Brian Friedman

• MS in Advanced Technology, Specialization in Computer Science
  Thomas J. Watson School of Engineering
• University Center at Binghamton
  Graduated with academic honors in May 1988
• BS in Computer Science/Electronics
  State University of New York at Binghamton
  Graduated with academic honors in May 1986

• B. D. Friedman, M. J. Burns and J. Cao, "Enterprise Social Networking Data Analytics Within Alcatel-Lucent," Bell Labs Tech. J., 18:1 (2014), 89-109.
• J. Cao, H. Gao, L. E. Li, and B. Friedman, "Enterprise Social Network Analysis and Modeling: A Tale of Two Graphs," Proc. 32nd IEEE Internat. Conf. on Comput. Commun. (INFOCOM '13) (Turin, Ita., 2013), pp. 2382-2390. 
• M. J. Burns and B. D. Friedman, "A Multidimensional Approach to Characterizing and Visualizing Latent Relationships in Enterprise Social Networks," Bell Labs Tech. J., 17:1 (2012), 201-217. 
• M. J. Burns, R. B. Craig, Jr., B. D. Friedman, P. D. Schott, and C. Senot, "Transforming Enterprise Communications Through the Blending of Social Networking and Unified Communications," Bell Labs Tech. J., 16:1 (2011), 19-34. 

I am primarily a software developer, but I also have considerable experience in UI design, databases, systems administration and software configuration management.   I've also done some project management and have written my share of documentation over the years.

• System and method of determining enterprise social network usage
  Inventors:    Jin Cao, Li Erran Li, Hongyu GAO, Brian D. Friedman
  Publication number:    US20130151429 A1
  Publication type:    Application
  Application number:    US 13/688,885
  Publication date:    Jun 13, 2013
  Filing date:    Nov 29, 2012
  Abstract:     According to an embodiment, a computing system includes at least one computing device including a processor configured to use a logistic regression model to provide an indication of a relationship between a user's position within an enterprise and how the user interacts with <span class="_wysihtml5-temp-placeholder"></span>other users of an enterprise social network. 
• Technique For Multi-Dimensionally Determining Strength Of An Item In A Weighted List Based On Tagging
  Inventors:    Brian D. Friedman, Christophe Senot, Michael J. Burns
  Original Assignee:    Alcatel-Lucent Usa Inc.
  Publication number:    US20110295859 A1
  Publication type:    Application
  Application number:    US 12/945,929
  Publication date:    Dec 1, 2011
  Filing date:    Nov 15, 2010
  Abstract:     A weighted list may be visually shown on a website, e.g., as a tag cloud, object cloud, etc. The font size of each item in the weighted list may indicate its strength relative to the other items in the same. The strength of a weighted list item is determined based at least on multi-dimensional weights accorded to applications by users of a given tag to an object. For example, each multi-dimensional weight may be an aggregate of weight measures of two or more of quantity-based dimensions, time-based dimensions, social distance-based dimensions, semantic similarity dimensions, etc. 
• Technique For Determining And Indicating Strength Of An Item In A Weighted List Based On Tagging
  Inventors:    Brian D. Friedman
  Original Assignee:    Alcatel-Lucent Usa Inc.
  Publication number:    US20110296345 A1
  Publication type:    Application
  Application number:    US 12/788,442
  Publication date:    Dec 1, 2011
  Filing date:    May 27, 2010
  Abstract:     A weighted list may be visually shown on a website, e.g., as a tag cloud, object cloud, etc. The font size of each item in the weighted list indicates its strength relative to the other items in the same. The strength of a weighted list item is determined based at least on individual weights associated with the users who applied a given tag to an object. For example, the item in the weighted list may represent the given tag in a tag cloud, or the object in an object cloud. The respective weights associated with the users when applying the given tag to the object may be different from one another because of different qualities of the users. For example, the weight associated with a user varies with the number of times the given tag was applied to that user.