Maneesh Agrawala

About

Maneesh Agrawala is the Forest Baskett Professor of Computer Science at Stanford University and the Director of the Brown Institute for Media Innovation. He holds a Ph.D. in Computer Science from Stanford University, obtained in 2002, and a B.S. in Mathematics from Stanford University, earned in 1994. His academic appointments include professorships in the Departments of Computer Science and Electrical Engineering at Stanford, where he is also a faculty affiliate of the Institute for Human-Centered Artificial Intelligence (HAI). Prior to his current position, he was a Professor of Electrical Engineering and Computer Science at the University of California, Berkeley from 2005 to 2015. Agrawala's research focuses on computer graphics, human-computer interaction, and visualization, with an emphasis on investigating how cognitive design principles can be used to improve the effectiveness of audio/visual media. His work aims to discover design principles and implement them in both interactive and automated design tools. Throughout his career, he has received numerous honors and awards, including a MacArthur Foundation Fellowship, an NSF CAREER Award, a SIGGRAPH Significant New Researcher Award, and fellowships from the Sloan Foundation and the ACM. He also serves as an advisor for the Human Computation Journal and is involved in various professional organizations and advisory roles.

Research topics

• Computer Science
• Artificial Intelligence
• Multimedia
• World Wide Web
• Algorithm
• Physics
• Business
• Programming language
• Advertising
• Political Science

Selected publications

• Adding Conditional Control to Text-to-Image Diffusion Models

  2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2023 · 3476 citations

  Senior authorCorresponding

  We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters fro…

  DOI

• Adding Conditional Control to Text-to-Image Diffusion Models

  arXiv (Cornell University) · 2023 · 208 citations

  Senior authorCorresponding

  We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters fro…

  DOI

• Crosscast: Adding Visuals to Audio Travel Podcasts

  2020 · 45 citations

  Senior authorCorresponding

  Audio travel podcasts are a valuable source of information for travelers. Yet, travel is, in many ways, a visual experience and the lack of visuals in travel podcasts can make it difficult for listeners to fully understand the places being discussed. We present Crosscast: a system for automatically adding visuals to audio travel podcasts. Given an audio travel podcast as input, Crosscast uses natural language processing and text mining to identify geographic locations and descriptive keywords wi…

  DOI

• Analysis of Faces in a Decade of US Cable TV News

  2021 · 25 citations

  Cable (TV) news reaches millions of US households each day. News stakeholders such as communications researchers, journalists, and media monitoring organizations are interested in the visual content of cable news, especially who is on-screen. Manual analysis, however, is labor intensive and limits the size of prior studies. We conduct a large-scale, quantitative analysis of the faces in a decade of cable news video from the top three US cable news networks (CNN, FOX, and MSNBC), totaling 244,038…

  PublisherDOI

• A mathematical foundation for foundation paper pieceable quilts

  ACM Transactions on Graphics · 2021 · 16 citations

  Senior authorCorresponding

  Foundation paper piecing is a popular technique for constructing fabric patchwork quilts using printed paper patterns. But, the construction process imposes constraints on the geometry of the pattern and the order in which the fabric pieces are attached to the quilt. Manually designing foundation paper pieceable patterns that meet all of these constraints is challenging. In this work we mathematically formalize the foundation paper piecing process and use this formalization to develop an algorit…

  PublisherOA PDFDOI

Recent grants

• DC: Medium: Collaborative Research: Data Intensive Computing: Scalable, Social Data Analysis

  NSF · $667k · 2010–2014

• CAREER: Design Principles, Algorithms, and Interfaces for Visual Communication

  NSF · $400k · 2007–2012

• III: Small: Extracting Data and Structure from Charts and Graphs for Analysis, Reuse and Indexing

  NSF · $499k · 2017–2021

Frequent coauthors

• David Salesin

  University of Washington

  39 shared

• Pat Hanrahan

  Stanford University

  38 shared

• Doantam Phan

  30 shared

• Barbara Tversky

  Stanford University

  30 shared

• Julie Heiser

  30 shared

• Jeff Klingner

  Perfect Harmony Health

  28 shared

• Chris Stolte

  27 shared

• Wilmot Li

  Adobe Systems (United States)

  27 shared

Labs

• Maneesh Agrawala's LabPI

  Computer Graphics, Human-Computer Interaction, Visualization, Information Visualization, Scientific Visualization

Education

• Ph.D.

  Stanford University

• M.S.

  University of California, Berkeley

• B.S.

  University of California, Berkeley

Awards & honors

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

Similar researchers at Stanford University

• Russell Poldrackh-155
• Vinod Menonh-127
• Russ B. Altmanh-119