Scholar
Yanfang (Fanny) Ye
Google Scholar ID: egjr888AAAAJ
Galassi Family Collegiate Professor in Computer Science and Engineering, University of Notre Dame
Citations & Impact
Citations
11,776
H-index
45
i10-index
127
Publications
20
Co-authors
0
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- Over 170 publications in top conferences and journals such as ACM CSUR, IEEE TNNLS, IEEE TKDE, SIGKDD, ICDM, CIKM, WWW, NeurIPS, ACL, NAACL, ICLR, ICML, AAAI, IJCAI, USENIX Security, NDSS, ACSAC. Nine best paper awards including the AAAI-DCAA 2023 Best Paper Runner-Up Award, the ACM CIKM 2021 Best Paper Award (Full Paper Track), the ACM CIKM 2021 Best Paper Runner-Up Award (Applied Paper Track), the AICS 2019 Challenge Problem Winner, the SIGKDD 2017 Best Paper Award and SIGKDD 2017 Best Student Paper Award (Applied Data Science Track), and the IEEE EISIC 2017 Best Paper Award. Also received the Innovation Award (2020-2021), the Research Award (2019-2020), the MetroLab Innovation of the Month (2020), the NSF Career Award (2019), the IJCAI Early Career Spotlight (2019), the ICDM 2018 Outstanding Service Award, and the New Researcher of the Year Award (2016-2017) at WVU. As the Lead/Sole PI, she has received multiple awards from the NSF, DoJ/NIJ, Notre Dame Strategic Framework Research Grant, and Notre Dame Poverty Research Package.
Research Experience
- Currently, the Galassi Family Collegiate Professor in Computer Science and Engineering (Full Professor) in the Department of Computer Science and Engineering (CSE) and the Associate Director of Applied Analytics in the Lucy Family Institute for Data & Society at the University of Notre Dame. Before joining Notre Dame, she was the Theodore L. and Dana J. Schroeder Associate Professor in the Department of Computer and Data Sciences at Case Western Reserve University.
Background
- Research interests include Artificial Intelligence (AI), Machine Learning (ML), Data Mining, Cybersecurity, and Public Health. By harnessing large-scale, multi-source, multi-modality data, she discovers new research problems, tackles fundamental challenges in AI and machine learning, and deploys developed techniques into real-world applications with broader impacts. Specifically, she strives to advance knowledge and science in graph learning, trustworthy LLMs, and multimodal learning; bridge AI/ML and cybersecurity focusing on AI security and safety, large-scale malware detection, and the study of the evolving underground ecosystem; and develop AI and data-driven techniques to combat the opioid crisis and infectious disease outbreaks.
Miscellany
- Currently looking for multiple Ph.D. students and Postdocs doing supervised research or independent study with her in the CSE department.
Co-authors