Prince Osei Aboagye, PhD
Applied Scientist at Amazon Alexa | Building large-scale AI systems for search, ranking, and personalization | Research in Generative AI, Multimodal Learning & Reasoning Systems
Applied Scientist at Amazon Alexa | Building large-scale AI systems for search, ranking, and personalization | Research in Generative AI, Multimodal Learning & Reasoning Systems
Applied Scientist building large-scale AI systems for search, ranking, and personalization. Working on LLM training, efficient inference, and production deployment.
Former Research Scientist focused on generative AI for complex sequences, parameter-efficient fine-tuning, and multimodal data curation.
LLM Post-Training, Vision-Language Alignment, Reasoning, Data-Centric AI, Synthetic Data Generation, Model Compression, Computational Geometry.
Prince Osei Aboagye, Jeff Phillips, Yan Zheng, et al.
Prince Osei Aboagye, Yan Zheng, Jack Shunn, et al.
Chin-Chia Michael Yeh, et al., Prince Osei Aboagye, et al.
Chin-Chia Michael Yeh, et al., Prince Osei Aboagye, et al.
Prince Osei Aboagye, et al.
Built ML models to classify movie reviews as positive or negative. Explored multiple algorithms and feature engineering techniques for sentiment analysis.
Used deep neural networks to classify music genres (Hip-Hop, Metal, Rock, Pop, Country) from 380,000+ song lyrics. Compared DNNs against KNN, Random Forest, XGBoost, and SVM.
Applied topological data analysis (persistent homology) to sentiment classification, using topological signatures as features for text analysis.
University of Utah
2018 – 2023
Advisor: Dr. Jeff Phillips
Dissertation: Understanding the Geometry of Structured Vectorized Representations
University of Texas at El Paso
2016 – 2018
Advisor: Dr. Michael Pokojovy
University of Ghana
2011 – 2015