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Supreme Court LLM Data Trainer

Supreme Court LLM Data Trainer
RStataPythonPostgreSQLAWSGCPPyTorchTensorFlowHugging Face
GitHub Repository

AI models for predicting Supreme Court decisions with statistical analysis

Conducted advanced statistical analyses on over 5,000 survey entries with 150+ variables to assess public opinion's influence on Supreme Court decisions, employing chi-squared tests, linear regression, and confidence intervals. Developed data visualizations in R, Stata, and Python to explore SCOTUS docket trends, case alignments, and political beliefs of small businesses. Collaborated with Dr. Neil Malhotra on a research publication and a forthcoming book, "Majority Opinions: How an Out-of-Step Supreme Court Can Affect the Rule of Law," while fine-tuning AI models leveraging LLMs for predicting Supreme Court decisions.

Research Summary: Key Contributions

Statistical Analysis

  • Conducted advanced statistical analyses, including:

  • Chi-squared tests.

  • Linear regression.

  • Confidence intervals.

  • Analyzed over 5,000 survey entries with 150+ variables to assess public opinion and its impact on Supreme Court decisions.

Data Visualization

  • Developed over 100 lines of code in R, Stata, and Python to create:

  • Plots.

  • Tables.

  • Diagrams.

  • Visualized relationships between:

  • Public perception and Supreme Court docket fluctuations.

  • The influence of SCOTUS decisions on policy.

Database Management

  • Structured and managed extensive databases to:

  • Analyze survey data.

  • Identify trends in SCOTUS case alignments (liberal vs. conservative).

  • Examine political beliefs among small businesses.

Research and Publications

  • Book Publication:

  • Authored and presented a research publication to a board of 10+ professors.

  • Conducted meticulous book analysis, proof writing, and academic writing.

  • Successfully released the book "Majority Opinions: How an Out-of-Step Supreme Court Can Affect the Rule of Law" with Dr. Neil Malhotra.

  • Paper Publication:

  • Collaborated on a paper with Dr. Malhotra: "The Politics of Small Business Owners."

AI Development

  • Fine-tuned an AI model leveraging Large Language Models (LLMs) to:

  • Predict Supreme Court decisions.

  • Process over 1,500 files, including 800 court documents and personal appeals.

  • Classify cases based on specific justices.

  • Engineered Python-based SCOTUS case file data loaders for Google Cloud processors, optimizing pre-trained LLM models.