Conferences are an important part of any field’s development. They offer an opportunity to gather the community to learn, share, and collaborate. ODSC’s founding goal was to help grow the data science community and our hope is to continue with that mission.
At ODSC 2020, we are unveiling our first ever 4-day ODSC Global Virtual Conference, an online and on-demand version of ODSC. You’ll be able to follow specific tracks, choose from over 110 different training sessions and workshops, watch over 100 talks with insights from experts, gain access to 380+ hours of content, and virtually connect with over 5000 other ODSC attendees.
If you are working in a healthcare/biotech/life science/government or NGO to better understand the pandemic using data and data science, please refer to our Free Passes section under Registration. Free passes are also available to those made unemployed due to this crisis.
There’s a lot of content to choose from. Here are our picks for 20 talks that show how diverse and thorough the ODSC Global Virtual Conference will be this April 14-17.
1. Keynote: Michael Kearns, PhD, Professor, National Center Chair, Founding Director | Warren Center for Network and Data Sciences, UPenn
Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he holds the National Center Chair and has joint appointments in the Wharton School. He is founder of Penn’s Networked and Social Systems Engineering (NETS) program, and director of Penn’s Warren Center for Network and Data Sciences. Michael is the author of “The Ethical Algorithm: The Science of Socially Aware Algorithm Design,” which is gaining traction as a must-read for understanding the ethics behind AI and how we can better plan for the future.
2. Keynote: Margaret Mitchell, PhD, AI Researcher | Google Research and Machine Intelligence
Margaret Mitchell is a Senior Research Scientist at Google, where she focuses on research around vision-language and language generation. Her work is helping Google push the bounds of computer vision and NLP, even utilizing statistics and cognitive science insights. Before Google, she was a founding member of the famous Microsoft Research “Cognition” group, which is focused on developing AI at Microsoft. Margaret’s Ted Talk, “How We Can Build AI to Help Humans, Not Hurt Us,” has been viewed almost 1.2M times, making it a popular video choice for AI enthusiasts.
3. Keynote: Mike Stonebraker, PhD, A.M. Turing Award Laureate, Professor, Co-Founder | MIT CSAIL, Tamr
Dr. Stonebraker has been a pioneer of database research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. Much of his current research focuses on database technology, operating systems and the architecture of system software services.
4. Simplifying Data Science with Delta Lake and MLflow: Matei Zaharia, PhD Professor, Co-Founder & Chief Technologist | Stanford, Databricks
As the potential for data science and AI increases, it becomes difficult to streamline all machine learning processes and applications. Older methods have become automated or simplified, but the best companies want to keep pushing further. In this track keynote, Matei will discuss how you can use open-source tools like Delta Lake and MLflow to improve and simplify your data science process.
5. Smart Technologies in Enhancing Browsing Experiences: Zona Kostic, PhD, Research Fellow: Harvard University
Physical and virtual searching follow different rules, logic, and procedures, each with their own pros and setbacks. How do you develop applications that seek to intertwine two types of search, such as with augmented reality? In her talk at ODSC East, Zona will discuss some of the design methods that her teams have created for use in visualization systems for these novel information search processes.
6. The Art (and Importance) of Data Storytelling: Diedre Downing, Lead Data Storytelling Trainer | StoryIQ
Your data is useless unless it tells a story, and your story is useless if people can’t read or interpret it., which is why you need to be careful when developing data visualizations for the masses. At ODSC East, Diedre will discuss the importance of developing a story for your data and some of the initial ways to build a cohesive narrative.
7. Advances in Machine Learning: Finance Perspective: Gary Kazantsev, PhD, Head of Quant Technology Strategy | Bloomberg.
The finance industry is also a major implementer of machine learning. At ODSC East, Gary will discuss the latest developments in machine learning research viewed through the lens of the finance industry, such as interpretability, causality, nonstationarity, sample efficiency, etc.
8. Managing Data Projects Like a Software Engineer: Michael Jalkio, Data Engineer | Amazon
Black box concerns aside, data shouldn’t exist in a silo. Other teammates might join in, code may be reproduced, and in general, data and code should be easy to work with. At ODSC East, Michael will discuss how to write code that is reproducible and easy for other people to work with, including virtual environments, version control, and project structure.
9. Machine Learning and Artificial Intelligence in 2020: Recent Trends, Technologies, and Challenges: Sebastian Raschka, PhD, Professor, Researcher, Author of “Python Machine Learning” | University of Wisconsin-Madison
Advances in computing hardware, and especially the utilization of GPUs for training deep neural networks, make it feasible to develop predictive models that achieve human-level performance in various natural language processing and image recognition challenges. This talk will highlight the research and technology advances and trends of the last year(s), concerning GPU-accelerated machine learning and deep learning, and focusing on the most profound hardware and software paradigms that have enabled it.
10. Deep Learning (with TensorFlow 2): Dr. Jon Krohn, Chief Data Scientist, Author of Deep Learning Illustrated | Untapt
Tensorflow is often used for solving deep learning problems and for training and evaluating processes up to the model deployment, making it a popular choice for deep learning enthusiasts. Dr. Krohn will give a thorough primer on deep learning using TensorFlow, first discussing the foundations of deep learning and neural networks up to actually creating usable neural networks.
11. Improving Subseasonal Forecasting in the Western U.S. with Machine Learning: Lester Mackey, PhD, ML Researcher, Professor | Microsoft Research New England, Stanford University
Weather affects everyone daily, and traditional methods of forecasting aren’t always perfect. In an effort to better predict temperatures over a longer period of time, NOAA launched a year-long real-time forecasting challenge in which participants aimed to skillfully predict temperature and precipitation in the western U.S. In his talk at ODSC East, Lester will present and evaluate his machine learning approach to the competition and release the SubseasonalRodeo dataset, collected to train and evaluate said forecasting system.
12. A Data Science Playbook for Explainable AI – Navigating Predictive and Interpretable Models: Joshua Poduska, Chief Data Scientist | Domino Data Lab
Model ethics, interpretability, and trust will be seminal issues in data science in the coming decade. This technical talk discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry.
13. Accelerate ML Lifecycle with Kubernetes and Containerized Data Science Tools: Abhinav Joshi (Sr. Principal Marketing Manager) and Tushar Katarki (Sr. Principal Product Manager) | Red Hat
Data scientists desire a self-service, cloud-like experience to access ML modeling tools, data, & compute resources to rapidly build, scale, reproduce, & share ML modeling results with peers & software developers. The session at the ODSC Global Virtual Conference will provide an overview of containers and Kubernetes, and how these technologies can help solve the challenges faced by data scientists, ML engineers, and application developers.
14. The Hamiltonian Monte Carlo Revolution is Open Source: Probabilistic Programming with PyMC3: Austin Rochford, Chief Data Scientist | Monetate Labs
In the last ten years, there have been a number of advancements in the study of Hamiltonian Monte Carlo algorithms that have enabled effective Bayesian statistical computation for much more complicated models than were previously feasible. This talk will give an introduction to probabilistic programming with PyMC3.
15. The Software GPU: Making Inference Scale in the Real World: Nir Shavit, PhD, CEO, Professor | Neural Magic, MIT
It doesn’t matter how good your deep learning algorithms are if your hardware can’t handle it. Though, how great would it be if you could scale your deep learning without having to worry about your GPU? This talk will demonstrate how to make inference scale in the real world using only software on commodity CPUs.
16. Transfer Learning in NLP: Joan Xiao, PhD, Principal Data Scientist | Linc Global
Transfer learning enables leveraging knowledge acquired from related data to improve performance on a target task. In this session, you’ll learn the different types of transfer learning, the architecture of these pre-trained language models, and how different transfer learning techniques can be used to solve various NLP tasks.
17. Applying State-of-the-art Natural Language Processing for Personalized Healthcare: David Talby, PhD, CTO | Pacific AI
More than half of the clinically relevant data in oncology is only found in free-text pathology reports, radiology reports, sequencing reports, and progress notes, meaning any data scientist who wants to be involved in healthcare should be able to do NLP. This talk will discuss how to use SparkNLP in a healthcare setting from start to finish.
18. Gaining Machine Learning Observability: Josh Benamram (Co-founder) and Evgeny Shulman (CTO & Cofounder) | Databand.ai
This session is a hands-on workshop (with coding) to demonstrate how to gain observability (monitoring & alerting) for production machine learning pipelines. We will provide background on why observability is important to run successful MLOps, then walk through in detail how to set up a robust observability system.
19. Training and Operationalizing Interpretable Machine Learning Models: Francesca Lazzeri, PhD, Senior ML Scientist | Microsoft
Companies have to learn how to successfully build, train, test, and push hundreds of machine learning models in production, in ways that are robust, explainable, and repeatable. In her ODSC East talk, Francesca will introduce some common challenges of machine learning model deployment such as choosing the right tools, how to use autoML, and more.
20. Explainable AI for Training with Weakly Annotated Data: Evan Schwab, PhD, Research Scientist | Philips Research North America
Unlike natural images where local annotations of everyday objects can be more easily crowd-sourced, in the medical domain, acquiring reliably labeled data for large datasets is an expensive undertaking requiring detailed pixel-level annotations for a multitude of findings. This talk will address these shortcomings with an interpretable AI algorithm that can classify and localize critical findings in medical images without the need of expensive pixel-level annotations.
Ready to learn the latest in machine & deep learning, NLP, AI engineering, and more – but from the comfort of your home or office? Sign up now for the ODSC Global Virtual Conference this April 14-17. Register by March 27th for 20% off all ticket types.
You can also use this as a good time to try to convince your manager to allow you to livestream the ODSC Global Virtual Conference. Consider pitching the virtual conference to them so you and your entire team can learn about the latest in AI without ever leaving home.