Event detail

Name: Machine Learning & AI in Quant Finance Conference NYC 2018

From: 01 Mar 2018

To: 02 Mar 2018

Address: New York, United States

Organizer: QuanTech

Key speakers: Peter Hafez, Chief Data Scientist, Ravenpack Gordon Ritter, Senior Portfolio Manager, GSA Capital Marcos dePrado, Senior Managing Director, Guggenheim Partners Ediz Ozkaya, Executive Director & Machine Learning Strategist, Goldman Sachs Miquel Alonso, Executive Director, UBS Abdel Lantere, Data Scientist & Quantitative Consultant, HSBC Paul Bilokon, Senior Quantitative Consultant, BNP Paribas Arun Verma, Quantitative Researcher, Bloomberg LP Rajesh Krishnamachari, Quantitative Strategist and Data Scientist, J.P. Morgan Mariano Zeron, Head of R&D, MoCaX Intelligence Suhail Shergill, Head of R&D and Innovation Lead, Scotiabank Ryan Faulkner, Officer, Federal Reserve Bank of New York

Web page: http://quantechconference.com/

Pricing: - variable

Description: Program:
Overcoming the Trade-off: Predictive Power vs. Expressiveness of Machine Learning Models
“Black-box Machine Learning: Improving Transparency”.
Machine Learning Models
Applying Machine Learning to Evaluate Systemic Risk and Contribution of Individual SIFIs
Machine Learning – Recent Trends and Applicability to Risk and Related Areas
Fast MVA Optimisation using Chebyshev Interpolants
Practical Aspects of Applying Deep Learning for Market Making
A look at Latest Hardware Developments in Machine & Deep Learning
Machine Learning & Ai in Quantitative Finance Panel
The 7 Reasons Most Machine Learning Funds Fail
Machine Learning, High-Frequency Trading and Kdb+/q for Quants and Data Scientists
Machine Learning for Trading
Big Data and AI Strategies: Machine Learning and Alternative Data Approach to Investing
Text Mining and Market Sentiment
Machine Learning & Event Detection for Trading Energy and Metal Futures
Extracting embedded alpha in Stocks and Commodity underlyings using statistical arbitrage/ML techniques from News/Social data

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