Mackenzie Leake

Mackenzie Leake

I am a final-year PhD student in computer science at Stanford University advised by Maneesh Agrawala. I received a MS in computer science at Stanford and a BA in computational science and studio art at Scripps College. I've been fortunate to intern at Chatham Labs, Adobe Research, and IBM Research Almaden.

Contact Information:

first initial + lastname @cs.stanford.edu

Recent Projects

PaperPiecing
This paper presents a mathematical formalization of a quilting technique called foundation paper piecing. We propose an algorithm for checking paper pieceability and implement it in a design tool.

(SIGGRAPH 2021 paper - coming soon!)

Improv
PatchProv is a design tool that supports improvisational quilt design. Quilters capture images of their fabrics and utilize the digital tool to iterate between design and sewing steps.
(CHI'21 paper | website)
Slideshow
We present a method for automatically generating audio-visual slideshows from a text article by identifying representative concrete phrases from the text and searching for visuals that match these words.
(CHI'20 paper | video | website)
Roughcut
We designed a tool that uses the structure of dialogue-driven scenes and cinematographic principles to edit video sequences automatically. The input is a script and multiple video takes, capturing different camera framings or performances of the scene. Our system automatically cuts together a video based on film-editing idioms.
(SIGGRAPH'17 paper | video | website)

Publications

  • Leake, M., Bernstein, G., Davis, A., & Agrawala, M. (2021). A Mathematical Foundation for Foundation Paper Pieceable Quilts. SIGGRAPH 2021 (to appear).
  • Leake, M., Lai, F., Grossman, T., Wigdor, D., & Lafreniere, B. (2021). PatchProv: Supporting Improvisational Design Practices for Modern Quilting. CHI Conference on Human Factors in Computing Systems (CHI'21).
  • Leake, M., Kim, J., Shin, H., & Agrawala, M. (2020). Generating Audio-Visual Slideshows from Text Articles Using Word Concreteness. CHI Conference on Human Factors in Computing Systems (CHI'20).
  • Leake, M., Davis, A., Truong, A., & Agrawala, M. (2017). Computational Video Editing for Dialogue-Driven Scenes. ACM Transactions on Graphics (SIGGRAPH'17), 36(4), 130:1-130:14.
  • Leake, M. & Lewis, C.M. (2017). Designing CS Resource Sharing Sites for All Teachers. Proceedings of the 48th Technical Symposium on Computer Science Education (SIGCSE'17). 357-362.
  • Leake, M. & Lewis, C.M. (2016). Designing a New System for Sharing Computer Science Teaching Resources. Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work & Social Computing Companion (CSCW'16). 321-324.
  • Leake, M. (2015). Chart-based Strategies for Solving Bayesian Inference Problems. Journal of Computing Sciences in Colleges. 30(4), 107-114.
  • Leake, M., Xia, L., Rocki, K., & Imaino, W. (2015). Effect of Spatial Pooler Initialization on Column Activity in Hierarchical Temporal Memory. 29th AAAI Conference on Artificial Intelligence (AAAI'15), 4176-4177.

Awards

  • Adobe Research Fellowship, 2017
  • Brown Institute for Media Innovation Magic Grant, 2016
  • Stanford EDGE-STEM Fellowship, 2015
  • Stanford School of Engineering Fellowship, 2015
  • Goldwater Scholar, 2014
  • Amgen Scholar, 2013
  • James E. Scripps Scholarship, 2011-2015