Brought to you by NumFOCUS Foundation and O’Reilly Media Inc.
The official Jupyter Conference
August 22-23, 2017: Training
August 23-25, 2017: Tutorials & Conference
New York, NY
 
Beekman/Sutton North
Add Jupyter frontends: From the classic Jupyter Notebook to JupyterLab, nteract, and beyond to your personal schedule
11:05am Jupyter frontends: From the classic Jupyter Notebook to JupyterLab, nteract, and beyond Kyle Kelley (Netflix), Brian Granger (Cal Poly San Luis Obispo)
Add JupyterLab: The next-generation Jupyter frontend to your personal schedule
11:55am JupyterLab: The next-generation Jupyter frontend Brian Granger (Cal Poly San Luis Obispo), Chris Colbert (Project Jupyter), Ian Rose (UC Berkeley)
Add GenePattern Notebook: Jupyter for integrative genomics to your personal schedule
1:50pm GenePattern Notebook: Jupyter for integrative genomics Thorin Tabor (University of California, San Diego)
Add GeoNotebook: An extension to the Jupyter Notebook for exploratory geospatial analysis to your personal schedule
2:40pm GeoNotebook: An extension to the Jupyter Notebook for exploratory geospatial analysis Christopher Kotfila (Kitware)
Add JupyterHub: A roadmap of recent developments and future directions to your personal schedule
4:10pm JupyterHub: A roadmap of recent developments and future directions Min Ragan-Kelley (Simula Research Laboratory), Carol Willing (Cal Poly San Luis Obispo)
Add A billion stars in the Jupyter Notebook to your personal schedule
5:00pm A billion stars in the Jupyter Notebook Maarten Breddels (Maarten Breddels)
Sutton Center/Sutton South
Add Leveraging Jupyter to build an Excel-Python bridge to your personal schedule
11:05am Leveraging Jupyter to build an Excel-Python bridge Christine Doig (Anaconda ), Fabio Pliger (Anaconda)
Add Data science made easy in Jupyter notebooks using PixieDust and InsightFactory to your personal schedule
11:55am Data science made easy in Jupyter notebooks using PixieDust and InsightFactory David Taieb (IBM), Prithwish Chakraborty (IBM Watson Health), Faisal Farooq (IBM Watson Health)
Add Beautiful networks and network analytics made simpler with Jupyter to your personal schedule
1:50pm Beautiful networks and network analytics made simpler with Jupyter Daina Bouquin (Harvard-Smithsonian Center for Astrophysics), John D (CUNY Building Performance Lab)
Add Jupyter at Netflix to your personal schedule
2:40pm Jupyter at Netflix Kyle Kelley (Netflix)
Add Humans in the loop: Jupyter notebooks as a frontend for AI pipelines at scale to your personal schedule
4:10pm Humans in the loop: Jupyter notebooks as a frontend for AI pipelines at scale Paco Nathan (derwen.ai)
Add Data science at UC Berkeley: 2,000 undergraduates, 50 majors, no command line to your personal schedule
5:00pm Data science at UC Berkeley: 2,000 undergraduates, 50 majors, no command line Gunjan Baid (UC Berkeley), Vinitra Swamy (UC Berkeley)
Murray Hill
Add Writing (and publishing) a book written in Jupyter notebooks to your personal schedule
11:05am Writing (and publishing) a book written in Jupyter notebooks Andreas Mueller (Columbia University)
Add Music and Jupyter: A combo for creating collaborative narratives for teaching to your personal schedule
11:55am Music and Jupyter: A combo for creating collaborative narratives for teaching Carol Willing (Cal Poly San Luis Obispo)
Add Deep learning and Elastic GPUs using Jupyter to your personal schedule
1:50pm Deep learning and Elastic GPUs using Jupyter Tim Gasper (data.world), Subbu Rama (Bitfusion)
Add How Jupyter makes experimental and computational collaborations easy to your personal schedule
2:40pm How Jupyter makes experimental and computational collaborations easy Zach Sailer (University of Oregon)
Add Opinionated analysis development to your personal schedule
4:10pm Opinionated analysis development Hilary Parker (Stitch Fix)
Add Lessons learned from tens of thousands of Kaggle notebooks to your personal schedule
5:00pm Lessons learned from tens of thousands of Kaggle notebooks Megan Risdal (Kaggle), Wendy Chih-wen Kan (Kaggle)
Nassau
Add How JupyterHub tamed big science: Experiences deploying Jupyter at a supercomputing center to your personal schedule
11:05am How JupyterHub tamed big science: Experiences deploying Jupyter at a supercomputing center Shreyas Cholia (Lawrence Berkeley National Laboratory), Rollin Thomas (Lawrence Berkeley National Laboratory), Shane Canon (Lawrence Berkeley National Laboratory)
Add Building a notebook platform for 100,000 users to your personal schedule
11:55am Building a notebook platform for 100,000 users Scott Sanderson (Quantopian)
Add Managing a 1,000+ student JupyterHub without losing your sanity to your personal schedule
1:50pm Managing a 1,000+ student JupyterHub without losing your sanity Ryan Lovett (Department of Statistics, UC Berkeley), Yuvi Panda (Data Science Education Program (UC Berkeley))
Add From Beaker to BeakerX to your personal schedule
2:40pm From Beaker to BeakerX Matt Greenwood (Two Sigma Investments)
Add Enhancing data journalism with Jupyter to your personal schedule
4:10pm Enhancing data journalism with Jupyter Karlijn Willems (DataCamp)
Add Industry and open source: Working together to drive advancements in Jupyter for quants and data scientists to your personal schedule
5:00pm Industry and open source: Working together to drive advancements in Jupyter for quants and data scientists Srinivas Kumar Sunkara (Bloomberg LP), Cheryl Quah (Bloomberg LP)
Regent Parlor
Add Data science platforms: Your key to actionable analytics (sponsored by DataScience.com) to your personal schedule
11:05am Data science platforms: Your key to actionable analytics (sponsored by DataScience.com) William Merchan (DataScience.com)
Add Fueling open innovation in a data-centric world (sponsored by Anaconda Powered by Continuum Analytics) to your personal schedule
11:55am Fueling open innovation in a data-centric world (sponsored by Anaconda Powered by Continuum Analytics) Peter Wang (Anaconda)
Add Building interactive applications and dashboards in the Jupyter Notebook (sponsored by Bloomberg) to your personal schedule
1:50pm Building interactive applications and dashboards in the Jupyter Notebook (sponsored by Bloomberg) Romain Menegaux (Bloomberg LP), Chakri Cherukuri (Bloomberg LP)
Add Reproducible dashboards and other great things to do with Jupyter (sponsored by Domino Data Lab) to your personal schedule
2:40pm Reproducible dashboards and other great things to do with Jupyter (sponsored by Domino Data Lab) Mac Rogers (Domino Data Lab)
Add Mapping data in Jupyter notebooks with PixieDust (sponsored by IBM) to your personal schedule
4:10pm Mapping data in Jupyter notebooks with PixieDust (sponsored by IBM) RAJ SINGH (IBM Cloud Data Services)
Add Thursday opening welcome to your personal schedule
Grand Ballroom
8:50am Thursday opening welcome Andrew Odewahn (O'Reilly Media), Fernando Perez (UC Berkeley and Lawrence Berkeley National Laboratory)
Add Project Jupyter: From interactive Python to open science to your personal schedule
8:55am Project Jupyter: From interactive Python to open science Fernando Perez (UC Berkeley and Lawrence Berkeley National Laboratory)
Add Jupyter and Anaconda: Shaking up the enterprise (sponsored by Anaconda Powered by Continuum Analytics) to your personal schedule
9:15am Jupyter and Anaconda: Shaking up the enterprise (sponsored by Anaconda Powered by Continuum Analytics) Peter Wang (Anaconda)
Add How the Jupyter Notebook helped fast.ai teach deep learning to 50,000 students to your personal schedule
9:25am How the Jupyter Notebook helped fast.ai teach deep learning to 50,000 students Rachel Thomas (fast.ai)
Add Data science without borders to your personal schedule
9:45am Data science without borders Wes McKinney (Two Sigma Investments)
Add Labz 'N Da Wild 2.0: Teaching signal and data processing at scale using Jupyter notebooks in the cloud to your personal schedule
10:05am Labz 'N Da Wild 2.0: Teaching signal and data processing at scale using Jupyter notebooks in the cloud Demba Ba (Harvard University)
Add Closing remarks to your personal schedule
10:25am Closing remarks
8:00am Morning Coffee sponsored by Domino Data Lab | Room: Sponsor Pavilion (Grand Ballroom Foyer)
Add Speed Networking to your personal schedule
8:00am Speed Networking | Room: 3rd floor promenade
10:30am Break | Room: Sponsor Pavilion (Grand Ballroom Foyer)
3:20pm Afternoon Break sponsored by Two Sigma | Room: Sponsor Pavilion (Grand Ballroom Foyer)
Add Attendee Reception to your personal schedule
5:45pm Attendee Reception | Room: Sponsor Pavilion (Grand Ballroom Foyer)
8:30am this slot is just to eliminate grey space
Add Lunch (sponsored by Anaconda Powered by Continuum Analytics) and Thursday Industry Tables to your personal schedule
12:35pm Lunch (sponsored by Anaconda Powered by Continuum Analytics) and Thursday Industry Tables | Room: America's Hall 1
11:05am-11:45am (40m) Programmatic
Jupyter frontends: From the classic Jupyter Notebook to JupyterLab, nteract, and beyond
Kyle Kelley (Netflix), Brian Granger (Cal Poly San Luis Obispo)
Kyle Kelley and Brian Granger offer a broad look at Jupyter frontends, describing their common aspects and explaining how their differences help Jupyter reach a broader set of users. They also share ongoing challenges in building these frontends (real-time collaboration, security, rich output, different Markdown formats, etc.) as well as their ongoing work to address these questions.
11:55am-12:35pm (40m) Programmatic
JupyterLab: The next-generation Jupyter frontend
Brian Granger (Cal Poly San Luis Obispo), Chris Colbert (Project Jupyter), Ian Rose (UC Berkeley)
Brian Granger, Chris Colbert, and Ian Rose offer an overview of JupyterLab, which enables users to work with the core building blocks of the classic Jupyter Notebook in a more flexible and integrated manner.
1:50pm-2:30pm (40m) Reproducible research and open science
GenePattern Notebook: Jupyter for integrative genomics
Thorin Tabor (University of California, San Diego)
Thorin Tabor offers an overview of the GenePattern Notebook, which allows Jupyter to communicate with the open source GenePattern environment for integrative genomics analysis. It wraps hundreds of software tools for analyzing omics data types, as well as general machine learning methods, and makes them available through a user-friendly interface.
2:40pm-3:20pm (40m) Extensions and customization
GeoNotebook: An extension to the Jupyter Notebook for exploratory geospatial analysis
Christopher Kotfila (Kitware)
Chris Kotfila offers an overview of the GeoNotebook extension to the Jupyter Notebook, which provides interactive visualization and analysis of geospatial data. Unlike other geospatial extensions to the Jupyter Notebook, GeoNotebook includes a fully integrated tile server providing easy visualization of vector and raster data formats.
4:10pm-4:50pm (40m) Core architecture
JupyterHub: A roadmap of recent developments and future directions
Min Ragan-Kelley (Simula Research Laboratory), Carol Willing (Cal Poly San Luis Obispo)
JupyterHub is a multiuser server for Jupyter notebooks. Min Ragan-Kelley and Carol Willing discuss exciting recent additions and future plans for the project, including the ability to share notebooks with students and collaborators.
5:00pm-5:40pm (40m) Usage and application
A billion stars in the Jupyter Notebook
Maarten Breddels (Maarten Breddels)
Maarten Breddels offers an overview of vaex, a Python library that enables calculating statistics for a billion samples per second on a regular n-dimensional grid, and ipyvolume, a library that enables volume and glyph rendering in Jupyter notebooks. Together, these libraries allow the interactive visualization and exploration of large, high-dimensional datasets in the Jupyter Notebook.
11:05am-11:45am (40m) Usage and application
Leveraging Jupyter to build an Excel-Python bridge
Christine Doig (Anaconda ), Fabio Pliger (Anaconda)
Christine Doig and Fabio Pliger explain how they built a commercial product on top Jupyter to help Excel users access the capabilities of the rich data science Python ecosystem and share examples and use cases from a variety of industries that illustrate the collaborative workflow between analysts and data scientists that the application has enabled.
11:55am-12:35pm (40m) Development and community
Data science made easy in Jupyter notebooks using PixieDust and InsightFactory
David Taieb (IBM), Prithwish Chakraborty (IBM Watson Health), Faisal Farooq (IBM Watson Health)
David Taieb, Prithwish Chakraborty, and Faisal Farooq offer an overview of PixieDust, a new open source library that speeds data exploration with interactive autovisualizations that make creating charts easy and fun.
1:50pm-2:30pm (40m) Extensions and customization
Beautiful networks and network analytics made simpler with Jupyter
Daina Bouquin (Harvard-Smithsonian Center for Astrophysics), John D (CUNY Building Performance Lab)
Performing network analytics with NetworkX and Jupyter often results in difficult-to-examine hairballs rather than useful visualizations. Meanwhile, more flexible tools like SigmaJS have high learning curves for people new to JavaScript. Daina Bouquin and John DeBlase share a simple, flexible architecture that can help create beautiful JavaScript networks without ditching the Jupyter Notebook.
2:40pm-3:20pm (40m) Development and community
Jupyter at Netflix
Kyle Kelley (Netflix)
So, Netflix's data scientists and engineers. . .do they know things? Join Kyle Kelley to find out. Kyle explores how Netflix uses Jupyter and explains how you can learn from Netflix's experience to enable analysts at your organization.
4:10pm-4:50pm (40m) Usage and application
Humans in the loop: Jupyter notebooks as a frontend for AI pipelines at scale
Paco Nathan (derwen.ai)
Paco Nathan reviews use cases where Jupyter provides a frontend to AI as the means for keeping humans in the loop. This process enhances the feedback loop between people and machines, and the end result is that a smaller group of people can handle a wider range of responsibilities for building and maintaining a complex system of automation.
5:00pm-5:40pm (40m) Usage and application
Data science at UC Berkeley: 2,000 undergraduates, 50 majors, no command line
Gunjan Baid (UC Berkeley), Vinitra Swamy (UC Berkeley)
Engaging critically with data is now a required skill for students in all areas, but many traditional data science programs aren’t easily accessible to those without prior computing experience. Gunjan Baid and Vinitra Swamy explore UC Berkeley's Data Science program—2,000 students across 50 majors—explaining how its pedagogy was designed to make data science accessible to everyone.
11:05am-11:45am (40m) Extensions and customization
Writing (and publishing) a book written in Jupyter notebooks
Andreas Mueller (Columbia University)
The Jupyter Notebook can combine narrative, code, and graphics—the ideal combination for teaching anything programming related. That's why Andreas Müller chose to write his book, Introduction to Machine Learning with Python, in a Jupyter notebook. However, going from notebook to book was not easy. Andreas shares challenges and tricks for converting notebooks for print.
11:55am-12:35pm (40m) Documentation
Music and Jupyter: A combo for creating collaborative narratives for teaching
Carol Willing (Cal Poly San Luis Obispo)
Music engages and delights. Carol Willing explains how to explore and teach the basics of interactive computing and data science by combining music with Jupyter notebooks, using music21, a tool for computer-aided musicology, and Magenta, a TensorFlow project for making music with machine learning, to create collaborative narratives and publishing materials for teaching and learning.
1:50pm-2:30pm (40m) Kernels
Deep learning and Elastic GPUs using Jupyter
Tim Gasper (data.world), Subbu Rama (Bitfusion)
Combined with GPUs, Jupyter makes for fast development and fast execution, but it is not always easy to switch from a CPU execution context to GPUs and back. Tim Gasper and Subbu Rama share best practices for doing deep learning with Jupyter and explain how to work with CPUs and GPUs more easily by using Elastic GPUs and quick-switching between custom kernels.
2:40pm-3:20pm (40m) Reproducible research and open science
How Jupyter makes experimental and computational collaborations easy
Zach Sailer (University of Oregon)
Scientific research thrives on collaborations between computational and experimental groups, who work together to solve problems using their separate expertise. Zach Sailer highlights how tools like the Jupyter Notebook, JupyterHub, and ipywidgets can be used to make these collaborations smoother and more effective.
4:10pm-4:50pm (40m) Reproducible research and open science
Opinionated analysis development
Hilary Parker (Stitch Fix)
Traditionally, statistical training has focused on statistical methods and tests, without addressing the process of developing a technical artifact, such as a report. Hilary Parker argues that it's critical to teach students how to go about developing an analysis so they avoid common pitfalls and explains why we must adopt a blameless postmortem culture to address these pitfalls as they occur.
5:00pm-5:40pm (40m) Reproducible research and open science
Lessons learned from tens of thousands of Kaggle notebooks
Megan Risdal (Kaggle), Wendy Chih-wen Kan (Kaggle)
Kaggle Kernels, an in-browser code execution environment that includes a version of Jupyter Notebooks, has allowed Kaggle to flourish in new ways. Drawing on a diverse repository of user-created notebooks paired with competitions and public datasets, Megan Risdal and Wendy Chih-wen Kan explain how Kernels has impacted machine learning trends, collaborative data science, and learning.
11:05am-11:45am (40m) JupyterHub deployments
How JupyterHub tamed big science: Experiences deploying Jupyter at a supercomputing center
Shreyas Cholia (Lawrence Berkeley National Laboratory), Rollin Thomas (Lawrence Berkeley National Laboratory), Shane Canon (Lawrence Berkeley National Laboratory)
Shreyas Cholia, Rollin Thomas, and Shane Canon share their experience leveraging JupyterHub to enable notebook services for data-intensive supercomputing on the Cray XC40 Cori system at the National Energy Research Scientific Computing Center (NERSC).
11:55am-12:35pm (40m) JupyterHub deployments
Building a notebook platform for 100,000 users
Scott Sanderson (Quantopian)
Scott Sanderson describes the architecture of the Quantopian Research Platform, a Jupyter Notebook deployment serving a community of over 100,000 users, explaining how, using standard extension mechanisms, it provides robust storage and retrieval of hundreds of gigabytes of notebooks, integrates notebooks into an existing web application, and enables sharing notebooks between users.
1:50pm-2:30pm (40m) JupyterHub deployments
Managing a 1,000+ student JupyterHub without losing your sanity
Ryan Lovett (Department of Statistics, UC Berkeley), Yuvi Panda (Data Science Education Program (UC Berkeley))
The UC Berkeley Data Science Education program uses Jupyter notebooks on a JupyterHub. Ryan Lovett and Yuvi Panda outline the DevOps principles that keep the largest reported educational hub (with 1,000+ users) stable and performant while enabling all the features instructors and students require.
2:40pm-3:20pm (40m) Extensions and customization
From Beaker to BeakerX
Matt Greenwood (Two Sigma Investments)
Matt Greenwood introduces BeakerX, a set of Jupyter Notebook extensions that enable polyglot data science, time series plotting and processing, research publication, and integration with Apache Spark. Matt reviews the Jupyter extension architecture and how BeakerX plugs into it, covers the current set of BeakerX capabilities, and discusses the pivot from Beaker, a standalone notebook, to BeakerX.
4:10pm-4:50pm (40m) Usage and application
Enhancing data journalism with Jupyter
Karlijn Willems (DataCamp)
Drawing inspiration from narrative theory and design thinking, Karlijn Willems walks you through effectively using Jupyter notebooks to guide the data journalism workflow and tackle some of the challenges that data can pose to data journalism.
5:00pm-5:40pm (40m) Usage and application
Industry and open source: Working together to drive advancements in Jupyter for quants and data scientists
Srinivas Kumar Sunkara (Bloomberg LP), Cheryl Quah (Bloomberg LP)
Strong partnerships between the open source community and industry have driven many recent developments in Jupyter. Srinivas Sunkara and Cheryl Quah discuss the results of some of these collaborations, including JupyterLab, bqplot, and enhancements to ipywidgets that greatly enrich Jupyter as an environment for data science and quantitative financial research.
11:05am-11:45am (40m) Sponsored
Data science platforms: Your key to actionable analytics (sponsored by DataScience.com)
William Merchan (DataScience.com)
Ian Swanson explores the key components of a data science platform and explains how they are enabling organizations to realize the potential of their data science teams.
11:55am-12:35pm (40m) Sponsored
Fueling open innovation in a data-centric world (sponsored by Anaconda Powered by Continuum Analytics)
Peter Wang (Anaconda)
Peter Wang explores open source commercial companies, offering a firsthand account of the unique challenges of building a company that is fundamentally centered around sustainable open source innovation and sharing guidelines for how to carry volunteer-based open source values forward, intentionally and thoughtfully, in a data-centric world.
1:50pm-2:30pm (40m) Sponsored
Building interactive applications and dashboards in the Jupyter Notebook (sponsored by Bloomberg)
Romain Menegaux (Bloomberg LP), Chakri Cherukuri (Bloomberg LP)
Romain Menegaux and Chakri Cherukuri demonstrate how to develop advanced applications and dashboards using open source projects, illustrated with examples in machine learning, finance, and neuroscience.
2:40pm-3:20pm (40m) Sponsored
Reproducible dashboards and other great things to do with Jupyter (sponsored by Domino Data Lab)
Mac Rogers (Domino Data Lab)
Mac Rogers shares best practices for creating Jupyter dashboards and some lesser-known tricks for making Jupyter dashboards interactive and attractive.
4:10pm-4:50pm (40m) Sponsored
Mapping data in Jupyter notebooks with PixieDust (sponsored by IBM)
RAJ SINGH (IBM Cloud Data Services)
Raj Singh offers an overview of PixieDust, a Jupyter Notebook extension that provides an easy way to make interactive maps from DataFrames for visual exploratory data analysis. Raj explains how he built mapping into PixieDust, putting data from Apache Spark-based analytics on maps using Mapbox GL.
8:50am-8:55am (5m)
Thursday opening welcome
Andrew Odewahn (O'Reilly Media), Fernando Perez (UC Berkeley and Lawrence Berkeley National Laboratory)
Program chairs Andrew Odewahn and Fernando Pérez open the first day of keynotes.
8:55am-9:15am (20m)
Project Jupyter: From interactive Python to open science
Fernando Perez (UC Berkeley and Lawrence Berkeley National Laboratory)
Fernando Pérez opens JupyterCon with an overview of Project Jupyter, describing how it fits into a vision of collaborative, community-based open development of tools applicable to research, education, and industry.
9:15am-9:25am (10m) Sponsored Keynote
Jupyter and Anaconda: Shaking up the enterprise (sponsored by Anaconda Powered by Continuum Analytics)
Peter Wang (Anaconda)
In recent years, open source has emerged as a valuable player in the enterprise, and companies like Jupyter and Anaconda are leading the way. Peter Wang discusses the coevolution of these two major players in the new open data science ecosystem and shares next steps to a sustainable future.
9:25am-9:45am (20m)
How the Jupyter Notebook helped fast.ai teach deep learning to 50,000 students
Rachel Thomas (fast.ai)
Although some claim you must start with advanced math to use deep learning, the best way for any coder to get started is with code. Rachel Thomas explains how fast.ai's Practical Deep Learning for Coders course uses Jupyter notebooks to provide an environment that encourages students to learn deep learning through experimentation.
9:45am-10:05am (20m)
Data science without borders
Wes McKinney (Two Sigma Investments)
Wes McKinney makes the case for a shared infrastructure for data science, discusses the open source community's efforts on Apache Arrow, and offers a vision for seamless computation and data sharing across languages.
10:05am-10:25am (20m)
Labz 'N Da Wild 2.0: Teaching signal and data processing at scale using Jupyter notebooks in the cloud
Demba Ba (Harvard University)
Demba Ba discusses two new signal processing/statistical modeling courses he designed and implemented at Harvard, exploring his perspective as an educator and that of the students as well as the steps that led him to adopt the current cloudJHub architecture. Along the way, Demba outlines the potential of architectures such as cloudJHub to help to democratize data science education.
10:25am-10:30am (5m)
Closing remarks
Program chairs Fernando Pérez and Andrew Odewahn close the first day of keynotes.
8:00am-8:50am (50m)
Break: Morning Coffee sponsored by Domino Data Lab
8:00am-8:30am (30m)
Speed Networking
Gather before keynotes on Thursday and Friday morning for a speed networking event. Enjoy casual conversation while meeting new attendees.
10:30am-11:05am (35m)
Break
3:20pm-4:10pm (50m)
Break: Afternoon Break sponsored by Two Sigma
5:45pm-6:45pm (1h)
Attendee Reception
Come enjoy delicious snacks and beverages with fellow JupyterCon attendees, speakers, and sponsors.
8:30am-8:50am (20m)
Plenary: this slot is just to eliminate grey space
12:35pm-1:50pm (1h 15m)
Lunch (sponsored by Anaconda Powered by Continuum Analytics) and Thursday Industry Tables
Industry Table discussions are a great way to informally network with people in similar industries or interested in the same topics. Industry Table discussions will happen during lunch on Thursday, August 24, and Friday, August 25.