Agenda

Welcome to CASIS and LLNL! 7:55 AM

Geophysics Signal Processing — Christina Morency (LLNL) 8:00 AM

• Electromagnetic Wave Propagation Based Upon Spectral-element Method —
  1. Christina Morency
• Finite Frequency Sensitivity Kernels for Multi Scale Electromagnetic Imaging Applications —
  1. Saptarshi Mukherjee
  2. Christina Morency
• Seismic to Electric Conversion: a Tool for Earth Subsurface Characterization —
  1. Christina Morency
  2. Saptarshi Mukherjee

Graph Theory — Goran Konjevod (LLNL) 9:00 AM

• Dynamic Graph Clustering with Variable Time Resolution —
  1. Timothy La Fond
• Learning the Nodes of a Graph from Interactions —
  1. Jose Cadena
  2. Goran Konjevod
• Latent Hub Networks —
  1. Clifford Anderson-Bergman

Break 10:00 AM

Machine Learning 1 — Judy Liebman (LLNL) 10:20 AM

• A very short Tutorial on Deep Reinforcement Learning —
  1. Brenden Petersen
• Evolution of Machine Learning for NIF Optics Inspection —
  1. Laura Kegelmeyer

Applications of Signal Processing — Ryan Goldhahn (LLNL) 11:20 AM

• Similarity Data Based TBI Patient Stratification and Feature Selection —
  1. Qi Cheng
  2. Alan Kaplan
  1. Geoffrey Manley
  2. "Track TBI Consortium"
• Applications of Signal Processing in Power Systems Research at LLNL —
  1. Philip Top
  2. Emma Stewart

Lunch Break 12:00 PM

Welcome 12:45 PM
Patricia Falcone Deputy Director for Science & Technology, LLNL

Keynote Address: Cyber Attacks on Internet of things Sensor Systems for Inference 1:00 PM
Professor Rick S. Blum Lehigh University

Poster Session 2:00 PM

• A 1000x Stabler Spectrograph using an Interferometer with Crossfaded Delays —
  1. David J. Erskine
  1. Eric V. Linder
• Automated Annotation of Satellite Imagery using Model-based Projections —
  1. Randy Roberts
  2. John Goforth
  3. George Weinert
  4. Charles Grant
  1. Will R. Ray, Brad Stinson, Andrew Duncan
• Towards Representative Metric of Behavior Style in Imitation and Reinforcement Learning —
  1. Igor Borovikov
  2. Jesse Harder
  3. Ahmad Beirami
  1. Michael Sadovsky Krasnoyarsk
• Deconvolution Uncertainty for Power Sensors at the National Ignition Facility —
  1. Dominic Carrano
  1. Ryan Muir
• An Expectation-Maximization (EM) Algorithm for Orbit Linkage and Determination —
  1. Jason Bernstein
• NIF Diffraction Efficiency, Heating & Optical Damage of Gratings Debris Shield (GDS): Modeling With COMSOL —
  1. Alice Wong
  2. Michael Rushford
  3. Hoang Nguyen
  4. Marcus Monticelli
  5. Christopher Miller
  6. David Cross
• Fiber Optic Seismic Sensing of the Subsurface —
  1. Robert Mellors
  2. Michael Messerly
  3. Paul Pax
  4. Charles Yu
  5. Cody Mart
  6. Christopher Sherman
  7. Rick Ryerson
  8. Graham Allen
  9. Karl Fisher
  10. Christina Morency
  11. Rengin Gok
• Biomedical Data to Knowledge: An Integrative Informatics Approach With Machine Learning for Brain Tumor Research —
  1. Xiaoqing Jin
  2. Xuanyu Mao
  3. Antoine Snijders
  4. Jian-Hua Mao
  5. Hang Chang
• Classifying Static X-Ray Imager Diagnostic Using Convolutional Neural Networks —
  1. William Leach
  1. James Henrikson
• Detecting Small Objects in Large Microscopy Images —
  1. Piyush Karande
  2. Jose Cadena Pico
  3. Alan Kaplan
  4. Doris Lam
  5. Heather Enright
  6. Sandra Peters
  7. Allison M. Yorita
  8. Razi Haque
  9. Ana Paula Sales
  10. Nicholas Fischer
• Uncertainty Quantification and Prediction for X-ray Systems Using a Systems Approach —
  1. Jefferson A. Cuadra
  2. Aditya K. Mohan
  3. Robert M. Panas
• Qubit Readout at the LLNL Quantum Testbed —
  1. Luis Martinez
  2. Y.J. Rosen
  3. E.T. Holland
  4. X. Wu
  5. A. R. Castelli
  6. J.L DuBois
• Optical Compressive Sensing of Ultra-Wideband RF Spectra —
  1. Jacky Chan
  2. David Perlmutter
  3. Jason Chou
  4. John Heebner
• Out of Band Acoustic Fields: Theory and Application —
  1. Brian M. Worthmann
  1. David R. Dowling

Featured Presentation: Data Extraction from the Atmospheric Tests Films 3:00 PM
Greg Spriggs LLNL

Non-Destructive Evaluation — Kyle Champley (LLNL) 3:40 PM

• Methods for Few-View CT Reconstruction —
  1. Kyle Champley
• Deep Learning based 3D Volume Segmentation for Few View X-ray Computed Tomography —
  1. Aditya Mohan
  2. Kyle Champley
  3. Rushil Anirudh
  4. Hyojin Kim

Machine Learning 2 — Judy Liebman (LLNL) 4:30 PM

• Feedstock Optimization Using Computer Vision and Machine Learning Techniques —
  1. Matthew Rever
  2. T. Nathan Mundhenk
  3. Donald Loveland
  4. Brian Gallagher
  5. T. Yong Han
• Robust Projection onto Image Manifolds with Corruption Mimicking —
  1. Rushil Anirudh
  2. Jayaraman J. Thiagarajan
  3. Bhavya Kailkhura
  4. Timo Bremer
• Measuring Generalization in Machine Learning —
  1. Gerald Friedland