I am a technical and scientific leader with expertise in building and leading large, high-impact machine learning research and product teams in the areas of science, technology, and quantitative trading. I am currently Chief Scientist at Delphia, working to democratize data. 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.