You are invited to attend an AI Roundtable event this Friday, November 1. This event is entitled “Ethical and effective use of AI in academic settings. How should we use AI effectively in our classrooms?”

We will have representatives from various departments across campus (Law/Academic Integrity/Library Sciences) and the panel will discuss the use of Gen AI in classrooms. You will have the opportunity to provide feedback to them as both faculty and students.

If you are interested in attending, please register here: https://forms.office.com/r/n4UznxruNg

Held online and in person at the AI Institute 
Room 513, 1112 Greene St. Columbia, SC 29208 (Science and Technology Building)
10 am ET to 12 PM ET (refreshments will be provided)

*Limited to 50 in-person participants

DISSERTATION DEFENSE

Department of Computer Science and Engineering

University of South Carolina

Author : Fengyao Yan

Advisor : Dr. Yan Tong, Dr. Jijun Tang

Date : Oct 24th, 2024

Time:  9:30 am

Place : Zoom Meeting

Link: https://us05web.zoom.us/j/82857019481?pwd=ueKsnxBVTLySbXb4yj4Z93pAzb7va…

Meeting ID: 828 5701 9481
Passcode: 747882

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Meeting ID: 828 5701 9481
Passcode: 747882

Find your local number: https://us05web.zoom.us/u/leCEnSLjPT

Abstract

      Genes can be treated as a graph that can be mapped. Tremendous information is coded in genes to ensure a complex functioning organism. Decoding this information is critical to understanding our biology and developing treatments for various diseases including cancer. Deep learning, a new branch of computer science, has gained traction over the past decade. It offers more insight into the data that is processed by the deep-learning models. Our study has shown that deep-learning models can be an effective tool in decoding genetic data such as gene tissue-deconvolution, gene graph mapping and genomic imputation. In tasks such as tissue deconvolution, our research has demonstrated the superior capability of deep learning-based approaches in capturing sample variations compared to traditional numerical analytical methods. While our approach requires large relevant datasets for effective deep learning training, this challenge can be addressed with increasing data availability. In gene graphing and mapping, our Graph Neural Network based approach consistently outperforms traditional regression techniques by a significant margin. The primary challenge here lies in the demand for substantial computing resources; however, the ongoing growth in average computing power and the enhanced accessibility of computational resources are expected to alleviate this constraint over time. Moreover, in the realm of generating and imputing missing biological data, cutting-edge generative AI models have proven to be invaluable. We are actively exploring the potential of generative AI to aid in imputing common missing biological data such as gene expression or methylation states. Overall, the evolution of advanced deep learning models has introduced fresh perspectives and possibilities to the field of biology and medicine, albeit accompanied by certain challenges. By addressing these challenges, deep learning models exhibit remarkable efficacy in resolving complex biomedical issues. In the foreseeable future, these advancements hold the promise of unveiling novel biological insights and facilitating the development of innovative treatments, thereby propelling biomedical research forward and ultimately benefiting humanity. 

Join Zoom Meeting

https://sc-edu.zoom.us/j/87569507267

 Meeting ID: 875 6950 7267

Abstract:

Artificial Intelligence (AI) is THE topic of the hour - praised to solve the most pressing problems of humankind while at the same time damned as the end of civilization as we know it. In manufacturing, every company today has been told by service providers, vendors, and the media that they need to invest in AI and other Smart Manufacturing technologies or face certain failure. At the same time, there is a lack of understanding of how Applied AI and other Smart Manufacturing technologies impact the business models, value creation, and sustainability in a manufacturing and digital supply network context. This seminar highlights recent promising efforts to apply AI to improve manufacturing. First, we will be looking at selected advanced, smart, and sustainable manufacturing related projects including i) a recently completed collaborative project on ‘Hybrid modeling for energy efficient CNC grinding’ funded by CESMII which achieved a 37% reduction in energy and 41% in processing time, ii) an ongoing effort funded by NSF on factory to factory communication, digital twins, and time-series analytics, iii) a new DoD funded project on time-series analytics in composites additive manufacturing, and iv) a NSF funded effort combining federated learning and blockchain technology in manufacturing networks. Concluding, we will be venturing out a bit by exploring more visionary ‘tomorrow’s’ opportunities of advanced and smart manufacturing technologies. The seminar content is based on projects funded by CESMII, NSF, DoD, NIST, and the EPA, and partially based on the presenter's book 'Digital Supply Networks' by McGraw-Hill that won the IISE Book of the Year 2021 award - www.digitalsupplynetwork.com

Short Bio:

Dr. Thorsten Wuest is a Full Professor of Mechanical Engineering in the Molinaroli College of Engineering and Computing at the University of South Carolina. His research focusses on Smart and Advanced Manufacturing, AI/ML incl., Hybrid Analytics and Federated Learning, Industry 4.0, Servitization and Product Service Systems, as well as closed-loop, item-level Product Lifecycle Management. Dr. Wuest's research is funded by a variety of federal agencies (incl. NSF, NIST, DoD, EPA, NIH, CESMII/DoE), international agencies (incl. Thomas Jefferson Fund, DFG, EC, BMBF, etc.), and industry. He is a globally recognized Smart Manufacturing thought leader and one of SME's 20 most influential professors in smart manufacturing. In addition to publishing his work in the premier academic outlets of his field, he was featured by Forbes, Futurism, IndustryWeek, the World Economic Forum, CBC Radio, and World Manufacturing Forum, etc. Dr. Wuest gave invited talks in more than 10 countries and published three award-winning books and over 170 peer-reviewed articles in international archival journals and conferences gathering over 11,000 citations to-date, and serves as a reviewer for many. He serves as Vice-Chair Americas for the IFIP WG 5.7, is an Associate Editor for the Robotics & Computer-Integrated Manufacturing (RCIM), ASTM Journal Smart and Sustainable Manufacturing Systems (SSMS), and the International Journal of Manufacturing Research (IJMR), and a member of the Editorial Board for the Journal of Manufacturing Systems (JMSY) and Production & Manufacturing Research (PMR) and several more. He serves on the Advisory Board for multiple companies and startups, including the Knudsen Institute, Maven Machines, Veepio, Sustainment, and SavePlanetEarth. Learn more at www.SmartMfg.info

Details at https://research.cec.sc.edu/aii/roundtable-discussion

AI-athon: Bring your data, research problem, and a potential Machine Learning approach (perhaps developed during AI-ification) for full implementation during a one-day hands-on workshop. The AII will provide space and expertise to guide you through installation, coding, and development of a Machine Learning engine. To remain effective, we aim to offer a low participant-to-instructor ratio, therefore, space is limited. At the end of this one-day workshop, you will walk away with a functional ML engine, the knowledge of how to improve the core engine, and have formed a collaborative research in AI/ML.
Registration form: https://forms.office.com/r/muzySJTtnY

AI-ification: Present your research that can benefit from modern AI approaches to a panel of friendly and knowledgeable AI practitioners during the first hour of this meeting. During the second hour of the meeting, the panel will brainstorm and recommend ways of integrating modern AI techniques into your existing research. Form new collaborations and partnerships during the brainstorming session, take the formed ideas to AI-athon, and embark on your path to Deep AI-ification.
Registration form: https://forms.office.com/r/n5dMWBFCXT

Details at https://research.cec.sc.edu/aii/ai-ification

Roundtable Discussion: Join us in a 2-hour meeting when an AI-related topic (suggested by the USC community) is presented by a panel of experts (during the first hour) and discussed by the broader community of participants and experts (during the second hour). The topics will be suggested by the participants and selected based on popularity. 
Registration form: https://forms.office.com/r/n4UznxruNg

More details at https://research.cec.sc.edu/aii/roundtable-discussion

Hope you have had a wonderful start of the semester. Women in Computing will be hosting its first meeting of the Fall semester 6 – 7:30pm, Thursday August 29, in Honors Residence Hall B110! Women in Computing is open to all majors and students interesting in topics of computing technology, and diversity/inclusion within the tech industry. Everyone – all genders and majors is welcome!