I am a hands-on technical and scientific expert with experience both as a senior individual contributor and org leader delivering complex, high impact applied machine learning research projects in the areas of science, technology and quantitative trading. Currently, Principal Applied Machine Learning Engineer at Shopify, building machine learning-based products to help make commerce better for everyone. Previously Chief Scientist at Oracle Alpha leading machine learning and natural language processing research and production for an emerging systematic fundamental hedge fund. I am also an Adjunct Professor in NYU's Department of Finance and Risk Engineering, lecturing on natural language processing and machine learning applied to quantitative trading and finance. I received my Ph.D. in Machine Learning from Carnegie Mellon University and my BA in Computer Science and Artificial Intelligence from Columbia University.
With expertise in machine learning, natural language processing, and quantitative trading, my personal research interests are in robust machine learning, developing models and features that are robust to:
- extremely low signal to noise ratio and low sample size regimes
- changes in the distribution of features and labels across train and test sets (transfer learning)
- extracting features from unstructured data
- I am particularly interested in applications of robust machine learning to time series and natural language processing models in financial and other domains.