Accelerate Your Research from Code to Cluster

UCLA SSC provides comprehensive statistical consulting, custom data pipeline development, and High-Performance Computing (SSC HPC) support for UCLA faculty, researchers, staff, and graduate students.

Whether you need to refine a complex statistical model, debug R or Python code, automate web scraping, or scale memory-intensive simulations to the Cluster, we offer dedicated end-to-end computational guidance tailored to your research goals.

How We Can Help

We bridge the gap between statistical theory, modern data science workflows, and advanced computing infrastructure.

1. Statistical Consulting & Methodology

• Study & Survey Design: Complex sampling weights, missing data imputation, scale construction, and survey aggregation.
• Causal Inference & Econometrics: Panel data analysis, fixed/random effects, difference-in-differences, instrumental variables, and matching methods.
• Advanced Applied Modeling: Generalized linear models (GLMs), survival analysis, multilevel/hierarchical modeling, and structural equation modeling.

2. Data Science & Machine Learning

• Data Wrangling & Automation: Building clean ETL pipelines, API extraction, and web scraping using R and Python.
• Text Analysis & Machine Learning: Natural Language Processing (Old fashioned NLP + LLMs), topic modeling, classification, predictive analytics, and supervised/unsupervised learning for social data.
• Reproducible Workflows: Best practices for version control (Git/GitHub), containerized environments, and reproducible dynamic documents (Quarto / R Markdown).

3. High-Performance Computing (SSC HPC)

• Code Optimization & Parallelization: Translating desktop R/Python scripts into multi-core, parallelized batch jobs on our local cluster.
• Environment Setup: SLURM job scheduling, Jupyter / RStudio Server configuration, custom Linux software environments, and GPU acceleration.
• Big Data Handling: Managing high-memory workloads and out-of-core statistical computing.

4. Data Science & Coding Help Desk

• Hands-on Debugging: One-on-one troubleshooting for broken code, package installation failures, syntax errors, and data cleaning roadblocks in R, Python, or Julia. SAS, SPSS, Stata also welcome.
• Revise & Resubmits: Decipher cryptic peer review comments and get your paper done.

Serving the Broad UCLA Research Community

Looking for statistical advice, R/Python coding help, or computational support following recent campus resource shifts?

SSC Research Computing welcomes researchers across the Social Sciences, Humanities, and partner UCLA campus units. Led by Staff Stats Consultant Neal Fultz, our consultations provide high-touch, senior-level expertise, taking your project from initial research design to industrial-level compute on the SSC HPC cluster.