Sukrit Gupta
Nanyang Technological University, School of Computer Science & Engineering, Graduate Student
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I am a PhD student at the School for Computer Science & Engineering, Nanyang Technological University, Singapore. My research interests are in the fields of Complex Networks Analysis, Network Science and Graph Theory. At NTU, my research and course intake will focus on study and detection of functional modules in the brain. I am also the Teaching Assistant for Prof. Jagath Rajapakse's Neural Networks class for undergrads. I have a B. Eng. in Computer Science from PEC U... moreI am a PhD student at the School for Computer Science & Engineering, Nanyang Technological University, Singapore. My research interests are in the fields of Complex Networks Analysis, Network Science and Graph Theory. At NTU, my research and course intake will focus on study and detection of functional modules in the brain. I am also the Teaching Assistant for Prof. Jagath Rajapakse's Neural Networks class for undergrads.
I have a B. Eng. in Computer Science from PEC University of Technology (formerly Punjab Engineering College, Chandigarh), one of the premier technical institutes in India. The institute awarded me the highest honor given to a graduating student, the Institute Color for research activities during my undergraduate degree. Apart from PEC, I worked with researchers at IASI-CNR, Rome (Italy), Carnegie Mellon University (U.S.A.), Ben Gurion University of the Negev (Israel), Indian Institute of Technology Ropar (India).
Besides being interested in learning and research, I am an avid reader, I like to keep myself updated on current happenings, I am into photography and I am getting back to playing the guitar. edit
Comparative network analysis is an emerging line of research that provides insights into the structure and dynamics of networks by finding similarities and discrepancies in their topologies. Unfortunately, comparing networks directly is... more
Comparative network analysis is an emerging line of research that provides insights into the structure and dynamics of networks by finding similarities and discrepancies in their topologies. Unfortunately, comparing networks directly is not feasible on large scales. Existing works resort to representing networks with vectors of features extracted from their topologies and employ various distance metrics to compare between these feature vectors. In this paper, instead of relying on feature vectors to represent the studied networks, we suggest fitting a network model (such as Kronecker Graph) to encode the network structure. We present the directed fitting-distance measure, where the distance from a network A to another network B is captured by the quality of B's fit to the model derived from A. Evaluation on five classes of real networks shows that KronFit based distances perform surprisingly well.
Research Interests:
Functional connectivity of the human brain and the hierarchical modular architecture of functional networks can be investigated using functional magnetic resonance imaging (fMRI). Various network models, such as power-law networks and... more
Functional connectivity of the human brain and the hierarchical modular architecture of functional networks can be investigated using functional magnetic resonance imaging (fMRI). Various network models, such as power-law networks and modular networks have been explored before to study brain networks. In order to investigate the plausibility of mod-eling functional brain networks with network models based on distribution of node degree and connection weights, we will compute the goodness-of-fit of several network models on resting-state fMRI scans gathered in the Human Connec-tome Project. Our experiments suggest that the power-law networks and stochastic block models aptly fit functional con-nectivity of the subjects and the stochastic block models have the potential to detect functional modules of the brain.