Data Science and its Implications for Business and Society

Seminario promosso dal Laboratorio Interdipartimentale BigData@PoliTo 


Data Science and its Implications for Business and Society 

Prof. Vasant Dhar 

Stern School of Business and Center for Data ScienceNYU


Mercoledì 16 marzo - Ore 9.30

Aula 7T - Cittadella Politecnica Fabbricato Ex Tornerie, Castelfidardo, 39 


This seminar will cover the broad questions at the intersection of machine learning and big data that make up the emerging landscape of data science. The seminar addresses four basic questions:

  1. What is different now relative to ve years ago in data science and machine learning?
  2. What are the new methods we have at our disposal that have emerged from machine learning and when do they work best?
  3.  What are the challenges and major unsolved problems associated with intelligent automated systems?
  4. How well we should expect these approaches to work for prediction and decision-making in management and engineering- related problems?

The seminar will answer these questions based on 20+ years of research and trading experience using machine learning methods for predictions in nance, sports, healthcare, and  education. 

Short Bio 

Vasant Dhar is Professor at the Stern School of Business and the Center for Data Science at NYU. He is also Editor-in-Chief of the Big Data journal. For the last 20+ years, a core part of Professor Dhar’s research has addressed the following question: when do computers make better decisions than humans? 

His June 2015 editorial in Big Data answers this question in the nancial arena, namely, “Should You Trust Your Money to a Robot?”.
Dhar’s created the Adaptive Quant 
Trading (AQT) program in 2009, a data-driven learning machine that trades the world’s most liquid futures contracts systematically for institutional investors.
He has written over 100 research articles and dozens of opinion editorials in media including 
the Financial Times, Wall Street Journal, Forbes, and Wired Magazine.
Professor Dhar is also active in 
the education arena, using big data captured through childrens’ interactions with mobile devices to personalize education for 4 to 10 year olds with the objective of improve their learning in math, reading, and comprehension.
In effect, the research addresses the question, “Should you trust your child’s education to a robot?”.