Fortune magazine recently published an article on The new thing on campus: Why universities are appointing their first chief AI officers, now known as CAIOs.  In describing the trend, the article quoted the CAIOs from George Mason University, UCLA, the University of Utah, and UMBC, which appointed Professor Anupam Joshi as its Chief AI Officer and Vice Provost in June 2025. 

Joshi, who joined UMBC as an Assistant Professor in 1998, started working on AI and neural networks as a graduate student at Purdue in the early 1990s. His research has continued to use a broad range of evolving AI concepts and systems to a variety of problems in different application domains, including cybersecurity, healthcare, social media, pervasive computing systems, high-performance computing, and multi-agent systems.  Here’s an excerpt from the Fortune article.

“Anupam Joshi had served as CAIO for less than a month at the University of Maryland, Baltimore County when he spoke to Fortune. He shares that the school’s leadership had realized that AI had been generating excitement beyond the typical areas of computer and information science. … As he settles into his new work, Joshi says he’s exploring research partnerships that can be extended with companies like Google and Microsoft, while also listening to how professors want to utilize and teach AI. Many college professors are already using AI in their work, at times to the consternation of the students they teach. Joshi says teachers also tend to be a vocal crowd that isn’t shy about sharing their scholarly opinions.

“I think whoever comes into these kinds of roles needs to start with the position of humility,” says Joshi. “Because it’s one thing to do your own research in AI, trying to push the state of the art, and it’s another thing to use that as a tool to transform enterprises.”

To all HPC users,

Based on guidance provided by the Shared Infrastructure Governance subcommittee on advanced GPU resources, the DoIT Research Computing Team has made some changes to the Slurm model that governs chip-gpu. In this new Slurm model, all GPU machines are available for general use so that resources never sit idle, but machines contributed by funds from specific research groups can opt for dedicated access to those machines through Slurm. Jobs running on contributed machines may be preempted if and when a user from a contributing research group opts for this dedicated access. For jobs run by all other users, these jobs may occasionally be preempted if the job is running on a contributed machine.

In this way, all users have access to all GPU machines, but users belonging to contributing research groups retain dedicated access to the contributed machines.

Users will notice that the chip-gpu Slurm cluster now shows three partitions. The default gpu partition encompasses all GPU machines, the gpu-contrib partition encompasses only machines where jobs may be preempted, and the gpu-general partition encompasses all machines where jobs will not be preempted due to dedicated contributor access. Note that the gpu-contrib partition is meant only to show users which nodes may see jobs preempted due to dedicated contributor access.

The three day preemption rule is unchanged across chip-gpu.

For more information about any of these hardware partitions and how preemption works across the chip HPC cluster, please see the wiki page: https://umbc.atlassian.net/wiki/spaces/faq/pages/1249509377/chip+Partitions+and+Usage

As always, let us know if you notice any issues or otherwise have questions or concerns by submitting a descriptive help request form found at the following link.

https://rtforms.umbc.edu/rt_authenticated/doit/DoIT-support.php?auto=Research%20Computing

Thanks for reading,

Roy Prouty

Assistant Director for Research Computing

(& Max!)