Advisory Board

Dr. Xiang Li

Xiang Li, Ph.D. is Research Assistant at the Temple University and Data Scientist for the Temple University Health System. His main research is Emotion of Artificial Intelligence. He is a member of the Temple AGI Team, where they do research and experiments in the field of Artificial General Intelligence.

As Data Scientist for Temple Health, Xiang predicted the readmission of COPD patients. COPD or Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. Xiang was trying to find the reason why the readmission rate of COPD is high. He created regression models to predict the length of stay of COPD, pulmonary, and Sepsis patients.

He also created regression models to predict the number of available beds for hospital units and improved accuracy from 50% to 90%. He used similar models for patient flow and the length of stay of ED patients based on multiple factors. He significantly improved accuracy in both models.

Xiang earned his Doctor of Philosophy in Artificial General Intelligence in 2019 from Temple University with his research on the Emotion of Artificial Intelligence. He earned his Bachelor’s Degree of Science in Mathematics and Computer Science in 2014 from the Bloomsburg University of Pennsylvania.

When at Bloomsburg University, Xiang was working also as Software Engineer where he collaborated with professional programmers, students, and professors to develop the Bloomsburg University Weather Viewer. This weather visualization program is designed to enhance the educational value of the weather data.

In 2016, Xiang did his Internship as Data Scientist in Jakarta, Indonesia for Mediatric Big Data Analytics. While there, Xiang guided the Data Analytic team in solving problems of making decisions based on insufficient knowledge in offline situations.

From 2016 to 2017, Xiang became Graduate Teaching Assistant at Temple University, Assisting on Statistics, Probability, and Discrete Mathematics.

As the Research Assistant at Temple University between 2018 and 2019, Xiang worked on his dissertation about Emotion in Artificial General Intelligence. The objective of this research was to understand motivation and emotion processing in an AGI system NARS (Non-Axiomatic Reasoning System). Under the basic assumption that an AGI system should work with insufficient resources and knowledge, the emotion module can help direct the selection of internal tasks, and allow the autonomous allocation of internal resources and rapid response with urgency, so that the inference capability of AGI system can be improved.

Read Functionalist Emotion Model in Artificial General Intelligence, Self in NARS, an AGI System, and Cumulative learning.

Visit his LinkedIn profile and ResearchGate profile.