Big Data is a fact, fueling a transformation of finance and the world of business in yet unpredictable ways. Scientific parsing of big data and Data Analytics are encountering challenges to process data, produce meaningful inferences and maintain the integrity and the safety of its use.
Irene Aldridge is Industry Assistant Professor, Dept. of Finance and Risk Engineering, Polytechnic Institute of New York University (NYU). At NYU, Aldridge specializes in cutting edge HFT and other data-driven finance research and teaches courses on HFT and algorithmic execution. Aldridge is the author of several books, including “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (2nd edition just published by Wiley, 2013), translated into several languages.
Aldridge worked for various institutions on Wall Street and in Toronto, including Goldman Sachs and CIBC, and is a founder of ABLE Alpha Trading, LTD, a company specializing in high frequency trading technologies, including quantitative market-making. Her research has been profiled on BBC, CNBC, FOX Business, CBC, BNN, German ZDF, National Public Radio (NPR), Bloomberg Radio, as well as in the New York Times, the Wall Street Journal, Associated Press, Financial Times, Thomson/Reuters, Bloomberg LP, Forbes and other major business news outlets.
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