ODSC West 2018

Online Oct 3 1:00 am to Nov 3, 2018 3:00 am

Price: Free


We are very excited to our ODSC West next month! As we get closer to the conference, we want to invite you to participate in ODSC West's Online Warm-Up.

To access this webinar, please register using the link below:https://attendee.gotowebinar.com/register/2294337013425062657

We will features 4 speaker from our upcoming ODSC West conference in San Francisco each of which will present a 30 minute sessions including:

Marc Fridson - Balancing ML Accuracy, Interpretability and Costs When Building a Model

Sean Patrick Gorman, PhD & Steven Pousty - How to use Satellite Imagery to be a Machine Learning Mantis Shrimp

Michael Mahnoney, PhD - Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap4th speaker TDB

Full Agenda Detail

Session 1 - Balancing ML Accuracy, Interpretability and Costs When Building a Model (30 Minutes)

Speaker:Marc FridsonBio:Marc Fridson is the Principal Data Scientist of Cross Brand Digital @ Carnival Cruise Line, a Part-Time Lecturer for the Applied Analytics Program Masters Program @ Columbia University and the founder of tech start-up Instant Analytics. He holds a B.S. in Industrial and Systems Engineering from Rutgers University.

Abstract:This workshop will use real-world coding examples in Python to demonstrate how to be mindful of these constraints when developing your models.

Session 2 - How to use Satellite Imagery to be a Machine Learning Mantis Shrimp (30 Minutes)

Speaker:Sean Patrick Gorman, PhD & Steven PoustyBio:Sean is the Head of Technical Product Management at DigitalGlobe helping build GBDX and next generation machine learning tools for satellite imagery. Sean received his PhD from George Mason University as the Provost's High Potential Research Candidate, Fisher Prize winner and an INFORMS Dissertation Prize recipientSteve is the Developer Relations lead for DIgitalGlobe. He goes around and shows off all the great work the DigitalGlobe engineers do. 

Steve has a Ph.D. in Ecology from University of Connecticut. He likes building interesting applications and helping developers and data scientists do more with spatial data

Abstract:In this session we are going to start by showing you how satellite imagery actually allows you to “see” in more bands of color than the mantis (how about 26 bands) – each band is a massive amount of data about the earth. Then we will show you how you can work with this data in Jupyter notebooks to extract all sorts of information about the world. Finally, we will wrap up with how to make ML models using this data, extract features we care about, and then run it through a cloud-based processing model.

Session 3 - Matrix Algorithms at Scale: Randomization and using Alchemist to bridge the Spark-MPI gap (30 Minutes)Speaker:Michael MahoneyBio:Michael Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He works on algorithmic and statistical aspects of modern large-scale data analysis. He received him PhD from Yale University with a dissertation in computational statistical mechanics.

Abstract:In this session we will describe some of the underlying randomized linear algebra techniques. Finally, we'll describe Alchemist, a system for interfacing between Spark and existing MPI libraries that is designed to address this performance gap. The libraries can be called from a Spark application with little effort, and we illustrate how the resulting system leads to efficient and scalable performance on large datasets. We describe use cases from scientific data analysis that motivated the development of Alchemist and that benefit from this system. We'll also describe related work on communication-avoiding machine learning, optimization-based methods that can call these algorithms, and extending Alchemist to provide an python notebook <=> MPI interface.

ODSC West 2018

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