In the second of two Q&As with R&D colleagues, SAS' Udo Sglavo provides a window into how we approach drug-development challenges with machine learning.
A note from Udo Sglavo: At SAS, what we deliver to our customers is a product of creative minds thinking differently, challenging the norm, taking risks, and learning from trial and error (The greatest teacher, failure is). For the return of World Creativity & Innovation Week, we want to share
SAS' Udo Sglavo and Jan Chvosta discuss the power of a regression framework and choosing the correct regression model.
SAS' Udo Sglavo interviews colleague Jan Chvosta, director of Scientific Computing at SAS, on regression analysis and how it works.
In the second of two posts spotlighting SAS R&D innovators, SAS' Udo Sglavo interviews Chris Barefoot, Matthew Galati, Courtney Ambrozic and Davood Hajinezhad.
In the first of two posts spotlighting SAS R&D innovators, SAS' Udo Sglavo introduces you to developers Amy Shi, Maggie Du and Phil Helmkamp.
It's been a great ride Since 2014, the Operations Research Blog has covered a broad range of topics related to real applications of operations research. Paraphrasing Principal Product Manager for Optimization Ed Hughes' first post - this blog covered how OR methods could be applied to organizational and business planning
The first principle of analytics is about bringing the right analytics technology to the right place at the right time. Whether your data are on-premises, in the cloud, or at the edges of the network – analytics needs to be there with it. Being true to this principle means we
“Technology is an industry that eats its young, it is rare to come across providers that have been around for more than a human generation.” Tony Bear, Big on Data With more than 40 years in the market, SAS is one of the rare technology providers that has been around
Remember Subconscious Musings? It was the name of the blog Radhika Kulkarni (now retired Vice President of SAS R&D) started in 2012. She wrote about trends that drove innovation and challenges that expanded the boundaries of what we thought was possible. It eventually evolved into what we now know as
My esteemed colleague and recently-published author Jared Dean shared some thoughts on how ensemble models help make better predictions. For predictive modeling, Jared explains the value of two main forms of ensembles --bagging and boosting. It should not be surprising that the idea of combining predictions from more than one
We all have some sort of intuitive idea of what time series data is – it’s a bunch of measurements or observations that are marked by a time stamp – we know when the measurement was taken, as well as what was measured. This natural temporal ordering of the data
I recently gave a talk to a group of engineering students at Duke University, located just down the road from our headquarters in North Carolina. A couple of days later, one of the students sent me an email asking a very good question: which skills should I build up to