The Department of Computer Science at the Florida State University has completed our 2024 faculty recruiting process with an incredibly successful year in terms of both the outcome and the procedure. “With the hiring of Yushun Dong, Shangqian Gao, Shibo Li, Xin Liu, and Shifat Mithila, we continue to strengthen our existing core systems research area and growing the caliber of our teaching portfolio,” said Weikuan Yu, Professor and Chair of Computer Science.
Yushun Dong
Dr. Yushun Dong joined the Department of Computer Science as an Assistant Professor in the fall of 2024. Prior to this, he received his Ph.D. degree in Electrical and Computer Engineering at the University of Virginia in 2024. His primary research focus is developing responsible AI that advances social good, with a particular emphasis on inclusive decision-making. As a result of his research work, he has published over 30 research papers in the areas of explainability, algorithmic fairness, AI Security, and AI/ML + X (Applications), garnering over 800 citations. He is the recipient of multiple prestigious awards, including the Louis T. Rader Graduate Research Award, Endowed Fellowship, and the Best Doctoral Forum Poster Award (runner-up) at SIAM 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. Dr. Gao has published over 30 papers in prestigious conferences and journals, including CVPR, ICCV, ECCV, ICLR, NeurIPS, ICML, EMNLP, NAACL, TPAMI, and JMLR. Additionally, he consistently serves as a reviewer for these leading conferences and journals.
Shibo Li
Dr. Li joined the Department of Computer Science as an assistant professor in August 2024. Before this appointment, he earned his Ph.D. from The University of Utah, his M.S. from the University of Pittsburgh, and his B.E. from the South China University of Technology. His research primarily focuses on probabilistic machine learning, uncertainty quantification in deep models, and interactive machine learning to tackle complex challenges prevalent in various scientific disciplines. His research group will work on advancing interdisciplinary studies between machine learning and scientific computing, contributing to developing a reciprocal relationship between these fields. During his Ph.D. studies, Dr. Li published 17 papers in top-tier machine learning venues, including NeurIPS, AISTATS, ICLR, and ICML, with 11 of these papers as first author. He has also served as a program committee member and reviewer for leading machine learning conferences and prestigious journals such as the Journal of Computational Physics (JCP), Scientific Reports, and Neural Networks (NEUNET). Additionally, Dr. Li has gained professional experience through roles at Amazon and Schlumberger-Doll Research.
Xin Liu
Dr. 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, where he contributed to the NSF AI-EDGE Institute. 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. Additionally, he has served as a reviewer for conferences and journals, including the SIGCOMM Artifact Evaluation Committee.
Shifat Mithila
Dr. Mithila joined the Department of Computer Science as a Teaching Faculty I in the fall of 2024. Her professional experience includes roles as a Postdoctoral Researcher and Software Developer at the LSU Agricultural Center, where she developed multiple web-based tools analyzing geospatial data to aid in flood risk mitigation and irrigation strategies. She earned her Ph.D. in Computer Science from Louisiana State University in 2022, with a focus on cloud computing, and holds a Master’s degree in Applied Mathematics and Statistics from Florida Atlantic University, concentrating on cryptography. With extensive teaching experience in computer science and mathematics, Dr. Mithila has also contributed to numerous academic publications and conference presentations including IEEE CLOUD and AGU conferences. She has received several awards and scholarships, including the Verizon and Clinton Global Initiative University (CGI U) Social Innovation Challenge award, for her academic excellence and innovative projects. Dr. Mithila’s work and research interests lie at the intersection of cloud computing and cryptography, encompassing the optimization of computational costs in high-performance computing applications in hybrid clouds, as well as the development of innovative strategies to address data privacy and security challenges through cryptographic protocols in complex computing environments. Additionally, she is passionate about applying software engineering to real-world challenges and fostering interdisciplinary collaborations, particularly in environmental science, where computational techniques can address global issues.