• AI, Machine Learning, and Analytics in Federal Policy (hosted by AT&T Labs)

    We will have O'Reilly books to raffle! ------------------------------- AGENDA ------------------------------- 6:30 – 7:00 Networking with refreshments 7:00 - 7:15 Introduction 7:15 - 8:00 Jaime Anne Earnest – Future State: AI and Machine Learning in the Applied Human Services Policy Environment 8:00 - 8:30 More networking with food & drinks ------------------------------- SPEAKER & TALK INFO ------------------------------- Title: Future State: AI and Machine Learning in the Applied Human Services Policy Environment Abstract: The application of Artificial Intelligence and Machine Learning approaches in Federal and Military contexts is well-explored in certain domains: autonomous weapons development, national security, robotics, and process automation for government logistics and acquisition. However, AI and Machine Learning have exciting, less-understood applications in human services policy and legislative development. In this talk, we’ll explore some of the opportunities and challenges to applying AI/ML to human services programming delivery and the development of legislation, using current examples from the Federal infrastructure, exploring needs and possibilities for AI and ML in emergent domains. Speaker Bio: Jaime Anne Earnest, MPH PhD is a former Lord Kelvin/Adam Smith Scholar at the University of Glasgow, National Science Foundation grantee, and is currently an ORISE Fellow/Program Evaluation Translational Scientist at the Pentagon where she executes technical evaluation on human services programs and is a strategic scientific advisor to Army leadership. Her work explores methodological innovations for public health policy and program evaluation, strategic scientific capacity building in policy environments, and the intersections of medicine, psychology, politics, and society. https://www.linkedin.com/in/jaimeanneearnest/ ----------------------------------------- ABOUT AT&T LABS RESEARCH ----------------------------------------- AT&T Labs Research is a global leader in development and research drawing on an unparalleled 140-year heritage of creation and innovation. Our Data Science and AI Research division is a team of dedicated data scientists using big data technologies to help solve some of AT&T’s toughest problems. https://about.att.com/sites/labs_research ------------------------------- CODE OF CONDUCT ------------------------------- WiMLDS is dedicated to providing a harassment-free experience for everyone. We do not tolerate harassment of participants in any form. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate. Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate. Thank you for helping make this a welcoming, friendly community for all. All attendees should read the full Code of Conduct before participating: https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct You must provide your FULL NAME for security. NO GUESTs allowed.

  • Machine Learning Interpretability at Microsoft Research [@Deloitte Digital]

    We will have O'Reilly books to raffle! ------------------------------- AGENDA ------------------------------- 6:30 – 7:00 Networking with refreshments 7:00 - 7:15 Introduction – Deloitte Specific Content 7:15 - 8:00 Forough Poursabzi Sangdeh: Manipulating and Measuring Model Interpretability 8:00 - 8:30 More networking with food & drinks ------------------------------- SPEAKER & TALK INFO ------------------------------- Abstract: Machine learning is increasingly used to make decisions that affect people’s lives in critical domains like criminal justice, fair lending, and medicine. While most of the research in machine learning focuses on improving the performance of models on held-out datasets, this is seldom enough to convince end-users that these models are trustworthy and reliable in the wild. To address this problem, a new line of research has emerged that focuses on developing interpretable machine learning methods and helping end-users make informed decisions. Despite the growing body of work in developing interpretable models, there is still no consensus on the definition and quantification of interpretability. In this talk, I will argue that to understand interpretability, we need to bring humans in the loop and run human-subject experiments. I approach the problem of interpretability from an interdisciplinary perspective which builds on decades of research in psychology, cognitive science, and social science to understand human behavior and trust. I will talk about a set of controlled user experiments, where we manipulated various design factors in models that are commonly thought to make them more or less interpretable and measured their influence on users' behavior. Our findings emphasize the importance of studying how models are presented to people and empirically verifying that interpretable models achieve their intended effects on end-users. Speaker Bio: Forough is a post-doctoral researcher at Microsoft Research in New York City. She works in the interdisciplinary area of interpretable and interactive machine learning. Forough collaborates with psychologists to study human behavior when interacting with machine learning systems. She uses these insights to design machine learning models that humans can use effectively. She is also interested in several aspects of fairness, accountability, and transparency in machine learning and their effect on people 's decision-making process. Forough holds a BE in computer engineering from the University of Tehran and a PhD in computer science from the University of Colorado at Boulder. https://www.linkedin.com/in/forough-poursabzi-sangdeh-479156a1 ------------------------------- ABOUT DELOITTE ------------------------------- Hux by Deloitte Digital gives you increased visibility and control of customer data, machine learning-driven capabilities to determine how best to engage at an individual level, and system integration across the entire customer engagement ecosystem. This enables you to create more personalized, contextualized, and memorable end-to-end experiences for individuals at scale. These experiences can help humans create deep emotional connections to your products and brands, driving loyalty and business growth. ------------------------------- CODE OF CONDUCT ------------------------------- WiMLDS is dedicated to providing a harassment-free experience for everyone. We do not tolerate harassment of participants in any form. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate. Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate. Thank you for helping make this a welcoming, friendly community for all. All attendees should read the full Code of Conduct before participating: https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct

  • PyGotham Proposal (CFP) Brainstorming [** RSVP with PyLadies **]

    REGISTER HERE: https://www.meetup.com/NYC-PyLadies/events/260580451/ Ever wanted to submit a conference proposal but didn't know where to start? Join NYC PyLadies at DropBox for an evening of proposal brainstorming! The goal of the evening is for EVERYONE to craft a conference (PyGotham or imaginary) talk proposal -- we hope attendees will be excited to submit their proposal to PyGotham. We will have mentors/TAs who have given talks at PyGotham or other tech conferences to help you craft your proposal. This is a great opportunity to meet, hear and learn firsthand from the women who have given great talks at Python conferences. This event is meant to be accessible to Pyladies with all levels of experience in giving talks at conferences and we think there will be takeaway skills for everyone attending it. So whether your goal is to submit a conference proposal to PyGotham/ to hone your proposal-writing skills/ to learn best practices from some of the leading women in the field, just bring your ideas and your laptops and we will help you with the rest! ------------------------ AGENDA ------------------------ 6:30pm - 7:00pm: Doors open + networking 7:00pm - 8:00pm: What makes a good proposal + how to begin brainstorming 8:00pm - 8:45pm: Work together to draft up our very own proposals! 8:45pm - 9:00pm: Share with the group (if you feel comfortable doing so) ---------------------------- ABOUT PYGOTHAM ---------------------------- PyGotham is NYC's very own annual Python conference and is a great place for both first time and veteran speakers. The goal is to get you thinking about potential talk topics and how to construct a proposal that will make the organizers sing with joy! Submit your talks here: https://cfp.pygotham.org/ ---------------------------------------- PyGotham: the conference ---------------------------------------- https://2019.pygotham.org ---------------------------- ABOUT DROPBOX ---------------------------- Dropbox is a modern workspace designed to reduce busywork so you can focus on the things that matter. In our New York office, teams are driving significant impact towards Dropbox’s success, owning a breadth of user-facing products and low-level infrastructure. Dropbox is a welcoming environment for everyone, and we do our best to make sure all people feel supported and connected at work. Come join us! Dropbox is hiring: https://www.dropbox.com/jobs/locations/nyc ------------------------ RESOURCES ------------------------ "Write an Excellent Programming Blog" by Jesse Davis: Blog Post https://emptysqua.re/blog/write-an-excellent-programming-blog/ Video http://pyvideo.org/pycon-us-2016/a-jesse-jiryu-davis-write-an-excellent-programming-blog-pycon-2016.html "On Conference Speaking" by Hynek Schlawack: Blog Post https://hynek.me/articles/speaking/ PyGotham 2018 YouTube Channel: http://bit.ly/2Uj7pdU ------------------------ INFO ------------------------ MISSION: http://www.pyladies.com CODE OF CONDUCT: http://www.pyladies.com/CodeOfConduct/ ------------------------ WHAT TO BRING ------------------------ - Curiosity - Your laptop - A government-issued ID for getting into the building

  • Facebook Artificial Intelligence (FB AI)

    Needs a location

    Speakers 01 Abhijit Bose Title: Welcome and Overview of Facebook AI Research Abstract: Abhijit is an Engineering Manager on FAIR at Facebook's New York office and will give an overview of Facebook's AI Research team. 02 Lili Dworkin Title: Sequential Parameter Tuning Experiments Abstract: Both engineers and researchers are often faced with the problem of optimizing large parameter configurations. We've developed a platform that facilitates the process of running adaptive experiments to hone in on optimum configurations. 03 Wan-Yen Lo Title: Pushing the Limit of Computer Vision Research Abstract: In FAIR, our ambition is to solve AI, which can only be done by actively engaging with the research community. We publish our research findings, open-source the software we develop, and present at leading conferences. In this talk, we will share our recent and exciting progress in computer vision research made possible by our newly-released software, which we hope to foster future work worldwide. 04 Smriti Bhagat Title: A Hierarchy of Hashtags Abstract: Some hashtags are simple like #cat, but more often, they are complex abstractions of experiences, e.g., #fromwhereistand, #plantsmakepeoplehappy. We’ve developed algorithms for hierarchical representations of hashtags to study their semantic relatedness and gain a better understanding of the hashtag space. 05 Emily Dinan Title: Grounding Dialogue in Knowledge Abstract: In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. We collect a dialogue dataset grounded in knowledge and then design architectures capable of retrieving knowledge, reading and conditioning on it, and finally generating natural responses

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  • Machine Learning w/Graph Algorithms+Cypher for Apache Spark [register w/Neo4j]

    REGISTER HERE: https://neo4j.typeform.com/to/USb6It?event=nyc This event is part of GlobalGraphCelebrationDay.com. This event is in partnership with: Neo4j NYC - https://www.meetup.com/nycneo4j/ Women in Machine Learning and Data Science: https://www.meetup.com/NYC-Women-in-Machine-Learning-Data-Science/ H2O.ai & NYC Big Data Science: https://www.meetup.com/NYC-Big-Data-Science/ Metis: NY Data Science: https://www.meetup.com/Metis-New-York-Data-Science/ --------------- Talk 1: Graph analytics with Cypher for Apache Spark --------------- Cypher for Apache Spark allows users to construct, query, and analyze graphs directly within their Spark environments, as well as to bring those graphs into Neo4j for more advanced analysis. With Cypher becoming an officially supported part of Apache Spark 3.0, Martin Junhganns, one of the senior developers of Cypher for Apache Spark, will speak about how to create and manage graphs in you data lake, as well as how to bring those graphs into Neo4j for native graph functionality. Speaker: Martin Junghanns is part of the Cypher for Apache Spark Engineering team at Neo4j. He has a research background in distributed graph analytics, his main interests are query engines, graph algorithms and bringing graph querying into the world of Apache Spark. Martin holds a MSc Computer Science degree from the University of Leipzig. --------------- Talk 2: Using graphs to build more accurate machine learning models --------------- Graph data structures allow data scientists to leverage predictive features from relationships and network structures in powerful ways. Alicia Frame, a Senior Data Scientist at Neo4j, will walk through concrete examples of how to leverage the graph structure of your existing data, as well as how to use graph algorithms to add highly predictive features to machine learning models. Speaker: Alicia Frame is the Senior Data Scientist on Neo4j's Product team, where she is responsible for algorithm development and strategy. She earned a PhD in computational biology from the University of North Carolina at Chapel Hill and a BS in biology and mathematics from the College of William and Mary in Virginia, and has over 8 years experience in enterprise data science at BenevolentAI, Dow, and the EPA.

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  • Diversity as a Data Science Imperative [see link for registration]

    In order to gain entry you MUST RSVP in these 2 places: 1. DATAIKU: https://www.meetup.com/Analytics-Data-Science-by-Dataiku-NY/events/260066774/ 2. GENERAL ASSEMBLY https://generalassemb.ly/education/diversity-as-a-data-science-imperative-feat-spotify-mongodb-wimlds-turner/new-york-city/74871 Schedule 6:30pm: Pizza, Beer, Networking 7:00pm: Panel discussion 7:30pm: Break-outs w/ panelists Data science has drastically transformed in recent years, drawing attention to its ability to produce real impact across a number of industries. However, despite its many evolutions, the field has seriously lagged in successes in diversity - with the lowest variety in gender, race, and education diversity in technical fields (https://www.forbes.com/sites/priceonomics/2017/09/28/the-data-science-diversity-gap/#2c5ff90a5f58). Thus, we're bringing together leaders from Spotify, MongoDB, WiMLDS, and Turner Broadcasting to discuss the impacts diversity can have on the field, ranging from AI ethics to the perception of the data scientist role. Following the panel, we'll break out into speaker-led groups for smaller discussions on how we can actively contribute to building diversity in our own work. Panelists Inga Chen, Product Manager @ Spotify and NYC Chapter Lead for Women in Product Haile Owusu, SVP of Analytics, Decision and Data Sciences @ Turner Broadcasting Jesse Jiryu Davis, Staff Software Engineer @ MongoDB Reshama Shaikh, Board Member of Women in Machine Learning & Data Science Triveni Ghandi, Data Scientist @ Dataiku Speaker bios Inga Chen leads two personalization & discovery product teams at Spotify focused on building data and machine learning models that power personalized listening experiences, including Discover Weekly, Release Radar, Daily Mix, Home, Voice & Search. Before Spotify, she led user-facing analytics products at Squarespace, turning data into actionable insights across web & mobile to help millions of websites make better decisions about their businesses. Haile Owusu is senior vice president of analytics, decisions & data sciences at Turner. In this role, Owusu focuses on building out Turner’s DS capabilities, expanding the company’s scope in applying analytics, data and decision sciences to enhance its products. His DS team works closely with many of Turner’s business groups to translate strategies into execution plans for new decision support systems and audience insight strategies. Jesse is a Staff Engineer at MongoDB in New York City. He and Guido van Rossum are coauthors of "A Web Crawler With asyncio Coroutines", a chapter in the "500 Lines or Less" book in the Architecture of Open Source Applications series. Jesse lives in Manhattan with his partner Jennifer Armstrong, and their dwarf hamsters Hazel and Gertrude. Reshama is a freelance data scientist/statistician with skills in Python, R and SAS. She earned her M.S. in statistics from Rutgers University. She earned her M.B.A. from NYU Stern School of Business studying strategy, business analytics & technology management. She began her career at Educational Testing Service, then worked for over 10 years as a biostatistician in the pharmaceutical industry at companies including PPD, Merck, Thomas Jefferson University and Pfizer. She also taught math and statistics for 2 years at Temple University. Triveni is a Data Scientist with Dataiku, working with clients to determine best practices around DS and their specific projects. She previously worked as a Data Analyst with a large non-profit dedicated to improving education outcomes in NYC. She holds a Ph.D in Political Science from Cornell University.

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  • Data Science & Artificial Intelligence Research at AT&T Labs & Xandr (AppNexus)

    The event will feature speakers from the Data Science and AI Research (DSAIR) division of AT&T Labs Research and Xandr (former AppNexus). We will have O'Reilly books & swags to raffle! ----------------------------- AGENDA ----------------------------- 6:30 - 6:55 Networking, food & drinks 6:55 - 7:00 Becca Conneely - Welcoming by Xandr 7:00 - 7:05 DeDe Paul - Intro to AT&T Labs Research & Xandr 7:05 - 7:20 Emily Dodwell & Ritwik Mitra - From Theory to Practice: A Machine Learning Use Case for Advertising at AT&T 7:20 - 7:35 Cheryl Flynn Brooks - Detecting and Mitigating Bias in Targeted Advertising 7:35 - 7:50 Yana Volkovich - Click Prediction for Bid Valuation 7:50 - 8:05 Laura Yu - Adult Content Classifier for Web Pages 8:05 - 8:20 Subho Majumdar - To Watch or not to Watch: Early prediction of viewer engagement in a TV show using machine learning 8:20 - 9:00 More networking with food & drinks ----------------------------------------------------------- ABOUT AT&T LABS RESEARCH AND XANDR ----------------------------------------------------------- AT&T Labs Research AT&T Labs Research is a global leader in development and research drawing on an unparalleled 140-year heritage of creation and innovation. Our Data Science and AI Research division is a team of dedicated data scientists using big data technologies to help solve some of AT&T’s toughest problems. https://about.att.com/sites/labs_research Xandr We are a collective with a common purpose: make advertising matter to brands and consumers alike. Leveraging the spirit of innovation that began with Alexander Graham Bell more than 140 years ago and has continued on as a part of AT&T’s legacy, we are uniquely positioned to move the industry forward. With one of the world’s largest collections of digital, film and TV properties via our sister company WarnerMedia, we provide a premium option for advertisers and publishers looking to reach specific audiences at scale in premium and brand-safe environments. https://www.xandr.com ----------------------------- CODE OF CONDUCT ----------------------------- WiMLDS is dedicated to providing a harassment-free experience for everyone. We do not tolerate harassment of participants in any form. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate. Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate. Thank you for helping make this a welcoming, friendly community for all. All attendees should read the full Code of Conduct before participating: https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct

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  • Women in Data Science (WiDS) Conference NYC [register with SAP]

    * EVENT IS FULL * Registration will not be processed through meetup. Register here: https://events.sap.com/us/widsnyc2019/en/home --------------------- 8:00 - 8:50 am: Check In 8:50 - 9:00 am: Welcome by host Ann Rosenberg, SVP & Global Head of SAP Next-Gen 9:00 - 9:30 am: Opening Keynote - Cassie Kozyrkov, Chief Decision Scientist at Google 9:30 - 10:15 am: Panel - Data Science Live from the Trenches Moderator: Parinaz Vahabzadeh, Head of Data Labs at Tapestry Sinziana Eckner, Data Scientist at JP Morgan Chase Anna Coenen, Senior Data Scientist at The New York Times Nami Choe, Head of the Platforms Data & Measurement Team at Google Claudia Perlich, Senior Data Scientist at Two Sigma 10:15 - 10:45 am: Keynote - Christine Hung, Head of Data Solutions at Spotify 10:45 - 11:00 am: Coffee & Bio Break 11:00 - 11:45 am: Panel - Career Paths in Data Science Moderator: Nancy Fessatidis Emily Robinson, Data Scientist at DataCamp Tamar Shapiro, Head of Analytics at Instagram Dara Chen, Data & Analytics at EY 11:45 - 12:15 am: Keynote - Jeannette M. Wing, Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University 12:15 - 1:15 pm: Networking Lunch 1:15 - 2:00 pm: Panel - Data for Good Moderator: Reshama Shaikh, Freelance Data Scientist/Statistician Shira Mitchell, Statistician at the NYC Mayor's Office of Data Analytics Lauren Kennedy, Postdoctoral Research Scientist at Columbia University Davar Ardalan, Founder and Storyteller in Chief at IVOW AI 2:05 pm: Open Networking

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  • Understanding Probability Theory With Exercises in R and Python

    33 Thomas Street

    $5.00

    Formal probability theory is a rich and sophisticated field of mathematics that forms the foundation of statistics and data science. Unfortunately it also has reputation for being confusing, if not outright impenetrable. Much of that intimidation, however, is due not to the abstract mathematics but rather how they are taught. Many introductions to probability theory confound the abstract mathematics with their practical implementations, convoluting _what_ we can calculate in the theory with _how_ we implement those calculations. To make matters even worse, probability theory is used to model a variety of subtlety different systems, which then burdens the already confused mathematics with the distinct and often conflicting philosophical connotations of those applications. In this course we will attempt to untangle this pedagogical knot and illuminate the basic concepts and manipulations of probability theory and their applications. Our ultimate goal is to demystify what we can calculate in probability theory and how we can perform those calculations in practice, the latter being demonstrated with interactive exercises in R and Python. -------------------------- Speaker Bio -------------------------- Michael Betancourt (https://betanalpha.github.io) is the principle research scientist at Symplectomorphic, LLC, where he develops theoretical and methodological tools to support practical Bayesian inference. He is also a core developer of Stan, where he implements and tests these tools. In addition to hosting tutorials and workshops on Bayesian inference with Stan he also collaborates on analyses in, amongst others, epidemiology, pharmacology, and physics. Michael on Twitter: https://twitter.com/betanalpha -------------------------- Agenda -------------------------- 10:00 - 12:00 Lecture / workshop 12:00 - 01:00 Lunch 01:00 - 05:00 Lecture / workshop -------------------------- Requirements -------------------------- The course will assume some familiarity with the basics of calculus and linear algebra. In order to participate in the interactive exercises attendees must provide a laptop with R or Python. Some exercises will optionally utilize Stan and we suggest that you have the the latest version of RStan (https://cran.r-project.org/web/packages/rstan/index.html) or PyStan (https://pystan.readthedocs.io/en/latest/) installed. Please verify that you can run the 8schools model as discussed in the RStan Quick Start Guide (https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started) or the PyStan Quick Start Guide (https://pystan.readthedocs.io/en/latest/getting_started.html) and report any installation issues on the Stan Forums (https://discourse.mc-stan.org) as early as possible. -------------------------- POLICY -------------------------- No refunds. ----------------------------- CODE OF CONDUCT ----------------------------- WiMLDS is dedicated to providing a harassment-free experience for everyone. We do not tolerate harassment of participants in any form. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate. Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate. Thank you for helping make this a welcoming, friendly community for all. All attendees should read the full Code of Conduct before participating: https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct

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  • Tips for Better Predictive Models (@Foursquare)

    ----------------------------- AGENDA ----------------------------- 6:30-6:55pm Mingling and Networking 6:55-7:00pm Careers at Foursquare 7:00-7:45pm Talk by Stephanie Yang 7:45-8:15pm Networking This talk will cover the history of Foursquare, from its roots in consumer apps to its recent pivot to building a location technology platform. Lessons learned and to be discussed with examples include: - designing the right metrics to track - the value of feature engineering - and the importance of researching your data ------------------------------- ABOUT FOURSQUARE ------------------------------- Since our inception in 2009, Foursquare has been a leading force in changing how location information enriches our real-world and digital lives. As a location intelligence company, Foursquare is comprised of two well-known consumer apps, Foursquare and Swarm, as well as thriving media and enterprise products. Our B2B offerings include Places (for developers), Pinpoint and Attribution (for marketers), and Place Insights (for analysts, based on the world's largest foot traffic panel). With more than 200 people across our offices in New York, San Francisco, and in sales offices around the globe, we’re dedicated to our trailblazing mission—enriching consumer experiences and informing business decisions. Join us! ----------------------------- SPEAKER BIO ----------------------------- Stephanie Yang is a data scientist and technical lead who has worked at Foursquare since 2014. She leads a team of machine learning engineers who build quantitative models and application servers for all things location. Prior to Foursquare, she worked as a high frequency trader for several years. She holds a Ph.D. in algebraic geometry from Harvard University. ----------------------------- CODE OF CONDUCT ----------------------------- WiMLDS is dedicated to providing a harassment-free experience for everyone. We do not tolerate harassment of participants in any form. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate. Be kind to others. Do not insult or put down others. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate. Thank you for helping make this a welcoming, friendly community for all. All attendees should read the full Code of Conduct before participating: https://github.com/WiMLDS/starter-kit/wiki/Code-of-conduct