01 About

Charles Benello

I'm a software engineer and systems researcher at the University of Chicago. I'm currently at Amazon Web Services in Palo Alto as a Software Development Engineer Intern on the Redshift query optimizer team, working on the data-sharing query path, having wrapped an internship at Alter Domus building AI systems for financial asset management earlier this year. My background spans database internals, high-performance computing, and full-stack development.

I design and implement large-scale systems - from parallel sorting algorithms in Rust to GPU-accelerated query processing in CUDA to production AI pipelines in Python. I've published in VLDB and CIDR, working with Professor Aaron Elmore at UChicago's ChiData Lab and Professor Anastasia Ailamaki at EPFL's DIAS Lab.

GitHub LinkedIn

Education

University of Chicago

Master's degree / Financial Mathematics

Jun 2025 - Mar 2027

University of Chicago

BS / Mathematics and Computer Science (Systems specialization); Minor in Korean

Sep 2021 - Jun 2025

Dean's List

St Paul's School

2015 - 2021

Mathematics: A*; Further Mathematics: A*; Computer Science: D1

Current Work

SDE intern at AWS Redshift (Palo Alto): re-architecting metadata loading on the data-sharing query path
Research: adaptive sort configuration across disaggregated and cloud-native hardware
Research: GPU-accelerated compression for analytical databases
Evaluating policy compliance gaps in Rust LLM code generation benchmarks
CovTSP: near-optimal transit coverage solver using real GTFS network data

Experience

Software Development Engineer Intern

AWS Redshift·Aug 2026 - Nov 2026

On the Redshift query optimizer team in Palo Alto, working on the data-sharing query path - the route a query takes when it reads another warehouse's data.

Cut end-to-end latency ~15% and p99 tail latency ~30% in benchmarks on contended data-sharing workloads by re-architecting the query path to fetch remote storage metadata just-in-time, after workload-management admission rather than before it
Eliminated ~10% of remote metadata downloads in benchmark workloads by proving the deferral safe - tracing planner dependencies across path generation, cardinality estimation, and result caching, then re-sourcing per-slice table statistics from lightweight catalog metadata

Skills

Query OptimizationDistributed SystemsCloud Data WarehousingPerformance EngineeringC++

Research + Publications

Research Focus

Large-scale data processing
Database internals
High-performance systems
GPU-accelerated compression

Research Highlights

Predictive modelling for CrocSort configuration on disaggregated and cloud-native hardware - finding diminishing returns in thread counts and near-optimal config selection without brute-force sweeps
Resource-adaptive query execution with paged memory management
GPU-accelerated compression: decompression strategies, workload modelling, and cost-based trade-offs
Policy gap in Rust code generation benchmarks: showing that code passing SWE-bench tests can still violate project-defined safety and style policies
Exploring external sort on GPUs, disaggregated hardware, and cloud-native environments - understanding how storage/compute separation and elastic resources reshape sorting performance

Advisors + Labs