Associate Professor of Biology
(610) 330-5846


  • Ph.D., Rutgers University (2010)

My Love for Teaching

To paraphrase the great physicist Richard Feynman, if you want to learn and fully understand something, you must think about how you would teach it to a 12-year-old. I am passionate about teaching because I love to learn. In my courses, I connect learning materials to the real world, and I provide concrete experiences for students to put concepts into practice. My students learn ideas, technologies, and skills that scientists practicing in academia and industry use in doing their research.

Although I teach statistics, molecular biology, human genetics, and bioinformatics, the recurrent theme of my courses is to teach students simple computer programming. I see programming as the third competency after language and mathematics, especially in the era of big data.

My Research Interests

My expertise lies in computational biology. I was personally thrilled by the completion of the Human Genome Project in 2001. A genome represents the source code of life; in other words, the mystery of biology is buried in a long string of As, Cs, Gs, and Ts. Unlocking the information embedded in a genome can offer us a fundamental understanding of biology.

Recently, my research focused on applying machine learning (i.e., artificial intelligence) in biology. My thesis student Zhaoyi Ding Bio ’21 and I co-authored a research article that proposed a machine learning model to differentiate between the electrocardiograms of elderly people who have suffered from stroke and people who have never had a stroke. The model can be used to help prevent recurrent strokes in post-stroke patients by monitoring the patterns of their heartbeats.

Given the success of the two mRNA COVID vaccines, thesis student Erica Chen Bio ’23 and I are currently studying the use of mRNA-based therapeutic technology in antibiotics development. One proven advantage of mRNA vaccines is their power to adapt to COVID variants. Like viruses, bacteria are rapidly mutating, leading to antibiotics resistance. Leveraging the adaptability of mRNA technology in combating fast-evolving bacteria could become a promising direction in developing effective antibiotics in the future. Currently, our goal is to develop an array of bioinformatics tools to identify viable bacterial genes in three common types of bacteria.

My Personal Interests/Community Work

Table tennis is one of my hobbies. I practice and play games on a weekly basis.

Selected Publications

Ho, E. S., & Ding, Z. (2022). Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mappingArtificial intelligence in medicine130, 102342.

Li, M. X., Weng, J. W., Ho, E. S., Chow, S. F., & Tsang, C. K. (2022). Brain delivering RNA-based therapeutic strategies by targeting mTOR pathway for axon regeneration after central nervous system injuryNeural regeneration research17(10), 2157–2165.