![]() ![]() ![]() The work is made available under the Creative Commons CC0 public domain dedicationĭata Availability: The C. įunding: This work was partially supported by the Research Program in Applied Neuroscience (JV, RJV, CEP, ), a National Security Science and Engineering Faculty Fellowship (CEP, ), Johns Hopkins University Human Language Technology Center of Excellence (DEF, JV, CEP, VL, ), and the XDATA program of the Defense Advanced Research Projects Agency (CEP, ) administered through Air Force Research Laboratory contract FA-0303. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. In its most general form, the graph matching problem (GMP)-finding an alignment of the vertices of two graphs which minimizes the number of induced edge disagreements-is equivalent to a quadratic assignment problem (QAP). QAPs were first devised by Koopmans and Beckmann in 1957 to solve a ubiquitous problem in distributed resource allocation, and many important problems in combinatorial optimization (for example, the “traveling salesman problem,” and the GMP) have been shown to be specialized QAPs. While QAPs are known to be NP-hard in general, they are widely applicable and there is a large literature devoted to their approximation and formulation see for a comprehensive literature survey. In casting the GMP as a QAP, we bring to bear a host of existing optimization theoretic tools and methodologies for addressing graph matching: Frank-Wolfe, gradient-descent, etc. Graph matching has applications in a wide variety of disciplines, spanning computer vision, image analysis, pattern recognition, and neuroscience see for a comprehensive survey of the graph matching literature. We are motivated by applications in “connectomics,” an emerging discipline within neuroscience devoted to the study of brain-graphs, where vertices represent (collections of) neurons and edges represent connections between them. 1460 researchers recently won a contract to provide test and evaluation support for a new IARPA program: Machine Intelligence from Cortical Networks (MICrONS).Analyzing connectomes is an important step for many neurobiological inference tasks. The MICrONS program aims to advance a new generation of neural-inspired machine learning algorithms by reverse engineering the algorithms and computations of the brain. The Sandia team’s efforts will include applying sensitivity analysis to validate computational neural models, developing novel challenge stimuli and evaluation metrics to assess the performance of novel machine learning algorithms, and designing evaluation methodologies for assessing computational neural model designs and the neural fidelity of machine learning algorithms. This work will book through the Defense Systems and Assessments PMU and supports the Synergistic Defense Products Mission Area. The team includes (in alphabetical order):īrad Aimone (1462), Kristofor Carlson (1462), Brad Carvey (1461), Warren Davis (1461), Michael Haass (1461), Jacob Hobbs (6132), Kiran Lakkaraju (1463), Kim Pfeiffer (1720), Fred Rothganger (1462), Timothy Shead (1461), Craig Vineyard (1462), Christina Warrender (1461) The Sandia team is highly interdisciplinary and includes computational neuroscientists (a growing capability within 1460) as well as researchers from existing 1460 strengths in machine learning, data analytics, and computation. Rising Stars in Computational & Data Sciences is an intensive academic and research career workshop series for women graduate students and postdocs. Co-organized by Sandia and UT-Austin’s Oden Institute for Computational Engineering & Sciences, Rising Stars brings together top women PhD students and postdocs for technical talks, panels, and networking events. The workshop series began in 2019 with a two-day event in Austin, TX. ![]() Due to travel limitations associated with the pandemic, the 2020 Rising Stars event went virtual with a compressed half-day format. Nonetheless, it was an overwhelming success with 28 attendees selected from a highly competitive pool of over 100 applicants. The workshop featured an inspiring keynote talk by Dr. Rachel Kuske, Chair of Mathematics at Georgia Institute of Technology, as well as lightning-round talks and breakout sessions. Several Sandia managers and staff also participated. ![]() The Rising Stars organizing committee includes Sandians Tammy Kolda (Distinguished Member of Technical Staff, Extreme-scale Data Science & Analytics Dept.) and James Stewart (Sr. Manager, Computational Sciences & Math Group), as well as UT Austin faculty Karen Willcox (Director, Oden Institute) and Rachel Ward (Assoc. ![]()
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