Maneesh Agrawala is the Forest Baskett Professor of Computer Science and Director of the Brown Institute for Media Innovation at Stanford University. He was previously a Professor of Electrical Engineering and Computer Science at the University of California, Berkeley (2005 - 2015). He works on computer graphics, human computer interaction and visualization. His focus is on investigating how cognitive design principles can be used to improve the effectiveness of audio/visual media. The goals of this work are to discover the design principles and then instantiate them in both interactive and automated design tools. He received an Okawa Foundation Research Grant in 2006, an Alfred P. Sloan Foundation Fellowship and an NSF CAREER Award in 2007, a SIGGRAPH Significant New Researcher Award in 2008, and a MacArthur Foundation Fellowship in 2009.

Academic Appointments

Administrative Appointments

  • Director, David and Helen Gurley Brown Institute for Media Innovation (2015 - Present)
  • Professor, Computer Science (2015 - Present)

Honors & Awards

  • Research Grant, Okawa Foundation (2006)
  • Research Fellow, Alfred P. Sloan Foundation (2007)
  • CAREER Award, National Science Foundation (2007)
  • Significant New Researcher Award, ACM SIGGRAPH (2008)
  • Fellow, MacArthur Foundation (2009)

Boards, Advisory Committees, Professional Organizations

  • Associate Editor, ACM Transactions on Graphics (2013 - Present)
  • Advisor, Human Computation Journal (2013 - Present)
  • Science and Creativity Advisor, Studio 360 with Kurt Andersen (2012 - Present)

Professional Education

  • Ph.D., Stanford University, Computer Science (2002)
  • B.S., Stanford University, Mathematics (1994)

Current Research and Scholarly Interests

Computer Graphics, Human Computer Interaction and Visualization.

All Publications

  • Saliency in VR: How do people explore virtual environments? Sitzmann, V., Serrano, A., Pavel, A., Agrawala, M., Gutierrez, D., Masia, B., Wetzstein, G. IEEE COMPUTER SOC. 2018: 1633–42


    Understanding how people explore immersive virtual environments is crucial for many applications, such as designing virtual reality (VR) content, developing new compression algorithms, or learning computational models of saliency or visual attention. Whereas a body of recent work has focused on modeling saliency in desktop viewing conditions, VR is very different from these conditions in that viewing behavior is governed by stereoscopic vision and by the complex interaction of head orientation, gaze, and other kinematic constraints. To further our understanding of viewing behavior and saliency in VR, we capture and analyze gaze and head orientation data of 169 users exploring stereoscopic, static omni-directional panoramas, for a total of 1980 head and gaze trajectories for three different viewing conditions. We provide a thorough analysis of our data, which leads to several important insights, such as the existence of a particular fixation bias, which we then use to adapt existing saliency predictors to immersive VR conditions. In addition, we explore other applications of our data and analysis, including automatic alignment of VR video cuts, panorama thumbnails, panorama video synopsis, and saliency-basedcompression.

    View details for DOI 10.1109/TVCG.2018.2793599

    View details for Web of Science ID 000427682500026

    View details for PubMedID 29553930

  • Converting Basic D3 Charts into Reusable Style Templates IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Harper, J., Agrawala, M. 2018; 24 (3): 1274–86


    We present a technique for converting a basic D3 chart into a reusable style template. Then, given a new data source we can apply the style template to generate a chart that depicts the new data, but in the style of the template. To construct the style template we first deconstruct the input D3 chart to recover its underlying structure: the data, the marks and the mappings that describe how the marks encode the data. We then rank the perceptual effectiveness of the deconstructed mappings. To apply the resulting style template to a new data source we first obtain importance ranks for each new data field. We then adjust the template mappings to depict the source data by matching the most important data fields to the most perceptually effective mappings. We show how the style templates can be applied to source data in the form of either a data table or another D3 chart. While our implementation focuses on generating templates for basic chart types (e.g., variants of bar charts, line charts, dot plots, scatterplots, etc.), these are the most commonly used chart types today. Users can easily find such basic D3 charts on the Web, turn them into templates, and immediately see how their own data would look in the visual style (e.g., colors, shapes, fonts, etc.) of the templates. We demonstrate the effectiveness of our approach by applying a diverse set of style templates to a variety of source datasets.

    View details for DOI 10.1109/TVCG.2017.2659744

    View details for Web of Science ID 000423541200005

    View details for PubMedID 28186898

  • Interactive Design and Stability Analysis of Decorative Joinery for Furniture ACM TRANSACTIONS ON GRAPHICS Yao, J., Kaufman, D. M., Gingold, Y., Agrawala, M. 2017; 36 (2)

    View details for DOI 10.1145/3054740

    View details for Web of Science ID 000400160000008

  • Interactive Furniture Layout Using Interior Design Guidelines ACM TRANSACTIONS ON GRAPHICS Merrell, P., Schkufza, E., Li, Z., Agrawala, M., Koltun, V. 2011; 30 (4)
  • Perceptual Guidelines for Creating Rectangular Treemaps IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Kong, N., Heer, J., Agrawala, M. 2010; 16 (6): 990-998


    Treemaps are space-filling visualizations that make efficient use of limited display space to depict large amounts of hierarchical data. Creating perceptually effective treemaps requires carefully managing a number of design parameters including the aspect ratio and luminance of rectangles. Moreover, treemaps encode values using area, which has been found to be less accurate than judgments of other visual encodings, such as length. We conduct a series of controlled experiments aimed at producing a set of design guidelines for creating effective rectangular treemaps. We find no evidence that luminance affects area judgments, but observe that aspect ratio does have an effect. Specifically, we find that the accuracy of area comparisons suffers when the compared rectangles have extreme aspect ratios or when both are squares. Contrary to common assumptions, the optimal distribution of rectangle aspect ratios within a treemap should include non-squares, but should avoid extremes. We then compare treemaps with hierarchical bar chart displays to identify the data densities at which length-encoded bar charts become less effective than area-encoded treemaps. We report the transition points at which treemaps exhibit judgment accuracy on par with bar charts for both leaf and non-leaf tree nodes. We also find that even at relatively low data densities treemaps result in faster comparisons than bar charts. Based on these results, we present a set of guidelines for the effective use of treemaps and suggest alternate approaches for treemap layout.

    View details for Web of Science ID 000283758600016

    View details for PubMedID 20975136

  • Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations 27th Annual CHI Conference on Human Factors in Computing Systems Heer, J., Kong, N., Agrawala, M. ASSOC COMPUTING MACHINERY. 2009: 1303–1312
  • Visualizing dynamic architectural environments COMMUNICATIONS OF THE ACM Houston, M., Niederauer, C., Agrawala, M., Humphreys, G. 2004; 47 (8): 54-59
  • Non-invasive interactive visualization of dynamic architectural environments Annual Symposium of the ACM SIGGRAPH Niederauer, C., Houston, M., Agrawala, M., Humphreys, G. ASSOC COMPUTING MACHINERY. 2003: 700–700
  • Designing effective step-by-step assembly instructions Annual Symposium of the ACM SIGGRAPH Agrawala, M., Phan, D., Heiser, J., Hayrnaker, J., Klingner, J., Hanrahan, P., Tversky, B. ASSOC COMPUTING MACHINERY. 2003: 828–37
  • Cognitive design principles for visualizations: Revealing and instantiating 25th Annual Conference of the Cognitive-Science-Society Heiser, J., Tversky, B., Agrawala, M., Hanrahan, P. LAWRENCE ERLBAUM ASSOC PUBL. 2003: 545–550
  • Sketches for design and design of sketches Conference on Human Behaviour in Design Tversky, B., Suwa, M., Agrawala, M., Heiser, J., Stolte, C., Hanrahan, P., Phan, D., Klingner, J., Daniel, M. P., Lee, P., Haymaker, J. SPRINGER-VERLAG BERLIN. 2003: 79–86
  • Conveying shape and features with image-based relighting IEEE Visualization 2003 Conference Akers, D., Losasso, F., Klingner, J., Agrawala, M., Rick, J., Hanrahan, P. IEEE. 2003: 349–354
  • Rendering effective route maps: Improving usability through generalization SIGGRAPH 2001 Agrawala, M., Stolte, C. ASSOC COMPUTING MACHINERY. 2001: 241–250
  • Efficient image-based methods for rendering soft shadows Computer Graphics Annual Conference Agrawala, M., Ramamoorthi, R., Heirich, A., Moll, L. ASSOC COMPUTING MACHINERY. 2000: 375–384
  • Artistic multiprojection rendering 11th Eurographics Workshop on Rendering Agrawala, M., Zorin, D., Munzner, T. SPRINGER-VERLAG WIEN. 2000: 125-?
  • Model-based compression for synthetic animations International Conference on Image Processing (ICIP-96) Chaddha, N., Agrawala, M., Beers, A. IEEE. 1996: 417–420