MARC PIETTE
Pilots at Sanchez St, San Francisco, CA
License number
California A5292663
Issued Date
Feb 2016
Expiration Date
Feb 2021
Category
Airmen
Type
Authorized Aircraft Instructor
Address
Address
237 Sanchez St, San Francisco, CA 94114
Professional information
Entrepreneur, Co-Founder And Coo At Locu
Position:
COO and Co-Founder at Locu, Inc.
Location:
San Francisco, California
Industry:
Information Technology and Services
Work:
Locu, Inc.
- Cambridge, MA since Nov 2010
-
COO and Co-Founder
MIT Sloan School of Management
Sep 2010 - May 2011
-
Teaching Assistant
VMware
2010 - 2010
-
Product Management (vSphere)
Bloomberg
- Greater New York City Area Oct 2007 - Jul 2009
-
Senior Engineering Team Lead (R&D)
IBM
Jul 2002 - Oct 2007
-
Security Solutions Architect
Education:
Massachusetts Institute of Technology - Sloan School of Management 2009 - 2011
Master of Business Administration (M.B.A.) Université catholique de Louvain 1996 - 2001
B.S./M.S., Computer Science and Engineering
Master of Business Administration (M.B.A.) Université catholique de Louvain 1996 - 2001
B.S./M.S., Computer Science and Engineering
Honor & Awards:
- IBM Ovation Award for Excellent Performance in Working with Clients (2007)
- Cisco Achievement Program Award for Excellent Performance during Internship (2000)
Languages:
French
Method And Apparatus For Forming A Structured Document From Unstructured Information
US Patent:
2013006, Mar 14, 2013
Filed:
Sep 6, 2012
Appl. No.:
13/605051
Inventors:
Marek Olszewski - San Francisco CA, US
Stylianos Sidiroglou - Cambridge MA, US
Jason Ansel - Cambridge MA, US
Marc Piette - San Francisco CA, US
Rene Reinsberg - San Francisco CA, US
Stylianos Sidiroglou - Cambridge MA, US
Jason Ansel - Cambridge MA, US
Marc Piette - San Francisco CA, US
Rene Reinsberg - San Francisco CA, US
Assignee:
LOCU, INC. - Cambridge MA
International Classification:
G06F 17/24
US Classification:
715234
Abstract:
Illustrative embodiments improve upon prior machine learning techniques by introducing an additional classification layer that mimics human visual pattern recognition. Building upon classification passes that extract contextual information, illustrative embodiments look for hints of high-level semantic categorization that manifest as visual artifacts in the document, such as font family, font weight, text color, text justification, white space, or CSS class name. An improved lightweight markup language enables display of machine-categorized tokens on a screen for human correction, thereby providing ground truths for further machine classification.