COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Calibration for probabilistic classification – Oscar Olmedo
COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR
Oscar Olmedo, PhD
CACI International, Inc.
Calibration for Probabilistic Classification
Monday, August 28, 4:30-5:45
Exploratory Hall, Room 3301
ABSTRACT: This talk will be a review of calibration methods for classifiers that make probabilistic predictions on a scale 0 to 1. It is known that certain classification methods, such as Naïve Bayes or Random Forest make biased predictions that to not match the true posterior probabilities. By calibrating the predictions made by classifiers the true probability of the predicted class can be determined. This type of calibration can be crucial for real-world decision making problems in medicine, business, marketing, and finance. In this talk I will focus on applications in marketing.
Part two: Marketing yourself for future careers outside of academia
It is known that the number of jobs in academia is not rising as fast as the number of PhD’s graduating. Currently a new career option is available to these PhDs, the Data Scientist. But how does one make the transition out of academia to this hot new field? I will discuss strategies for marketing yourself as well as tools necessary to be successful in your transition.
Dr. Oscar Olmedo is an alumnus of George Mason University who studied physics in undergrad (2004), Computational Sciences and Informatics Masters (2007), and Computational Sciences and Informatics PhD (2011) with a concentration in solar physics under Dr. Jie Zhang. After graduating in 2011, Dr. Olmedo went on to NRL as an NRC fellow for two years, and briefly worked at NASA Goddard for a few months in 2013 before moving to Syntasa, a startup focusing on ecommerce/marketing analytics. In 2015, he moved to CACI to work on cyber security research as a DARPA contractor.
A copy of Dr. Olmedo’s presentation is found here: OLMEDO_PRESENTATION_8.28.17