Edward Y. Chang

Edward Y. Chang

Stanford, California, United States
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About

Home Page: http://infolab.stanford.edu/~echang/

Edward Chang is an adjunct…

Activity

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Experience

  • Stanford University Graphic

    Stanford University

    Stanford, California

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    Tokyo, Japan; Palo Alto, CA

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    San Francisco Bay Area, Taipei

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    Berkeley, CA

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    Mountain View, California

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    Santa Barbara, California

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    Mountain View, California

Education

  • Stanford University Graphic

    Stanford University

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    Databases, Machine Learning, Information Retrieval

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    Operating Systems, Distributed Databases

  • Operations research, optimization, programming language

Publications

  • Big Data Analytics for Large-Scale Multimedia Search

    WILEY

    A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability

    The amount of multimedia data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for large scale multimedia search. This book addresses that need, covering the area of multimedia retrieval and placing a special emphasis on scalability. It reports the recent works in large scale multimedia search…

    A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability

    The amount of multimedia data available every day is enormous and is growing at an exponential rate, creating a great need for new and more efficient approaches for large scale multimedia search. This book addresses that need, covering the area of multimedia retrieval and placing a special emphasis on scalability. It reports the recent works in large scale multimedia search, including research methods and applications, and is structured so that readers with basic knowledge can grasp the core message while still allowing experts and specialists to drill further down into the analytical sections.

    Big Data Analytics for Large-Scale Multimedia Search covers: representation learning, concept and event-based video search in large collections; big data multimedia mining, large scale video understanding, big multimedia data fusion, large-scale social multimedia analysis, privacy and audiovisual content, data storage and management for big multimedia, large scale multimedia search, multimedia tagging using deep learning, interactive interfaces for big multimedia and medical decision support applications using large multimodal data.

    Addresses the area of multimedia retrieval and pays close attention to the issue of scalability
    Presents problem driven techniques with solutions that are demonstrated through realistic case studies and user scenarios
    Includes tables, illustrations, and figures
    Offers a Wiley-hosted BCS that features links to open source algorithms, data sets and tools
    Big Data Analytics for Large-Scale Multimedia Search is an excellent book for academics, industrial researchers, and developers interested in big multimedia data search retrieval. It will also appeal to consultants in computer science problems and professionals in the multimedia industry.

    See publication

  • Nomadic Eternity 游牧人的永恒

    Tsinghua Publisher

    A collection of 24 poems written in 20 years depicting the four stages of my journey of faith. The four stages are youth (innocence), suffering, healing (hope), and mission (faith).

    See publication

  • Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception, Springer, September 2011

    Springer

    "Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception" covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge…

    "Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception" covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions. The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, Machine Learning, Large-scale Data Mining, Database, and Multimedia Information Retrieval. Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.

    See publication

  • 200+ Technical Publications

    Google Scholar

    See publication

Patents

  • 100+ US Granted Patents

    US 100+

    100+ patents filed at Google and HTC (DeepQ).

Honors & Awards

  • ACM Fellow for contributions to scalable machine learning and healthcare

    ACM

    Data-centric machine learning is the paradigm of learning representation from data. When data is voluminous, the algorithms must be able to run on parallel CPUs/GPUs to ensure fast training completion. When training data is inadequate, methods must be employed to improve data quality and diversity for effective representation learning. Ed's work on parallel machine learning algorithms since 2005 and active learning since 2000 are pioneer work in scalable machine learning. From 2013, his team…

    Data-centric machine learning is the paradigm of learning representation from data. When data is voluminous, the algorithms must be able to run on parallel CPUs/GPUs to ensure fast training completion. When training data is inadequate, methods must be employed to improve data quality and diversity for effective representation learning. Ed's work on parallel machine learning algorithms since 2005 and active learning since 2000 are pioneer work in scalable machine learning. From 2013, his team of researchers, engineers, and clinicians implemented a set of medical IoTs powered by AI for mobile diagnosis of 12 common diseases. His team in 2017 won the top-2 XPRIZE award of US$1M. Tricorder marks the future of AI healthcare, which can provide affordable, accessible, and high-quality care.

  • ACM SigMM Test of Time Paper Award

    ACM

    This award presented annually to a researcher who has made significant and lasting contributions to multimedia computing, communication and applications. My paper published in 2001 (20 years ago), received this prestigious award.

    Support Vector Machine Active Learning for Image Retrieval,
    S Tong, EY Chang, ACM Multimedia Conference, 107-118, 2001

  • XPRIZE Tricorder top-2 (out of 310)

    XPRIZE Foundation

    Of the 300 teams that joined the pursuit of the Qualcomm Tricorder XPRIZE, Final Frontier Medical Devices and Dynamical Biomarkers Group were both announced winners at the Qualcomm Tricorder XPRIZE awards ceremony on April 12, 2017.

    Final Frontier Medical Devices was announced the highest performing team and received $2.5M for their achievement and Dynamical Biomarkers Group received $1M for 2nd place. Both teams exceeded the competition requirements for user experience, nearly met the…

    Of the 300 teams that joined the pursuit of the Qualcomm Tricorder XPRIZE, Final Frontier Medical Devices and Dynamical Biomarkers Group were both announced winners at the Qualcomm Tricorder XPRIZE awards ceremony on April 12, 2017.

    Final Frontier Medical Devices was announced the highest performing team and received $2.5M for their achievement and Dynamical Biomarkers Group received $1M for 2nd place. Both teams exceeded the competition requirements for user experience, nearly met the challenging audacious benchmarks for diagnosing the 13 disease states, and with their prototypes, have taken humanity one step closer to realizing Gene Roddenberry’s 23rd century sci-fi vision. XPRIZE congratulates Final Frontier Medical Devices and Dynamical Biomarkers Group on their amazing achievements.

  • IEEE Fellow for contributions to scalable machine learning (foundation of AI)

    IEEE

    Scalable machine learning is an essential foundation of artificial intelligence, which nowadays relies on quickly learning knowledge from big data to achieve inference intelligence. When I started this project at Google in 2006, my colleagues wrote it off immediately for a good reason. Then, a peer director considered that only sub-linear algorithms should be worked on because of voluminous data. My team insisted that more complex data types such as imagery and video demand super-linear…

    Scalable machine learning is an essential foundation of artificial intelligence, which nowadays relies on quickly learning knowledge from big data to achieve inference intelligence. When I started this project at Google in 2006, my colleagues wrote it off immediately for a good reason. Then, a peer director considered that only sub-linear algorithms should be worked on because of voluminous data. My team insisted that more complex data types such as imagery and video demand super-linear algorithms to be effective. In 2011, our data-driven invited paper to analyze images was rejected by Transactions of IEEE. One of the reviewers (I believe to be one of the CNN inventors) insisted that models are more critical, not data. Despite I argued that even a good model requires a large volume of training data to improve classification accuracy, the "invited" paper was rejected. Around 2010, I championed Stanford professor Fei-Fei Li's ImageNet project with a 260k Google research grant. When in 2012, AlexNet successfully used CNN with ImageNet to demonstrate what big data plus a good model could accomplish, years of endeavors on scalable machine learning started to bear fruit. These days, from computer vision, robotics, to AlphaGo, effective intelligence requires big data, and big data requires scalable machine learning.

    Many researchers have worked on improving computation speed for years. This is a milestone that acknowledges all predecessors. AI is still in its infancy, and the goal of high machine intelligence is still beyond reach. Let us work diligently to advance the field.

  • CES Best Wearable Award

    CES, Las Vegas

    HTC/Under Amour Healthbox was awarded as the best wearable for its design and capabilities.

  • Best Technical Demonstration, Panorama 360

    ACM Conference of Multimedia

    HTC developed the first 360 x 180 panorama and launched with HTC M8 flag-ship phone. The significance of this Pan360 invention is that it uses inertial navigation sensors to facilitate image-frame stitching. Through such, frame stitching is fast and accurate. Pan360 also intelligently introduces non-invasive sensor calibration in each panorama shooting session to ensure sensors are properly calibrated.

    The innovative UI, which provides a user positions to point the camera, is adopted…

    HTC developed the first 360 x 180 panorama and launched with HTC M8 flag-ship phone. The significance of this Pan360 invention is that it uses inertial navigation sensors to facilitate image-frame stitching. Through such, frame stitching is fast and accurate. Pan360 also intelligently introduces non-invasive sensor calibration in each panorama shooting session to ensure sensors are properly calibrated.

    The innovative UI, which provides a user positions to point the camera, is adopted by Google since Pixel 3.

  • Best Paper Award

    WWW International Conference

    For the idea of propagating ads on social networks based on a heat diffusion model.

  • Google Innovation Award

    Google

    For using scalable machine learning to power Google Q&A system and successfully launched the product in 60+ nations.

  • NSF Career Award

    National Science Foundation

Languages

  • English

    Native or bilingual proficiency

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