Publications

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In many online social networking platforms, the participation of an individual is motivated by the participation of others. If an individual chooses to leave a platform, this may produce a cascade in which that person’s friends then choose to leave, causing their friends to leave, and so on. In some cases, it may be possible to incentivize key individuals to stay active within the network, thus preventing such a cascade. This problem is modeled using the anchored k-core of a network, which, for a network G and set of anchor nodes A, is the maximal subgraph of G in which every node has a total of at least k neighbors between the subgraph and anchors. In this work, we propose Residual Core Maximization (RCM), a novel algorithm for finding b anchor nodes so that the size of the anchored k-core is maximized. We perform a comprehensive experimental evaluation on numerous real-world networks and compare RCM to various baselines. We observe that RCM is more effective and efficient than the state-of-the-art methods: on average, RCM produces anchored k-cores that are 1.65 times larger than those produced by the baseline algorithm, and is approximately 500 times faster on average.

Securing cyber systems is of paramount importance, but rigorous, evidence-based techniques to support decision makers for high-consequence decisions have been missing. The need for bringing rigor into cybersecurity is well-recognized, but little progress has been made over the last decades. We introduce a new project, SECURE, that aims to bring more rigor into cyber experimentation. The core idea is to follow the footsteps of computational science and engineering and expand similar capabilities to support rigorous cyber experimentation. In this paper, we review the cyber experimentation process, present the research areas that underlie our effort, discuss the underlying research challenges, and report on our progress to date. This paper is based on work in progress, and we expect to have more complete results for the conference.

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Background: The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Results: Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. Conclusions: The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.

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With the advent of large-scale neuromorphic platforms, we seek to better understand the applications of neuromorphic computing to more general-purpose computing domains. Graph analysis problems have grown increasingly relevant in the wake of readily available massive data. We demonstrate that a broad class of combinatorial and graph problems known as dynamic programs enjoy simple and efficient neuromorphic implementations, by developing a general technique to convert dynamic programs to spiking neuromorphic algorithms. Dynamic programs have been studied for over 50 years and have dozens of applications across many fields.

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Community detection is often used to understand the nature of a network. However, there may exist an adversarial member of the network who wishes to evade that understanding. We analyze one such specific situation, quantifying the efficacy of certain attacks against a particular analytic use of community detection and providing a preliminary assessment of a possible defense.

Increasing penetration levels of renewables have transformed how power systems are operated. High levels of uncertainty in production make it increasingly difficulty to guarantee operational feasibility; instead, constraints may only be satisfied with high probability. We present a chance-constrained economic dispatch model that efficiently integrates energy storage and high renewable penetration to satisfy renewable portfolio requirements. Specifically, we require that wind energy contribute at least a prespecified proportion of the total demand and that the scheduled wind energy is deliverable with high probability. We develop an approximate partial sample average approximation (PSAA) framework to enable efficient solution of large-scale chance-constrained economic dispatch problems. Computational experiments on the IEEE-24 bus system show that the proposed PSAA approach is more accurate, closer to the prescribed satisfaction tolerance, and approximately 100 times faster than standard sample average approximation. Finally, the improved efficiency of our PSAA approach enables solution of a larger WECC-240 test system in minutes.

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Through long-term investments in computing, algorithms, facilities, and instrumentation, DOE is an established leader in massive-scale, high-fidelity simulations, as well as science-leading experimentation. In both cases, DOE is generating more data than it can analyze and the problem is intensifying quickly. The need for advanced algorithms that can automatically convert the abundance of data into a wealth of useful information by discovering hidden structures is well recognized. Such efforts however, are hindered by the massive volume of the data and its high velocity. Here, the challenge is developing unsupervised learning methods to discover hidden structure in high-volume, high-velocity data.

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Unprecedented amounts of data are continuously being generated by sensors (“hard” data) and by humans (“soft” data), and this data needs to be exploited to its full potential. The first step in exploiting this data is determine how the hard and soft data are related to each other. In this project we fuse hard and soft data, using the attributes of each (e.g., time and space), to gain more information about interesting events. Next, we attempt to use social networking textual data to predict the present (i.e., predict that an interesting event is occurring and details about the event) using data mining, machine learning, natural language processing, and text analysis techniques.

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