Employing Natural Language Processing and Latent Dirichlet Allocation (LDA) to model transcripts from TED Talks, then creating a recommender system based on language similarity.

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Exploring the Netflix movie dataset containing 100M movie ratings, then creating a recommender system based on vector similarity using sparse matrixes.

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Leveraging Spark and XGBoost to build a distributed recommender system for large-scale restaurant recommendation on Yelp.

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Evolutionary Genetic Algorithm to solve an inverse recommendation problem and predict inputs (experiment variables) necessary for achieving a target output (experimental results) in synthetic food development.

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Combining text feature engineering and Bidirectional Encoder Representations from Transformers (BERT) to automatically classify a TED Talk's topic.

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