Resume

• 2011

  Penn State University

  Quantitative Scientific Solutions

  Rudjer Boskovic Institute

  University of Dubrovnik

  Education Through Music

  Penn State University

  Harlem

  NYC

  Promote the integration of music into the curricula of disadvantaged schools in order to enhance students' academic performance and general development.

  Volunteer

  Education Through Music

  Dubrovnik

  Croatia

  Institute of Mathematical Chemistry

  Research Associate

  University of Dubrovnik

  Scientific and technical adviser to the Defense Advanced Research Projects Agency (DARPA) and the Intelligence Advanced Research Projects Agency (IARPA)

  Quantitative Scientific Solutions

  University of Dubrovnik

  Dubrovnik

  Croatia

  Department of Applied Computer Science\nCourses taught: Calculus

  Multivariable Calculus

  Numerical Analysis

  Statistics

  Teaching Assistant

  Zagreb

  Croatia

  Theoretical Chemistry Group

  Visiting Researcher

  Rudjer Boskovic Institute

  Applied Research Laboratory\nInformation Sciences and Technology Division\nDistributed Systems Group

  Penn State University

  Postdoctoral Fellow

  Mathematics Department

  College of Information Sciences and Technology\nCourses Taught: Discrete Math (IST 230)

  Graph Theory (Math 485)

  Penn State University

  Penn State College of Information Sciences and Technology

  State College

  PA

  Assistant Professor

• 2005

  French

  Croatian

  PhD

  Faculty of Natural Sciences

  Department of Mathematics\nDissertation: Community Structure and Hub Detection in Complex Networks

  Mathematics

  Association for Women in Mathematics

  Association for Women in Science

  University of Zagreb/Sveuciliste u Zagrebu

• 2001

  BA

  Dean's List 2001/2

  2002/3

  2003/4

  2004/5

  Dean's Scholarship Recipient 2001/2

  2002/3

  2003/4

  2004/5

  Robert C. Byrd Scholarship Recipient 2001/2

  2002/3

  2003/4

  2004/5

  Mathematics

  Music

  Columbia University Undergraduate Mathematics Society

  Columbia University Orchestra

  Columbia Classical Performers

  Bach Society Orchestra

  Delta Gamma

  Columbia University in the City of New York

• 1999

  Winner of the Monmouth Symphony Orchestra Young Artist Concerto Competition

  Winner of the Manhattan School of Music Parent's Association Award

  Flute Performance

  Music Theory

  Manhattan School of Music Precollege Program

  Mathematics

  Physics

  Columbia University Science Honors Program

• 1997

  Salutatorian

  National Merit Scholarship Finalist

  New Jersey State Youth Orchestra

  New Jersey Youth Orchestra Festival

  Hartwick College Summer Music Festival and Institute

  Middletown High School North

• Matlab

  LaTeX

  Public Speaking

  Data Analysis

  University Teaching

  Teaching

  Social Media

  Physics

  C++

  Numerical Analysis

  Machine Learning

  Science

  Graph Theory

  Chemistry

  Algorithms

  Mathematics

  Statistics

  Mathematical Modeling

  An Ultimatum Game Model for the Evolution of Privacy in Jointly Managed Content

  Content sharing in social networks is now one of the most common activities of internet users. In sharing content

  users often have to make access control or privacy decisions that impact other stakeholders or co-owners. These decisions involve negotiation

  either implicitly or explicitly. Over time

  as users engage in these interactions

  their own privacy attitudes evolve

  influenced by and consequently influencing their peers. In this paper

  we present a variation of the one-shot Ultimatum Game

  wherein we model individual users interacting with their peers to make privacy decisions about shared content. We analyze the effects of sharing dynamics on individuals’ privacy preferences over repeated interactions of the game. We theoretically demonstrate conditions under which users’ access decisions eventually converge

  and characterize this limit as a function of inherent individual preferences at the start of the game and willingness to concede these preferences over time. We provide simulations highlighting specific insights on global and local influence

  short-term interactions and the effects of homophily on consensus.

  An Ultimatum Game Model for the Evolution of Privacy in Jointly Managed Content

  In this paper

  we present a novel generalized framework for expressing peer influence dynamics over time in a set of connected individuals

  or agents. The proposed framework supports the representation of individual variability through parametrization accounting for differences in susceptibility to peer influence and pairwise relationship strengths. Modeling agents’ opinions and behaviors as strategies changing discretely and simultaneously

  we formally describe the evolution of strategies in a social network as the composition of contraction maps. We identify points of convergence and analyze these points under various conditions.

  Consensus on Social Graphs under Increasing Peer Pressure

  Given a specific scenario for the border control problem

  we propose a dynamic data-driven adaptation of the associated sensor network via embedded software agents which make sensor network control

  adaptation and collaboration decisions based on the contextual information value of competing data provided by different multi-modal sensors. We further propose the use of influence diagrams to guide data-driven decision making in selecting the appropriate action or course of actions which maximize a given utility function by designing a sensor embedded software agent that uses an influence diagram to make decisions about whether to engage or not engage higher level sensors for accurately detecting human presence in the region. The overarching goal of the sensor system is to increase the probability of target detection and classification and reduce the rate of false alarms. The proposed decision support software agent is validated experimentally on a laboratory testbed for multiple border control scenarios. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

  Dynamic data-driven sensor network adaptation for border control

  In this paper

  we describe a novel approach to investigate negative behavior dynamics in online social networks as epidemic phenomena. We present a finite-state machine model for time-varying epidemic dynamics

  and validate this model with experiments over a large dataset of Youtube commentaries

  indicating how different epidemic patterns of behavior can be tied to specific interaction patterns among users. A full version of this paper is available on arXiv.org.

  A Hybrid Epidemic Model for Antinormative Behavior in Online Social Networks

  The development of fair and practical policies for shared content online is a primary goal of the access control community. Multi-party access control

  in which access control policies are determined by multiple users each with vested interest in a piece of shared content

  remains an outstanding challenge. Purposeful or accidental disclosures by one user in an online social network (OSN) may have negative consequences for others

  highlighting the importance of appropriate sharing mechanisms. In this work

  we develop a game-theoretic framework for modeling multi-party privacy decisions for shared content. We assume that the content owner (uploader) selects an initial privacy policy that constrains the privacy settings of other users. We prove the convergence of users’ access control policies assuming a multi-round consensus-building game in which all players are fully rational and investigate a variation of rational play\nthat better describes user behavior and also leads to the rational equilibrium. Additionally

  in an effort to better approximate human behavior

  we study a bounded rationality model and simulate real user choices in this context. Finally

  we validate model assumptions and conclusions using experimental data obtained through a study of 95 individuals in a mock-social network.

  Constrained Social-Energy Minimization for Multi-Party Sharing in Online Social Networks

  We discuss the results of a 159-participant human-subject study on peer influence in multiparty access control decisions in social network sites. Our two-part research study considers users privacy attitudes and behaviors when choosing a privacy policy for joint content

  in the context of both synchronous and asynchronous sharing.

  A Model of Paradoxical Privacy Behavior (Invited Paper)

  Objective: In the cognitive and clinical neurosciences

  the past decade has been marked by dramatic growth in a literature examining brain \"connectivity\" using noninvasive methods. We offer a critical review of the blood oxygen level dependent functional MRI (BOLD fMRI) literature examining neural connectivity changes in neurological disorders with focus on brain injury and dementia. The goal is to demonstrate that there are identifiable shifts in local and large-scale network connectivity that can be predicted by the degree of pathology. We anticipate that the most common network response to neurological insult is hyperconnectivity but that this response depends upon demand and resource availability. Method: To examine this hypothesis

  we initially reviewed the results from 1

  426 studies examining functional brain connectivity in individuals diagnosed with multiple sclerosis

  traumatic brain injury

  mild cognitive impairment

  and Alzheimer's disease. Based upon inclusionary criteria

  126 studies were included for detailed analysis. Results: Results from 126 studies examining local and whole brain connectivity demonstrated increased connectivity in traumatic brain injury and multiple sclerosis. This finding is juxtaposed with findings in mild cognitive impairment and Alzheimer's disease where there is a shift to diminished connectivity as degeneration progresses. Conclusion: This summary of the functional imaging literature using fMRI methods reveals that hyperconnectivity is a common response to neurological disruption and that it may be differentially observable across brain regions. We discuss the factors contributing to both hyper- and hypoconnectivity results after neurological disruption and the implications these findings have for network plasticity. (PsycINFO Database Record (c) 2014 APA

  all rights reserved).

  Hyperconnectivity is a Fundamental Response to Neurological Disruption

  We study detection of cyberbullying in photo sharing networks

  with an eye on developing early warning mechanisms for the prediction of posted images vulnerable to attack. Given the overwhelming increase in media accompanying text in online social networks

  we investigate use of posted images and captions for improved detection of bullying in response to shared content. We validate our approaches on a dataset of over 3000 images along with peer-generated comments posted on the Instagram photo-sharing network

  running comprehensive experiments using a variety of classifiers and feature sets. In addition to standard image and text features

  we leverage several novel features including topics determined from image captions and a pretrained convolutional neural network on image pixels. We identify the importance of these advanced\nfeatures in assisting detection of cyberbullying in posted comments. We also provide results\non classification of images and captions themselves as potential targets for cyberbullies.

  Content-driven Detection of of Cyberbullying on the Instagram Social Network

  We propose the first use of a non-negative sparse autoencoder (NNSAE) neural network for community structure detection in complex networks. The NNSAE learns a compressed representation of a set of fixed-length

  weighted random walks over the network

  and communities are detected as subsets of network nodes corresponding to non-negligible elements of the basis vectors of this compression. The NNSAE model is efficient and online. When utilized for community structure detection

  it is able to uncover potentially overlapping and hierarchical community structure in large networks.

  Non-negative sparse autoencoder neural networks for the detection of overlapping

  hierarchical communities in networked datasets

  In this paper

  we discuss the results of a 159-participant human-subject study on peer influence in multiparty access control decisions in social network sites. Our two-part research study considers users privacy attitudes and behaviors when choosing a privacy policy for joint content in the context of both synchronous and asynchronous sharing.

  An In-Depth Study of Peer Influence on Collective Decision Making for Multi-Party Access Control (Invited Paper)

  We study a model of agent consensus in a social network in the presence increasing inter-agent influence

  i.e.

  increasing peer pressure. Each agent in the social network has a distinct social stress function given by a weighted sum of internal and external behavioral pressures. We assume a weighted average update rule consistent with the classic DeGroot model and prove conditions under which a connected group of agents converge to a fixed opinion distribution

  and under which conditions the group reaches consensus. We show that the update rule converges to gradient descent and explain its transient and asymptotic convergence properties. Through simulation

  we study the rate of convergence on a scale-free network.

  Opinion Dynamics in the Presence of Increasing Agreement Pressure

  Despite exciting advances in the functional imaging of the brain

  it remains a challenge to define regions of interest (ROIs) that do not require investigator supervision and permit examination of change in networks over time (or plasticity). Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques

  but these approaches are not data-driven

  requiring definition based on prior experience (e.g.

  choice of seed-region

  anatomical landmarks). These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning

  maturation effects

  or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g.

  whole-brain voxelwise approaches) ameliorate concerns regarding investigator bias

  but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach

  aggregate-initialized label propagation (AILP)

  which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity). To do so

  we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility

  we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.

  A voxelwise approach to determine consensus regions-of-interest for the study of brain network plasticity

  There remains much unknown about how large-scale neural networks accommodate neurological disruption

  such as moderate and severe traumatic brain injury (TBI). A primary goal in this study was to examine the alterations in network topology occurring during the first year of recovery following TBI. To do so we examined 21 individuals with moderate and severe TBI at 3 and 6 months after resolution of posttraumatic amnesia and 15 age- and education-matched healthy adults using functional MRI and graph theoretical analyses. There were two central hypotheses in this study: 1) physical disruption results in increased functional connectivity

  or hyperconnectivity

  and 2) hyperconnectivity occurs in regions typically observed to be the most highly connected cortical hubs

  or the “rich club”. The current findings generally support the hyperconnectivity hypothesis showing that during the first year of recovery after TBI

  neural networks show increased connectivity

  and this change is disproportionately represented in brain regions belonging to the brain's core subnetworks. The selective increases in connectivity observed here are consistent with the preferential attachment model underlying scale-free network development. This study is the largest of its kind and provides the unique opportunity to examine how neural systems adapt to significant neurological disruption during the first year after injury.

  The Rich Get Richer: Brain Injury Elicits Hyperconnectivity in Core Subnetworks

  Cyberbullying is an increasingly prevalent phenomenon impacting young adults. In this paper

  we present a study on both detecting cyberbullies in online social networks and identifying the pairwise interactions between users through which the influence of bullies seems to spread. In particular

  we investigate the role of user demographics and social network features in predicting how users will respond to a cyberbullying comment. We characterize the influencer/influenced relationship by which a user who has no history of abuse observes a peer engaging in bullying and follows suit. To our knowledge

  this is the first effort modeling peer pressure and social dynamics with analytical models. We validate our models on two distinct social network datasets

  totalling over 16

  000 posts. Our results offer insight into the dynamics of bullying and confirm social theories on the power of peer groups in the cyberworld. A full version of this paper is available on arXiv.org.

  Identification and characterization of cyberbullying dynamics in an online social network

  Crowdsourcing sites heavily rely on paid workers to ensure completion of tasks. Yet

  designing a pricing strategies able to incentivize users’ quality and retention is non trivial. Existing payment strategies either simply set a fixed payment per task without considering changes in workers’ behaviors

  or rule out poor quality responses and workers based on coarse criteria. Hence

  task requesters may be investing significantly in work that is inaccurate or even misleading. In this paper

  we design a dynamic contract to incentivize high-quality work. Our proposed approach offers a theoretically proven algorithm to calculate the contract for each worker in a cost-efficient manner. In contrast to existing work

  our contract design is not only adaptive to changes in workers’ behavior

  but also adjusts pricing policy in the presence of malicious behavior. Both theoretical and experimental analysis over real Amazon review traces show\nthat our contract design can achieve a near optimal solution. Furthermore

  experimental results demonstrate that our contract design 1) can promote high-quality work and prevent malicious\nbehavior

  and 2) outperforms the intuitive strategy of excluding all malicious workers in terms of the requester’s utility.

  Dynamic Contract Design for Heterogeneous Workers in Crowdsourcing for Quality Control

  Online Social Networks (SNs) thrive on shared content which attracts and engages users

  and in turn begets more shared content. It is in the interest of a SN site to mandate a minimal amount of shared information required from users to join the system. Required minimal information should be consistent with the site's business model

  while also ensuring that some options are in the hands of the individual user

  to allow users a comfortable experience. Accordingly

  establishing bounds on minimal information sharing is at the heart of a SN site's success. In this paper

  we develop a game theoretical model for a SN site's selection of a strategic lower bound on the extent of information sharing required of its users. We develop a bi-layer game with which we model user behavior as an evolving interaction between personal comfort and peer pressure

  and underlying core constraints\nplaced on this behavior by the policies of the SN site. We allow the site to play the role of the leader in a Stackelberg competition and provide a quantitative strategy by which a site can optimally determine a minimal bound on the shared information required of its users in order to simultaneously maximize user happiness and overall profit. We predict how users

  as followers in this game

  will respond to the site's policy and how this response will evolve over time. Finally

  \nwe validate our model on real-world user data and demonstrate that the quantitative predictions of our model match well with actual outcome.

  Site-Constrained Privacy Options for Users in Social Networks through Stackelberg Games

  Use of online social networks has grown dramatically since the first Web 2.0 technologies were deployed in the early 2000s. Our ability to capture user data

  in particular behavioral data has grown in concert with increased use of these social systems. In this study

  we survey methods for modeling and analyzing online user behavior. We focus on negative behaviors (social spamming and cyberbullying) and mitigation techniques for these behaviors. We also provide information on the interplay between privacy and deception in social networks and conclude by looking at trending and cascading models in social media.

  Positive and Negative Behavioral Analysis in Social Networks

  With the increasing popularity of user-contributed sites

  the phenomenon of “social pollution”

  the presence of abusive posts has become increasingly prevalent. In this paper

  we describe a novel approach to investigate negative behavior dynamics in online social networks as epidemic phenomena. We show that using hybrid automata

  it is possible to explain the contagion of antinormative behavior in certain online commentaries.We present two variations of a finite-state machine model for time-varying epidemic dynamics

  namely triggered state transition and\niterative local regression

  which differ with respect to accuracy and complexity. We validate the model with experiments over a dataset of 400

  000 comments on 800 YouTube videos

  classified by genre

  and indicate how different epidemic patterns of behavior can be tied to specific interaction patterns among users.

  A hybrid epidemic model for deindividuation and antinormative behavior in online social networks

  We present an evolutionary game-theoretic model for the spread of non-cooperative behavior in online social networks. We formulate a two-strategy game wherein each player’s behavior\nis classified as normal (cooperate) or abusive (defect) and pairwise interactions between adjacent players in the network graph yield a unique payoff to each according a prisoner’s dilemma payoff structure. Player strategies evolve by imitation of successful behavior in observable neighborhoods. We demonstrate convergence of player behavior over time to a final strategy vector. Proof-of-concept is given for a real-world dataset collected from a popular online forum.

  An evolutionary game model for the spread of non-cooperative behavior in online social networks

  The privacy policies of an online social network play an important role in determining user involvement and satisfaction

  and in turn site profit and success. In this paper

  we develop a game theoretic framework to model the relationship between the set of privacy options offered by a social network site and the sharing decisions of its users within these constraints. We model the site and the users in this scenario as the leader and followers

  respectively

  in a Stackelberg game. We formally establish the conditions under which this game reaches a Nash equilibrium in pure strategies and provide an approximation algorithm for the site to determine a discrete set of privacy options to maximize payoff. We validate hypotheses in our model on data collected from a mock-social network of users’ privacy preferences both within and outside the context of peer influence

  and demonstrate that the qualitative assumptions of our model are well-founded.

  Determining a discrete set of site-constrained privacy options for users in social networks through Stackelberg Game

  Rajtmajer

  Penn State College of Information Sciences and Technology