Postdoctoral Fellow in Data Mining in Social Systems
I am looking for postdoctoral fellows the area of data management
preferably with an emphasis on social networks, social media, text
mining, and recommender systems. Position is subject to the
availability of funding. The fellow will be working in the
Data Management and Mining Lab in the Department of Computer Science at
the University of British Columbia, Vancouver.
The focal area of research for this position is exemplified by the
following projects. Firstly, while tremendous advances have been made
in the area of influence maximization in social networks with viral
marketing as its main touted application, to date the research has seen
a modest penetration in industry-strength viral marketing case studies.
The aim of this project is to identify the limitations of known
research advances in influence maximization that have prevented the
techniques from being applied in practice, and devise techniques for
taking viral marketing out of the lab.
A second project will focus on developing techniques for recommending
novel and non-traditional kinds of “items” to “users” in unconventional
settings. Is prediction accuracy (along with its add-ons such as
diversity, serendipity etc) the only metric with which to gauge the
efficacy of a recommender system? What should a company powering its
business with a recommendation engine focus on in order to maximize
revenue? What features should a product have to attract customers? What
are some non-traditional settings where recommendations could benefit
applications? These are just some of the questions this project
How can we detect fake claims and more generally fake news? What kind
of intervention is effective in containing its propagation?
"Filter bubble" is a well-known problem in social media as well as in
news consumption? Given a topic, can we automatically identify the
stance taken by articles? What can we do to encourage a "balanced"
consumption of news covering opposing viewpoints on a given issue?
A PhD in computer science or a related field is required. The
successful candidate will have a strong publication record and the
ability and drive to work independently as well as with a team of
talented and passionate researchers. Interested applicants should
contact Professor Laks V.S. Lakshmanan at [first-name AT department DOT
university DOT ca].
See this for an idea of related publications. Salary will be commesurate with
qualifications and experience. The fellow will benefit from the strong
ties we enjoy with several industry collaborators such as AT&T
Labs, Google, LinkedIn, Oracle, Technicolor, and Yahoo! Research, and Walmart