Udo leads Applied Artificial Intelligence and Modeling Research and Development at SAS. With a 25-year track record of fostering technology innovation and excellence, Udo heads a team of expert developers and data scientists dedicated to pioneering cutting-edge software and leveraging advanced models to transform the way the world works. By imagining and building the next generation of AI-driven models and software solutions, Udo helps organizations harness the power of data and analytics to solve their toughest business challenges and outpace the world around them. Udo’s commitment to pushing the boundaries of what AI can achieve has earned him recognition as a global thought leader shaping the future of technology. In addition, Udo has written three publications and holds four patents in advanced analytics.
In the ever-expanding universe of AI, one concept has been steadily gaining ground: Human-out-of-the-loop AI. This term encapsulates a profound shift in how we interact with technology, prompting us to reconsider the boundaries between human and machine intelligence. But what does it entail? And what implications does it hold for
I've been fortunate to work on advanced analytics, machine learning and AI for more than 25 years. It’s more than a job. It’s my passion. So naturally, I was very interested in tuning into Microsoft AI CEO Mustafa Suleyman’s TED talk, What is an AI anyway? From my perspective, Mustafa
In 2023, businesses recognize the need to be resilient. With the global disruptions we’ve faced over the last few years, and continuing disruptions and instabilities, the need for organizational resilience has never been clearer. By building resiliency, organizations can adapt to changing circumstances, maintain stability and minimize damage from unexpected events.
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.
Learn how a computer vision application makes it safer, post-pandemic, for employees to return to factories.
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
