Maintaining Diversity in the Age of AI
Postdoctoral Researcher at Microsoft Research NYC focused on diversifying AI outputs and preserving user autonomy in both creative and technical domains.
Combining technical expertise with a focus on AI diversity and human agency
I'm a postdoctoral researcher at Microsoft Research in NYC, with a focus on diversifying the output of AI in both creative and technical domains. With a background in computer science and expertise in deep learning, generative models, and music AI, I approach artificial intelligence from a unique perspective: how can we ensure AI systems enhance human creativity and autonomy rather than diminish them?
My research addresses two related but distinct dangers in current AI development:
Developing techniques to maintain diversity in AI outputs across domains
Creating systems that enhance rather than replace human decision-making
Deep knowledge of generative models, formal methods, and creative AI
Exploring the technical and social aspects of AI diversity
For me, diversity is both a technical term (maximizing expected pairwise distance between two answers conditioned on them both being "good") and a concept with profound creative, economic, and social implications.
In a recent study (paper in submission), I found that 249 out of 250 poems generated by ChatGPT without special prompting were all identified by readers as being derived from the same poet (John Keats). This striking homogeneity illustrates how large language models tend to converge toward a "safe" mean statisticala problem that extends beyond poetry to code, design, business ideas, and other domains where innovation requires deviation from the norm.
My research develops methods to maintain diversity in AI outputs in cases where individuals, companies, or society would benefit from exploring a range of good but different possibilities rather than a single "optimal" solution.
Collaborating with professional poets
Working with professional poets to develop methods for creating "Counter-Egos" rather than AI replacements. This approach focuses on reinterpreting poetic styles through the lens of Webpage Poetry, allowing for new forms of expression.
IUI (HAI-GEN) 2024
Enabling people to explore their identity using LLMs with sophisticated algorithms that lead to real-world usable outputs, providing tools for personal exploration and discovery guided by user agency.
70,000+ listens on SoundCloud
Developing music generation models that enhance musician creativity while preserving artistic control and avoiding output homogenization.
Research initiatives focused on diversity in AI
A hub for research, tools, and essays on maintaining creative diversity in the age of AI. Provides resources for artists, developers, and researchers interested in building AI systems that enhance rather than constrain human creativity.
Showcasing research on autonomy in music generation with practical demonstrations that have reached a substantial audience.
Current Research
Developing techniques to ensure large language models can produce diverse, creative outputs rather than converging on a statistical mean.
Research contributions to AI diversity and controllability
Developed a method for measuring and producing meaningful global coherence in domains such as music and poetry, enabling generated outputs that maintain both local quality and structural integrity.
Developed a novel approach to steer music transformer models without manipulating their parameters, giving users greater control over generated music while preserving model capabilities.
Developed generative models based on latent structures detectable via program synthesis, laying groundwork for more interpretable and controllable generative AI.
Academic and industry roles focused on AI diversity
Postdoctoral Researcher
Leading research on diversifying AI outputs in creative and technical domains, developing new algorithms and evaluation metrics for measuring and promoting diversity in generative models.
Research Intern
Developed controllable generative music models to avoid both user de-agentification and output homogeny. Created tools that allow musicians to guide AI systems rather than be replaced by them.
Software Engineer/Researcher
Performed research in automated music production for therapeutic purposes leading to a patent.
Teaching Assistant
TA in Graduate Artificial Intelligence, Head TA in Graduate Applied Machine Learning. Designed curriculum that emphasized ethical considerations alongside technical skills.
University of Pennsylvania
Awarded the Chateaubriand Fellowship for Humanities and Social Sciences (declined due to health issues). Dissertation focused on maintaining controllability in generative AI systems.
New York University
Awarded the Faculty Memorial Prize, the Computer Science Prize, and the Computer Science Prize for Outstanding Performance in Computer Science.
University of Chicago
Awarded the Lilian Gertrude Selz Prize.
Get in touch to discuss research collaboration or speaking opportunities