Upcoming Duke+Data Science Learning Opportunities

Join us Wednesday afternoon for the next Proposal Studio vLE

Tomorrow: Proposal Studio on Novel Data Sources and Platform Development
Wednesday, April 7 | 1:00-2:00 PM
Hosted by Ricardo Henao and Michael Pencina

Proposal concepts on Wednesday will include a repository for global injury, cardiovascular risk in pregnancy, and inpatient hypoglycemia. Register at https://training.oit.duke.edu/enroll/common/show/21/175411

Please join us tomorrow for the third in a new series for spring 2021. Anyone in the Duke community is welcome to attend, and we especially encourage Duke early-stage investigators, postdocs, and trainees to join us.

Registration now open for the Machine Learning Virtual Summer School

Registration is now open for the 2021 Duke Machine Learning Virtual Summer School (MLvSS), which will be held in a virtual format June 14-17, 2021.

The 3.5 day curriculum in the MLvSS is targeted to individuals interested in learning about machine learning, with a focus on recent deep learning methodology. The MLvSS will introduce the mathematics and statistics at the foundation of modern machine learning, and provide context for the methods that have formed the foundations of rapid growth in artificial intelligence (AI). Additionally, the MLvSS will provide hands-on training in the latest machine learning software, using the widely used (and free) PyTorch framework. The MLvSS is particularly well-suited to members of academia and industry, including students and trainees, who seek a thorough introduction to the methods of machine learning, including interpretation and commentary by respected leaders in the field. Learn more at https://plus.datascience.duke.edu/mlvss2021

Other upcoming +DS learning experiences

Begins tomorrow: Applying Deep Learning to Biological Sequence Data (A two-part basic sciences session)
Wednesday, April 7 & Thursday, April 8 | 4:30-5:30 PM
Akshay Bareja

Recurrent neural networks (RNNs) are a class of neural networks that can process sequential data, such as text. RNNs have been successfully applied to many natural language processing tasks, including text generation, classification, and translation. In this two-part vLE, we will first introduce you to RNNs and their specific application to biological sequence data. In the second part, we will demonstrate how to build an RNN using PyTorch that can predict protein function based on amino acid sequence data.

Proposal Studio on Structured Data Analyses, Part 2
Wednesday, April 21 | 12:00 – 1:00 PM
Hosted by Samuel Berchuck and Michael Pencina

Proposal concepts will include AI-based pulmonary function testing, sickle cell severity, long-term follow up for diabetic retinopathy, and ovarian cancer care recommender. Register at https://training.oit.duke.edu/enroll/common/show/21/175412

New: Network Architecture for Researchers
Tuesday, May 4 | 12:00 – 1:00 PM
Charley Kniefel and Will Brockelsby

Join network architecture experts from Duke’s Office of Information Technology (OIT) as they discuss networking for researchers. The session will include foundational concepts of networking for scientific applications, with discussion of Duke’s successful implementation of a Science DMZ to support high-performance scientific computing across multiple years, and new initiatives such as the Archipelago project to deploy purpose-driven hybrid architectures and design of a new regional shared science DMZ. We’ll also cover ways that Duke researchers can recognize potential to improve their own network-based performance. While this session will be especially relevant for campus investigators who have interest in NSF cyberinfrastructure initiatives, we encourage anyone who wants to learn more about the campus network infrastructure or who is frustrated in moving large amounts of data into, out of, or around Duke’s campus to attend. Register at https://training.oit.duke.edu/enroll/common/show/21/175413


Duke+DataScience logo

+Data Science (+DS) is a Duke-wide program, operating in partnership with departments, schools, and institutes to enable faculty, students, and staff to employ data science at a level tailored to their needs, level of expertise, and interests. For more information, please visit our website at https://plus.datascience.duke.edu.