Nathalie Singh-Corcoran is the director of Communication Across the Curriculum in the Eberly College.  In addition to her administrative role, she teaches classes in  Undergraduate Writing and Professional & Technical Writing. Dr. Singh-Corcoran has held several leadership positions including the President of the International Writing Centers Association and WVU Faculty Senate Chair. Her research interests include writing pedagogy, writing program administration, and Writing Across & Within the Disciplines.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, despite remarkable advances in diagnostics, imaging, and therapeutics. The rapid expansion of multimodal cardiovascular data—including electrocardiograms (ECGs), cardiac imaging, electronic health records, wearable sensors, and emerging molecular data—has created unprecedented opportunities for artificial intelligence (AI) to transform cardiovascular care. This presentation will explore the evolving landscape of AI in cardiovascular health, highlighting recent advances in AI-assisted cardiac imaging, ECG analysis, multimodal data integration, and clinical decision support. Emphasizing the concept of augmented intelligence, the talk will discuss how AI can complement clinical expertise by integrating diverse data streams to enable more precise, efficient, and equitable cardiovascular care. Drawing on our research in multimodal cardiovascular imaging and AI-enabled clinical decision support, we will present emerging approaches for advancing precision diagnostics while addressing key challenges in robustness, interpretability, fairness, and real-world clinical implementation. The presentation will conclude with a vision for the next generation of trustworthy cardiovascular AI, where interdisciplinary collaboration among clinicians, engineers, and data scientists will accelerate the translation of AI innovations into scalable, human-centered solutions that improve patient outcomes and advance precision cardiovascular medicine.

Additive Manufacturing (AM) has emerged as a transformative technology, redefining design freedom and enabling groundbreaking advancements across diverse fields, from bioprinting tissues and organs to fabricating complex aerospace components and functional materials. Among AM technologies, metal additive manufacturing presents unique challenges due to high- temperature processing, stringent performance requirements, and the need for precise control of material properties, particularly in aerospace, biomedical, and high-performance engineering applications. These challenges have driven extensive research to improve process reliability, material performance, and scalability. This talk presents an overview of research conducted at the Manufacturing and Additive Manufacturing Innovation Center (MAGIC) at Arizona State University, highlighting key research thrusts in advancing materials and processes for metal additive manufacturing. Particular emphasis is placed on the development of high-conductivity functional materials, including Cu–CNT composites, and the optimization of laser powder bed fusion processes to achieve superior microstructural and functional characteristics. The lecture also explores emerging directions in multi-material additive manufacturing, artificial intelligence-assisted process optimization, biomimetic and adaptive structures, and multi-scale hierarchical architectures. The manufacturing challenges associated with these advanced systems are critically examined, and strategies for process modification and innovation are discussed. The presentation concludes with an outlook on future research directions that will enable the next generation of high-performance, intelligent, and multifunctional additively manufactured materials and structures.

Leila Ladani is a professor of mechanical and aerospace engineering at Arizona State University and the Graduate dean’s Fellow. She is the founding director of the Manufacturing Innovation Center and director of National Science Foundation National Research Traineeship Program at ASU. She received her PhD in Mechanical Engineering from the University of Maryland. Her research focuses on design and manufacturing, including micro/nano and additive manufacturing and computational physics-based modeling of the manufacturing processes. Furthermore, she has been active in the development of advanced adaptive and biomimetic materials for different applications including bio-medical and aerospace. She has been the keynote and invited speaker in over 40 talks. A few of Ladani’s numerous accolades, include, invited attendee of National Academy of Engineering Frontier of engineering conference, ASME EPPD Women Engineer, and Connecticut Women of Innovation Finalist in the category of Academic Innovation and Leadership. She is the editor of Journal of Materials Science and Engineering A and has published more than 200 refereed manuscripts and patents and a textbook in additive manufacturing designed for engineers and scientists. She is an ASME Fellow.

The human brain contains a complex network of white matter neural pathways that enable communication between distant brain regions and support cognition, memory, and behavior. Diffusion magnetic resonance imaging (dMRI) is a noninvasive imaging technique capable of mapping these neural pathways in vivo by measuring the diffusion of water molecules within brain tissue. Using tractography, these diffusion measurements can be reconstructed into three-dimensional models of white matter fiber pathways, enabling visualization and quantitative analysis of the brain's structural connectivity. Combined with machine learning and advanced image analysis, diffusion MRI provides powerful tools for detecting subtle structural changes associated with neurological disease. This talk will provide an overview of diffusion MRI, tractography, and tractometry for characterizing the brain's structural connectome. It will also cover recent advances in machine learning for whole brain connectome reconstruction, white matter bundle segmentation, nonlinear bundle registration, tract-specific statistical analysis, and multi-site data harmonization, enabling robust and anatomically precise analyses of large neuroimaging datasets acquired across diverse imaging protocols. Using data from large, multisite Alzheimer's disease cohorts, this talk will highlight how tractometry and multimodal analyses of diffusion MRI reveal early white matter alterations associated with mild cognitive impairment (MCI), Alzheimer's disease, amyloid and tau pathology, and APOE genotype. The presentation will conclude by discussing how the integration of diffusion MRI, multimodal neuroimaging, machine learning, and large-scale datasets is accelerating biomarker discovery and advancing precision medicine for neurodegenerative diseases.

Dr. Bramsh Qamar Chandio is an Assistant Professor in the Department of Chemical and Biomedical Engineering at West Virginia University. Dr. Chandio’s research focuses on computational neuroimaging, diffusion MRI, white matter tractography and tractometry, machine learning, and statistical methods for studying brain disorders. She develops computational methods for large-scale analysis of white matter neural pathways to improve the detection and understanding of neurodegenerative and psychiatric diseases. Prior to joining West Virginia University, Dr. Chandio was a Postdoctoral Scholar at the Imaging Genetics Center, Keck School of Medicine of the University of Southern California, where she worked with Prof. Paul M. Thompson. She received her Ph.D. in Intelligent Systems Engineering from Indiana University Bloomington. Dr. Chandio is the developer of several widely used open- source neuroimaging tools, including the BUAN tractometry framework, BundleWarp for nonlinear white matter bundle registration, and tract-specific harmonization methods implemented in the DIPY ecosystem. Her research aims to advance precision brain mapping through diffusion MRI, machine learning, and large-scale neuroimaging analysis.

Dr. Cerasela Zoica Dinu, Associate Dean for Student, Faculty and Staff Engagement and Professor of Chemical and Biomedical Engineering at West Virginia University's Benjamin M. Statler College of Engineering and Mineral Resources, will discuss how artificial intelligence (AI) is transforming career readiness and professional development. Drawing on her leadership of the College's Career and Professional Development Center, she will explore the growing gap between employer expectations for AI skills and students' usage of AI, while sharing practical strategies for helping students leverage AI ethically, effectively, and confidently as they prepare for their careers.

Cerasela Zoica Dinu is the Associate Dean for Student, Faculty and Staff Engagement and a Professor of Chemical and Biomedical Engineering in the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University. As Associate Dean for Engagement, Dr. Dinu provides strategic leadership for initiatives that foster engagement, belonging, leadership development, and professional growth by creating programs and partnerships that strengthen connections among students, faculty, staff, and alumni. In addition to her engagement portfolio, Dr. Dinu oversees the Statler College Career and Professional Development Center and its staff. She works with the Center to strengthen employer partnerships, expand experiential learning opportunities, enhance career readiness programming, and prepare students for successful careers through internships, co-ops, networking, and professional development. As a faculty member, Dr. Dinu's research focuses on biomaterials, nanotechnology, enzyme engineering, biosensors, and sustainable materials for healthcare and environmental applications. She is recognized for her commitment to STEM education, student mentorship, innovative teaching, and advancing engineering education through interdisciplinary collaboration and community engagement.

Dr. Goldsmith will begin by discussing the importance of teaching computer scientists and practitioners about ethics, and mention several approaches for doing so.  In particular, she will talk about how she uses science fiction in her ethics class.  After a short break, she will do a lightening overview of five ethical frameworks, and why having multiple frameworks is important.  This will conclude the lecture-style presentation.  Next, there will be a group discussion of some current ethical challenges, followed by small-group discussions and reportbacks.  Before the concluding segment, there will be a long enough break for most people to read Dominica Phetteplace's 2023 story, Confession #443 (Comments Open), followed by a discussion of the story and the ethical challenges it raises.

Judy Goldsmith is Professor and Associate Chair of Computer Science at the University of Kentucky.  She has done research primarily in theoretical computer science (computational complexity) and AI (computational social choice), and is currently also interested in the pedagogy of computer and AI ethics.  She is coauthor of the textbook Computer and Technology Ethics:  Engaging through science fiction (Burton et al., MIT Press, 2023), as well as ~200 technical papers.  She has been recognized with multiple teaching and mentoring awards.

This section will cover recent court decisions regarding copyright claims made against technology companies that develop generative AI, as well as recent government actions against Anthropic in connection with its models. In addition, it will provide an overview of emerging research on GenAI uplift for malicious cyberattacks, and discuss some legal responses to that.