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Upcoming +Data Science opportunities

+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)

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

Tuesday, February 25 | 4:30-6:30 PM

Machine Learning for Synthetic and Quantitative Biology

Lingchong You

A central objective in synthetic biology is to control the dynamics of engineered cells or cell populations in a predictable manner. Achieving this objective requires a quantitative description of biological systems that are both reliable and can be solved fast enough to guide experiments. To date, this line of work has primarily relied on the use of kinetic models that account for the relevant interactions. For instance, differential equations can be formulated to describe how bacterial populations form self-organized patterns or respond to different environmental cues, such as antibiotic treatment. However, these models face different conceptual and technical limitations, depending on the specific application contexts. They may fail to capture the relevant complexity of the experimental system. Or they may be computationally prohibitive to solve when one attempts to explore a large parametric space. I will discuss specific examples where machine learning can be applied to overcome these limitations. The combination of mechanistic modeling and machine learning can lead to computational predictions that are both effective and interpretable. Register at https://training.oit.duke.edu/enroll/common/show/21/174922

Wednesday, March 4 | 4:30-6:30 PM

Social Media Echo Chambers and Political Polarization

Chris Bail

There is widespread concern that social media platforms have created filter bubbles that reinforce peoples’ pre-existing views and prevent them from being exposed to those who do not share them. Though many people believe popping filter bubbles will reduce political polarization on social media, this talk will present multiple field experiments that challenge this common wisdom. I will first describe two studies that employed bots, network analysis, and quantitative surveys in order to show that popping peoples’ filter bubbles makes them more polarized; not less. Drawing upon in-depth interview and text-based data, I will argue that this backfire effect occurs because of an identity-threat mechanism linked to both core theories in social science as well as recent advances in neuroscience. Finally, I will describe ongoing research currently being conducted within my Polarization Lab that experimentally manipulates the identity of people deliberating on a de novo social media platform in order to further test this hypothesis. The results may be of interest to those who study political communication, social media, or the emerging field of computational social science. Register at https://training.oit.duke.edu/enroll/common/show/21/174918

Thursday, March 5 | 4:30-6:30 PM

Neuroscience Applications of Machine Learning

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/21/174972

Tuesday, March 17 | 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, microbiome, and proteomics data. Register at https://training.oit.duke.edu/enroll/common/show/21/174978

Wednesday, March 18 | 4:30-6:30 PM

AI for the Digital Humanities

Matthew Kenney

AI is playing an increasingly large role in the Digital Humanities. The use of AI throughout the humanities can accelerate research, open up new forms of investigation, and create novel approaches to interacting with data. In this IPLE we will overview how digital humanities researchers can leverage AI in their work. First, we will survey approaches, models, and applications common to AI and Digital Humanities research. Next, we’ll review successful projects at the intersection of DH and AI, and discuss how these projects are shifting the landscape of what is possible within the digital humanities. Lastly, we’ll review several models and architectures through case studies, to better understand how we can use AI in our own research. 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/21/174919

Thursday, March 19 | 4:30-6:30 PM

The Impact of Machine Learning in Astrophysics and Cosmology

Dan Scolnic

There is a big data revolution happening in astrophysics as the next generation of telescopes are coming online, with 20 terabytes of data coming from a single telescope per night. From this large amount of data, scientists are trying to find subtle clues that can help uncover the most profound mysteries in the universe. Here, I will focus on some of the machine learning and deep learning techniques that have been employed to classify different types of stars, galaxies and transients. I will go over recent successes and challenges, and show off the work I do, demonstrated by one of the latest and most popular Kaggle machine-learning competitions ever called ‘PLAsTiCC Astronomical Classification: Can you help make sense of the Universe?’ Register at https://training.oit.duke.edu/enroll/common/show/21/174921

Tuesday, March 24 | 4:30-5:30pm

Overview of Ethical Issues with Emerging AI

Nita Farahany

Artificial intelligence (AI) can reduce costs, improve efficiency, and potentially improve accuracy in many critical areas of life that impact humans. And yet, many of the tools of AI lack transparency, have inherent biases, and are difficult to govern. Where does this leave society and what are some of the known and unknown risks of what has come to be known as the “Fourth Industrial Revolution” underwritten by AI? This discussion will focus on AI and its implications for changes in humanity, the need for greater transparency, the growing use of AI in critical areas of decision-making, the importance of safeguarding against biases, explore issues about privacy, safety and security, and the future of work. Register at https://training.oit.duke.edu/enroll/common/show/21/174920

Wednesday, March 25 | 4:30-6:30 PM

PyTorch for Computer Vision

Kevin Liang

The goal of computer vision is for computers to be able to understand visual content (e.g. images, videos, 3D, stereo), usually for the purpose of making predictions (classification, detection, captioning, generation, etc.). Modern computer vision models are almost universally based on convolutional neural networks (CNNs), whose recent developments have lead to increasing adoption and deployment of deep learning models in a wide number of fields. In this hands-on session, we’ll introduce how to build CNNs in PyTorch, as well as how to load datasets and pre-trained models using PyTorch’s vision library, Torchvision. Bring a laptop and be ready to code! Register at https://training.oit.duke.edu/enroll/common/show/21/174973

Thursday, March 26 | 4:30-6:30 PM

Attention Networks for Natural Language Processing

Lawrence Carin

Neural-network-based methods for natural language processing (NLP) constitute an area of significant recent technical progress, with many interesting real-world applications. The Transformer Network is one of the newest and most powerful approaches of this type. This algorithm is based on repeated application of attention networks, in an encoder-decoder framework. In this presentation the basics of all-attention models (the Transformer) for NLP will be described, with application in areas like text synthesis (e.g., suggesting email text) and language translation. Register at https://training.oit.duke.edu/enroll/common/show/21/174974

Thursday, April 16 | 4:30-6:30 PM

Machine Learning in Neuroimaging

Andrew Michael

This training will consist of two main sections: (1) application of ML to brain images from a clinical archive to detect brain disorders and (2) extraction of brain features from a large publicly available dataset to better understand mental health. After a brief introduction to the fundamentals of brain imaging, the first part of the class will focus on using structural brain MRI to diagnose and predict autism. Next, a deep learning technique will be applied to estimate brain volume from head CT (computed tomography) images that have poor image contrast. This technique’s potential for early detection and tracking Alzheimer’s disease will be presented. In the second part of the class, resting-state functional MRI (rsfMRI) data will be used to identify brain markers that may help to better understand the gender disparity in mental health. The class will conclude with evidence that suggests that rsfMRI has individually unique patterns that may serve as brain markers of certain behavioral characteristics. Register at https://training.oit.duke.edu/enroll/common/show/21/174975

+DS AI in Art Show/Reception

Tuesday, March 3, 2020 | 5:30-7:30 PM

Ruby Lounge, Rubenstein Arts Center

Please join us on Tuesday, March 3 for an art show and reception at the Rubenstein Arts Center, hosted by Duke+DataScience (+DS). The event is open to all, no registration is needed, and light refreshments will be served.

This art show will highlight the Second Annual AI in Art Competition, with entries created by Duke students, faculty, and staff. You’ll have the opportunity to view these works, converse with the participants, and learn about how their creations were generated. During the spoken program (beginning at 6 PM) we will announce the prizes for this competition (First Place: $5000, Second Place: $2500, and Third Place: $1000) with presentations and remarks from the participants.

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. We invite you to join this dialogue with us.

The art featured in this flyer was created by participants in the first annual AI for Art competition.

Read more about last year’s competition in this Duke Today piece: https://today.duke.edu/2019/03/these-works-art-were-created-artificial-intelligence