getPaid
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Created a SparkML RandomForest model to predict total employee compensation. Queried data with SparkSQL, ran PySpark scripts to run EDA, pre-process data, and train model achieving with 0.98 R2 score.
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projects
Cancer Detection as a Microservice
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Trained a Voting Classifier to predict cancer type with the Wisconsin Breast Cancer Dataset. Hosted the model as a Flask-based microservice running within a Docker container.
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Product Shipping Status Classifier
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Created a RandomForest model to predict issues in the product supplychain which might delay product shipment to customers. Employed PCA and LDA optimization for visualization and dimension reduction. RandomForest model achieved accuracy of 97% (with PCA) and 92% (with LDA).
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Interpretable Parking Ticket Location Classifier
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Built a SparkML RandomForest classifier to predict the police-precinct of ticketed vehicles using the NYC parking-ticket dataset. Used PySpark for parallel computation. Achieved 98% accuracy, and identified police precincts with anomalous ticketing practices.
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AI Alignment in Criminal Justice - Fair Jury Selection and Deliberation
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Created a theoretical hybrid model for fair jury selection and intra-jury deliberation using optimization and game theory with a novel extension to nash equilibrium.
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LLM-as-a-SupremeCourt-Judge
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Developed an AI framework with LLMs (GPT API, fine-tuning, chain-of-thought reasoning) to evaluate 200+ Supreme Court cases, revealing how factors such as panel size, prompting strategies, and ideology personas influence alignment between LLM reasoning and human judges.
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Detecting Deception: Intelligent Systems for Fighting Misinformation
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Designed a logic-gated fact-checking system integrating RoBERTa classifiers with contradiction detection, validated on 7,200+ claims. Improved explainability and reliability by maintaining 83% accuracy while enabling selective overrides for low-confidence predictions.
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Beyond Hallucinations: A Multi-Agent Approach To Fact-Checked AI Knowledge
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Developed a theoretical framework consisting of multiple LLM agents producing verifiable responses through debate and consensus.
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Two Centuries of Sexism in British Parliament: A Computational Analysis of Women’s Representation in the Hansard Corpus
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Built an LLM-as-judge pipeline to mine 6.7M UK parliamentary speeches for suffrage debates, achieving stance-classification agreement with human experts (k=0.71) that exceeded inter-annotator agreement itself (k=0.64). Designed a psychology-grounded sexism taxonomy and applied it at corpus scale, quantifying that anti-suffrage speeches were 2.6x more likely to contain sexist rhetoric (54% vs. 21%) and revealing a century-long shift from hostile to benevolent sexism..
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