Knit: Integrating Human Based Partial Analyses of Big Data
We are developing means to automatically assist analysts/experts to
identify patterns and detect anomalies in big data streams as they arise
when heterogeneous, unstructured data sources are consulted. Our
approach solely relies upon analysts and their ability to group/categorize "common situations" into patterns. As one can imagine, an analyst can only process a subset of the big data. We are developing machine learning
algorithms that will use these partial groupings for a subset of the big data from each analyst and knit together, i.e. synthesize, integrate and/or merge, these discerned similarities providing a coherent global assessment.
Specifically, your goal will be to develop and analyze different machine learning methods under different characteristics of analysts and different
situational scenarios. We have emulated analyst behavior and data
streams through a probabilistic model of an analyst response to the
data streams emulating a scenario. Our goal is to analyze different ML
methods and develop new ones. You will be co-advised by a post-doc
with extensive experience in machine learnng.
The student must demonstrate excellent programming skills. Experience
in machine learning and data mining is a plus.
Please send a CV to Kalyan Veeramachaneni (firstname.lastname@example.org).