With a research theme in applied probability and statistical bioinformatics, several ongoing funded projects can be classified into a trio of major theoretical concepts: scan statistics, Poisson approximation, and excursion theory. The most exciting investigation is in the overlapping area of all three concepts for our research work on prediction algorithms. The prediction can be very computationally intensive for longer structural sequences, while much mathematical groundwork needs to done on this concept trio, especially for the new excursion theory and the concept of compound Poisson approximation. On the other hand, scan statistics with a better mathematical foundation is now a popular concept in genomics studies and health data surveillance. Here are brief descriptions of various research interests at different levels:


  • Undergraduate Participation in Bioinformatics Training (UPBiT):

    Individualized 3-year training programs with activities including communication workshops, lab training, research rotations, field trips, conferences, and specialized bioinformatics research projects that will typically involve techniques in discrete math, linear algebra, optimization, probability, and statistics.

  • Genomics and Sequence Analysis (Master's in Bioinformatics):

    Development and application of more specific bioinformatics computing tools to predict genomic structures and analyze molecular sequences. Students are encouraged to modify the computer programming for existing tools and to integrate with the wet-lab equipment involved.

  • Applied Probability and Biostatistics (Master's in Statistics or Mathematics):

    Investigation into probabilistic models and other techniques (e.g., Poisson approximation) for statistical analysis of biomolecular sequences. The emphasis will vary for students interested in mathematics, applied mathematics, or statistics.

  • Computational Data Bioscience (PhD in Computational Science or Data Science):

    Development of new probabilistic models (e.g., excursions) and distributed computing techniques to establish new approaches to computationally intensive problems stemming from biomolecular sequence and structure analyses. A current problem of interest is RNA structure prediction.

  • Mathematical Modeling and Prediction Algorithms (Postdoctoral Studies):

    Research into new mathematical and computational frameworks for developing prediction algorithms and data mining systems to address emerging bioinformatics problems.