cv

Contact Information

Name Robin Liu
Email robin28liu@gmail.com
Location Santa Barbara, CA
Website https://roobnloo.github.io

Experience

  • 2025 - 2026

    New York City

    Ph.D. Research Fellow
    Uniswap Labs
    • Developed and deployed the competitiveness index to compare DeFi protocols
    • Drew insights from large databases of transactions and A/B test results
    • Created shareable software and data resources used by data scientists and researchers
  • 2024 - 2024

    Grenoble, FR

    Research Engineer
    Inria Grenoble
    • Developed scalable tools for fitting statistical mixed-effects models to brain imaging data
    • Leveraged distributed high-performance computing clusters to analyze large datasets
    • Accelerated computations by 300% through detailed analysis and simplification of numerical operations
  • 2013 - 2020

    Chicago

    Software Developer
    Quantitative Risk Management Inc.
    • Developed the software framework for assessing interest rate, liquidity, and other risks associated with asset and liability management
    • Designed and maintained database systems for complex financial products
    • Built a system for cleaning and aggregating financial transaction data using K-means clustering
    • Enhanced the portfolio optimization engine to support haircut modeling
  • 2012 - 2012

    Chicago

    Software Development Intern
    Spot Trading LLC
    • Developed the trade management system of a proprietary options trading firm
    • Implemented low-latency trade execution on the Boston Options Exchange

Education

  • 2020 - 2026

    Santa Barbara, CA

    Ph.D.
    University of California, Santa Barbara
    Statistics
    • Topics in joint estimation of the mean and covariance structure in high-dimensional data
    • Advisor: Prof. Guo Yu
  • 2013 - 2013

    Ann Arbor, MI

    BS
    University of Michigan
    Honors Mathematics, Computer Science

Projects

  • Deep residual networks for crystallography
    • Developed a deep learning model in PyTorch for X-ray crystallography experiments
    • Trained CNNs for regression and classification of experimental results
    • Implemented transfer learning with ImageNet to improve prediction accuracy by 10%

Publications

Selected Honors

  • 2024 UCSB PSTAT Departmental Travel Grant
  • 2024-25 UCSB Doctoral Student Travel Grant

Technical Skills

Languages: Python, R, Julia, C++, C#, SQL
Packages: PyTorch, Jupyter, SciPy, tidyverse
Technologies: .NET, Linux, Docker, BigQuery, Google Cloud Platform, Apache Spark, Databricks
Statistical and machine learning: Proficiency with deep learning, xgboost, and other nonlinear prediction methods. Proficiency with classical methods such as OLS, LASSO, multivariate statistics, covariance estimation, time series analysis.

Service

  • Reviewer for Nature Scientific Reports

Presentations

A mixed model of regional functional connectivity from voxel-level BOLD signals: WNAR/IMS Annual Meeting 2025; Whistler, BC
Covariate-adjusted Gaussian graphical models via natural parametrization: WNAR/IMS/Graybill Annual Meeting 2024; Fort Collins, CO
Covariate-adjusted Gaussian graphical models via natural parametrization: Joint Statistical Meetings 2024; Portland, OR
Covariate-adjusted Gaussian graphical models via natural parametrization: CFE-CMStatistics 2024; London, UK
Covariate-adjusted Gaussian graphical models via natural parametrization: Inria statistical research seminar 2024; Grenoble, FR

Teaching

Lead instructor: (PSTAT 10) Principles of Data Science
Teaching assistant: (PSTAT 232) Computational Statistics, (PSTAT 231) Statistical Machine Learning, (PSTAT 234) Statistical Data Science, (PSTAT 235) Big Data Analytics, (PSTAT 120B) Probability and Statistics II