University of Rochester Institute for Data Science

Abstracts & Biographies

David Carey
Carey, David J.

Associate Chief Research Officer, Director of Weis Center for Research, Geisinger Clinic

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Aran Garcia-Bellido
Garcia-Bellido, Aran

Assistant Professor of Department of Physics & Astronomy

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Eric Gawiser
Gawiser, Eric

Associate Professor of Department of Physics & Astronomy at Rutgers, the State University of New Jersey

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

M. Eshan Hoque
Hoque, M. Ehsan

Assistant Professor of Computer Science

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Saurabh Kataria
Kataria, Saurabh

Research Scientist of Xerox

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

John Kessler
Kessler, John

Associate Professor of Earth and Environmental Sciences Department, University of Rochester

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

John Langford
Langford, John

Principal Research of Microsoft Research

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Edith Law
Law, Edith

CRCS Postdoctoral Fellow of School of Engineering and Applied Sciences at Harvard University

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Amit Mukherjee
Mukherjee, Amit

Computational Post Doctoral Fellow of Cold Spring Harbor Lab NY

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Alex Paciorkowski
Paciorkowski, Alex

Senior Instructor in Neurology, Biomedical Genetics and Pediatrics of University of Rochester, School of Medicine and Dentistry

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Salakhutdinov
Salakhutdinov, Ruslan

Assistant Professor of Department of Computer Science and Department of Statistics, University of Toronto

Email: rsalakhu@cs.toronto.edu

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Marc Schieber
Schieber, Marc H.

Professor of Neurology, of Neurobiology, and of Biomedical Engineering of University of Rochester
Attending Neurologist of Unity Health, Rochester, NY

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Vikas Sindhwani
Sindhwani, Vikas

Research Staff Member of Machine Learning group at IBM T.J. Watson Research Center

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Ellen Voorhees
Voorhees, Ellen

Senior Computer Scientist of U.S. National Institute of Standards and Technology (NIST)

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Fei Wang
Wang, Fei

Research Staff Member of Healthcare Analytics Research group, IBM T. J. Watson Research Center

Email: feiwang03@gmail.com

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.

Bin Zhang
Zhang, Bin

Assistant Professor of Department of Management Information Systems, Temple University

Email: bzhang@temple.edu

Comparing Peer Influences in Large Social Networks

The prevalence of social networks analysis has made technology diffusion an important topic in the Information Systems literature. Resent research suggests that adoption by individuals can be predicted not only from their personal tastes and characteristics, but also from the preferences of people who are close to them in their networks. Most models addressing these issues only consider one operative network. In reality there are often several networks influencing a targeted population, for example, friendship and colleagueship. However, researchers faces two challenges before they can compare multiple network influences on individuals' behaviors and attitudes in large social network contexts are high heterogeneity across subsets of the network and computing cost of processing the whole network. I developed a novel technique that can efficiently extract high quality sub graphs from large-scale networks so that the researcher can analyze these sub graphs as stand-alone subpopulations, instead of analyzing the whole population. In addition, I developed a hierarchical, multiple network-regime autocorrelation model for this class of problem and propose two algorithms for fitting it, one based on Expectation-Maximization (E-M) approach and the other on a Bayesian model using Markov Chain Monte Carlo (MCMC). I also illustrate how this approach can be applied to real social networks to compare the explanatory power of cohesion versus structural equivalence for social influence.

Bin Zhang’s primary research interests are social network analysis and business analytics. He is currently designing new algorithms and statistical models to analyze large-scale social networks, and studying their applications in technology diffusion and online social media. Bin has played key roles in research projects for NASA, NSF, and the Louisiana Department of Transportation and Development.