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Fuzzy Day 2017
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SQA Fuzzy Day 2017 Agenda: “Big Data! Big Deal?”


If you are an SQA Member, sign in and click here for access to the presentation materials


March 9, 2017



250 Greenwich St.

New York, NY



8:30 am: Registration & Breakfast

9:00 am: Overview & Historical Context

‘50 years of Data Science’

Dave Donoho (Stanford University and Renaissance Technologies)



David Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description. David’s research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. His applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems.

10:00 am: Machine Learning 101 

Probably Approximately Correct: A Brief Tour of All of Machine Learning

Shane Conway (Kepos Capital)



Shane Conway is a researcher at Kepos Capital, a systematic Global Macro hedge fund in New York.  Kepos investment strategies are data driven and the research process employs a variety of statistical and machine learning techniques.  Prior to Kepos, Shane was a researcher at AQR Capital.  Formerly, he worked at Millburn Ridgefield and Goldman Sachs.  Shane’s research primarily focuses on market microstructure and optimal execution problems.  He has a degree in Electrical Engineering from Columbia University.


11:00 am: Coffee Break

11:15 am: Example of a Specific Big Data Application

‘What Do Terabytes of Weekly Scanner Data Say About Economic Conditions?’

Serena Ng (Columbia University)



Serena Ng is a Professor of Economics at Columbia University, a fellow of the Econometric Society, and a member of the National Bureau of Economic Research. She received her B.A. from the University of Western Ontario and Ph.D from Princeton University. Her primary research is in methods for analyzing economic time series. She has written extensively on estimation and inference when data are non-stationary, model selection, and regressions with principal components as predictors in a data rich environment. Her recent work focuses on the opportunities and challenges that big data might pose for economic research. She uses terabytes of scanner data on consumer purchases to see what can be learned about aggregate economic conditions from micro level data.

12:30 pm: Big Data Mining (Working Lunch)

‘Big Data in Investment Finance: A Cautionary Comment’

Dan diBartolomeo (Northfield)



Mr. diBartolomeo is President and founder of Northfield Information Services, Inc.  Based in Boston since 1986, Northfield develops quantitative models of financial markets.   He sits on boards of numerous industry organizations include IAQF and CQA, and is a director and past president of the Boston Economic Club.  His publication record includes more than thirty books, book chapters and research journal articles.   In addition, Dan is a Visiting Professor at Brunel University and has been admitted as an expert witness in litigation matters regarding investment management practices and derivatives in both US federal and state courts.  

1:30 pm: Example of a Specific Big Data Application

‘Does Unusual News Forecast Market Stress?’

Paul Glasserman (Columbia University)



Paul Glasserman is the Jack R. Anderson Professor of Business at Columbia Business. He leads the risk management initiative of the school's Program for Financial Studies, and chairs the Financial and Business Analytics Center of Columbia's Data Science Institute. In 2011-2012, he was on leave from Columbia and working at the Office of Financial Research in the U.S. Treasury Department, where he continues to serve as a part-time consultant. He has also held visiting positions at Princeton University, NYU, and the Federal Reserve Bank of New York. Glasserman's publications include the book Monte Carlo Methods in Financial Engineering (Springer, 2004), which received the 2006 Lanchester Prize and the 2005 I-Sim Outstanding Publication Award. He was named Risk Magazine's 2007 Quant of the Year.He is a past recipient of the National Young Investigator Award from the National Science Foundation (1994-1999), IBM University Partnership Awards (1998-2001), the TIMS Outstanding Simulation Publication Award (1992), the Erlang Prize (1996), an IMS Medallion from the Institute of Mathematical Statistics (2006), and a fellowship from the FDIC Center for Financial Research (2004). He received the INFORMS Saul Gass award in 2016.Glasserman was senior vice dean of Columbia Business School in 2004-2008 and served as interim director of the Sanford C. Bernstein & Co. Center for Leadership and Ethics in 2005-2007.


2:30 pm: Coffee Break 

2:45 pm: Example of a Specific Big Data Application

             ‘Tales from the Data Trenches of Display Advertising’

              Claudia Perlich (Dstillery and NYU Stern)



Claudia Perlich leads the machine learning efforts that power Dstillerys digital intelligence for marketers and media companies.  With more than 50 published scientific articles, she is a widely acclaimed expert on big data and machine learning applications, and an active speaker at data science and marketing conferences around the world.

Claudia is the past winner of the Advertising Research Foundations (ARF) Grand Innovation Award and has been selected for Crains New Yorks 40 Under 40 list, Wired Magazines Smart List, and Fast Companys 100 Most Creative People.

Claudia holds multiple patents in machine learning. She has won many data mining competitions and awards at Knowledge Discovery and Data Mining (KDD) conferences, and served as the organizations General Chair in 2014.

Prior to joining Dstillery in 2010, Claudia worked at IBMs Watson Research Center, focusing on data analytics and machine learning.  She holds a PhD in Information Systems from New York University (where she continues to teach at the Stern School of Business), and an MA in Computer Science from the University of Colorado.


3:45 pm:  Lightning Presentations

‘Specific examples where big data, machine learning, AI &/or Data Science is having an impact

Speakers including:

·         Patrick Wood  (Kensho) -  Interacting with data to make better decisions

·         Afshin Goodarzi (1010data)

·         Manish Aurora (Rational Investing) - Value investing globally (& data preparation)

·         Sylvain Raynes (CreditSpectrum) - Asset Backed Securities (& credit ratings)




Patrick Wood is Head of Academic Research at Kensho Technologies, the leading provider of data analytics to sophisticated financial institutions and critical government agencies. In addition to serving the firm’s commercial clients, Patrick is responsible for Kensho's many collaborations with distinguished academic institutions and research teams worldwide.  Patrick Wood joined Kensho from The Advisory Board Company, a global research and technology company based in Washington, DC. Prior to his time in Washington, Patrick was a professor at Boston University. In addition to publishing in peer-reviewed journals, he has delivered lectures and seminars at institutions including the Max-Planck-Institut in Berlin, Oxford University, Yale University, the University of Pennsylvania, and the European University Institute in Florence. He also serves on the Board of Directors for CAIR Coalition, a Washington-based non-profit for immigrants’ rights.  Patrick holds a PhD from Princeton University and an undergraduate degree from Oxford University.



A recognized leader in the field of Big Data analytics, Afshin has led several teams in designing, building and delivering predictive models and analytical products to a diverse set of industries. Prior to joining 1010data, Afshin was a Managing Director of Mortgage Analytics at Equifax, where he was responsible for the creation of new data products and the supporting analytics to the financial services industry. Previously, he led the development of various classes of predictive models aimed at the mortgage industry during his tenure at Loan Performance (Corelogic). Prior to that he worked at BlackRock, the research center for NYNEX (present day Verizon), and Norkom Technologies. Afshin's publications span the fields of data mining, data visualization, optimization and artificial intelligence.



Manish Aurora, Managing Principal  -  Methodology and Product Architecture

         Co-founded Rational Investing LLC and built its first valuations starting in 1998.  The firm is now 20 professionals modeling the G7 and MSCI World markets

         Designed and developed the FX trading platform of FXCM, at peak the world’s largest non-bank online FX dealer

         Converted Merrill’s European FX derivatives exposure at NYC, London, Singapore offices to the Euro

         Reprogrammed JP Morgan’s global swaps pricing and counterparty credit risk calculation using Massively Parallel Supercomputing technology

         Designed the Value at Risk calculator for the merger of Chase and Chemical, then the biggest bank merger ever, under a tight deadline from the Federal Reserve

         Designed and constructed the first CMBS and Corporate Bond credit risk models at BlackRock

         Sell-side analyst at Nomura Securities covering real estate equity, debt, CMBS

         Built the first commercial paper direct issuance and investment management and reporting system for GE Capital, ITT, Ford at Financial Sciences
         MBA from University of Chicago; BS in computer science, University of Scranton


Dr. Sylvain Raynes is a founding principal of R&R Consulting, a structured finance consultancy dedicated to advising issuers, investors, intermediaries, non-profit corporations, and regulatory bodies in credit risk management and structured financial techniques using fine-grained analytical methods. Dr. Raynes started his structured finance work on the origination teams of UBS and CSFB. He developed methods for standardizing the credit risk analysis of exotic ABS while in the Structured Finance Group Moody's Investors Service in the mid-1990s. Earlier, at Goldman Sachs, he was involved in the statistical modeling of Derivative Product Companies, and at Citicorp he was responsible for the design of the credit scoring model for Citi's credit card portfolio. Dr. Raynes has a PhD in aerospace engineering from Princeton University and an M.S. in Numerical Methods from the Von Karman Institute in Brussels.

5:00 pm: Cocktail Reception 


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