Phd Positions at WAI University of Waikato

 New systems with novel mining techniques are necessary due to the velocity, but
also variety and variability, of high-speed data streams, particularly in IoT
applications. This challenging setting requires algorithms that use an extremely
small amount of time and memory resources, and are able to adapt to changes while
not stopping the learning process. Moreover, they should be distributed to allow
them to run on top of Big Data infrastructures. How to do this accurately in a
fully automatic and transparent elastic, real-time, system is the main challenge
for real-time analytics. In this scenario, high-performing ensemble setups for
machine learning from data streams, such as online bagging, leveraging bagging,
and random forests, are currently state-of-the-art. On the other hand, deep
neural networks are immensely popular and successful in offline settings, owing
in part to the proliferation of interest and oft-advertised successes in deep
learning. These algorithms can learn incrementally, but they have so far proved
too sensitive to hyper-parameter options and initial conditions to be considered
for the IoT data stream setting.

In this context, we are looking for candidates for a PhD funding opportunity in
the area of Machine Learning for Data Streams.

Please submit your CV, a cover letter, Master level transcripts, a reference letter (or the coordinates of a person willing to give one).

 
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