Mark Hopkins

COMPUTER SCIENCE, REED COLLEGE

I’m a Visiting Associate Professor of Computer Science at Reed College. My research focuses on machine learning, knowledge representation, and uncertain reasoning, particularly as applied to natural language tasks.

Recent Research Highlights

Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (ACL 2018): We revisit domain adaptation for parsers in the neural era. (pdf) (bib)

Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers (EMNLP 2017): Describes work done at the Allen Institute for Artificial Intelligence to build a system that can take the math portion of the Scholastic Aptitude Test (SAT). This approach provides the foundation of the Euclid system. (pdf) (bib) (demo)

Odd Man Out: Exploring the Associative Power of Lexical Resources (EMNLP 2018): A simple task, determining the “odd-man-out” from a set of choices, can be an effective means of comparing and contrasting the power of taxonomies and vector embeddings. (pdf) (bib)

Teaching

CSCI 378: Deep Learning (Spring 2019, course page):This course is an introduction to deep neural architectures and their training. Beginning with the fundamentals of regression, optimization, and regularization, the course will then survey a variety of architectures and their associated applications. Students will develop projects that implement deep learning systems to perform various tasks. Prerequisites: Mathematics 201, 202, and Computer Science 221.

CSCI 121 – Computer Science Fundamentals I (Spring 2019, course page): An introduction to computer science, covering topics including elementary algorithms and data structures, functional and procedural abstraction, data abstraction, object orientation, logic, and the digital representations of numbers. Emphasis is on mathematical problems and calculations and on recursive algorithms and data structures. The course includes a significant programming laboratory component where students will solve computational problems using a high-level language. The mechanisms for processing and executing programs will be surveyed. 

 

Previous courses:

  • CSCI 377 (Artificial Intelligence, Fall 2018)
  • CSCI 121 (Fundamentals of Computer Science, Fall 2018)