Data Science & AI
With 2.5 quintillion bytes of data being created EVERY DAY, the problem of the modern world isn’t how to get information, but how do you make sense of it, and then what do you do with it. Engineers at Speed School are creating novel ways to collect and interpret massive amounts of data to solve an array of complex problems like organ rejection, pavement deterioration, energy storage systems, and in one of the most exciting fields AI.
grant awarded to Speed School's Dr. Nihat Altiparmak by the National Science Foundation
Faculty doing research in this area
Dr. Olfa Nasraoui, Computer Science & EngineeringOur Faculty
“AI has been through many winters, and we happen to be in an awakening period right now. AI has to interact with humans at a level that is completely unprecedented. And I’m very interested in exploring these areas, which are at the intersection between human beings and technology.”
The Latest in Data Science
Recipient of Grand Challenger Award presented to Hermann Frieboes
Hunter West Named as Commencement Banner Bearer
Speed School Professor Honored at Presidential Excellence Awards
Facility Computational Intelligence Lab
Recent research into Computational Intelligence Tools for Medical Diagnostics was conducted at the Computational Intelligence Lab by Speed School students. This facility is just one of our state-of-the-art, on-campus learning spaces.
This paper reveals a trap for artificial general intelligence (AGI) theorists who use economists' standard method of discounting. This trap is implicitly and falsely assuming that a rational AGI would have time-consistent preferences.
A major driving force behind the increasing popularity of data science is the increasing need for data-driven analytics fuelled by massive amounts of complex data. Increasingly, parallel processing has become a cost-effective method for computationally large and data-intensive problems.
"Advances in high throughput methodologies, such as DNA/RNA sequencing and single cell sequencing have resulted in an explosion of the amount of biological data at a growth rate faster than Moore's Law."Eric Rouchka, Professor, Computer Science & Engineering