Trending Data Science Topics Coming to ODSC West 2020 Trending Data Science Topics Coming to ODSC West 2020
Even with the dynamic and unpredictable year that 2020 has been, data science is still growing, evolving, and adapting. At the... Trending Data Science Topics Coming to ODSC West 2020

Even with the dynamic and unpredictable year that 2020 has been, data science is still growing, evolving, and adapting. At the ODSC West 2020 Virtual Conference this October 27-30, we will host over 200 speakers who will present on the most cutting-edge topics in data science and artificial intelligence. Here are a few standout trending data science topics and presentations to keep an eye on:

[Related article: Applied AI October 2020: A Free One-Day Virtual Event]

1. Building Content Embedding with Self Supervised Learning: Sijun He & Kenny Leung | Twitter Cortex

Twitter is what’s happening in the world right now. In order to understand and organize content on the platform, they leverage a semantic text representation that is useful across a variety of tasks. In this talk, Sijun and Kenny will share their experience building and serving self-supervised content representations for heterogeneous content on Twitter. 

2. Data for Good: Ensuring the Responsible Use of Data to Benefit Society: Jeannette M. Wing, PhD, Avanessians Director of the Data Science Institute | Professor of Computer Science at Columbia University

The Data Science Institute at Columbia University promotes “Data for Good” as trending data science topics: using data to address societal challenges and bringing humanistic perspectives as—not after—new science and technology is invented. In this talk, Jeannette will present the mission of the Institute and highlights of their educational and research activities—all to ensure the responsible use of data to benefit society.

3. Advances and Frontiers in Auto AI & Machine Learning: Lisa Amini, PhD, Director | IBM Research

While current capabilities in Auto ML enable users to complete these steps in a few mouse clicks or lines of code, it still automates only a small portion of the data scientist and ML engineer’s workloads. In this talk, Lisa will focus on recent advances that will have a dramatic impact on driving automation across the entire AI/ML lifecycle, from data discovery and curation; to advanced model building with business and fairness constraints; to automation to monitor models in deployment, recognizing deficiencies and recommending corrections.

4. State-of-the-art natural language processing with Spark NLP: David Talby, PhD, CTO | Pacific AI, John Snow Labs

This is a hands-on tutorial on applying the latest advances in deep learning and transfer learning for common NLP tasks such as named entity recognition, document classification, spell checking, and sentiment analysis. Learn to build complete text analysis pipelines using the highly accurate, high performant, open-source Spark NLP library in Python.

5. Creating Equality and Inclusivity with Feature Engineering: Vida Williams, Advanced Analytics Solutions Lead | SingleStone

The more we recognize data as the undergirding of each and every company, one goal becomes clear. We must talk about demystifying Machine Learning lexicons, and methods. This talk will help business stakeholders understand the intimate way that deep analytics is redefining customer personas for the better and the worse.

6. Bayesian Workflow as Demonstrated with a Coronavirus Example: Andrew Gelman, PhD, Director of Applied Statistics Center | Columbia University

Andrew’s team recently fit a series of models to account for uncertainty and variation in coronavirus tests (see here). Andrew will talk about the background of this problem and analysis, and then expand into a general discussion of Bayesian workflow.

[Related trending data science topics article: The Bayesians are Coming! The Bayesians are Coming, to Time Series]

7. Continuous-time Deep Models for Forecasting Sparse Time Series: David Duvenaud, PhD Assistant Professor | University of Toronto

Much real-world data such as medical records, customer interactions, or financial transactions are recorded at irregular time intervals. However, most deep learning time series models, such as recurrent neural networks, require data to be recorded at regular intervals, such as hourly or daily. This talk will explain some recent advances in building deep stochastic differential equation models that specify continuous-time dynamics. 

8. GPU-accelerated Data Science with RAPIDS: John Zedlewski, Director of GPU-accelerated machine learning | NVIDIA

Traditionally, PyData frameworks were only executable on CPUs, making it difficult for users to take advantage of the increasingly-powerful GPUs that have already revolutionized deep learning and related fields. In this talk, we’ll introduce RAPIDS, an open-source framework that brings transparent GPU backends to popular Python APIs, such as those from Pandas, scikit-learn, and NetworkX.

9. Causal Inference in Data Science: Vinod Bakthavachalam, Senior Data Scientist | Coursera

The use of causal inference techniques can provide additional value from historical data as well to understand drivers of key metrics and other valuable insights. The session will be practical, focused on both theory and how to perform techniques in R. The end of the session will close with recent advances from combining machine learning with causal inference techniques to do things such as speed up AB testing.

10. A Human-Machine Collaboration Built on Trust and Accountability: Biplav Srivastava, PhD, Professor | AI Institute, University of South Carolina

As humans and AI technologies collaborate more closely than ever before in crucial areas of economy and everyday lives, there is a need to establish accountability for joint action taken based on trust, human values, engineering principles, and societal ethics. In this talk, we will discuss some of the opportunities and barriers to human-machine collaboration, and technological and policy advances to address them.

[Interested in the business side of data science and AI? Check out the Ai x business summit!]

See more trending data science topics at ODSC West by registering now!

Ready to learn more trending data science topics? Register for ODSC West 2020 now and gain access to more ODSC West 2020 topics, 200 speakers, 300+ hours of content, and more across four days of events that you won’t get anywhere else.



ODSC gathers the attendees, presenters, and companies that are shaping the present and future of data science and AI. ODSC hosts one of the largest gatherings of professional data scientists with major conferences in USA, Europe, and Asia.