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    [{"authors":null,"categories":null,"content":"Hi! I’m a computational social scientist interested in digital persuasion, misinformation, and the wisdom of crowds. I’m currently in my 5th Year as a PhD Student in Marketing at MIT Sloan School of Management advised by David Rand. I am also affiliated with the MIT Initiative for the Digital Economy. My research uses computational methods and real-world data to study how digital media contributes to societal problems, and how we can design scalable solutions. In my dissertation work, I combine experiments, crowdsourcing, and natural-language-processing to quantify the impact that COVID vaccine (mis)information on Facebook had on US vaccine refusal.\nPrior to my PhD, I worked as a software engineer at Meta on the News and Civic teams, and then as a research assistant at Microsoft Research with the Computational Social Science Group. Before that, I graduated from Yale in 2016 with a degree in Computer Science and Psychology. In my spare time, I enjoy pop culture trivia, yoga, and 5-star NYTimes recipes (wisdom of crowds!).\n","date":1696118400,"expirydate":-62135596800,"kind":"taxonomy","lang":"en","lastmod":1692759720,"objectID":"c6a72605ae78ed9155e0cc8112d190d2","permalink":"https://jennyallen.github.io/authors/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/","section":"authors","summary":"Hi! I’m a computational social scientist interested in digital persuasion, misinformation, and the wisdom of crowds. I’m currently in my 5th Year as a PhD Student in Marketing at MIT Sloan School of Management advised by David Rand.","tags":null,"title":"Jennifer Allen","type":"authors"},{"authors":null,"categories":null,"content":"Hello!\nI am a Post-Doctoral Researcher in the Computational Social Science Lab at the University of Pennsylvania, supervised by Duncan Watts. In Fall 2025, I will be joining NYU Stern as an Assistant Professor of Technology, Operations, and Statistics, as well as a research affiliate of the Center for Social Media and Politics.\nI’m a computational social scientist interested in digital persuasion, misinformation, and the wisdom of crowds. I received my PhD in Marketing from MIT Sloan School of Management advised by David Rand. My research uses computational methods and real-world data to study how digital media contributes to societal problems, and how we can design scalable solutions.\nPrior to my PhD, I worked as a software engineer at Meta on the News and Civic teams, and then as a research assistant at Microsoft Research with the Computational Social Science Group. Before that, I graduated from Yale in 2016 with a degree in Computer Science and Psychology. In my spare time, I enjoy pop culture trivia, yoga, and 5-star NYTimes recipes (wisdom of crowds!).\n","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1692759720,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://jennyallen.github.io/authors/admin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/authors/admin/","section":"authors","summary":"Hello!\nI am a Post-Doctoral Researcher in the Computational Social Science Lab at the University of Pennsylvania, supervised by Duncan Watts. In Fall 2025, I will be joining NYU Stern as an Assistant Professor of Technology, Operations, and Statistics, as well as a research affiliate of the Center for Social Media and Politics.","tags":null,"title":"Jennifer Allen","type":"authors"},{"authors":[],"categories":null,"content":" Click on the Slides button above to view the built-in slides feature. Slides can be added in a few ways:\nCreate slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further event details, including page elements such as image galleries, can be added to the body of this page.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"a8edef490afe42206247b6ac05657af0","permalink":"https://jennyallen.github.io/talk/example-talk/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/talk/example-talk/","section":"event","summary":"An example talk using Wowchemy's Markdown slides feature.","tags":[],"title":"Example Talk","type":"event"},{"authors":["Jennifer Allen","Duncan J Watts","David G Rand"],"categories":[],"content":"","date":1696118400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1692759720,"objectID":"d67fdd1cedd092f240f052f3dbad3812","permalink":"https://jennyallen.github.io/publication/allen-2023-vaccine/","publishdate":"2023-10-24T12:44:47.781018Z","relpermalink":"/publication/allen-2023-vaccine/","section":"publication","summary":"Researchers and public health officials have attributed low uptake of the COVID-19 vaccine in the US to social media misinformation. To evaluate this claim, we introduce a framework combining lab experiments, crowdsourcing, and machine learning to estimate the causal effect of 13,206 vaccine-related URLs shared on Facebook on US vaccination intentions. Our model predicts this content reduced intentions by-2.3 percentage points (95% QI:-3.5,-1.0) per US Facebook user. Strikingly, we estimate the impact of misinformation was 50X less than that of content not flagged by fact-checkers that nonetheless expressed vaccine skepticism. Although misinformation was significantly more harmful when viewed, its exposure on Facebook was limited. In contrast, mainstream stories highlighting rare vaccine deaths both increased vaccine hesitancy and were among Facebook’s most-viewed stories. Our work suggests that curbing misinformation benefits public health, but highlights the need to scrutinize factually correct but potentially misleading content.","tags":[],"title":"Quantifying the Impact of Misinformation and Vaccine-Skeptical Content on Facebook","type":"publication"},{"authors":["Minali Aggarwal","Jennifer Allen","Alexander Coppock","Dan Frankowski","Solomon Messing","Kelly Zhang","James Barnes","Andrew Beasley","Harry Hantman","Sylvan Zheng"],"categories":[],"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803392,"objectID":"0133fbd057515a281710aa04940d7017","permalink":"https://jennyallen.github.io/publication/aggarwal-20232/","publishdate":"2023-10-24T12:44:50.210447Z","relpermalink":"/publication/aggarwal-20232/","section":"publication","summary":"We present the results of a large, US$8.9 million campaign-wide field experiment, conducted among 2 million moderate- and low-information persuadable voters in five battleground states during the 2020 US presidential election. Treatment group participants were exposed to an 8-month-long advertising programme delivered via social media, designed to persuade people to vote against Donald Trump and for Joe Biden. We found no evidence that the programme increased or decreased turnout on average. We found evidence of differential turnout effects by modelled level of Trump support: the campaign increased voting among Biden leaners by 0.4 percentage points (s.e.=0.2pp) and decreased voting among Trump leaners by 0.3 percentage points (s.e.=0.3pp) for a difference in conditional average treatment effects of 0.7 points (t(1,035,571)=−2.09; P=0.036; points; 95% confidence interval=−0.014 to 0). An important but exploratory finding is that the strongest differential effects appear in early voting data, which may inform future work on early campaigning in a post-COVID electoral environment. Our results indicate that differential mobilization effects of even large digital advertising campaigns in presidential elections are likely to be modest.","tags":[],"title":"A 2 million-person, campaign-wide field experiment shows how digital advertising affects voter turnout","type":"publication"},{"authors":["Antonio A Arechar","Jennifer Allen","Adam J Berinsky","Rocky Cole","Ziv Epstein","Kiran Garimella","Andrew Gully","Jackson G Lu","Robert M Ross","Michael N Stagnaro"," others"],"categories":[],"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803392,"objectID":"68b9ad71cec8c4f28653df1dee4dc83d","permalink":"https://jennyallen.github.io/publication/arechar-2023-understanding/","publishdate":"2023-07-19T21:54:09.490975Z","relpermalink":"/publication/arechar-2023-understanding/","section":"publication","summary":"","tags":[],"title":"Understanding and combatting misinformation across 16 countries on six continents","type":"publication"},{"authors":["Jennifer Allen","Markus Mobius","David M Rothschild","Duncan J Watts"],"categories":[],"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803392,"objectID":"788ee23b71c3e07aac5264d63897c1b6","permalink":"https://jennyallen.github.io/publication/allen-2022-addendum/","publishdate":"2023-10-24T12:44:49.756222Z","relpermalink":"/publication/allen-2022-addendum/","section":"publication","summary":"","tags":[],"title":"Addendum to: Research note: Examining potential bias in large-scale censored data","type":"publication"},{"authors":["Jennifer Allen","Cameron Martel","David G Rand"],"categories":[],"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803391,"objectID":"2507ae5195de6c659357a7817bd1a329","permalink":"https://jennyallen.github.io/publication/allen-2022-birds/","publishdate":"2023-07-19T21:54:08.859366Z","relpermalink":"/publication/allen-2022-birds/","section":"publication","summary":"There is a great deal of interest in the role that partisanship, and cross-party animosity in particular, plays in interactions on social media. Most prior research, however, must infer users’ judgments of others’ posts from engagement data. Here, we leverage data from Birdwatch, Twitter’s crowdsourced fact-checking pilot program, to directly measure judgments of whether other users’ tweets are misleading, and whether other users’ free-text evaluations of third-party tweets are helpful. For both sets of judgments, we find that contextual features – in particular, the partisanship of the users – are far more predictive of judgments than the content of the tweets and evaluations themselves. Specifically, users are more likely to write negative evaluations of tweets from counter-partisans; and are more likely to rate evaluations from counter-partisans as unhelpful. Our findings provide clear evidence that Birdwatch users preferentially challenge content from those with whom they disagree politically. While not necessarily indicating that Birdwatch is ineffective for identifying misleading content, these results demonstrate the important role that partisanship can play in content evaluation. Platform designers must consider the ramifications of partisanship when implementing crowdsourcing programs.","tags":[],"title":"Birds of a feather don’t fact-check each other: Partisanship and the evaluation of news in Twitter’s Birdwatch crowdsourced fact-checking program","type":"publication"},{"authors":["Cameron Martel","Jennifer Allen","Gordon Pennycook","David Rand"],"categories":[],"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803392,"objectID":"8a57b3c93b6b7d4a7efc213b1edae087","permalink":"https://jennyallen.github.io/publication/martel-2022-crowds/","publishdate":"2023-07-19T21:54:09.155785Z","relpermalink":"/publication/martel-2022-crowds/","section":"publication","summary":"","tags":[],"title":"Crowds can effectively identify misinformation at scale","type":"publication"},{"authors":["Tobias Konitzer","Jennifer Allen","Stephanie Eckman","Baird Howland","Markus Mobius","David Rothschild","Duncan J Watts"],"categories":[],"content":"","date":1609459200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803391,"objectID":"f6c12230ea5f69f4bfec46bf2a93ff0b","permalink":"https://jennyallen.github.io/publication/konitzer-2021-comparing/","publishdate":"2023-07-19T21:54:08.106213Z","relpermalink":"/publication/konitzer-2021-comparing/","section":"publication","summary":"","tags":[],"title":"Comparing estimates of news consumption from survey and passively collected behavioral data","type":"publication"},{"authors":["Jake M. Hofman","Daniel G. Goldstein","Siddhartha Sen","Forough Poursabzi-Sangdeh","Jennifer Allen","Ling Liang Dong","Brenda Fried","Harpreet Gaur","Adnan Hoq","Emeka Mbazor","Naomi Moreira","Cindy Muso","Etta Rapp","Roymil Terrero"],"categories":[],"content":"","date":1609459200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1692759720,"objectID":"3d69ee1652f7eda0aec28ff07806eece","permalink":"https://jennyallen.github.io/publication/hofman-2020-expanding/","publishdate":"2023-10-24T12:53:39.411624Z","relpermalink":"/publication/hofman-2020-expanding/","section":"publication","summary":"In recent years, researchers in several scientific disciplines have become concerned with published studies replicating less often than expected. A positive side effect of this concern is an appreciation that replicating other researchers’ work is an essential part of the scientific process. To date, many such efforts have come from the experimental sciences, where replication entails running new experiments, generating new data, and analyzing it. In this article, we emphasize not experimental replications but data analysis replications. We do so for three reasons. First, experimental replication excludes entire classes of publications that do not run experiments or even collect original data (e.g., archival data analysis). Second, experimental replication may in some cases be a needlessly high bar: there is great value in replicating just the data analyses of published experimental work. As data analysis replications require a lower investment of resources than experimental replications, their adoption should expand the number and variety of scientific reproducibility studies undertaken. Third, we propose that teaching undergraduate students to perform data analysis replications will greatly increase the number of replications done while providing them with research experience that should inform their decisions to pursue research or to attend graduate school. Towards this end, we provide details of a pilot program we created to teach undergraduates the skills necessary to conduct data analysis replications, and include a case study of the first set of students who completed this program and attempted to replicate the data analyses in a widely-cited social science paper on policing. In addition, we present a summary of ten additional data analysis replications carried out entirely by students in a university course.","tags":["Reproducibility","Replication","Robustness","Education","Data analysis"],"title":"Expanding the scope of reproducibility research through data analysis replications","type":"publication"},{"authors":["Jennifer Allen","Markus Mobius","David Rothschild","Duncan Watts"],"categories":[],"content":"","date":1609459200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803391,"objectID":"dd717d03fa6d45f8597ec094c125fb4c","permalink":"https://jennyallen.github.io/publication/allen-2021-research/","publishdate":"2023-10-24T12:44:49.32636Z","relpermalink":"/publication/allen-2021-research/","section":"publication","summary":"","tags":[],"title":"Research note: Examining potential bias in large-scale censored data","type":"publication"},{"authors":["Jennifer Allen","Antonio A Arechar","Gordon Pennycook","David G Rand"],"categories":[],"content":"","date":1609459200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803391,"objectID":"8fe54c1239eb32043355cd55ad77ccb9","permalink":"https://jennyallen.github.io/publication/allen-2021-scaling/","publishdate":"2023-10-24T12:44:48.863087Z","relpermalink":"/publication/allen-2021-scaling/","section":"publication","summary":"Professional fact-checking, a prominent approach to combating misinformation, does not scale easily. Furthermore, some distrust fact-checkers because of alleged liberal bias. We explore a solution to these problems: using politically balanced groups of laypeople to identify misinformation at scale. Examining 207 news articles flagged for fact-checking by Facebook algorithms, we compare accuracy ratings of three professional fact-checkers who researched each article to those of 1128 Americans from Amazon Mechanical Turk who rated each article’s headline and lede. The average ratings of small, politically balanced crowds of laypeople (i) correlate with the average fact-checker ratings as well as the fact-checkers’ ratings correlate with each other and (ii) predict whether the majority of fact-checkers rated a headline as “true” with high accuracy. Furthermore, cognitive reflection, political knowledge, and Democratic Party preference are positively related to agreement with fact-checkers, and identifying each headline’s publisher leads to a small increase in agreement with fact-checkers.","tags":[],"title":"Scaling up fact-checking using the wisdom of crowds","type":"publication"},{"authors":["Jennifer Allen","吳恩達"],"categories":["Demo","教程"],"content":"Overview The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more Get Started 👉 Create a new site 📚 Personalize your site 💬 Chat with the Wowchemy community or Hugo community 🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy 💡 Request a feature or report a bug for Wowchemy ⬆️ Updating Wowchemy? View the Update Tutorial and Release Notes Crowd-funded open-source software To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.\n❤️ Click here to become a sponsor and help support Wowchemy’s future ❤️ As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨\nEcosystem Hugo Academic CLI: Automatically import publications from BibTeX Inspiration Check out the latest demo of what you’ll get in less than 10 minutes, or view the showcase of personal, project, and business sites.\nFeatures Page builder - Create anything with widgets and elements Edit any type of content - Blog posts, publications, talks, slides, projects, and more! Create content in Markdown, Jupyter, or RStudio Plugin System - Fully customizable color and font themes Display Code and Math - Code highlighting and LaTeX math supported Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more! Beautiful Site - Simple and refreshing one page design Industry-Leading SEO - Help get your website found on search engines and social media Media Galleries - Display your images and videos with captions in a customizable gallery Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site Multi-language - 34+ language packs including English, 中文, and Português Multi-user - Each author gets their own profile page Privacy Pack - Assists with GDPR Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects One-Click Deployment - No servers. No databases. Only files. Themes Wowchemy and its templates come with automatic day (light) and night (dark) mode built-in. Alternatively, visitors can choose their preferred mode - click the moon icon in the top right of the Demo to see it in action! Day/night mode can also be disabled by the site admin in params.toml.\nChoose a stunning theme and font for your site. Themes are fully customizable.\nLicense Copyright 2016-present George Cushen.\nReleased under the MIT license.\n","date":1607817600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1607817600,"objectID":"279b9966ca9cf3121ce924dca452bb1c","permalink":"https://jennyallen.github.io/post/getting-started/","publishdate":"2020-12-13T00:00:00Z","relpermalink":"/post/getting-started/","section":"post","summary":"Welcome 👋 We know that first impressions are important, so we've populated your new site with some initial content to help you get familiar with everything in no time.","tags":["Academic","开源"],"title":"Welcome to Wowchemy, the website builder for Hugo","type":"post"},{"authors":["Jennifer Allen","Baird Howland","Markus Mobius","David Rothschild","Duncan J Watts"],"categories":[],"content":"","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803390,"objectID":"a775c6460d925084273a731c8721d8cb","permalink":"https://jennyallen.github.io/publication/allen-2020-evaluating/","publishdate":"2023-10-24T12:44:48.230236Z","relpermalink":"/publication/allen-2020-evaluating/","section":"publication","summary":"“Fake news,” broadly defined as false or misleading information masquerading as legitimate news, is frequently asserted to be pervasive online with serious consequences for democracy. Using a unique multimode dataset that comprises a nationally representative sample of mobile, desktop, and television consumption, we refute this conventional wisdom on three levels. First, news consumption of any sort is heavily outweighed by other forms of media consumption, comprising at most 14.2% of Americans’ daily media diets. Second, to the extent that Americans do consume news, it is overwhelmingly from television, which accounts for roughly five times as much as news consumption as online. Third, fake news comprises only 0.15% of Americans’ daily media diet. Our results suggest that the origins of public misinformedness and polarization are more likely to lie in the content of ordinary news or the avoidance of news altogether as they are in overt fakery.","tags":[],"title":"Evaluating the fake news problem at the scale of the information ecosystem","type":"publication"},{"authors":["David Holtz","Michael Zhao","Seth G Benzell","Cathy Y Cao","Mohammad Amin Rahimian","Jeremy Yang","Jennifer Allen","Avinash Collis","Alex Moehring","Tara Sowrirajan"," others"],"categories":[],"content":"","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689803391,"objectID":"25e8b6b0887385fbf2c286687aad11b3","permalink":"https://jennyallen.github.io/publication/holtz-2020-interdependence/","publishdate":"2023-07-19T21:54:08.251774Z","relpermalink":"/publication/holtz-2020-interdependence/","section":"publication","summary":"","tags":[],"title":"Interdependence and the cost of uncoordinated responses to COVID-19","type":"publication"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Wowchemy Wowchemy | Documentation\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026#34;blueberry\u0026#34; if porridge == \u0026#34;blueberry\u0026#34;: print(\u0026#34;Eating...\u0026#34;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three A fragment can accept two optional parameters:\nclass: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\nOnly the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026#34;/media/boards.jpg\u0026#34; \u0026gt;}} {{\u0026lt; slide background-color=\u0026#34;#0000FF\u0026#34; \u0026gt;}} {{\u0026lt; slide class=\u0026#34;my-style\u0026#34; \u0026gt;}} Custom CSS Example Let’s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://jennyallen.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Wowchemy's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.\nNullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.\nCras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.\nSuspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.\nAliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.\n","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1461715200,"objectID":"e8f8d235e8e7f2efd912bfe865363fc3","permalink":"https://jennyallen.github.io/project/example/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/example/","section":"project","summary":"An example of using the in-built project page.","tags":["Deep Learning"],"title":"Example Project","type":"project"}]