Shih
Researcher
Also affiliated: University of Southern California (2022); University of California, Los Angeles (2022); Regeneron (United States) (2022); Department of Physics, Mathematics and Informatics (1998); Jacobs Institute (2022); Paulo Picanço School of Dentistry (1998); University at Buffalo, State University of New York (2022)
Faculty Researcher
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Shih's research program focuses on health literacy within dermatology patient populations, aiming to improve patient understanding and engagement with their care. Their work has explored methods for leveling the playing field in healthcare communication, ensuring equitable access to information for all patients. This line of inquiry is supported by publications that examine the practical application of health literacy principles in clinical settings. Additionally, Shih has investigated advanced machine learning techniques for personalized content recommendations, specifically in the context of email promotions. This work utilizes contextual bandit algorithms to optimize user engagement. Shih's scholarly contributions are reflected in a citation count of 218 and an h-index of 4 across 28 publications.
Metrics
- h-index: 4
- Publications: 28
- Citations: 218
Selected Publications
-
Health Literacy in Dermatology Patients: How to Level the Playing Field (2022)
Collaboration Network
Top Collaborators
- Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
- Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
- Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
- Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
- Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
- Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
- Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations
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