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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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About me
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This is a page not in th emain menu
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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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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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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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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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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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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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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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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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Developed a theoretical framework consisting of multiple LLM agents producing verifiable responses through debate and consensus.
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Developing NLP pipeline over 200 years of Hansard debates (1803–2005) using LLMs to study gender bias, framing, and speaker dynamics. Applying representation analysis and framing detection to uncover temporal shifts in discourse, generating insights for bias and fairness in AI systems.
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