Daniel Lowd, Department of Computer and Information Science

Daniel Lowd

lowd@uoregon.edu
Academic Areas:
Machine Learning, Artificial Intelligence

Daniel Lowd is an academic expert in machine learning and artificial intelligence. At the University of Oregon, he is an associate professor of computer and information science. His specific expertise includes adversarial machine learning. These are problems like spam and fraud detection, where it's difficult to build good models because people will always be working to trick them. Daniel researches a range of problems in this area, including understanding the vulnerabilities of popular machine learning methods, how an attacker could exploit them, and how we can build models that are more robust and reliable.

Contact: lowd@uoregon.edu | 541-346-4154

Website: http://ix.cs.uoregon.edu/~lowd/

Recent Media: 
Why AI is still terrible at spotting violence online (CNN, March 18, 2019)
Nonprofit OpenAI looks at the bill to craft a Holy Grail AGI, gulps, spawns commercial arm to bag investors' mega-bucks (The Register UK, March 13, 2019)
Can Facebook use AI to fight online abuse? (The Conversation, June 12, 2018)
The tiny changes that can cause AI to fail (BBC, April 11, 2017)