Tsai-Hsuan (Angel) Chung

Doctoral Candidate

Contact Information

Primary Email:
angelchg@wharton.upenn.edu

office Address:
3730 Walnut Street
526.8 Jon M Hunstman Hall
Philadelphia, PA 19104

Research Interests: Generative AI and machine learning for healthcare, education, and social good

Links: Personal Website, LinkedIn

Research

Hamsa Bastani, Osbert Bastani, Tsai-Hsuan (Angel) Chung, “Optimizing Health Supply Chains in LMICs with Machine Learning: A Case Study in Sierra Leone”. In Responsible and Sustainable Operations: The New Frontier, edited by Tang, Christopher S. (Switzerland: Springer Nature, 2024), pp. 187-202 Abstract
This chapter overviews the challenges in pharmaceutical supply chains (PSCs) in Low- and Middle-Income Countries (LMICs), with a focus on Sierra Leone. Furthermore, it describes how traditional supply chain optimization strategies can be used to improve performance of PSCs in Sierra Leone. Finally, it describes the significant potential for using machine learning in this framework for effective demand forecasting. We highlight challenges such as limited data availability, the need to ensure equitable distribution, as well as the potential for transfer learning to address some of these challenges.

Teaching

Past Courses

EDUC5414 - Econ of Ed in Dev Countries
This is a course on economics of education, a field within the subject of economics that draws upon many areas of economic specialization. The course focuses on lower middle income countries and includes papers and case-studies covering themes such as returns to investment in education, production, costs and financing of education, teacher labor markets, economic growth, education markets, and equity issues.
EDUC6462 - Monitrng & Eval in Intl Ed Dev
This course covers theories, methods, and applications of monitoring and evaluation for educational and social programs, with special emphasis on international education development. Topics include basic statistical concepts, program theory, process and outcome assessment, concepts in survey methods, introduction to causal inference, introductory regression analysis, and an overview of impact assessments and cost-benefit/cost-effectiveness analysis.
EDUC7462 - Adv Monitoring & Eval in IED
This advanced-level course builds on foundational statistical knowledge to provide students with practical, hands-on experience in quantitative methods for monitoring and evaluation (M&E) in international education and development contexts. Open to graduate students across programs, the course combines theoretical understanding with applied data analysis using STATA, emphasizing essential methods such as randomized experiments, and various quasi-experimental designs for causal inference. Students will explore a range of quantitative methodologies, including logit/probit models, propensity score matching, difference-in-differences, regression discontinuity, and other quasi-experimental approaches. Through interactive labs, research papers/case studies, and guided practice, students will strengthen their ability to analyze complex data, interpret results critically, and design effective research tools—skills vital for careers in research, policy evaluation, and international development. Prerequisites: Introductory Statistics and basic familiarity with STATA.

Activity

Latest Research

Hamsa Bastani, Osbert Bastani, Tsai-Hsuan (Angel) Chung, “Optimizing Health Supply Chains in LMICs with Machine Learning: A Case Study in Sierra Leone”. In Responsible and Sustainable Operations: The New Frontier, edited by Tang, Christopher S. (Switzerland: Springer Nature, 2024), pp. 187-202

All Research