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Soulaymane Kachani

from New York, NY
Age ~47
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Also known as:
Soulaymane Kachman, Soulaymane I
Connected to:
Caroline Kachanovsky, 62Mark S Kachanuk, 67Pamela A Kachar, 72Srikanth Kacharam, 41Deepa Kacharia, 51Paul E Kachaturian, 74Elaine J Kachavos, 80Guyane Kachbalyan, 44Randa L Kachef, 40Agnes C Kachel, 109

Soulaymane Kachani Phones & Addresses

  • New York, NY
  • Cambridge, MA

Resumes

Resumes

Soulaymane Kachani Photo 1

Young Global Leader

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Location:
New York, NY
Industry:
Higher Education
Work:
Strategic Capital Allocation Group Jul 2003 - Dec 2016
Chief Strategy Advisor
Columbia University In the City of New York Jul 2003 - Dec 2016
Vice Provost For Teaching, Learning, and Innovation
Columbia University In the City of New York Jan 2014 - Jun 2014
Special Assistant To the Provost For Online Education
Columbia University In the City of New York Jul 2008 - Jun 2014
Director of Master's Programs and Director of Executive Education, Ieor Department
Mckinsey & Company Jul 2003 - Jun 2013
Part-Time Expert Consultant
Education:
Harvard Kennedy School
University of Oxford
Massachusetts Institute of Technology
Doctorates, Doctor of Philosophy Massachusetts Institute of Technology
Master of Science, Masters Centralesupelec
Lycée Louis - Le - Grand
Skills:
Data Analysis
Strategy
Management Consulting
Program Management
Operations Research
Mathematical Modeling
Financial Modeling
Statistics
Matlab
Research
Quantitative Analytics
R
Analysis
Business Strategy
Qualitative Research
Strategic Planning
Statistical Modeling
Econometrics
Optimization
Sas
Financial Analysis
Quantitative Finance
Applied Mathematics
Supply Chain Optimization
Derivatives
Consulting
Business Analysis
Data Mining
Project Management
Mentoring
Risk Management
Analytics
Java
Leadership Development
Management
Valuation
Economics
Vba
Microsoft Office
Negotiation
Soulaymane Kachani Photo 2

Soulaymane Kachani

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Location:
Greater New York City Area
Industry:
Higher Education

Publications

Wikipedia

Soulaymane Kachani

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Soulaymane Kachani is a Morocco-born academic and businessman, and a professor at Columbia University. At Columbia University, Kachani teaches courses in ...

Us Patents

System And Methods For Business To Business Price Modeling Using Price Change Optimization

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US Patent:
7680686, Mar 16, 2010
Filed:
Aug 29, 2006
Appl. No.:
11/468013
Inventors:
Jens E. Tellefsen - Mountain View CA, US
Soulaymane Kachani - New York NY, US
Assignee:
Vendavo, Inc. - Palo Alto CA
International Classification:
G06F 17/30
G06F 17/00
US Classification:
705 10, 705400
Abstract:
The present invention relates to business to business market price control and management systems. More particularly, the present invention relates to systems and methods for generating price modeling and optimization modules in a business to business market setting wherein price changes are optimized to achieve desired business results.

Resource Allocation Techniques

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US Patent:
20030046212, Mar 6, 2003
Filed:
Dec 13, 2001
Appl. No.:
10/018696
Inventors:
Brian Hunter - Boston MA, US
Soulaymane Kachani - Cambridge MA, US
International Classification:
G06F017/60
US Classification:
705/036000
Abstract:
Resource allocation techniques for determining an allocation of investment funds among a set of at least two asset classes for a period of time which maximizes return on the investment funds over the period of time. In one aspect of the techniques, the return on the investment funds is determined using real options. In another aspect of the techniques, reliability of return is used to quantify the effects of the diversification resulting from the use of different classes of assets (). The reliability of return is then used as a constraint on the maximization of the return. The reliability of return is determined () by establishing correlations between the asset classes with regard to risk, using the correlations with the standard deviations for the asset classes to determine covariances between the asset classes, and using the covariances to determine the standard deviation for the risk for the entire set. The standard deviation is then used together with the return to determine the reliability of the return ().

Resource Allocation Technique

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US Patent:
20060200400, Sep 7, 2006
Filed:
Jun 18, 2004
Appl. No.:
10/561095
Inventors:
Brian Hunter - Boston MA, US
Ashish Kulkarni - Brighton MA, US
Soulaymane Kachani - New York NY, US
International Classification:
G06F 19/00
US Classification:
70503600R, 700097000
Abstract:
An improved resource allocation system comprising a reliability decision engine (), which allocates the portfolio's assets as required for the desired reliability portfolio. The reliability decision engine including two reliability decision engines, a basic reliability decision engine () and a robust reliability decision engine (). The use of robust optimization makes it possible to determine the sensitivity of the optimized portfolio. Scenarios can be specified directly by the user or automatically generated by the system in response to a selection by the user. Inputs () are applied to basic the basic reliability decision engine () and inputs () are applied to robust reliability decision engine ().

Resource Allocation Techniques

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US Patent:
20100185557, Jul 22, 2010
Filed:
Dec 31, 2009
Appl. No.:
12/651272
Inventors:
Brian A. Hunter - Boston MA, US
Ashish Kulkarni - Cambridge MA, US
Soulaymane Kachani - New York NY, US
Assignee:
Strategic Capital Network, LLC - Boston MA
International Classification:
G06Q 40/00
G06Q 10/00
US Classification:
705 36 R, 705348
Abstract:
Resource allocation techniques for robust optimization of a set of assets. In these techniques, a user defines or selects scenarios that model investment conditions including normal and/or extreme conditions. The set of assets is optimized across the scenarios to produce weights for the assets in the set that optimize the worst-case value of the assets. A resource allocation system is disclosed which first selects a reliable set of assets for optimization and then optimizes the reliable set of assets. Optimization of the set of assets may involve robust or non-robust optimization, many different kinds of constraints and/or multiple constraints, different objective functions, and different adjustments for the objective functions. Selection of the set of assets and selection of the kind of optimization, of the constraints, of the objective function, and of the adjustments to the objective function is done using a graphical user interface.
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Soulaymane Kachani from New York, NY, age ~47 Get Report