ECE Seminar - AI for Social Good in the Era of Large Language Models

10:00am - 11:00am
Room 2516 (lift 25/26), Academic Building

Abstract: In the era of large language models (LLMs), the landscape of artificial intelligence (AI) has transformed dramatically, offering unprecedented opportunities for social impact. This talk will explore the potential of LLMs to drive significant advancements in areas critical for social good, including mitigating online abuse and making games safer for kids. This talk will also present the potential applications of LLMs to both network security and software security. In addition, the talk will discuss the safety/security challenges and responsibilities inherent in deploying LLMs. By highlighting both the successes and challenges, the talk aims to foster a nuanced understanding of how LLMs can be safely and effectively utilized for the betterment of society.

講者/ 表演者:
Prof. Hongxin Hu
Department of Computer Science and Engineering at University at Buffalo, SUNY

Hongxin Hu is a Professor and Associate Chair of the Department of Computer Science and Engineering at University at Buffalo, SUNY. He is a recipient of the NSF CAREER Award (2019) and Amazon Research Award (2022). His research spans security, machine learning, and networking. He has participated in multiple cross-university, cross-disciplinary projects funded by NSF. His research has also been funded by NSA, U.S. Army, USDOT, Google, VMware, Amazon, etc. He has published over 150 refereed technical papers, many of which appeared in top-tier conferences such as S&P, CCS, USENIX Security, NDSS, SIGCOMM, NSDI, NeurIPS, ICML, and CHI, and well-recognized journals such as IEEE TIFS, IEEE TDSC, IEEE/ACM TON, and IEEE TKDE. He is the recipient of ACM SACMAT Test-of-Time Award in 2024, and the Best Paper Awards from ACM ASIACCS (2022), ACSAC (2020), IEEE ICC (2020), ACM SIGCSE (2018), and ACM CODASPY (2014). His research has also been featured by the IEEE Special Technical Community on Social Networking and received 50+ press coverage including ACM TechNews, InformationWeek, Slashdot, etc.

語言
英文
適合對象
教職員
研究生
本科生
主辦單位
電子及計算機工程學系
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