+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
Upcoming In-Person Learning Experiences (IPLEs)
+DS will offer 7 IPLEs in November
These sessions offer the opportunity to dive deeper into topics and target diverse units at Duke: from those that desire a broad understanding of what is possible with data science, and those who wish to use data-science tools (software) without a need for deep understanding of underlying methodology, to those who desire a rigorous technical proficiency of the details and methodology of data science. Anyone in the Duke community is welcome to join, there is no fee to attend, and no prior experience is necessary. Learn more about IPLEs on the +DS website: https://plus.datascience.duke.edu/learn-ds#iple
Monday, November 11 | 4:30-6:30 PM
Facial Recognition – a Case Study in the Challenges of Implementing AI Technologies
Michael Waitzkin
Facial recognition is currently one of the most visible applications of artificial intelligence. With a promising range of highly beneficial uses, it can also be deployed as a very effective tool of political repression. What lessons can be learned as other AI applications emerge into public focus? Register at: https://training.oit.duke.edu/enroll/common/show/21/174642
Tuesday, November 12 | 4:30-6:30 PM
Some Examples of Machine Learning in Neuroscience
David Carlson
Like many fields, neuroscience is experiencing a data deluge. Machine learning techniques are being used to learn better biomarkers, make sense of the brain, and automate tasks. I will introduce some of the methodology and ideas being worked on within this research area, including domain adaptation and automatic behavior extraction from video. These techniques, when harnessed correctly, can help improve the quality of information extracted from collected data and accelerate science; however, interpretation and validation pitfalls can derail researchers from making proper conclusions. I’ll introduce how model validation can vary depending on the scientific hypothesis. Register at: https://training.oit.duke.edu/enroll/common/show/21/174661
Thursday, November 14 | 4:30-6:30 PM
Natural Language Processing with Attention-Based Neural Networks
Lawrence Carin
There has been a recent surge in the quality of natural language processing technology, and much of this has been driven by a new class of neural networks, based on the concept of "attention." Attention networks localize (pay attention to) a portion of input text, when performing a task. For example, in the context of translation, when converting text from Language A to Language B, when producing the text in Language B the neural network adaptively focuses its attention on the appropriate subset of input text in Language A. In this session we will discuss how attention networks are manifested, and we will use this insight to describe the recently-developed Transformer Network, which is based entirely on the concept of attention. The Transformer Network has achieved state-of-the-art performance in many areas, such as translation, summarization and other text-synthesis tasks. Register at: https://training.oit.duke.edu/enroll/common/show/21/174659
Monday, November 18 | 4:30-6:30 PM
Molecular (Omics) Data Analysis
Ricardo Henao
Omics aims to understand biological processes by leveraging high-throughput technologies and data science. Aided by subject matter expertise, this combination has resulted in accelerated discoveries in health and disease. In this session we will go through the characteristics of the molecular data generated by some of this technologies and the fundamental processing and statistical analysis tools (including machine learning methods) that can be used to generate knowledge from these complex, high-dimensional data. Use cases include analysis of gene expression, metabolomics and proteomics data. Register at: https://training.oit.duke.edu/enroll/common/show/21/174651
Tuesday, November 19 | 4:30-6:30 PM
Case Study: NLP for Mobile Devices
Dinghan Shen
Mobile devices have become a vital platform for deploying state-of-the-art natural language processing (NLP) techniques, due to their ubiquity and portable nature. This presentation will discuss a case study for how deep NLP models are employed to greatly improve mobile users’ productivity while replying to emails on the go. This feature, called Smart Reply, has been actively used on both Gmail and Outlook since being launched. Specifically, the architectural design of this NLP system will be discussed, including the concrete engineering challenges to address in practice. Further, motivated by the rising concern about user/data privacy, a federated learning paradigm is leveraged to serve billions of users without data leaving their devices. Considering the low-resource nature of mobile devices, a special type of word embeddings is introduced to meet the computation and storage requirements. Potential extensions to more application scenarios will also be discussed for this technology. Register at: https://training.oit.duke.edu/enroll/common/show/21/174657
Wednesday, November 20 | 4:30-6:30 PM
AI for Visual Art
Matthew Kenney
The democratization of AI has touched every industry, including the visual and interactive arts. In this IPLE we will overview how artists have adopted AI into their art practices, and have leveraged AI to make new and exciting works. First, we will survey prominent artist in the field engaging with AI, and how their works are reshaping landscape of visual art. Next, we’ll consider how these works have shifted definitions of authorship, collaboration, and art production for the AI-Art community. Lastly, we’ll review several models and architectures that artists have worked with, and discuss how we might leverage them to produce our own creative works. This session is part of the Humanities/Social Sciences +DS track, which focuses on engagements with AI for the Digital Humanities and Social Sciences. Register at: https://training.oit.duke.edu/enroll/common/show/21/174652
Thursday, November 21 | 4:30-6:30 PM
Deep Convolutional Object Detection
Kevin Liang
While image classification to a single class label can be useful, we are often also interested in where an object instance is located within an image. Such a problem is known as Object Detection. Like other computer vision tasks, the advent of deep learning (particularly convolutional neural networks) has resulted in significant advances in recent years. In this session, a real-world application of object detection is discussed, followed by an introduction of popular convolutional object detection models. Register at: https://training.oit.duke.edu/enroll/common/show/21/174662
Lunch and Learn on Tuesday, November 5
Please join us for tomorrow’s session!
Tuesday, November 5, 2019 | 12:15-1:30 PM | Trent Semens Learning Hall
Early Autism Screening with Machine Learning
· Geraldine Dawson, PhD, William Cleland Professor of Psychiatry & Behavioral Sciences; Director, Duke Center for Autism and Brain Development; Director, Duke Institute for Brain Sciences
· Guillermo Sapiro, PhD, James B. Duke Professor of Electrical and Computer Engineering; Professor of Mathematics
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
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 experiences (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.
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.
For more information about the NLP-WS, and to register, please see https://strategicplan.duke.edu/initiatives/natural-language-processing-winter-school/
