01 About

Charles Benello

I am a master's student in Financial Mathematics at the University of Chicago, where I completed my undergraduate degree in Mathematics and Computer Science with a specialization in computer systems and a minor in Korean.

My academic and research background spans large-scale data processing, database internals, and high-performance systems, with a growing focus on applying these skills to quantitative finance. This summer, I am conducting research at EPFL under Professor Anastasia Ailamaki on GPU-accelerated compression techniques for analytical database systems.

Previously, I was a researcher at the ChiData Lab at the University of Chicago, working with Professor Aaron Elmore.

Financial MathematicsDatabase SystemsGPU CompressionQuantitative Research

Education

University of Chicago

Master's degree / Financial Mathematics

Jun 2025 - Dec 2026

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 Research

  • GPU-accelerated compression techniques for analytical database systems
  • Parallel compression algorithms optimized for modern hardware
  • Applying systems research to quantitative finance

02 Experience

Experience

Teaching Assistant

Sep 2023 - Present

University of Chicago Department of Computer Science · Part-time

Quantitative Researcher - University of Chicago Project Lab

Oct 2025 - Dec 2025

Bank of America

Lead Residential Assistant

2023 - 2024

University of Chicago · Full-time

Teaching Assistant

Sep 2022 - Dec 2023

University of Chicago Department of Mathematics

03 Research + Teaching

Research + Teaching

Research in database systems and GPU compression, plus teaching in computer science and mathematics.

Research Focus

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

Research Highlights

  • Parallel compression algorithms optimized for modern hardware
  • Analytical and simulation-based performance models for GPU decompression
  • Resource contention modeling across I/O, decompression, and compute

Teaching

  • Winter 2026: CMSC 235 - Databases Systems
  • Fall 2025: CMSC 141 - Intro to CS I
  • Spring 2025: CMSC 235 - Databases Systems
  • Winter 2025: CMSC 142 - Intro to CS II
  • Fall 2024: CMSC 144 - Systems Programming II
  • Spring 2024: CMSC 235 - Databases Systems
  • Fall 2023: CMSC 143 - Systems Programming I
  • Fall 2023: Math 131 - Elem Functions and Calculus I
  • Spring 2023: Math 159 - Intro to Proofs in Analysis
  • Winter 2023: Math 159 - Intro to Proofs in Analysis
  • Fall 2022: Math 159 - Intro to Proofs in Analysis

Advisors + Labs

  • Professor Aaron Elmore (ChiData Lab, University of Chicago)
  • Professor Anastasia Ailamaki (DIAS Lab, EPFL)

04 Projects

Projects

277

contributions

Machine Learning
Machine Learning
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