About Adam Purtee

Adam Purtee

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

Email: apurtee@cs.rochester.edu
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

CSC 171 – Introduction to Computer Science – TR 200-315
CSC 246 – Machine Learning – TR 1105-1220

Recent Courses

CSC 171 – Introduction to Computer Science (every term Fall 2019 through Fall 2023)
CSC 257/457 – Computer Networks (Fall 2019, Fall 2020, Fall 2021, Fall 2022)
CSC 246 – Machine Learning (Spring 2020, Spring 2021, Spring 2022, Spring 2023, Fall 2023)
CSC 442 – Artificial Intelligence (Fall 2018, Fall 2019, Fall 2020, Fall 2021)

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:

A. Purtee, Toward Better Reasoning from Natural Language, PhD Thesis, University of Rochester, 2018.

A. Purtee and L.K. Schubert, Simple Rules for Probabilistic Commonsense Reasoning, in Proc. Advances in Cognitive Systems, Troy, NY, May 13 2017.

A. Purtee and L.K. Schubert, TTT: A tree transduction language for syntactic and semantic processing, in Proc. EACL 2012 Workshop on Applications of Tree Automata Techniques in Natural Language Processing, pages 21–30, Avignon, France, April 24 2012. Source Code

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.