I am a researcher in the field of machine learning with a focus on the use of foundation models in scientific applications. I am currently a research scientist at Polymathic AI and New York University. For more information see my CV and blog.


Counting in Context
We introduce the Contextual Counting task, a new toy problem aimed at exploring interpretability of Transformer models in quantitative domains.
xVal Number Encoding
We introduce xVal, a new number encoding scheme for LLMs. Using xVal with a modified number inference method makes LLMs continuous function approximators. This makes them have a better inductive bias for data analysis in scientific domains.


Answers to frequently asked questions about the research field, methods, specific projects, and how to get involved. 

I am primarily interested in the development of cutting edge machine learning tools for heterogenous data analysis. That is, any task that requires processing and understanding of different types of input (images, text, videos, spectral densities, etc.)

I am currently involved in the Polymathic AI project, an initiative aimed at building foundation models for scientific data analysis.

The home page hero image is a collage of a number of figures from my publications. The pastel themed images on this site are generated using OpenAI Dall-E models. In 2023, I did an overhaul of the website and ported over the posts that I had made in my old blog. To maintain consistency and generate new hero-images for the new template, I used DALL-E to generate pastel colored cartoon images for the older posts. 

Depending on the situation. Please reach out directly. I am in particular happy to help non-profit organizations with humanitarian mission statements.


For questions, comments and collaborations, please reach out to my email or socials below.