Andrew Arnold

Adjunct Professor NYU

Andrew Arnold works on robust machine learning at Google Research, New York, and serves as an Adjunct Professor at New York University where he lectures on machine learning and natural language processing applied to quantitative trading and finance. Previously, he was a portfolio manager and research director at Cubist Systematic Strategies (Point72), applying machine learning to quantitative trading. Before that, he was variously a hedge fund cofounder, chief technology officer, quantitative portfolio manager, machine learning researcher and software engineer at Ophir Partners, Trexquant, WorldQuant, Merrill Lynch, Microsoft Research, IBM Research, Google and Bloomberg. He received his PhD in machine learning from Carnegie Mellon University and his BA in computer science from Columbia University.

Conference Day One: June 30 2020

Tuesday, June 30th, 2020

1:35 PM Transfer learning for machine learning and NLP: adapting models to changing markets

- Challenges of transfer learning within financial markets
- Identifying how to use transfer learning to your advantage 
- Techniques and case studies to adapt models across new instruments and markets

2:25 PM Panel: Building the optimal data/ML teams

- Hiring and retaining the perfect data team
- The war for talent and where to find the best data professionals 
- Identifying the skills needed for the best data professional in the industry and where to find them

Conference Day Two: July 1 2020

Wednesday, July 1st, 2020

2:15 PM Open forum: Navigating data challenges with latest ML/AI applications

Opportunity to bring your challenges and put them to our expert Machine Learning panel.