Dr. Juan Jenny Li has dedicated the last 28 years of her career to applying artificial intelligence across various fields, including software testing and analysis, healthcare, and cybersecurity. In her dissertation, she invented a conditional-belief-based method to represent knowledge with considerable uncertainty, subsequently adapting it to AI as contingency-belief planning for reliable monitoring of telecom systems. At Bellcore Research, Dr. Li innovated, implemented, and delivered for field usage a tool-suite of system workflow design and analysis tools. Transitioning to Avaya Labs Research (formerly part of Bell Labs Research), she first invented and released an internet topology discovery and diagnosis tool, followed by a software tool suite for program testing and analysis on various platforms. During her eight years as a professor at Kean University, Dr. Li has advised over 70 undergraduate and graduate students in numerous research projects in AI and machine learning, focusing on applications such as health and cybersecurity. Over 70% of the students she advised are from underrepresented minority (URM) groups, and over 60% are female. Dr. Li's students have won awards and scholarships such as NSF SSTEM, PosterOntheHill, McNair, and GraceHopper. Her expertise and experience cover computer science, physical sciences, life sciences, health, mathematics, and engineering. Dr. Li manages programs at the National Science Foundation (NSF) across various directorates, fostering interdisciplinary involvement. Besides her two NSF grants from the Education (EDU) and Computer and Information Science and Engineering (CISE) directorates, she has also received grants from NSF's Mathematical and Physical Sciences (MPS) and Engineering directorates. Beyond being an NSF grant recipient and program manager, Dr. Li also has extensive experience in receiving industrial grants and executing them with successful outcomes, providing valuable insights into prioritizing research projects effectively.
This material is based upon work supported by the National Science Foundation under Grant HRD-1834620 and No. 1551221. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.