Instruction & Teaching philosophy

Data Science is a relatively new and rapidly evolving field. While the basics of the field remain the same and can be taught from the textbooks, the advances must be learned from competitions and/or high-impact papers. Not only do they update the collective knowledge about a particular topic, but they also contain implementational aspects. I believe that a great combination of learning from textbooks /papers, and participation in extracurricular competitions can be beneficial for students' academic outcomes and preparation for the job market. While teaching a course, I will help students take ownership of their learning and development.  After completing what a textbook has to offer on a particular topic, I would like to encourage student(s), individually or in a group, to present the latest developments they could find (in competitions/papers) to promote student involvement and independent learning.

Research Interests

My research bridges the areas of Computer Science, Bioinformatics, and Biology and centers around using state-of-the-art Big Data and Machine Learning technologies to address the challenges in genomics and metagenomics.  

Selected Publications

Fernando Meyer, Adrian Fritz, [et al, including Lizhen Shi]. Critical Assessment of Metagenome Interpretation - the second round of challenges, Nature Methods, January 2022

Lizhen Shi, Bo Chen. LSHvec: A Vector Representation of DNA Sequences Using Locality Sensitive Hashing and Word Embeddings. ACMBCB’21: Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, August 2021

Lizhen Shi, Xiandong Meng, Elizabeth Tseng, Michael Mascagni, Zhong Wang. SpaRC: scalable sequence clustering using Apache Spark. Bioinformatics 35.5 (2018): 760-768.