Professors Scott Acton and Mathews Jacob, esteemed faculty members from the University of Virginia’s Charles L. Brown Department of Electrical and Computer Engineering, have been selected to join the prestigious IEEE Signal Processing Society’s 2025 Class of Distinguished Lecturers. This elite group is comprised of only five appointees from around the world, underscoring the remarkable achievements of both Acton and Jacob in the fields of signal processing and machine learning. Their two-year terms in this role, designated for 2025 and 2026, signify a substantial recognition of their contributions to the engineering community and their ongoing commitment to education and research.
The Distinguished Lecturer Program of the IEEE Signal Processing Society plays a vital role in advancing the professional development of its members. It not only allows members access to world-renowned educators, but also allocates funds to support the chapters that host these lectures at their meetings. The program emphasizes the importance of sharing knowledge and fostering an environment conducive to learning and innovation within the field of signal processing. Acton and Jacob’s inclusion in this program will undoubtedly inspire future engineers and lay the groundwork for continued collaboration among academia, industry, and the engineering community at large.
Professor Acton serves as the Lawrence R. Quarles Professor and chair of his department, where he leads groundbreaking research at the Virginia Image and Video Analysis lab. His expertise lies chiefly in artificial intelligence applications for video analysis, a field that has garnered significant attention due to its potential to transform various industries, including healthcare, surveillance, and autonomous vehicle navigation. Recently, Acton spearheaded a project designed to develop an AI-driven system capable of characterizing human actions in video content with unparalleled clarity and accuracy. This advanced system could revolutionize how we interact with video technologies in high-stakes scenarios and everyday applications alike.
This ambitious research initiative aims to create AI solutions that can interpret human behavior in video data, thus providing unprecedented precision for security and safety applications. The implications extend beyond mere technological advancement; they involve potential transformative impacts on education as well. Acton’s work aligns with a National Science Foundation-supported program that focuses on harnessing artificial intelligence to enhance instructional effectiveness. The project, known as Artificial Intelligence for Advancing Instruction, seeks to automate video analysis in educational settings, ultimately giving teachers tools that could lead to improved outcomes in the classroom environment.
Professor Jacob, known for his innovation and expertise in developing machine learning algorithms tailored for medical imaging, is pursuing a mission to make advanced medical imaging techniques more accessible. He leads the Computational Biomedical Imaging Group and has recently secured a $3.9 million multi-institute grant aimed at identifying early signs of Alzheimer’s disease and dementia through advanced imaging technologies. Jacob’s work employs magnetic resonance spectroscopic imaging, a non-invasive method that tracks the brain’s metabolic changes, helping to advance the understanding of neuronal health and function in patients.
Continuing his dedication to fostering healthcare enhancement through technology, Jacob’s ongoing research also includes projects focused on ultrahigh resolution imaging of the brain using 7-Tesla MRI systems. These advanced imaging modalities allow scientists and healthcare professionals to gain insight into the brain’s architecture and the pathological changes associated with various neurodegenerative diseases. Additionally, Jacob is developing pioneering “free-breathing” cardiac MRI techniques that enable patients to undergo scanning without the strenuous requirement of holding their breath, thereby improving patient comfort and accessibility in clinical settings.
The overarching goal of both professors is to leverage their research in signal processing and imaging technologies for the betterment of society. As healthcare costs continue to rise dramatically, Jacob envisions that machine learning and advanced signal processing methods can significantly reduce the expenses associated with medical imaging, thereby enhancing accessibility. He hopes that the recognition granted through the Distinguished Lecturer position will facilitate his interaction with local IEEE Signal Processing Society chapters. Such engagement will provide him with a platform to inspire young engineers about the profound impacts of signal processing and machine learning in medical settings and beyond.
Both Acton and Jacob are harnessing their expertise to address critical challenges faced by society. Through their efforts, they seek not only to advance technological capabilities but also to inspire the next generation of engineers and researchers. Effective education and mentorship are paramount for nurturing talent and innovation in an ever-evolving field like engineering. By participating in the Distinguished Lecturer Program, they can share their insights and research outcomes with a broader audience, potentially sparking interest in burgeoning technologies and facilitating collaborations across various disciplines.
As the landscape of engineering continues to expand with technological advancements, the engagement of established professionals like Acton and Jacob is vital. Their commitment to education and research exemplifies the core values of the IEEE community, emphasizing a collaborative spirit aimed at overcoming both academic and societal challenges. As they embark on their journey with the Distinguished Lecturer Program, their message will resonate widely—encouraging the exploration of innovative technologies while showcasing the importance of interdisciplinary collaboration in addressing pressing global issues.
In summary, the achievements of Professors Scott Acton and Mathews Jacob stand as a testament to the university’s dedication to excellence in engineering education and research. By receiving this coveted honorary designation, they not only solidify their positions as leaders in their respective fields but also lay the groundwork for future advancements in signal processing, artificial intelligence, and medical imaging. Their involvement with the IEEE Signal Processing Society is set to inspire others while championing the transformative potential of engineering innovation in the modern world.
Professors Acton and Jacob’s work represents the convergence of technology and human impact, highlighting the essential role that engineers play in shaping our future. Their stories serve as a source of inspiration for aspiring engineers and researchers, showcasing the possibilities that lie at the intersection of theory and practical application. As they prepare to share their knowledge through lectures and workshops, the engineering community stands to benefit immensely from their insights, potentially leading to future breakthroughs that can further revolutionize our understanding of technology’s role in society.
The journey of discovery is ever-evolving, and as Acton and Jacob step into their roles as distinguished lecturers, the world of engineering moves closer to realizing the profound impacts of their research—an endeavor that promises to enhance lives and reshape industries across the globe.
Subject of Research: Advanced Signal Processing and AI Applications in Medical Imaging
Article Title: UVA Professors Selected as IEEE Distinguished Lecturers
News Publication Date: October 2023
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Image Credits: Tom Cogill/UVA School of Engineering and Applied Science
Keywords
Signal Processing, Image Analysis, Machine Learning, Healthcare, AI, Imaging Technology, Biomedical Engineering, Education
Tags: Academic ResearchAlzheimer’s disease researchArtificial Intelligencebiomedical engineeringEngineering EducationHealthcare InnovationIEEE Distinguished LecturersMachine learningMedical Imagingneurodegenerative diseasesSignal ProcessingVideo Analysis Technology