UMBC librarians will interview Dr. Ray Pun on all things AI! Do you have any burning questions about ChatGPT and other LLMs? What does the current landscape in libraries and higher education look like with the presence of AI? What about artificial intelligence and its impact on the environment? 

Raymond (Ray) Pun is the academic and research librarian at the Alder Graduate School of Education, a teacher residency program in California, where he supports library services by engaging with residents and teacher educators. With over 17 years of experience in the library field, Ray has previously worked at Stanford University, Fresno State, New York University Shanghai, and The New York Public Library in various roles. Ray holds a Doctorate in Education from Fresno State, a Master of Library Science from the City University of New York - Queens College, a Master of Arts in East Asian Studies, and a Bachelor of Arts in History from St. John's University. Pun has previously served as President of the Asian Pacific American Librarians Association (APALA) and the Chinese American Librarians Association (CALA), and on the American Library Association (ALA) Council as a Councilor At-Large. Pun is currently a candidate for the American Library Association President 2025-2026.

The EDUCAUSE AI Landscape report summarizes the higher education community's current sentiments and experiences related to AI in higher education. It is based on a survey was distributed from November 27 to December 8, 2023, and focuses on the impacts AI has had on higher education since the mainstreaming of generative AI tools.

The report's sections cover key findings, strategic planning and readiness, strategic leaders and partners, policies and procedures, workforce impact, the future of AI in higher education, and recommendations and resources.

UMBC Ph.D. student Farshad Safavi presented his dissertation proposal on recognizing emotions for better human-robot interaction on February 9, 2024.

One of the primary challenges in Human-Robot Interaction (HRI) is enabling robots to effectively understand and respond to human emotions. Humans express emotions through verbal and non-verbal cues, while robots typically rely on pre-programmed algorithms and physical gestures. Our research aims to develop HRI that bridges this gap by leveraging multimodal emotion detection. Emotions play a crucial role in human communication and decision-making, significantly influencing human-robot interactions. We aim for robots to understand and respond to human emotions by integrating neurophysiological and behavioral channels. Initially, we examine unimodal facial expression recognition using Convolutional Neural Networks (CNN) and Vision Transformers (ViT). Next, we enhance the model with a Mixture of Transformers (MiT). Using this enhanced model, we have developed a human-robot interaction perception system. Subsequently, we investigate multimodal emotion recognition in conveying emotions in Human-Robot Interaction (HRI). While unimodal techniques have been used to recognize emotions from various sources, research indicates that emotion recognition is inherently multimodal. Fusion representations provide a more comprehensive view of the emotional state, thereby enhancing emotion recognition accuracy. Therefore, exploring the role of multimodal fusion through computational models and neurophysiological experiments is essential. Our framework uses machine learning and deep learning to interpret complex physiological and facial expression data, enabling nuanced human-robot interactions. We focus on the offline fusion of multimodal methods, combining brain and behavior models, and exploring real-time fusion solutions. These human-robot interactions, based on emotions, will be validated through neurophysiological experiments, aiming for seamless and intuitive interactions based on a thorough understanding of human emotions.

Committee: Drs. Ramana Kumar Vinjamuri (Chair/Advisor), Tulay Adali, Nilanjan Banerjee, Justin Brooks, Scott Kerick

UMBC Professor Cynthia Matuszek is the PI on a new award from the National Science Foundation to support the 2024 joint Principal Investigator Meeting for NSF's two research programs on robotics and related areas, National Robotics Initiative (NRI) and Foundational Research in Robotics (FRR). Professor Jie Yin from North Carolina State University is the grant Co-Principal investigator. Dr. Matuszek will chair the meeting, which will take place in downtown Baltimore, Maryland, on 28-30 April 2024. 

The meeting will serve as a conference bringing together a community of robotics researchers whose work is specifically relevant to the NRI and FRR programs. The agenda includes talks from selected projects, poster sessions, workshops, panel discussions, multiple keynote talks, a diversity-focused event, and Federal agency program updates from leaders in the research community. This meeting will also support the participation of a cohort of potential future proposers to the FRR program. 

The annual PI Meeting for the NSF FRR and NRI research communities enables investigators to meet and share their research and best practices, discuss new research opportunities, explore new ideas and partnerships, and interact with Federal agency representatives, industry, and other stakeholders interested in NSF robotics research.

1-2 pm Wednesday, March 6, online 

The three-day workshop, Toward an Ethical Framework for Artificial Intelligence in Biomedical and Behavioral Research: Transparency for Data and Model Reuse, explored and assessed the landscape of ethical AI in the biomedical research context by gathering expert input on opportunities and challenges concerning the ethical use and reuse of data and foundation models in the AI development cycle.

Specific objectives of the interactive workshop included 

• Developing transparency guidelines for NIH awardees using, developing, or contributing to AI;
• Identifying tools and capability gaps for ethical and transparent AI; 
• Predicting trends and future states.

The UMBC Faculty Development Center will hold a one hour online session on Encouraging Academic Integrity in the Era of AI vis WebEx from 12-1 pm on February 14, 2024.

While fostering and maintaining academic integrity has always been an important goal of instruction at UMBC, advances in "the other AI" -- artificial intelligence -- has complicated this effort. Students are not always aware of what constitutes plagiarism where tools such as ChatGPT are concerned. Meanwhile, faculty often feel pressure to ensure that students meet course learning objectives and also help them learn how to use AI tools ethically and responsibly. The fact that effective tools to detect students' usage of AI in completing assignments do not currently exist further complicates the academic integrity landscape. 

In this session we will revisit best practices in course, syllabus, and assignment design in light of the opportunities and temptations that generative AI tools present. Faculty will learn how to make their policies around AI use clear to students and will gain strategies for dissuading students from inappropriate use of these tools.

The goal of Computer Vision, as coined by Marr, is to develop algorithms to answer "What are", "Where at", "When from" visual appearance. The speaker, among others, recognizes the importance of studying underlying entities and relations beyond visual appearance, following an Active Perception paradigm. This talk will present the speaker's efforts over the last decade, ranging from 1) reasoning beyond appearance for vision and language tasks (VQA, captioning, T2I, etc.), and addressing their evaluation misalignment, through 2) reasoning about implicit properties, to 3) their roles in a Robotic visual concept learning framework. The talk will also feature the Active Perception Group (APG)'s projects addressing emerging challenges of the nation in automated mobility and intelligent transportation domains, at the ASU School of Computing and Augmented Intelligence (SCAI).

Yezhou (YZ) Yang is an Associate Professor and a Fulton Entrepreneurial Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University. He founded and directs the ASU Active Perception Group, and currently serves as the topic lead (situation awareness) at the Institute of Automated Mobility, Arizona Commerce Authority. He is also a thrust lead (AVAI) at Advanced Communications Technologies (ACT, a Science and Technology Center under the New Economy Initiative, Arizona). His work includes exploring visual primitives and representation learning in visual (and language) understanding, grounding them by natural language and high-level reasoning over the primitives for intelligent systems, secure/robust AI, and V&L model evaluation alignment. Yang is a recipient of the Qualcomm Innovation Fellowship 2011, the NSF CAREER award 2018, and the Amazon AWS Machine Learning Research Award 2019. He received his Ph.D. from the University of Maryland at College Park, and B.E. from Zhejiang University, China. He is a co- founder of ARGOS Vision Inc, an ASU spin-off company.

The Advances in Perception, Prediction, and Reasoning (PPR) talks are organized and hosted by UMBC Professor Tejas Gokhale.

UMBC Training Centers established a new Center for Applied AI as a dedicated hub for advancing skills and fostering innovation in the ever-changing field of Artificial Intelligence. It will offer multiple learning opportunities through courses, webinars, and micro-sessions from a team of experienced instructors with extensive industry knowledge. 

How to apply: Fill out this Google form.