On December 5, 2019 there was a Mathematics in Industry Seminar
The speakers were Drs. Stephen Shauger, Sean Lynch, and Aaron Wiechmann from the Laboratory for Analytic Sciences
How do we know that we can trust our machine learning models? How do we detect when an adversary is exploiting our machine learning solutions? How does the explainability of machine learning models help the customers of such models versus helping the adversaries of such models? In this talk we will examine recent issues in Machine Learning Explainability and in Adversarial Machine Learning.
On November 13, 2019 there was a Mathematics in Industry Seminar.
The speakers were Drs. Rachel Clipp and Matt Brown from Kitware
Kitware researchers Dr. Rachel Clipp and Dr. Matt Brown will talk about their transition from academic research to R&D at a private company as well as some of their active projects. Kitware is a software research and development company with expertise in computer vision, data and analytics, high-performance computing and visualization, medical computing, and software process. Dr. Clipp works in Kitware’s Medical Computing group developing and supporting simulation platforms that power medical training, planning, and predictive applications for improved patient treatment and outcomes. Capabilities include whole-body computational physiology models for faster than real-time simulation, surgical planning and guidance applications, high-fidelity computational fluid dynamics for patient-specific treatment planning, and virtual/augmented reality solutions for immersive training, and patient-specific image-to-model and mode-to-simulation workflows. Dr. Brown works in Kitware’s Computer Vision group, developing technologies for 3-D reconstruction, deep-learning-based detection and tracking, and activity recognition. His work involves deploying state-of-the-art algorithms into real-world systems.