Schedule

The Core Value of the Student Seminars at Florida State University is to foster a seminar series as a dynamic intellectual hub dedicated to introducing and advancing the cutting-edge of computer science for the students at the academic community of FSU, with a particular focus on artificial intelligence, machine learning, and related emerging technologies. This regular seminar serves as a platform for faculty and students to share groundbreaking discoveries, innovative methodologies, and critical insights across the rapidly evolving landscape of computational sciences. By promoting interdisciplinary dialogue and collaboration, this seminar aims to catalyze innovation and cross-pollination of ideas spanning various domains within and beyond traditional computer science boundaries. It offers an essential opportunity for participants to engage with the latest advancements, challenge conventional paradigms, and inspire novel research directions.

2024

    11/13/2024 - Research Paper Presentation (by Hansong Zhou): Waste Not, Want Not: Service Migration-Assisted Federated Intelligence for Multi-Modality Mobile Edge Computing [Paper PDF] [Slides]
    11/20/2024 - Research Paper Presentation (by Daniel Riley): Multi-Phase Invariant Synthesis [Paper PDF] [Slides]
    12/04/2024 - Research Paper Presentation (by Ram Sharan Chaulagain): Enhanced UGAL Routing for Dragonfly Networks [Paper PDF] [Slides]
    12/11/2024 - Research Paper Presentation (by Zhankai Ye): Network Sensing Using Neural Network [Paper PDF] [Slides]

Target Audience

Anyone who are in the research of computer science, especially artificial intelligence, machine learning, and data mining. All students and faculty members are warmly welcomed to join.

Committee Members

Yushun Dong

Dr. Yushun Dong is an assistant professor with the Computer Science Department at Florida State University. He received Ph.D. degree in Electrical and Computer Engineering at the University of Virginia in 2024. His research interest mainly lies in achieving responsible AI to further advance social good such as facilitating inclusive decision-making. He has abundant research works under related topics with a particular focus on relational data, including 30+ published research papers in the areas of explainability, algorithmic fairness, AI Security, and AI/ML + X (Applications). He is the recipient of multiple prestigious awards including Louis T. Rader Graduate Research Award, Endowed Fellowship, and Best Poster at Doctoral Forum of SDM 2022.

Shangqian Gao

Dr. Gao joined the Department of Computer Science as an assistant professor in the fall of 2024. He earned his Ph.D. from the University of Pittsburgh in 2024, under the guidance of Prof. Heng Huang. Before his academic appointment, Dr. Gao spent a year as a research scientist at Samsung Research America (SRA), where his work on improving the efficiency of Large Language Models received the Presidential Award from SRA. His research interests span a broad range of topics in AI and machine learning, including efficient machine learning, cross-modal learning, reinforcement learning, and optimization methods. Recently, his research has focused on solving constrained optimization problems to reduce the size of large models, such as Large Language Models, Vision-Language models, and Diffusion models.

Shibo Li

Dr. Shibo Li is an assistant professor with the Computer Science Department at Florida State University. He obtained his Ph.D. degree in Computer Science from The Kahlert School of Computing (SoC) at The University of Utah. His primary research area, AI for Science, integrates physical system analysis with machine learning methodologies. Computational physics, developed over centuries, is essential for understanding the universe and creating new technologies. Meanwhile, AI has revolutionized many sectors. Though these fields may seem distinct, they are complementary: physical insights improve data-driven methods' efficacy, and data-driven methods capture physical laws flexibly. Leveraging abundant data, these methods provide statistical insights, augment traditional research, and offer efficient computing infrastructure for enhanced efficiency.

Xin Liu

Dr. Xin Liu joined the Department of Computer Science as an Assistant Professor in August 2024. Prior to this, he served for two years as a Postdoctoral Scholar at The Ohio State University. He earned his Ph.D. in Computer Engineering from the University of Maryland, Baltimore County, in 2022. Dr. Liu’s research focuses on leveraging machine learning to enhance the performance and security of next-generation wireless networks, with a particular emphasis on IoT, 6G, and autonomous vehicles. He has co-authored 15 papers in top-tier conferences, including SIGCOMM, NSDI, and USENIX Security. Beyond research, Dr. Liu is deeply committed to mentoring and community engagement. He co-chaired the AI-EDGE SPARKS initiative and has mentored numerous undergraduate research projects, fostering interdisciplinary collaboration and innovation in wireless networking and AI.

Contact

Powered by Dr. Yushun Dong (yd24f@fsu.edu).