About Dr. Andrew G. West
I am Andrew G. West, a Sr. Research Scientist at Verisign in the Washington D.C. area. In my role with the Strategy & Analytics group I apply deep learning, machine learning, forecasting, and other data science techniques across our domain name business and associated marketing efforts. I publish my research and pursue intellectual property protection via my affiliation with Verisign Labs. Prior to Verisign, I completed my Ph.D. at UPenn in 2013 (MSE 2010) and received my B.Sc. at Washington & Lee University in 2007.
My recent work has focused on: (1) Leveraging deep learning for generative and classification tasks, (2) modeling and forecasting many aspects of the domain name lifecycle, (3) applying big data techniques to Internet-scale quantification problems, and (4) designing crowd-sourced data collection for market measurement and competitive intelligence. Complementing this work are broader research interests that include reputation management, metadata analysis, email/Web 2.0 abuse, underground economies, behavioral profiling, and Internet virality. Though the majority of my insights are now internally facing, my research approach draws from a rigorous background of 35+ peer-reviewed publications and a desire to bring emergent techniques to bear on business problems.
Before my current role, my dissertation research investigated security in "open collaboration" applications. I examined how user-generated content and collaborative semantics change how abuses manifest and can be detected. This yielded tools still in popular use, notably “STiki”, which has been used to remove 1.1+ MILLION damaging revisions from Wikipedia. Media outlets such as the Chronicle of Higher Education and Gizmodo have recognized my contributions, and I remain an active volunteer in the Wikipedia community.
http://www.andrew-west.com -- my professional website, has my full C.V., copies of my publications, and enumerates my academic and professional involvement.
All PDF versions linked herein are authors' versions of the respective publications and posted to this personal/institutional page as permitted by policy.
deep learning, machine learning, forecasting, domains, domain name lifecycle, crowdsourcing, Internet measurement, NLP, big data, data science, Web 2.0 security, anti-abuse, underground economies, reputation management, and Wikipedia
|2007 ‐ 2013||PhD, University of Pennsylvania ‐ Computer and Information Science (CIS)|
|2007 ‐ 2010||MSE, University of Pennsylvania ‐ Computer and Information Science (CIS)|
|2003 ‐ 2007||BSc, Washington and Lee University ‐ Computer Science|
Trust and reputation management (4)
An Evaluation Framework for Reputation Management Systems
Trust Modeling and Management in Digital Environments: From Social Concept to System Development (2009)
Reputation management (RM) is employed in distributed and peer-to-peer networks to help users compute a measure of trust in other ...
QuanTM: A Quantitative Trust Management System
Proceedings of the Second European Workshop on System Security (EUROSEC '09) (2009)
Quantitative Trust Management (QTM) provides a dynamic interpretation of authorization policies for access control decisions based on upon evolving reputations ...
Routing reputation (4)
AS-TRUST: A Trust Quantification Scheme for Autonomous Systems in BGP
Lecture Notes in Computer Science: Trust and Trustworthy Computing (2011)
The Border Gateway Protocol (BGP) works by frequently exchanging updates that disseminate reachability information about IP prefixes (i.e., IP address ...
Miscellaneous security (5)
Towards the Effective Temporal Association Mining of Spam Blacklists
8th Annual Collaboration, Electronic Messaging, Anti-Abuse, and Spam Conference (2011)
IP blacklists are a well-regarded anti-spam mechanism that capture global spamming patterns. These properties make such lists a practical ground-truth ...
Undergraduate writings (2)
Bound Optimization for Parallel Quadratic Sieving Using Large Prime Variations
Undergraduate Honor's Thesis, Washington & Lee University (2007)
The Quadratic Sieve (QS) factorization algorithm is a powerful means to perform prime decompositions that combines number theory, linear algebra, ...