Quantum Research Interantional Feb 2004 - Aug 2008
Senior Operations Analyst
Us Army Feb 2004 - Aug 2008
Chief, I Corps G3 Force Management
U.s Army Apr 1984 - Apr 2004
Various Postions, Commisioned Officer
Education:
University of Detroit 1981 - 1984
Bachelors, Bachelor of Arts, History
Skills:
Military Operations Army Operational Planning Command Military Experience Military Logistics Special Operations Intelligence Analysis National Security Force Protection Afghanistan Project Management Counterinsurgency Top Secret Interagency Coordination Intelligence Military Training Tactics Defense Humint Reconnaissance Readiness Sigint Weapons Counterterrorism Active Dod Secret Clearance Military Liaison Exercises Dod Nato Combat C4Isr Command and Control U.s. Department of Defense Military
Languages:
English
Principal Research Software Engineer, Microsoft Research A.i
Microsoft
Principal Research Software Engineer, Microsoft Research A.i
Microsoft Jul 1, 2013 - Sep 2016
Principal Software Development Engineer, Microsoft Research, Fuse Labs
Imdb.com May 2012 - Jun 2013
Software Development Manager
Amazon Feb 2008 - May 2012
Senior Technical Program and Software Development Manager
Teachtown Jan 2003 - Oct 2007
Cto, Founder
Education:
Boston University 1994 - 1998
Doctorates, Doctor of Philosophy
Uc San Diego 1990 - 1994
Bachelors, Bachelor of Science, Cognitive Science
Skills:
Software Development Start Ups E Commerce Software Design Entrepreneurship Agile Methodologies Product Development User Interface Design Product Management Web Services Distributed Systems Team Leadership Program Management User Experience Machine Learning Scalability Mobile Applications Software Engineering Software Project Management C++ Strategy Web Applications Rest Artificial Intelligence Project Management Scrum Artificial Neural Networks C# Agile Project Management Project Planning Computer Science Javascript Pragmatic Marketing Certification Object Oriented Design Management Cordova Phone Gap
Certifications:
Neural Networks and Deep Learning Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Structuring Machine Learning Projects
IMDb.com since May 2012
Software Development Manager
Amazon.com Feb 2008 - May 2012
Senior Technical Program and Software Development Manager
TeachTown, Inc. Jan 2003 - Oct 2007
CTO, Founder
UW Digital Ventures Feb 2003 - Aug 2004
Software Technology Manager
Apex Nanotechnology Apr 2002 - Jan 2003
Technical Director, Founder
Education:
Boston University 1994 - 1998
University of California, San Diego 1990 - 1994
Skills:
Machine Learning Artificial Intelligence Artificial Neural Networks Team Leadership Product Management Product Development UI Design C# C++ Entrepreneur Start-ups Entrepreneurship User Interface Design Program Management Project Planning Pragmatic Marketing Certification Computer Science Project Management Software Development E-commerce Software Project Management
Isbn (Books And Publications)
Agarmakt Pa Avskrivning: En Debattbok Fran SNS Om Intressebalans Och Effektivitet I Naringslivet
Techniques and systems may be used to generate a list of interests of a user that interacts with a catalog of items, such as by purchasing the items from a host. The host may then generate a list of interests of the user using a taxonomy that is mapped to the catalog of items and the user interaction with the items. By generating the list of interests based on user interaction with the catalog, the list of interests may be generated based on factual data rather than user opinion. However, in some instances, the user may additionally provide a self-rating for an interest that is identified by the host. In various aspects, the list of interests may be associated with a reviewer of items of the catalog. In some aspects, a reader may identify reviewers that have similar interests by comparing reviewers' interests to the reader's interests.
A computing system causes instructional media to be played on a device to a user. An instructor in the instructional media provides guidance as to how to perform an activity when the instructional media is played on the device. The computing system obtains user data pertaining to performance of the activity by the user. The computing system generates a user-customized portion of the instructional media based upon the user data and a computer-implemented model. The computing system causes the user-customized portion to be played on the device to the user, where the device emits audible words reproduced in a voice of the instructor, where the audible words are based upon the user data, and further where the device displays generated images of the instructor depicting the instructor speaking the audible words as the device emits the audible words.
Training A User-System Dialog In A Task-Oriented Dialog System
- Redmond WA, US Lars Hasso LIDEN - Seattle WA, US Thomas PARK - Seattle WA, US Matthew David MAZZOLA - Seattle WA, US Shahin SHAYANDEH - Seattle WA, US Jianfeng GAO - Woodinville WA, US Eslam Kamal ABDELREHEEM - Sammamish WA, US
Methods and systems are disclosed for improving dialog management for task-oriented dialog systems. The disclosed dialog builder leverages machine teaching processing to improve development of dialog managers. In this way, the dialog builder combines the strengths of both rule-based and machine-learned approaches to allow dialog authors to: (1) import a dialog graph developed using popular dialog composers, (2) convert the dialog graph to text-based training dialogs, (3) continuously improve the trained dialogs based on log dialogs, and (4) generate a corrected dialog for retraining the machine learning.
Presenting Content Items Based On Received Facet Value Scores
- Redmond WA, US Lars Hasso Liden - Seattle WA, US
International Classification:
G06F 17/30
Abstract:
In a content item feed, such as a news feed associated with a user in a social network, facet values for multiple facets are determined for the content items in the feed. These facets may include a topic or subject associated with the content item, an author of the content item, and the number of comments associated with the content item. After the user views the content item, the user is asked to score each of the facet values that were determined for the content item. After some number of content items have been scored by the user, newly received content items have their facet values automatically scored based on the scores received from the user for content items having some or all of the same facet values. The content items are displayed to the user according to the scores.
- Seattle WA, US Lars Hasso Liden - Seattle WA, US
International Classification:
H04N 21/81 H04N 21/44 H04N 21/2668 H04N 21/478
Abstract:
Disclosed are various embodiments for presenting collections of items related to subjects in a performance to viewers. The collections are assumed to be identified prior to the performance by individuals with knowledge of subjects expected to appear in the performance. The subject's appearance may be detected based on a user input indicating the appearance or by a subject recognition algorithm. The collections may be presented to a customer in a display also rendering the performance or in a separate display. The customer may select the collections and/or items therein for performing an action with the item, such as, for example purchasing the item.
- Redmond WA, US LARS LIDEN - SEATTLE WA, US HAI LIU - SAMMAMISH WA, US WILLIAM PORTNOY - WOODENVILLE WA, US SHAHIN SHAYANDEH - KIRKLAND WA, US JANICE VON ITTER - SEATTLE WA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
H04L 12/18 H04L 12/58 H04L 29/06
Abstract:
Methods, computer systems, and computer-storage media are provided for connecting devices. Shared spaces may be created among one or more users and/or devices. Based on various factors including locations, histories among users, time of day, etc., spaces may be created among users such that content may be shared within the shared space. The spaces may be created using multiple technologies such that one user may connect to the space via a first identifier format while a second user may connect to the space via a second identifier format. Once created, content may be shared with one or more users of the space. Depending on privacy and/or security settings of the space, any user may invite other users to join the space. The content may be available to any participant once the space has terminated.