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)

Program Affiliations

  • Symbolic Systems Program

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.

2022-23 Courses

Stanford Advisees

All Publications

  • A Mathematical Foundation for Foundation Paper Pieceable Quilts ACM TRANSACTIONS ON GRAPHICS Leake, M., Bernstein, G., Davis, A., Agrawala, M. 2021; 40 (4)
  • Vid2Player: Controllable Video Sprites That Behave and Appear Like Professional Tennis Players ACM TRANSACTIONS ON GRAPHICS Zhang, H., Sciutto, C., Agrawala, M., Fatahalian, K. 2021; 40 (3)

    View details for DOI 10.1145/3448978

    View details for Web of Science ID 000695551400005

  • Iterative Text-Based Editing of Talking-Heads Using Neural Retargeting ACM TRANSACTIONS ON GRAPHICS Yao, X., Fried, O., Fatahalian, K., Agrawala, M. 2021; 40 (3)

    View details for DOI 10.1145/3449063

    View details for Web of Science ID 000695551400001

  • AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning Grunde-McLaughlin, M., Krishna, R., Agrawala, M., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 11282-11292
  • The Role of Working Memory in Program Tracing Crichton, W., Agrawala, M., Hanrahan, P., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2021
  • Analysis of Faces in a Decade of US Cable TV News Hong, J., Crichton, W., Zhang, H., Fu, D. Y., Ritchie, J., Barenholtz, J., Hannel, B., Yao, X., Murray, M., Moriba, G., Agrawala, M., Fatahalia, K., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2021: 3011-3021
  • Editing Self-Image COMMUNICATIONS OF THE ACM Fried, O., Jacobs, J., Finkelstein, A., Agrawala, M. 2020; 63 (3): 70–79

    View details for DOI 10.1145/3326601

    View details for Web of Science ID 000582584200023

  • Searching the Visual Style and Structure of D3 Visualizations IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Hoque, E., Agrawala, M. 2020; 26 (1): 1236–45


    We present a search engine for D3 visualizations that allows queries based on their visual style and underlying structure. To build the engine we crawl a collection of 7860 D3 visualizations from the Web and deconstruct each one to recover its data, its data-encoding marks and the encodings describing how the data is mapped to visual attributes of the marks. We also extract axes and other non-data-encoding attributes of marks (e.g., typeface, background color). Our search engine indexes this style and structure information as well as metadata about the webpage containing the chart. We show how visualization developers can search the collection to find visualizations that exhibit specific design characteristics and thereby explore the space of possible designs. We also demonstrate how researchers can use the search engine to identify commonly used visual design patterns and we perform such a demographic design analysis across our collection of D3 charts. A user study reveals that visualization developers found our style and structure based search engine to be significantly more useful and satisfying for finding different designs of D3 charts, than a baseline search engine that only allows keyword search over the webpage containing a chart.

    View details for DOI 10.1109/TVCG.2019.2934431

    View details for Web of Science ID 000506166100114

    View details for PubMedID 31442980

  • Supporting Visual Artists in Programming through Direct Inspection and Control of Program Execution Li, J., Brandt, J., Mech, R., Agrawala, M., Jacobs, J., ACM ASSOC COMPUTING MACHINERY. 2020
  • Answering Questions about Charts and Generating Visual Explanations Kim, D., Hoque, E., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2020
  • Generating Audio-Visual Slideshows from Text Articles Using Word Concreteness Leake, M., Shin, H., Kim, J. O., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2020
  • Text-based Editing of Talking-head Video ACM TRANSACTIONS ON GRAPHICS Fried, O., Tewari, A., Zollhofer, M., Finkelstein, A., Shechtman, E., Goldman, D. B., Genova, K., Jin, Z., Theobalt, C., Agrawala, M. 2019; 38 (4)
  • Pinpoint: A PCB Debugging Pipeline Using Interruptible Routing and Instrumentation Strasnick, E., Follmer, S., Agrawala, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Optimizing Portrait Lighting at Capture-Time Using a 360 Camera as a Light Probe Jane, L. E., Fried, O., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2019: 221–32
  • How to Design Voice Based Navigation for How-To Videos Chang, M., Anh Truong, Wang, O., Agrawala, M., Kim, J., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • VisiBlends: A Flexible Workflow for Visual Blends Chilton, L. B., Petridis, S., Agrawala, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • View-Dependent Video Textures for 360 degrees Video Liu, S. J., Agrawala, M., DiVerdi, S., Hertzmann, A., ACM ASSOC COMPUTING MACHINERY. 2019: 249–62
  • Editing Spatial Layouts through Tactile Templates for People with Visual Impairments Li, J., Kim, S., Miele, J. A., Agrawala, M., Follmer, S., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Visual Rhythm and Beat ACM TRANSACTIONS ON GRAPHICS Davis, A., Agrawala, M. 2018; 37 (4)
  • 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

  • An Interactive Pipeline for Creating Visual Blends Chilton, L. B., Petridis, S., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2018: 188–90
  • Improving Comprehension of Measurements Using Concrete Re-Expression Strategies Hullman, J., Kim, Y., Nguyen, F., Speers, L., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2018
  • RecipeScape: An Interactive Tool for Analyzing Cooking Instructions at Scale Chang, M., Guillain, L. V., Jung, H., Hare, V. M., Kim, J., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2018
  • Facilitating Document Reading by Linking Text and Tables Kim, D., Hoque, E., Kim, J., Agrawala, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 423–34
  • Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress DESIGN THINKING RESEARCH: MAKING DISTINCTIONS: COLLABORATION VERSUS COOPERATION Kim, J., Agrawala, M., Bernstein, M. S., Plattner, H., Meinel, C., Leifer, L. 2018: 105–29
  • Computational Video Editing for Dialogue-Driven Scenes ACM TRANSACTIONS ON GRAPHICS Leake, M., Davis, A., Truong, A., Agrawala, M. 2017; 36 (4)
  • 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

  • Mosaic: Designing Online Creative Communities for Sharing Works-in-Progress Kim, J., Agrawala, M., Bernstein, M. S., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2017: 246–58
  • Shot Orientation Controls for Interactive Cinematography with 360 degrees Video Pavel, A., Hartmann, B., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2017: 289-297
  • Scanalog: Interactive Design and Debugging of Analog Circuits with Programmable Hardware Strasnick, E., Agrawala, M., Follmer, S., ACM ASSOC COMPUTING MACHINERY. 2017: 321-330
  • Automatically Visualizing Audio Travel Podcasts Lee, J., Gordon, M., Agrawala, M., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2017: 165-167
  • Generating Personalized Spatial Analogies for Distances and Areas Kim, Y., Hullman, J., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2016: 38-48
  • Data-driven Adaptive History for Image Editing Chen, H., Wei, L., Hartmann, B., Agrawala, M., Spencer, S. N. ASSOC COMPUTING MACHINERY. 2016: 103-111
  • QuickCut: An Interactive Tool for Editing Narrated Video Anh Truong, Berthouzoz, F., Li, W., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2016: 497-507
  • VidCrit: Video-Based Asynchronous Video Review Pavel, A., Goldman, D. B., Hartmann, B., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2016: 517-528
  • Capture-Time Feedback for Recording Scripted Narration Rubin, S., Berthouzoz, F., Mysore, G. J., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2015: 191-199
  • SceneSkim: Searching and Browsing Movies Using Synchronized Captions, Scripts and Plot Summaries Pavel, A., Goldman, D. B., Hartmann, B., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2015: 181-190
  • Interactive Furniture Layout Using Interior Design Guidelines ACM TRANSACTIONS ON GRAPHICS Merrell, P., Schkufza, E., Li, Z., Agrawala, M., Koltun, V. 2011; 30 (4)
  • CommentSpace: Structured Support for Collaborative Visual Analysis Willett, W., Heer, J., Hellerstein, J. M., Agrawala, M., ACM ASSOC COMPUTING MACHINERY. 2011: 3131-3140
  • 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