+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
Lunch and Learn on Tuesday, December 3
Please join us for tomorrow’s session!
Tuesday, December 3, 2019 | 12:15-1:30 PM | Trent Semens Learning Hall
Recommending MyChart Responses with Natural Language Processing
- Jedrek Wosik, MD, Cardiology Fellow, Department of Medicine
- Ricardo Henao, PhD, Assistant Professor of Biostatistics and Bioinformatics; Principal Data Scientist, Duke Forge
Lunch will be provided, no registration is required, and there is no charge to attend. Anyone in the Duke community is welcome to join. The Trent Semens Center is next to the Duke Medicine Pavilion, and a short walk from the engineering campus. To learn more, please visit: https://plus.datascience.duke.edu/learn-ds#lunch-and-learn
Student Poster Session on Wednesday, December 4
More than 30 student posters will be presented
The +DS program will hold a student showcase on Wednesday, December 4 from 4:30-6:00 PM in the Energy Hub Atrium (first floor of Gross Hall).
We invite you to join us to learn about the work of the project teams, and converse with the students, mentors, faculty, and staff. Light refreshments will be served.
· 27 teams with 62 students will be presenting from the +DS fall 2019 course POE 190.01/POE 790.01 "Introduction to Machine Learning Methods and Practice." This mini-class has introduced students to machine learning methods that have become increasingly useful in practice, specifically deep learning and neural networks. The student have produced their end-of-semester projects with applications of machine learning to a problem of relevance to their field of study or major.
· 5 teams with 8 students will be presenting from the +DS Advanced Projects. Students who have previously completed the +DS curriculum are eligible to apply for the Advanced Projects, typically structured as an independent study project, where they are mentored by leading Duke faculty involved in data science research with areas of focus including dermatology, ophthalmology, pathology, radiology, and cardiology.
These +DS project-based learning teams offer Duke students, both undergraduate and graduate, the opportunity to be a part of teams applying advanced data science methods and machine learning to real-world problems, and as a means of learning the important field of machine learning in a manner that is accessible and adaptive to all Duke students.
Second Annual Duke AI for Art Competition
Deadline for submissions: December 9
Using computers to generate art promotes deeper understanding of the ways that artificial intelligence (AI) is changing the visual and media-based world around us, and provokes inquiry into the endeavor and humanity behind creativity. In this spirit, +DS is excited to announce the second annual Duke AI for Art Competition.
All Duke students, faculty and staff may submit visual art generated via artificial intelligence. Submission are open to all artistic mediums. The art should be submitted as a digital image, video, text file, or sound file (.mp3, .wav) and should be generated entirely by software run on a computer. The art should be accompanied by a statement outlining the technical and conceptual approaches to the work. We encourage people from any discipline or field to participate, regardless of prior experience. A +DS in-person learning experience (IPLE) is scheduled for rescomputing.
The deadline for submissions to this competition is midnight (EST) on December 9, 2019, and submissions should be emailed to plus-datascience. The submission should consist of the art (typically in the form of a high-resolution pdf file), and also the software that was used to generate it. The generation of the art must be reproducible, by running the submitted software; the software will not be shared beyond the judges. The submission should cite the source of any art used to seed the entry, and reference the source of any software that was not created directly by the submitting individual/team.
The prizes for this competition are as follows. First Place: $5000, Second Place: $2500, and Third Place: $1000. The submissions will be judged by faculty from Duke’s Art, Art History & Visual Studies Department, and from the Rhodes Information Initiative at Duke (Rhodes-iiD). A public event is planned for late January 2019, at which many of the submissions will be displayed, and the first through third-place winners will be asked to give short talks on their art, and how it was produced.
New Spring 2020 Class Offering Training in AI to All Duke Students
Class Title: "AI for Everyone"
EGR 190.06 (undergraduate students) / EGR 590.06 (graduate students)
This class will introduce the student to machine learning (ML) and artificial intelligence (AI) methods that have become increasingly useful in practice, specifically deep learning and neural networks. Application areas include image analysis, text analysis, and optimal decision making. This class is directed to any Duke student, independent of major, who is interested in learning the basics of ML and AI.
For more information, please visit https://plus.datascience.duke.edu/announcements/new-spring-2020-class-offering-training-ai-all-duke-students
Limited Seats Available: Natural Language Processing Winter School
Duke University, January 5-7, 2020
Machine learning is a field characterized by development of algorithms that are implemented in software and run on a machine (e.g., computer, mobile device, etc.). Recently, with increasing access to massive datasets, and to significant advances in computing power, machine learning performance has improved markedly. Further, over the last five years, significant advances have been made in a subfield of machine learning called “deep learning.”
In the Natural Language Processing (NLP) Winter School (WS), a focus will be placed on an area of machine learning that is impacting many areas of life: the capacity of machine learning to “read,” analyze and synthesize natural text. The NLP-WS will introduce participants to the deep-learning technology that has revolutionized (within the last several years) the capacity of machines to perform language translation, to answer questions posed for given text, and to generate (synthesize) text that is near human-generated quality. The NLP-WS is meant to be accessible to a wide audience, not just those with prior technical experience. The objective is to introduce the transformational field of deep-learning-based natural language processing to a diverse community of interested learners.
A limited number of seats are still available. We will also maintain a waitlist beyond the maximum registration, and will contact those on the waitlist as spots become available.
For more information about the NLP-WS, and to register, please see https://strategicplan.duke.edu/initiatives/natural-language-processing-winter-school/
