- [ONLINE] Causal Inference in the language of machine learning with Robert Ness
For July, we are featuring a joint meetup run by Statistical Seminars DC. Register here or at their event. https://www.meetup.com/Statistical-Seminars-DC/events/271973731/ Blurb: Causal inference is having a moment in the machine learning community. The next generation of AI will require algorithms that enable agents to reason causally about the world. But what is causal inference and how does it connect to machine learning? How are the big tech companies and data science organizations applying causal inference? How can we build causal models with deep learning tools like PyTorch? This talk introduces causal inference in the language of machine learning, and illustrates how to build causal models and perform causal reasoning using deep probabilistic models. Code examples are included. Bio: Robert didn't start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies. After switching to the tech industry, Robert's interests shifted to modeling data. He attained his Ph.D. in mathematical statistics from Purdue University, and then he worked as a research engineer in various AI startups. He has published in journals and venues across these spaces, including RECOMB and NeurIPS, on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. In addition to startup work, he is a machine learning professor at Northeastern University.
- Career Transitioning with Kevin O’Connell
For June, we are partnering with Statistical Seminars DC. Register here or at Statistical Seminars DC: https://www.meetup.com/Statistical-Seminars-DC/events/270699316/ Details Summary: Job transitions can be stressful. Whether they’re due to layoff, a new job, or moving into a new field after graduation. Sometimes even seemingly minor professional moves can leave you feeling as if you’ve teleported to a strange land. The pace of change in almost every field is dizzying and what were once steady fields and safe careers are being disrupted by COVID-19 and other factors. Join us as Kevin O’Connell shares his tips on how to effectively change careers. The format of this event is a discussion form so bring your questions. Kevin O’Connell is a serial entrepreneur, author, and instructor at George Washington University’s School of Business with 15 years of higher education experience. After working his first 8 years in student affairs, Kevin launched himself full time to turn his passion project into a grassroots movement. The Niche Movement, a global community reaching more than 5,000 people, helps college students and young professionals rethink the traditional career search and find a job they love. In 2014 Kevin delivered his first TEDx talk and year later, released his first book The New Rules to Finding a Career You Love. Since going out to work for himself, Kevin has also founded and built a substantial digital storytelling agency called FYN Creative - based in Arlington, VA. Kevin and his team produce video content, create campaigns, and provide trainings for universities and nonprofits up and down the east coast. FYN Creative has partnered with over 30 organizations including American University, ACPA, the National Education Association, and Leadership Greater Washington. FYN Creative: Website: http://bit.ly/fyncreative Email: [masked] Twitter: http://bit.ly/fyntwitter Instagram: http://bit.ly/fyninstagram Facebook: http://bit.ly/fynfacebook Kevin O’Connell: Website: http://bit.ly/kowebsite Email: [masked] Linkedin: https://www.linkedin.com/in/kevinocon... Twitter: http://bit.ly/koco83twitter Instagram: http://bit.ly/koco83instagram Snapchat: koco83
- [REMOTE] Shiny Deep Dive
This month, we are co-hosting with Statistical Programming DC. Sign up here or there. Description and Zoom link below. Join the next SPDC online meetup with two members of the Shiny team at RStudio. Barret Schloerke and Carson Sievert work full time on improving the experience of creating visualizations in R and building Shiny apps. Join them for two talks on new packages that can improve your experience building Shiny apps and data visualizations. Schedule: 7 - 7:20pm Reactlog 2.0: Debugging the State of Shiny (Barret) The revamped reactlog provides an updated visual display to traverse through the reactive behavior within your shiny application. Using live shiny applications, we will use reactlog’s directed dependency graph to find missing reactive dependencies in “working” applications and address suboptimal reactive coding patterns. Correcting these coding patterns will reduce the amount of calculations done by shiny and keep reactive objects from being created unnecessarily. 7:20-7:30 Q+A 7:30-7:50 Plot theming made easieR (Carson) The new thematic package provides a simplified and uniform approach to theming ggplot2, lattice, and base R graphics based on a few colors and font(s). This package provides the foundation for shiny’s new plot auto-theming feature, which automatically styles plots based on the application’s styling. 7:50-8 Q+A Zoom Link: https://zoom.us/j/92586102485?pwd=MFlvTThJMzNLWkgrR2xLcHo1dmVPUT09 Zoom Password: spdc-shiny
- Data Science 2x: Temporal Graphs Models & Machine Translation Quality Estimates
We've got two data science talks for you! Come out for pizza at 6:30 and we'll get the program started at 7. All are welcome! ------------------------ Talk 1: A system for machine translation quality estimation: a BERT-based model, novel data-set, and user interface. Machine translation is pretty accurate, so it’s critical to find the increasingly few instances of mistranslation. Quality estimation is an automatic method for estimating the quality of machine translation output at run-time, without relying on reference translations. In this presentation, we will introduce a new data-set for quality estimation, which has recently been released for the upcoming World Machine Translation Quality Estimation shared task at ACL in July. We will also share a BERT-based model for predicting translation quality, and a novel user interface for displaying results. Nina Lopatina is a research data scientist at IQT Labs, currently working on machine translation quality estimation. Prior to this project, Nina researched machine learning privacy attacks on speaker identification models. Before joining IQT Labs, Nina researched neural processes and computations underlying decision-making at Berkeley & the National Institute on Drug Abuse. --------------------------- Talk 2: Towards Modeling Temporal Graphs and Embeddings Many real-world graph applications operate on temporal streaming data. Most of the current graph analytics platforms do not natively support such temporal datasets. In this talk we present a way to model temporal graphs and we apply a sliding-window techniques to build temporal node embeddings. Such embeddings can explicitly characterize how nodes and communities change over time. We will use an adaptive authentication use-case to introduce these concepts. Srdjan Marinovic is co-founder and CTO of SignalFrame, a DC-based graph-analytics startup. He leads the development of the company’s temporal graph platform and streaming services. Prior to co-founding SignalFrame, Srdjan was in academia, at Imperial College London and ETH Zurich, focused on building secure systems founded in non-monotonic AI and formal methods. Agenda ---------------------------- 6:30pm – 7:00pm Networking & Refreshments 7:00pm – 7:15pm Introduction & Announcements 7:15pm – 8:30pm Presentations and Q&A DSDC Code of Conduct ----------------------------- bit.ly/dsdc_code
- Data Science Double Header: DevOps Lessons & Deploying ML in the Enterprise
Talk 1: Reflections on a Year Spent Talking to Data Scientists about DevOps As a Solutions Engineer at RStudio, I spend a good deal of time helping data scientists advocate for R within their organizations. These people are fully committed to ushering in better data practices and adopting the best tools and infrastructure for their team. Unfortunately, even highly motivated individuals can run into roadblocks when trying to get R recognized as an analytic standard. Commonly issues stem from a lack of management and governance around open source software; especially when IT is unfamiliar with requisite core competencies. In this talk, I present DevOps as a framework for understanding and navigating these kinds of organizational challenges. Kelly O’Briant is a solutions engineer at RStudio interested in configuration and workflow management with a passion for R administration. --------------------------- Talk 2: Deploying and Managing Machine Learning in Enterprise Environments You've Built and Trained a Model. Now What? An Overview of the ML lifecycle–what your company needs at every stage, from data collection to resource management, and how that impacts your deployment choices. We’ll then discuss custom deployment solutions from different industries and what you can learn from their success (and failures). Brendan Collins is the lead Solutions Engineer for Algorithmia’s east coast enterprise customers. He has worked in financial enterprise infrastructure for more than a decade, with groups ranging in size from the largest financial institutions in the world to community banks. Brendan has a true passion for helping enterprises use machine learning and data science to solve cutting edge problems, as well as a personal interest in serverless technologies of all shapes and sizes. Agenda ---------------------------- 6:30pm – 7:00pm Networking and Refreshments 7:00pm – 7:10pm Introduction, Announcements 7:15pm – 8:15pm Presentations and Q&A Join us for discussion and Data Drinks @ Tonic (2036 G St NW) following the event.
- Deep Learning with Tensorflow 2.0
Abstract ----------------------------- Developed by Google and written in python, Tensorflow is the most popular deep learning framework. However, it is not perfect. To improve the developer experience, consolidate functionality, and expand on the ability to be deployed, Tensorflow 2 was developed. As of September 30th 2019, Tensorflow 2 is out of alpha and has been given a full release. This talk will address the motivations behind Tensorflow 2 and how it fixes some of the pain points of its predecessor. I will also include a practical example of how to effectively develop deep learning models using some of Tensorflow 2's features. About the Speaker ----------------------------- Elliott is a Data Scientist at Blacksky inc., a Virginia Based Geospatial company. For the past 5 years he has worked on developing and productizing several aspects of machine learning, particularly in the areas of computer vision and natural language processing. He is always interested in what is making an impact in the world of understanding data. Outside of work he attends several local DC meetups and loves to engage with and learn from others in the field. Agenda ---------------------------- 6:30pm – 7:00pm Networking and Refreshments 7:00pm – 7:10pm Introduction, Announcements 7:10pm – 7:40pm Presentation 7:40pm – 7:55pm Q&A 8:00pm – 8:30pm Data Drinks @Tonic (2036 G St NW)
- What We Know about the Quality of Data Used in Data Science
What We Know about the Quality of Data Used in Data Science Dr. Stephanie Eckman Abstract ----------------------------- The insights we get from data science models are only as good as the data that go into the models. Data can suffer from errors of representation (some cases are missing) and errors of measurement (the values in the data set are wrong). Both can cause problems for data scientists training models. I will discuss common data sources and the errors that we find in them. I will provide ideas on how we can address these errors to improve data science models. About the Speaker ----------------------------- Dr. Stephanie Eckman is a Fellow in the Survey Research Division at RTI International. She has a Ph.D. in Survey Statistics & Methodology from the Joint Program in Survey Methodology at the University of Maryland. She specializes in understanding data quality and the social construction of data. Agenda ---------------------------- 6:30pm – 7:00pm Networking and Refreshments 7:00pm – 7:10pm Introduction, Announcements 7:10pm – 7:40pm Presentation 7:40pm – 7:55pm Q&A 8:00pm – 8:30pm Data Drinks @Tonic (2036 G St NW)
- Learn Python!
Come learn Python in a supportive environment! If you're new to coding or just new to Python, this MeetUp is for you! 🐍 You'll be working through exercises in the Memorable Python ebook. It's available for free in beta here: https://memorablepython.com Everyone will work through the problems at their own pace. Feel free to get started ahead of time. Also feel free to drop in any time during the evening. Bring - A laptop that can connect to wifi. - A positive attitude. 😀 - It's helpful to have a Google account set up, as we'll be using Google Colab. If you're an experienced Python pro and would like to help mentor at the event, please message us on MeetUp. Registration is limited due to space and mentor constraints. We'll be in Room 110 at Monroe Hall on the George Washington University campus. Note this is a new location. Monroe Hall[masked] G Street NW Washington, DC 20052 See you there!
- DataDive for Development with the World Bank
*The event is full!* To attend, you must be registered through this Google form: https://bit.ly/2kkbLFH Please join our MeetUp group to be notified for future events. *What's a DataDive?* A collaborative event where data practitioners work together to build cool things, discover relationships, and share insights. *Why Participate?* You have an opportunity to help the World Bank solve some of the world's most important problems. - Sharpen your creative problem solving, data cleaning, data analysis, data modeling, and presentation skills. - Make new friends and meet great people. - Learn about collaboration opportunities with the World Bank. - Help drive the conversation as the World Bank works to solve problems with data. *The event is full!* To attend, you must be registered through this Google form: https://bit.ly/2kkbLFH Food and beverages will be provided. *What to bring* Please bring a laptop and a government-issued photo ID. *Arrival* Please arrive by 4 pm to get through security. *More info is available on the event website*: http://bit.ly/dive4dev See you there! *The event is full!* To attend, you must be registered through this Google form: https://bit.ly/2kkbLFH
- Learn Python!
**Notice the corrected Date. This event is Tuesday, Oct. 1, 2019.** Please update your RSVP if you cannot make it. There are a number of people on the waitlist. To attend, you must be registered through this Google form: https://bit.ly/2kkbLFH . Come learn Python in a supportive environment! If you're new to coding or just new to Python, this MeetUp is for you! 🐍 You'll be working through exercises in the Memorable Python ebook. It's available here for free in beta here: https://memorablepython.com Everyone will work through the problems at their own pace. Feel free to get started ahead of time. Also feel free to drop in any time during the evening. Bring - a laptop that can connect to wifi. - a positive attitude. 😀 - It's helpful to have a Google Account set up, as we'll be using Google Colab. If you're an experience Python pro and would like to help mentor at the event, please message us on MeetUp. Registration is limited due to space and mentor constraints. We'll be in Room 110 at Phillips Hall on the George Washington University campus. See you there! **Notice the corrected Date. This event is Tuesday, Oct. 1, 2019.** Please update your RSVP if you cannot make it. There are a number of people on the waitlist. 👍