Brian Tjaden

Chung Family Professor in Data and Computational Science and Professor of Computer Science

Professor of computational biology and computer science.

My research is in the field of computational biology. My work focuses on the design, analysis, and implementation of algorithms with applications in genomics and molecular biology. I study the genomes of various organisms and develop computational methods for characterizing genes and other biologically important elements in the genomes. I design approaches for identifying novel genes in a genome as well as for characterizing the functions of genes and how gene products interact as part of a system that carries out important processes in a cell. My work often utilizes data from high-throughput DNA sequencing technologies. Before arriving at Wellesley, I worked in research groups at the Institute for Systems Biology, Biatech Organization, and Intel Corporation. While on leave from Wellesley, I spent a year as a visiting scholar at Harvard University. My research is funded by the National Science Foundation, the National Institutes of Health, and the Howard Hughes Medical Institute.

I teach at all levels of the curriculum, ranging from introductory courses in computer science to advanced courses in computational biology and bioinformatics.

I enjoy traveling, playing sports, and spending time with my family.

Education

  • B.A., Amherst College
  • M.S., University of Washington-Seattle Campus
  • Ph.D., University of Washington-Seattle Campus

Current and upcoming courses

  • Machine learning is the science of teaching computers how to learn from observations. It is ubiquitous in our interactions with society, such as in face recognition, web search, targeted advertising, speech processing, and genetic analysis. It is currently at the forefront of research in artificial intelligence, and has been making rapid strides given the vast availability of data today. This course is a broad introduction to the field, covering the theoretical ideas behind widely used algorithms like decision trees, linear regression, support vector machines, and many more. We will also study practical applications of these algorithms to problems in a variety of domains, including vision, speech, language, medicine, and the social sciences.
  • Machine learning is the science of teaching computers how to learn from observations. It is ubiquitous in our interactions with society, such as in face recognition, web search, targeted advertising, speech processing, and genetic analysis. It is currently at the forefront of research in artificial intelligence, and has been making rapid strides given the vast availability of data today. This course is a broad introduction to the field, covering the theoretical ideas behind widely used algorithms like decision trees, linear regression, support vector machines, and many more. We will also study practical applications of these algorithms to problems in a variety of domains, including vision, speech, language, medicine, and the social sciences.