Teaching Showcase

Have you taught, led a class, or guest-lectured and tried something new or different, or do you know of someone who has? If so, then please complete the brief form at https://duke.qualtrics.com/jfe/form/SV_5bEwvnN8ahHzwns by June 6th to share that experience with others at the 2nd Teaching being held at the Duke School of Nursing (room TBA). As an informal event for anyone interested in education, the showcase is an opportunity to exchange ideas about teaching innovations.

Space Diplomacy and Humanitarian Intervention: the case of Ukraine and beyond

Since Russia began its full-scale invasion of Ukraine on February 24, data from a wide array of government and commercial space assets has helped the general public understand the trajectory of the war in a transformative way. For diplomats and aid workers, the growing availability of data from multispectral satellite-based imagers has helped drive diplomatic strategies to support Ukrainian sovereignty, while providing the capability to develop effective strategies to deliver aid to Ukrainian citizens in need.

Coffee and Crypto

The Coffee and Crypto Chat Series is back for the 2021-2022 academic year!

Join Dr. Jimmie Lenz, the Director of the Master of Engineering Programs in Financial Technology and Cybersecurity, and Lee Reiners, the Executive Director of the Global Financial Markets Center at Duke University, for an informal discussion of the latest crypto news topics.

Please Register in advance for this meeting by clicking "More Event Information" below. Registration is free.

ECE SEMINAR: Deep Learning Based Medical Image Analysis: Challenges and New Approaches

New technologies for acquiring large amounts of medical image data give rise to an ever increasing demand for effective approaches for medical image analysis tasks. Recently, deep learning (DL) methods have yielded remarkably high quality solutions for many medical imaging applications, largely outperforming traditional image analysis methods. Comparing to natural scene images, medical image analysis faces several different challenges. Commonly, DL methods rely on lots of annotated data for model training.