Abstract:

Dr. Asim Ali, Executive Director of the Biggio Center and instructor in the Harbert College’s Department of Business Analytics & Information Systems, will discuss his modification to an essay assignment to use ChatGPT in his Intro to MIS class taught to 120 business students. He will share the benefits and challenges associated with implementing the redefined assignment. Dr. Ali will also share the Biggio Center’s strategy for building the campus community's capacity for A.I.’s impact in education. Topics will include a brief overview of Biggio Center’s Teaching with AI at AU Canvas course, an upcoming speaker series, and an open discussion about curricular enhancement opportunities.

A brief bio of the speaker is available online.

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Abstract:

They are man’s best friend and man’s best defense. They are smart, durable, adaptable, mobile, rapidly programmable, hypersensitive sensor platforms with ability to trace an odor to its source. With extreme odor sensitivity in the part per trillion range (e.g. 1 second/31,500 years) they can learn new targets, discriminate complex odor profiles in real-time, and search large areas and populations for targets in environmental extremes. They are DOGS!

Dogs have lived and worked with and for mankind for millennia. Our partners and companions since before the invention of agriculture, dogs have been by our sides through times of peace and war for centuries. This presentation will introduce and review aspects of the transdisciplinary, AU/AUCVM Canine Performance Sciences (CPS) Detection Canine Sciences, Innovation, Technology and Education (DCSITE) program with the objective of inviting discussion regarding opportunities for engagement with AI@AU.

A brief bio of the speaker is available online.

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Abstract:

The recent technological convergence of sensors, imaging, robotics, and artificial intelligence (AI) enables practical and novel applications for turbocharging scientific discoveries and production systems in agriculture and forestry. Such automation technologies not only reduce the dependency on a declining farm workforce, but also improve sustainability in multiple aspects via data-driven decision making. This seminar will present several such case studies in the field of phenomics and precision farming, covering high-throughput phenotyping of plant characteristics in field and controlled environments, AI-powered low-cost precision livestock monitoring, and robotic forest nursery inventory. Looking into the future, I envision several novel AI-based sensing and robotic systems to bridge the gap between high-end instrumentation systems for plant/animal research and accessible technologies for crop/livestock production. Interdisciplinary research between biologists, geneticists, engineers, and computer scientists is critical to the success of such endeavors to help feed and fuel a growing global population.

Bio:
Dr. Yin Bao is an Assistant Professor in the Department of Biosystems Engineering at Auburn University since 2019. He received his BS degree in Mechanical Engineering from China Agricultural University in 2012 with a focus on automotive electronics. Dr. Bao earned his PhD degree in Agricultural and Biosystems Engineering from Iowa State University in 2018 and he continued his postdoctoral research for another year at ISU before his current position. His research focuses on automation technology for facilitating scientific discoveries and advancing production systems in agriculture and forestry. Specifically, he leverages sensors, multimodal imaging, machine/deep learning-based predictive models, unmanned ground/aerial vehicles, and robotics to develop reliable, affordable, and efficient tools for rapid phenotyping and precision farming of crops and livestock.

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Abstract:

Digital technologies, artificial intelligence (AI), and decision support systems play a key role in agriculture as they support the implementation of site-specific crop management practices to increase productivity and protect the environment. Today, large volumes of data from crop, soils, and the environment are collected in near real-time from farm machinery, proximal and remote crop and soil sensors, as well as weather sensors. Various examples of how these data and AI are integrated to support farmers’ crop management decisions will be highlighted. Specific AI applications to row crops agriculture discussed will cover aspects of irrigation management, crop yield prediction, and crop maturity forecast.

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Bios:

Dr. Brenda Ortiz is a Full Professor in the Crop, Soil, and Environmental Sciences Department and she has a Research and extension appointment. Over the last 14 years, Dr. Brenda V. Ortiz has led several research and extension projects focused on precision agriculture with emphasis on irrigation and nutrient management and well as pest management. She has expertise in remote sensing, crop growth modeling, GIS, and precision agriculture data management. She had published 45 peer-review journal articles and she is a senior author on 30 of those. Besides her university faculty appointment, she serves now as the secretary of the International Society of Precision Agriculture.

Dr. Mailson Oliveira is a Postdoctoral Scientist in the Department of Crop Soil and Environmental Sciences at Auburn University since 2021. He received his BS degree in Agronomic Engineering from Federal Rural University of the Amazon in 2016 with a focus on weed science. Dr. Oliveira earned his PhD degree in Plant Production from Sao Paulo State University in 2021 before his current position. His research focuses on precision agriculture technology for advancing crop production systems. Specifically, he leverages machine learning predictive models, and remote sensing theory to develop new methods to solve real farming problems.

Abstract:

ChatGPT, Google Bard, and now Microsoft 365 Pilot—AI and machine learning tools are at the top of today’s news feeds. The rapid pace of development in this field and the almost-weekly appearance of powerful new AI tools have universities and other institutions scrambling to develop policies and applications for their use.

Academic libraries are no exception. Panels on AI/ML have started popping up at national library conferences to standing-room-only crowds. The Auburn University Libraries (AUL) have been experimenting with AI/ML since 2019, when librarians and library IT staff began using IBM Watson in an exploratory project with the Military REACH Program in the College of Human Sciences. Although that project has been eclipsed by internal developments at IBM and the advent of more-powerful and easier-to-use solutions, it introduced Auburn librarians to a pioneering suite of AI tools and provided them with hands-on experience in using AI for a real-world application. AUL Research Data Management Librarian Ali Krzton and AUL AD for Technology and Research Support Aaron Trehub will describe their experience of working with the IBM Watson team, their attempt to use Watson with the Military REACH library of research publications, and current discussions around the professional and ethical implications of AI and machine learning in the academic library world.

Bios:

Aaron Trehub joined the Auburn University Libraries in 2004. He has served the libraries in a number of capacities and is currently the Assistant Dean for Technology and Research Support. He is responsible for overseeing all aspects of library technology at Auburn, including the integrated library system, discovery tools, network administration, digital asset management, digital collections, and digital preservation. Aaron has served as a co-principal investigator or project director on two IMLS National Leadership Grants that led to the creation of a statewide digital repository (AlabamaMosaic: 2001-2004) and the LOCKSS-based Alabama Digital Preservation Network (ADPNet: 2006-2008). Aaron came to Auburn from the University of Illinois Library at Urbana-Champaign, where he held the rank of associate professor of library administration and managed two Web-based, revenue-generating reference services. Before becoming a librarian, Aaron was trained as a Russian specialist and worked as a research analyst at Radio Free Europe/Radio Liberty in Munich, Germany. Aaron has a B.A. from McGill University, an M.A. from the Johns Hopkins University School of Advanced International Studies (SAIS), and an MLS from the University of Illinois at Urbana-Champaign. He is especially interested in digital libraries, digital preservation, digital scholarship, and the application of new technologies (e.g. AI/ML) to library collections and operations. He is also interested in modern U.S. history and the effects of information technology on personal privacy and civil society.

Ali  Krzton  is the Research Data Management Librarian.  She came to Auburn University Libraries in 2017.  The most challenging aspect of her role is supporting data management planning, data workflows, and FAIR data sharing for research of all varieties taking place on campus, but that also gives her license to maintain a breadth of knowledge and keep learning.  Her recent scholarship has focused on AI and its implications for many aspects of information science.  Ali’s academic background is in ecology, evolution, and behavior.  Prior to becoming a librarian, she conducted field research on the social organization of the golden snub-nosed monkey in China, and she maintains a special interest in environmental science and conservation in her current role.  She received a B.A. in Evolutionary Biology from Dartmouth College, a M.A. in Anthropology from Texas A&M University, and a M.L.I.S. from Kent State University.

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Abstract:

Magnetic resonance imaging (MRI) is increasingly used in clinical settings to image the anatomy and physiological function of the cardiovascular system. While the image acquisition can be done in a reasonable amount of time, the analysis of these images to obtain quantitative measures of cardiac function is time consuming and requires considerable manual intervention. Consequently, only left ventricular volumes are measured at two points in the cardiac cycle, end-diastole and end-systole, to obtain stroke volume and ejection fraction. In this talk, techniques are presented to leverage the manual analysis that is typically done at two time points in the cardiac to compute parameters of cardiac function and shape throughout the cardiac cycle. Techniques for analyzing advanced cardiac MRI such as tagged MRI and phase contrast MRI are presented along with future directions including machine learning based techniques for automating the image analysis process for cardiac MRI.

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