Eugene Wu

Focus: Optimized FADE sparse-matrix evaluation with SIMD/multithreading for near-linear 8x speedups.

Rutgers University

Jun. 2023 - Sep. 2023

closely worked with Dong Deng

Focus: Implemented similarity-search baselines and parallel group-function analytics pipelines.

Nanyang Technological University

Jan. 2023 - Jul. 2023

Focus: Built low-latency disorder stream processing and Bayesian variational inference for complex event data.

Meituan

Apr. 2022 - Sep. 2022, Beijing, China

Backend Engineer Intern

Focus: Delivered short-video backend features, Kafka/Hive recommendation pipelines, and periodic refresh for low-bandwidth users.


© Hongbin Zhong