Undergraduate and Graduate Summer Research Experiences in Artificial
   Intelligence and Machine Learning at Bryn Mawr College
   Apply by *March 1, 2013* for full consideration.  We will continue to
   accept applications after this date until all positions are filled.
   Spend ten weeks of your summer working on exciting projects in artificial
   intelligence and machine learning at Bryn Mawr College!  We have openings
   for several undergraduate or graduate research assistants to work on two
   grant-sponsored research projects this summer.  Student participants will
   join a research team with other students, Prof. Eric Eaton, and one
   postdoctoral researcher to carry out a detailed program of research toward
   scholarly publications.  Students will present the results of their
   research during the final week of the program at Bryn Mawr College, and
   (if appropriate) at their home institutions and/or other academic venues,
   such as research conferences.
   All students who are beginning their junior or senior undergraduate year
   in Fall 2013 or who will graduate during the Spring 2013 semester, and
   all graduate students are eligible to apply.  To be considered, you should
   have a background in either computer science, mathematics, physics, or
   statistics and have strong grades in your major. Although it is not
   required, it would be beneficial if you have taken and done well in at
   least one course related to artificial intelligence, machine learning,
   robotics, statistics, or topology.
   Brief descriptions of the two sponsored research projects are listed
   below:
   LIFELONG MACHINE LEARNING -- Most current machine learning methods learn a
   model for a single data set, and then forget that model completely when
   they are applied to another data set.  In contrast, human learning retains
   information between learning tasks, developing skills over a lifetime of
   experience.  This project will focus on developing methods for computers
   to learn continually from and transfer knowledge between multiple learning
   tasks, enabling the continual development of skills
   through lifelong machine learning. The development of lifelong machine
   learning has the potential to enable a new class of learning systems
   capable of learning a diverse set of skills over time and then adapting
   those skills as needed to changes in the environment.  Within this
   project, we are also developing methods for users to instruct learning
   systems, enabling the users to teach and shape behaviors over time.  We
   are applying these lifelong learning methods to a variety of domains,
   including robotic control and coordination in a multi-agent simulator, and
   facial expression recognition from images.
   SOCIAL NETWORK ANALYSIS -- Networks occur in many different applications,
   including social networks, corporate organizations, chemical interactions,
   and biological regulatory networks.  Relational networks represent the
   connections between entities as a graph, providing an intuitive and
   structured representation of this knowledge.  Often, these networks
   contain natural communities of entities, such as groups of friends or
   project teams, that is useful for analyzing the structure of the network.
    This project focuses on automatically identifying these natural community
   structures in heterogeneous networks that contain more than one type of
   entity (e.g., a graph of connections between individuals, corporations,
   and locations).  We will then apply these natural community structures to
   learning and dimensionality reduction (i.e., compression) in the graph.
    This project builds on concepts from topology and statistical physics.
   On-campus housing and meals are available for student participants, along
   with a variety of professional development workshops and summer
   activities.
   Application instructions and further details are available online at
   http://cs.brynmawr.edu/~eeaton/openpositions.html
   Bryn Mawr College is an affirmative action/equal-opportunity employer.
   Minority candidates and women are especially encouraged to apply.  Hiring
   is contingent upon eligibility to work in the United States.  Bryn Mawr
   College is located in the suburbs of Philadelphia and is convenient to
   major air and train transportation hubs.