Adam Purtee

  Assistant Professor of Instruction
  Computer Science Department
  Rochester, NY 14627-0226

  Office: 2109 Wegmans Hall
  Office hours: Tuesday and Wednesday 330pm-430pm

About Me

I am a member of the instructional faculty in the Computer Science department at the University of Rochester. I generally teach 2-3 courses per semester ranging from a very large introductory course to upper level electives on machine learning and computer networks. Before teaching at this university, I was a lecturer at the Rochester Institute of Technology, where I developed a passion for teaching and working with students. I generally structure my courses around programming projects and other hands-on activities where students are able to immediately see the proof of their own understanding of the material. I try to present a classroom environment that is inclusive, welcoming, and accessible, while still providing a challenging and engaging experience for most students.

Current Courses - Spring 2024

Recent Courses

Research Interests

My research interests have been primarily directed at improving the scope and flexibility of symbolic reasoning methods applied in connection with natural language. In conjunction with my advisor Len Schubert, I wrote a pattern transduction tool for working with parse trees, explored semi-supervised methods for training statistical parsers, and developed an inferentially coherent modal embedding for probabilistic knowledge in first order (and higher order) logics. My current focus (as of 2023) is gaining a deeper understanding of human learning theory and pedagogy, with a particular interest in CS education. Relevant publications to my former work on symbolic AI are presented below:

Other Things I like to Discuss

I really like computer science. It is a vast field and I've found almost no aspect to be boring. A few topics are particularly fun for me though, so I've listed them below. If you're interested in these things, or working on them and want to chat, feel free to find me after class or in office hours:   Natural language understanding, knowledge representation, commonsense reasoning, machine learning, numerical programming, sampling algorithms, virtual machines, computer networking, privacy, network and computer security.