NEST

PI: Chjan Lim, co-PI: Gyorgy Korniss and Boleslaw Szymanski

We propose to answer theoretically and by comparisons with real data, whether (i) the initial conditions of a social system have significant effects on the rates and thresholds of committed minority dominance, and (ii) an entropy-like measure of initial diversity of opinions is sufficient to predict the serious dynamical consequences of zealots.

PI: Szymanski, Boleslaw, co-PI: Gyorgy Korniss

We study real data projected out from empirical or info-social network, to develop methodologies and techniques for extracting relevant information and behavioral patterns from the underlying social network. With the availability and accessibility of vast amount of data in recent years, our project advances our fundamental knowledge, from a network science viewpoint, and creates methodologies and techniques that could be applied to address strategic and urgent needs aligned with national priorities for network research. Using a novel combination of stochastic agent-based models for opinion formation and massive real-life data, we are developing models with predictive power for large-scale social networks.

PI: Szymanski, Boleslaw, co-PI: Brian Uzzi, Northwestern University

Our world is an interconnected system of threats. When one threat materializes, the materialization of connected threats change, and in turn they may materialize, creating spillovers of realized threats across the system. Identifying the spillover and self-materialization potentials of networked threats furnishes a new basis for predicting the spread of threats and creating intervention to curb them. Currently, high quality estimates of the spillover potential of threats have been developed (WEF). Estimates of the internal drivers of systems failure are however nascent. We propose a methodology that will advance the current state of the art regarding threat networks through collaborative man-&-machine learning that will produce a risk network specifically defined for forecasting and controlling the effects of spillover and self-materialization potentials for existing and emerging threats. Our proposal augments human decision-making with an interactive, novel human-machine modeling of dynamical complex systems. In this grant, we develop a new understanding of crowd-sourcing as a means to enhance human estimates of threats in noisy, uncertain environments, and in particular curb decision-makers’ cognitive biases.

PI: Kar, Koushik

This project involves implementation of a Loss-Tolerant Transport Protocol (LT-TCP) on a Linux platform. The project also includes evaluation of LT-TCP (with respect to TCP) in terms of goodput and latency under a wide range of loss rate conditions, and possible redesign and fine-tuning of the LT-TCP protocol as necessary. This work is being conducted in close cooperation with MIT Lincoln Laboratory.

PI: Spear, Frank, co-PIs: Sibel Adali, Peter Fox, Boleslaw Szymanski

MetPetDB is a database for metamorphic petrology that is being designed and built by a global community of metamorphic petrologists in collaboration with computer scientists at Rensselaer Polytechnic Institute as part of the National Cyberinfrastructure Initiative and supported by the National Science Foundation. This project supports the development, implementation and population of MetPetDB with the purpose of, archiving published data, storing new data for ready access to researchers and students, facilitating the gathering of information for researchers beginning new projects, providing a search mechanism for data relating to anywhere on the globe, providing a platform for collaborative studies among researchers, and serving as a portal for students beginning their studies of metamorphic geology.

PI: Szymanski, Boleslaw, RPI co-PIs: Sibel Adali, Jim Hendler, Heng Ji, Gyorgy Korniss, Malik Magdon-Ismail, Al Wallace

Website: scnarc.rpi.edu

The ARL Social Cognitive Network Academic Research Center (SCNARC) has been created and funded as a part of the US Army Network Science Collaborative Technology Alliance together with three other centers focusing on different kind of networks. The principal member of the Center is Rensselaer Polytechnic Institute while the remaining members are CUNY, IBM TJ Watson Research Laboratory, and Northeastern University. The Center includes also collaborators from the Army Research Laboratory, Northwestern University, University of Notre Dame. The Center collaborates closely with other centers of the Network Science CTA. Rapid growth of web-based social networks has redefined social interactions. Increasing popular, technology-based social networks do not require personal, direct contact but at the same time they provide rich traces of data about their activities. These kinds of social networks and the behaviors that govern their dynamics and evolution are the subject of our research.

PI: Szymanski, Boleslaw, co-PI: Gyorgy Korniss

We study vulnerability and recovery in complex networks with the aim to develop methods and design a prototype system for optimizing robustness in such networks. We are developing generic analytic, numerical, and simulation methods to model and analyze, "what if?" disruption scenarios and encapsulate such methods in a prototype system for such modeling and analysis. We also investigate methods for reallocation of resources for the surviving part of the network to keep it operational and efficient. We investigate flow in resistor networks and the resulting load landscapes that provide a fundamental model for transport, based on local routing and conservation laws. This basic model and the resulting load distribution and load landscape in a given network will be used to identify the nodes with the extreme loads in real-life empirical networks, and in turn, to optimize transport.

PI: Szymanski, Boleslaw

The project focuses on parallel computations over the Internet, including volunteer computing. Two large application has been developed, one called MilkyWay@home that focuses on searching for streams of stars from Milky Way neighboring galaxies into Milky Way by its gravitational pull. The other application, DNA@home focuses on transcriptional regulatory network using voluntary computing platforms. In both cases, we are using BOINC infrastructure and server to dispatch work to users and collected results. The grant provided initial impetus for the computational work and is now supplemented by NSF support for relevant science research.