Recent advances in sensing technology have enabled the capture of dynamic heterogeneous network data. However, due to limited resources it is not practical to measure a complete snapshot of the system at any given time. This topic focuses on inferring the full system or a close approximation from a minimal set of measurements. Relevant areas of interest include matrix completion, low-rank modeling, on-line subspace tracking, classification, clustering, and, ranking of single and multimodal data, all in the context of active learning and sampling of very large and dynamic datasets. Applications areas of interest include, but are not limited to communication, social, and computational network analysis; system monitoring; anomaly detection; and video processing. We are also interested in topological methods such as robust geometric inference, statistical topological data analysis, and computational homology and persistence. Candidates should have a strong research record in these areas.

 

References

Chi Y, Eldar YC, Calderbank R: IEEE Transactions on Signal Processing 61: 5947, 2013

Recht B: Journal of Machine Learning Research 12: 3413, 2011

 

key words

Active learning; Matrix completion; Multimodal data; Computational network analysis; Subspace estimation; Topological methods; Robust geometric inference; Low-rank modeling; Persistence;

Citizenship:  Open to U.S. citizens

Level:  Open to Postdoctoral and Senior applicants