Kathryn Leonard

Professor Kathryn Leonard
Professor, Computer Science
B.S., University of New Mexico; M.S., Ph.D., Brown University
Department Chair, Computer Science
Appointed In: 
2017
Office: 
Swan Hall #B101
Hours: 

M/F 4:00-5:30pm. Please contact Prof. Leonard for Discord sign-in information.

Kathryn Leonard studies geometric models for computer graphics, computer vision, and data analysis. 

Prof. Leonard's students have gone on to PhD programs and careers at Google, Amazon, and tech start-ups. With degrees in mathematics and English, she loves connecting computer science to other disciplines. She spends a large portion of her time promoting undergraduate research and representation of womxn and other marginalized groups in the computational sciences.

Jump to: Undergraduate research | Increasing representation in STEM | Research projects

Undergraduate research

A high impact practice, undergraduate research is one of the most transformative experiences a student can have—they themselves generate new knowledge in a field. Prof. Leonard has a long history with undergraduate research, most recently culminating in a $1.4 million NSF grant to fund the Center for Undergraduate Research in Mathematics, which she directs. Other activities supporting undergraduate research include:

  • PUMP Journal of Undergraduate Research, Editorial Board, 2017 – present.
  • Mathematical Association of America Haimo Teaching Award Committee, 2018 – present.
  • Scholarship and Practice in Undergraduate Research (SPUR), Math & Computer Science Editor, 2015 – 2018.
  • Council on Undergraduate Research, Facilitator, Councilor in Math/CS Division, 2015 – 2018
Publications:

Increasing representation in STEM

A healthy scientific community engages people from all walks of life. Prof. Leonard has extensive background in increasing representation in the computational sciences, particularly for women-identifying people, including significant work with the Association for Women in Mathematics (AWM), where, among other roles, she was on the Executive Committee as Meetings Coordinator from 20132018. She has organized several workshops and conferences to highlight and promote womxn’s work, including:

  • Women in the Science of Data and Mathematics Research Collaboration Conference, ICERM, Providence, RI, July 2019.
  • SkelNetOn Challenge and Workshop, Computer Vision and Pattern Recognition Annual Conference, June 2019.
  • Women in Computer Vision, Computer Vision and Pattern Recognition Annual Conference, June 2019.
  • Shape Modeling Special Session, AWM Research Symposium, Apr 2019.
  • Society for Industrial and Applied Mathematics Annual Meeting, Portland, OR, July 2018.
  • Women in Shape Modeling 3 Research Collaboration Conference, University of Trier, July 2018.
  • Women in the Science of Data and Mathematics Research Collaboration Conference, ICERM, Providence, RI, July 2017.
  • Shape Modeling, Theory and Applications Special Session, AWM Research Symposium, UCLA, April 2017.
  • Women in Shape Modeling 2 Research Collaboration Conference, Nesin Math Village, Turkey, June 2016.
  • AWM Workshop at Joint Mathematics Meetings (JMM): Mathematics of Image Analysis, Baltimore, MD, January 2014.
  • SIAM Minisymposium on Geometric Shape Analysis, JMM 2014.
  • Women in Shape Modeling Research Collaboration Workshop, Institute for Pure and Applied Mathematics, UCLA, July 2013.
  • Mathematical Sciences Research Institute (MSRI)/PREP: The Mathematics of Image Analysis, March 2005.
  • MSRI Workshop for Women: Introduction to Image Analysis, January 2005.
Publications:
  • J. Balén, N. Deans, B. Gillespie, K. Leonard, N. Parmar, B. Rasnow and C.Wyels. Developing Cultural Literacy in the STEM Disciplines, Journal of Multiculturalism in Education, September 2012.

Research projects

(* indicates undergraduate co-author)

Shape understanding: developing models for automatically understanding shapes and their parts in a way that mirrors human understanding, primarily based on the Blum Medial axis.
Robust skeleton extraction: Developing robust and efficient techniques for extracting skeletal models from shapes that are less sensitive to noise than traditional methods.
Applications of shape understanding to other fields
Foundational theory for shape modeling and understanding
Other work
  • K. Leonard, L. Contreras,* D. DeSantis,* On the geometric deformations of functions in L2[D], Involve, 6(3):233-241, 2012.
  • L. Cutler, A. Lerios, K. Leonard, G. Sarkis, et.al, Big data techniques applied to media and computer graphics applications, High Performance Transaction Systems, Sept. 2015.
  • A. Genctav, K. Leonard, S. Tari, E. Hubert, G. Morin, N. El-Zehiry, E. Chambers (ed.), Research in Shape Modeling (book), Springer-Verlag, May 2018.
  • K. Leonard, S. Tari (ed.), Research in Shape Analysis (book), Springer-Verlag, May 2015.