The Rising Stars organizing committee includes Sandians Tammy Kolda (Distinguished Member of Technical Staff, Extreme-scale Data Science & Analytics Dept.) and James Stewart (Sr. Several Sandia managers and staff also participated. Rachel Kuske, Chair of Mathematics at Georgia Institute of Technology, as well as lightning-round talks and breakout sessions. The workshop featured an inspiring keynote talk by Dr. Nonetheless, it was an overwhelming success with 28 attendees selected from a highly competitive pool of over 100 applicants.
Due to travel limitations associated with the pandemic, the 2020 Rising Stars event went virtual with a compressed half-day format.
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The workshop series began in 2019 with a two-day event in Austin, TX. 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. Rising Stars in Computational & Data Sciences is an intensive academic and research career workshop series for women graduate students and postdocs. 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. This work will book through the Defense Systems and Assessments PMU and supports the Synergistic Defense Products Mission Area. 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. 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. 1460 researchers recently won a contract to provide test and evaluation support for a new IARPA program: Machine Intelligence from Cortical Networks (MICrONS).
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