Portfolio

Portrait of Guanli Liu

Guanli (Leo) Liu · Backend Systems · Data Infrastructure · Research Engineering

I build data and AI systems, then turn them into reproducible, measurable products.

Postdoctoral researcher and engineer at the University of Melbourne. This site is organized as a growing portfolio of case studies across database systems, benchmarking, and LLM-assisted data workflows.

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Portfolio Tracks

Each track will continue to grow with technical write-ups, demo snapshots, and code references.

Data Layout and Query Performance Systems Lakehouse physical design, indexing, and cost-aware optimization.
Benchmarking and Drift-Aware Evaluation Controlled experiment pipelines for workload/data drift and reproducible system comparisons.
LLM-Assisted Data Workflows RAG and agent pipelines that convert natural language intent into executable database tasks.
Backend and Data Platform Delivery PostgreSQL services, API layers, automation workflows, and production-style engineering practices.

Featured Build Stories

Projects are presented as build stories: problem context, technical implementation, and measurable outcome.

System Design Lakehouse VLDB 2026 (Submitted)

LayoutPilot / Layout Advisory

Designed an advisory backend for analytical data lake workloads, including workload ingestion, SQL parsing, and reusable decision logic for partitioning and intra-file layout choices.

Stack: Python, SQL parsing workflows, experiment harnesses, Dockerized service environment.

Code

LayoutPilot paper figure
Benchmarking Drift-Aware VLDB 2026

DriftBench

Built a modular benchmarking framework for evaluating system behavior under workload and data drift, with configurable workload generation and repeatable performance analysis.

Stack: Python, benchmark DSL design, automation scripts, reproducible experiment pipelines.

Code

DriftBench paper figure
Agent Infrastructure Prototype

Metadata-Native Agent Workspace Infrastructure

Proposed and prototyped an infrastructure direction where metadata drives task-aware context routing, access control, and recoverable actions for agent workflows.

Public write-up and architecture notes will be published in an upcoming portfolio update.

LLM + DB Applied AI

LLM-Assisted Query Agents

Developed agents that translate natural language requests into executable database workflows through retrieval, query decomposition, and result-grounded evaluation.

Demo snapshots and prompt-to-query traces will be added as a case study.

Build Queue (Next Portfolio Drops)

  • Blockchain Data Platform at nftDb: Kafka + Airflow ingestion and BigQuery analytics workflow case study.
  • Baidu IM Backend: message protocol and deduplication reliability engineering breakdown.
  • Evaluation Tooling: reusable experiment templates and CI-style benchmark validation pipeline.

Timeline Snapshot

2024-Present: Postdoctoral Research Fellow, The University of Melbourne (backend systems, benchmarking frameworks, LLM-assisted data systems).

2023-2024: Data Scientist / Data Infrastructure Engineer, nftDb (pipeline automation, RAG assistant, analytics workflows).

2015-2017: Software Engineer, Baidu (IM backend services, protocol design, database performance tuning).