Announcing the Second Wave of ODSC East 2021 Speakers Announcing the Second Wave of ODSC East 2021 Speakers
ODSC East 2021 is a little more than a month away, and we know that the most exciting part of the... Announcing the Second Wave of ODSC East 2021 Speakers

ODSC East 2021 is a little more than a month away, and we know that the most exciting part of the event is learning from a diverse cohort of speakers, researchers, founders, and practitioners from under the AI and data science umbrella. This year, we’re expanding our reach into new focus areas as well, aiming to showcase the diverse potential of AI across industries. To help you get excited about the event ahead, here are a few recently added ODSC East 2021 speakers that we’re excited to learn from this March 30th to April 1st.

A New Measurements-Based Approach to Machine Learning: Dr. Gerald Friedland | CTO & Co-Founder / Adjunct Professor, Electrical Engineering and Computer Sciences | Brainome / University of California, Berkeley

In this talk, Gerald will discuss an entirely new and different approach to supervised machine learning – one that is rooted in measurements. We will explain how this new approach (which is actually as old as science itself) can be used to solve difficult machine learning problems, many of which have previously been out of reach.

An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?: Tamara Broderick, PhD | Associate Professor | MIT

This session proposes a method to assess the sensitivity of data analyses to the removal of a small fraction of the data set. Our metric, which we call the Approximate Maximum Influence Perturbation, approximately computes the fraction of observations with the greatest influence on a given result when dropped.

Data Mastering at Scale: Mike Stonebraker, PhD | A.M. Turing Award Laureate, Professor, Co-Founder | MIT CSAIL, Tamr

As enterprise data grows exponentially, decades of technologies have failed to address the challenge of large data volume and variety and unintended data silos. In this talk, Michael Stonebraker dives into why the data accessibility gap exists and the leading methods to solve this data problem.

The Clinician’s AI Partner: Augmenting Clinician Capabilities Across the Spectrum of Healthcare: Serena Yeung, PhD | Assistant Professor of Biomedical Data Science | Stanford University

In this talk, Serena will discuss the potential of AI to function as a partner to clinicians, and augment their capabilities across the spectrum of healthcare delivery. With an emphasis on AI capabilities for visual reasoning, she will present examples and ongoing work towards building clinician and AI partnerships in settings ranging from hospital treatment to remote care and beyond.

XAI – Explanation in AI: From Machine Learning to Knowledge Representation & Reasoning and Beyond: Freddy Lecue, PhD | Chief AI Scientist, Research Associate | Thales, Inria

The term XAI refers to a set of tools for explaining AI systems of any kind, beyond Machine Learning. Even though these tools aim at addressing explanations in the broader sense, they are not designed for all users, tasks, contexts and applications. This presentation will describe progress to date on XAI by reviewing its approaches, motivation, best practices, industrial applications, and limitations.

An Overview of Methods to Handle Missing Values: Julie Josse, PhD | Advanced Researcher | Inria

In this tutorial, we will review the main approaches and implementation (in R and python) to tackle the issue of missing data. We will start by the inferential framework, where the aim is to estimate at best the parameters and their variance in the presence of missing data.

Art of BERT: Unlock the Full Potential of BERT for Domain-Specific Tasks(TensorFlow): Thushan Ganegedara | Senior Data Scientist, AI&ML Instructor | QBE Insurance, DataCamp

In this workshop, you will go beyond just using BERT and explore techniques to suit it for the domain-specific task at hand. By the end of this workshop, you will understand the types of data used by the model(s), how to combine BERT with downstream models, and advanced techniques to get even more impressive results.

A/B Testing for Data Science Using Python: Mary C Boardman, PhD | Senior Data Scientist, Instructor | TI Health, University of South Florida

In this workshop, you will have familiarity with the types of questions A/B testing can answer, how to design your own experiment, and what to watch out for on a conceptual level. You will also learn how to randomize control and test groups in Python before you conduct the experiment and analyze/test the results after the experiment ends.

Register now for ODSC East 2021

These are only a few of the many ODSC East 2021 speakers who will present their expertise this March. There’s still time to register now for 30% off before ticket prices go up – don’t miss your chance to improve your data science game!



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.