Hedge Fund

Datacast Episode 103: Computational Economics, Statistical Arbitrage, and Adaptable Data Consolidation with Eric Daimler

Datacast Episode 103: Computational Economics, Statistical Arbitrage, and Adaptable Data Consolidation with Eric Daimler

Dr. Eric Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Eric has co-founded six technology companies that have done pioneering work in areas ranging from software systems to statistical arbitrage.

As a Presidential Innovation Fellow during the Obama Administration, Eric helped drive the agenda for U.S. leadership in research, commercialization, and public adoption of AI. He has also served as the Assistant Dean and an Assistant Professor of Software Engineering at Carnegie Mellon’s School of Computer Science. He specializes in public policy and economics, helped launch Carnegie Mellon’s Silicon Valley Campus, and founded its Entrepreneurial Management program. His academic research focuses on the intersection of Machine Learning, Computational Linguistics, and Network Science.

As a frequent keynote speaker, Eric has presented at venues including the engineering schools of MIT, Stanford, and Harvard. He studied at Stanford University, the University of Washington-Seattle, and Carnegie Mellon University, where he earned his Ph.D. in its School of Computer Science.

Datacast Episode 86: Risk Management, Open-Source Governance, and Negative Engineering with Jeremiah Lowin

Datacast Episode 86: Risk Management, Open-Source Governance, and Negative Engineering with Jeremiah Lowin

Jeremiah Lowin is the Founder & CEO of Prefect, a dataflow automation company. Before starting Prefect, Jeremiah gained extensive experience in all aspects of the modern data stack as a director of risk management, machine learning researcher, and data scientist at a number of institutional investment firms. Today, he lives with his wife and two sons in Washington, DC.

Datacast Episode 65: Chaos Theory, High-Frequency Trading, and Experimentations at Scale with David Sweet

Datacast Episode 65: Chaos Theory, High-Frequency Trading, and Experimentations at Scale with David Sweet

David Sweet was a quantitative trader at GETCO, where he used experimental methods to tune trading strategies, and a machine learning engineer at Instagram, where he experimented on a large-scale recommender system. He is currently writing a book called "Tuning Up," an extension of lectures given at NYU Stern on tuning high-frequency trading systems. Before working in the industry, he received a Ph.D. in Physics and published research in Physical Review Letters and Nature. The latter publication – an experiment demonstrating chaos in geometrical optics -- has become a source of inspiration for computer graphics artists, undergraduate Physics instructors, and an exhibit called TetraSphere at the Museum of Mathematics in New York City.