Network-Data Analytics — methodology development for community detection, ranking systems, network inference, and manifold learning; Dynamics on and of Networks — nonlinear and stochastic systems including social contagions, oscillator synchronization, and network evolution.
311 Mathematics Building
UB, North Campus
Buffalo NY, 14260-2900
Phone: (716) 645-8796
Fax: (716) 645-5039
PhD, Applied Mathematics, University of Colorado, Boulder
Networks are widespread in technology, biology, and society. Moreover, they can encode similarity between arbitrary objects (i.e., images, videos, etc.) and thus play a crucial role for data-analysis methodology in general. I develop mathematical, statistical and machine learning tools to study network representations of complex systems and data. My work falls under two broad groups:
Importantly, these two groups are closely intertwined. That is, realistic modeling of dynamical systems typically requires assimilation with empirical data. At the same, data-analysis methodology often stems from the study of dynamics. Random walks, for example, are central to algorithms including PageRank, Infomap, and Diffusion Map. I study network dynamics and network data as a synergistic pursuit.