CSEE Distinguished Speaker Series

Open Issues in Open World Learning

Walter J. Scheirer
Dennis O. Doughty Collegiate Professor of Engineering
University of Notre Dame

11:30-12:30 ET, Monday, March 30, 2026, UMBC ITE 459 and online

Meaningful progress has been made in open world learning (OWL), enhancing the ability of agents to detect, characterize, and incrementally learn novelty in dynamic environments. However, novelty remains a persistent challenge for agents relying on state-of-the-art learning algorithms. This talk considers the current state of OWL, drawing on insights from a recent DARPA research program on this topic. I identify open issues that impede further advancements spanning theory, design, and evaluation. In particular, I emphasize the challenges posed by dynamic scenarios that are crucial to understand for ensuring the viability of agents designed for real-world environments. The talk provides suggestions for setting a new research agenda that effectively addresses these open issues.

Dr. Scheirer's research interests within computer science include artificial intelligence, computer vision, machine learning, and digital humanities. His research has helped establish the areas of open set recognition & open world learning in computer vision.  He serves on the IEEE CS Board of Governors, CTO of the Computer Vision Foundation, and Chair Emeritus of IEEE PAMI-TC. He received his Ph.D. from the University of Colorado and his M.S. and B.A. degrees from Lehigh University. Prof. Scheirer is also a recognized cultural critic and historian, commenting on the social context of emerging technologies, such as his most recent book: A History of Fake Things on the Internet.

UMBC CSEE professor Mehdi Kiani and his colleagues and students are developing personalized and precise treatments to improve patients' quality of life using AI, wearable tech, and other innovations. They recently teamed up with colleagues at New York Institute of Technology and Pennsylvania State University to develop a system that combines state-of-the-art, millimeter-sized medical implants, computational models, and machine learning to better monitor and treat stomach disorders. 

The NIH-funded "CyberGut" project will use physics-informed neural networks that combine data-driven learning with fundamental physical laws. "When enhanced with human-like learning strategies, such as self-learning and adaptive optimization, these networks can evolve into significantly more powerful analytical tools," Penn State collaborator Farnaz Tehranchi says.

Using AI to Launch and Scale Your Startup

12-1 pm March 4, 2026, University Center 302, UMBC

The Computing Research Association (CRA) hosted its 2025 CRA Summit on July 28-31, 2025. Over the course of the week, attendees from academia, industry, and government explored varied perspectives on how artificial intelligence (AI)  is reshaping computing research, undergraduate and graduate education, and workforce development.

The CRA has now released a report summarizes the key discussions and takeaways from the two core academic convenings of the 2025 CRA Summit: the 2025 CRA Summit on AI Undergraduate Education and CRA Summit on AI Graduate Education and Research. 

You can read a short summary of the report and get the full 78 page report here.

AI in Practice: Preparing Students for Al-Driven Research Careers

12-1pm EST Nov 12, 2025, online

Seminar Recording

In 2024, information systems Ph.D. student Ommo Clark penned an opinion piece for BusinessDay Nigeria exploring why many Nigerians diagnose and treat their medical conditions themselves, often turning to unreliable online information.

While the essay was inspired by firsthand experiences in her native country, the impulse to consult “Dr. Google” is a worrying global trend, Clark says, and one that has motivated her Ph.D. work. It’s unlikely that people will stop going online with health questions, so Clark is researching ways that AI could help patients, healthcare providers, public health officials, and content platforms better understand and evaluate the sea of medically related content on the Internet.

Ommo’s efforts were recently recognized when one of her research papers, co-authored with information systems professor Karuna Joshi, won the Best Student Paper Award at the IEEE International Conference on Digital Health 2025, held in July in Helsinki, Finland. The paper, titled “Real-Time Detection of Online Health Misinformation using an Integrated Knowledgegraph-LLM Approach,” describes the results of combining two types of AI approaches to tackle the problem of identifying online health misinformation.

Read more about their work in this UMBC News article.

UMBC CSEE professors Konstantinos Kalpakis and Dong Li received research funding from the University of Maryland, Baltimore’s Institute for Clinical and Translational Research to collaborate with UMB partners to develop innovative healthcare technology.

Professor Kalpakis and his colleagues are developing a new approach to help trauma physicians make better-informed treatment decisions quickly. The team is working on a machine-learning framework that can help ER doctors predict medical outcomes, such as which patients will experience severe complications from trauma and what their blood transfusion needs may be.

Professor Li's work will use AI technology to develop a reliable method to monitor blood pressure on a smartphone outfitted with ultrasound sensors to measure blood flow and using the built-in microphone to record heart sounds. The partnership with UMB will allow the team to conduct clinical trials, with both healthy individuals and those with hypertension, to rigorously evaluate the system’s accuracy and effectiveness.

Naghmeh Karimi, an associate professor in the Department of Computer Science and Electrical Engineering, was recently granted more than $300,000 in funding from the Semiconductor Research Corporation (SRC) to study the security of promising hardware components that speed up the computing process. 

SRC brings together technology companies, academics, and government agencies to tackle large scientific and technical challenges, and Karimi’s research will be funded by three leading technology companies: IBM-Research, AMD, and Siemens. 

Karimi and her team, including graduate students and a collaborator from Arizona State University, will study computer chips whose design and structure allows computing-in-memory (CiM), where data processing happens directly within the computer’s memory. CiM architectures are promising for speeding up the use of machine learning algorithms because they consume less energy.

Different types of CiM devices (such as RRAM, MRAM, and SRAM) each have their own strengths and weaknesses in terms of performance, power use, and size, and to get the best results, engineers need to combine different CiM devices into one system. Building these systems in 3D layers can further improve their efficiency and performance. However, the security of these 3D architectures has received little attention to date. 

Karimi’s team will study the security of 3D CiM technologies used in AI applications. In particular, the research will focus on evaluating the security vulnerabilities of the technologies and developing mitigation strategies. 

“I’m excited about this project because the topic is very timely,” says Karimi. “The support from three leading companies in the AI field shows the importance of the problem and the promise of the solutions we are working on.”

The team aims to enhance the security of CiM-based AI accelerators against physical attacks that adversaries might launch to leak sensitive data or induce malfunctions. The researchers will work closely with the funding companies over the next three years in this area.

This post was written by Catherine Meyers and originally published online here.