Experienced AI Engineer and Data Scientist with a strong background in machine learning, natural language processing, and data analytics. My interdisciplinary interests extend to classics, philosophy, and music.
Researching Minds, Machines & Intelligence, focusing on Deep Learning/Artificial Intelligence Conceptual Spaces, AI Philosophy, and Consciousness Science.
Architecting and implementing cutting-edge Large Language Models and AI systems.
Teaching foundational ethics and philosophy in computer & data science, with a focus on AI governance and emerging technology challenges.
Spearheaded data science initiatives, focusing on predictive modeling, data pipeline engineering, and NLP implementations.
Leading technical initiatives and strategic planning for an international non-profit organization.
Conducted complex statistical analyses using Python, uncovering key insights that shaped the direction of multiple research projects.
Developed data-driven reports for the VP & CEO, highlighting key findings from extensive data mining efforts.
Applied advanced data science techniques to real-world projects, including time series forecasting and geospatial analysis.
Architected and implemented a responsive web platform, driving a 20% increase in user engagement and online visibility.
We present a large-scale empirical comparison of speech-to-text, LLM, and text-to-speech combinations using data from over 300,000 AI-conducted job interviews. Our analysis reveals that Google's STT with GPT-4.1 and Cartesia's TTS outperforms alternatives in both objective quality metrics and user satisfaction scores.
A cross-lingual transformer model measuring semantic relationships between ancient Greek and Latin lexicons, revealing significant alignments in philosophical concepts.
We introduce Abliterator, a framework for studying and manipulating personality traits in large language models (LLMs) through activation engineering.
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