UMBC's Center for Social Science Scholarship will hold a workshop series this Fall on generative AI, LLMs, and computational social science methods. This series will begin with the basics of computing in R and how to use generative AI/LLMs in social science research workflows. Each session will focus on helping faculty and students become comfortable with deploying AI and LLM models in their research, while also gaining a deeper understanding of the ethical, equity, and environmental implications of these models.
This series is supported through the Elkins Professorship, features several speakers, and will continue into the spring 2026 semester. The four sessions this Fall are:
Register for the sessions here.
Cosponsored by DoIT, the Center for Scalable Data and Computational Science, and CGC-SCIPE.
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A primary goal is to uncover what prompts the mode switch. “How does it decide when to switch? And the hypothesis that we’re considering is that it’s based on some internal measure of uncertainty in the fish, meaning that if the fish isn’t sure if it’s inside the tube, it’s going to move so it can gather sensory information,” Hoffman says.
To test this, the team integrates several methods. At the University of Minnesota, engineers led by Andrew Lamperski will apply machine learning to map relationships between sensory inputs and behavioral outputs in the form of mathematical functions. Hoffman handles data analysis, starting with manual pattern-spotting before coding.
Read more about this research here.
UMBC’s Department of English has added a new course, ENGL 211 Writing with AI. The course is being taught for the first time this Fall by Dr. Tanya Olsen, a Senior Lecturer and the Director of the Department's Writing and Rhetoric Division. Here is the description from UMBC's course catalog.
What if AI could help you solve problems, generate ideas, or tackle challenges simply by asking the right questions? This course introduces prompt writing - the art of crafting clear, purposeful inputs to guide AI tools, which is rooted in rhetorical skills. Rather than replacing human thinking, AI can enhance the creativity and analysis you bring to any writing situation. Students will master a range of prompt writing techniques while critically engaging with AI to design prompts for personal, academic, and professional purposes.
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The first episode in the USM’s AI, Unscripted podcast series is now available. The collection of 30-minute sessions is intended to encourage and support faculty in using AI for teaching and learning. In this first episode, Ben Shaw, a teaching and learning librarian at the University of Maryland, College Park, talks about a module on AI and Information Literacy that instructors can add to their courses.
The 1-2 hour module was developed to help university students in any course and with any background learn how to use AI tools such as ChatGPT, Microsoft Copilot, and Adobe Firefly. You can explore the module on the UMD site. If you are interested in adding or adapting the module for your own course website, you can fill out a request form on this page.
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.”
UMBC CWIT alumna Wendy Bickersteth will give an online talk on Existing Risks of Generative AI and Surveying Public Perceptions on Friday, September 5, 2025, from 11am-12pm EDT. She is currently a Societal Computing PhD student at the Carnegie Mellon University CyLab Security and Privacy Institute.
Working with Dr. Lorrie Cranor, she conducts research on usable privacy and security, focusing on privacy labels and the use of AI. Read more about this work in this recent paper.
Join the online event via this WebEx link.
UMBC professor Houbing Song and PhD student Safayat Bin Hakim will give a free online webinar on Neuro-symbolic AI: The Third Wave of AI, at 8:00am EDT on Thursday, 26 September 2025. The webinar is part of the IEEE Industrial Electronics Society (IES) Young Professionals Society "Inspire the Next Generation with IEEE IES SYP Forum and Webinar Series".
There are three waves of Artificial Intelligence. The first Wave of AI is Crafted Knowledge, which includes rule-based AI systems. The second wave of AI is Statistical Learning, which includes machines becoming intelligent by using statistical methods. The third wave of AI is contextual adaptation. In the third wave, instead of learning from data, intelligent machines will understand and perceive the world on its own, and learn by understanding the world and reason with it. Neuro-symbolic AI, which combines neural networks with symbolic representations, has emerged as a promising solution of the third wave of AI. In this talk, first Houbing Herbert Song will share his journey from counter-unmanned aircraft systems to explainable AI to neuro-symbolic AI. Then he will present the past, present, and future of neuro-symbolic AI. Prof. Song will also share his perspective on the emerging area of neuro-symbolic AI.
Register for the free webinar here. A recording will be available online sometime after the presentation.