MERL - Mitsubishi Electric Research Laboratories

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Alan Sullivan

Alan Sullivan
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  • Biography

    First at U.C. Berkeley, then at Lawrence Livermore National Laboratory, Alan studied interactions between ultra-high intensity femtosecond lasers and plasmas. Prior to joining MERL in 2007, he worked at a series of start-ups where he developed a novel volumetric 3D display technology. At MERL His research interests include computational geometry and computer graphics.

  • Recent News & Events

    •  NEWS   MERL Researchers Demonstrate Robot Learning Technology at CEATEC'18
      Date: October 15, 2018 - October 19, 2018
      Where: CEATEC'18, Makuhari Messe, Tokyo
      MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; Alan Sullivan; Jeroen van Baar; William Yerazunis
      Research Areas: Artificial Intelligence, Computer Vision, Data Analytics, Robotics
      Brief
      • MERL's work on robot learning algorithms was demonstrated at CEATEC'18, Japan's largest IT and electronics exhibition and conference held annually at Makuhari Messe near Tokyo. A team of researchers from the Data Analytics Group at MERL and the Artificial Intelligence Department of the Information Technology Center (ITC) of MELCO presented an interactive demonstration of a model-based artificial intelligence algorithm that learns how to control equipment autonomously. The algorithm developed at MERL constructs models of mechanical equipment through repeated trial and error, and then learns control policies based on these models. The demonstration used a circular maze, where the objective is to drive a ball to the center of the maze by tipping and tilting the maze, a task that is difficult even for humans; approximately half of the CEATEC'18 visitors who tried to steer the ball by means of a joystick could not bring it to the center of the maze within one minute. In contrast, MERL's algorithm successfully learned how to drive the ball to the goal within ten seconds without the need for human programming. The demo was at the entrance of MELCO's booth at CEATEC'18, inviting visitors to learn more about MELCO's many other AI technologies on display, and was seen by an estimated more than 50,000 visitors over the five days of the expo.
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    •  NEWS   MERL Researchers Demonstrate New Model-Based AI Learning Technology for Equipment Control
      Date: February 14, 2018
      Where: Tokyo, Japan
      MERL Contacts: Devesh Jha; Daniel Nikovski; Diego Romeres; William Yerazunis; Jeroen van Baar; Alan Sullivan
      Research Areas: Optimization, Computer Vision, Artificial Intelligence, Data Analytics, Robotics
      Brief
      • New technology for model-based AI learning for equipment control was demonstrated by MERL researchers at a recent press release event in Tokyo. The AI learning method constructs predictive models of the equipment through repeated trial and error, and then learns control rules based on these models. The new technology is expected to significantly reduce the cost and time needed to develop control programs in the future. Please see the link below for the full text of the Mitsubishi Electric press release.
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  • Awards

    •  AWARD   R&D100 award for Deep Learning-based Water Detector
      Date: November 16, 2018
      Awarded to: Ziming Zhang, Alan Sullivan, Hideaki Maehara, Kenji Taira, Kazuo Sugimoto
      MERL Contact: Alan Sullivan
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • Researchers and developers from MERL, Mitsubishi Electric and Mitsubishi Electric Engineering (MEE) have been recognized with an R&D100 award for the development of a deep learning-based water detector. Automatic detection of water levels in rivers and streams is critical for early warning of flash flooding. Existing systems require a height gauge be placed in the river or stream, something that is costly and sometimes impossible. The new deep learning-based water detector uses only images from a video camera along with 3D measurements of the river valley to determine water levels and warn of potential flooding. The system is robust to lighting and weather conditions working well during the night as well as during fog or rain. Deep learning is a relatively new technique that uses neural networks and AI that are trained from real data to perform human-level recognition tasks. This work is powered by Mitsubishi Electric's Maisart AI technology.
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  • Research Highlights

  • Internships with Alan

    • CV1385: Embedded Computer Vision

      MERL is seeking a self-motivated intern to work on embedded computer vision systems, with a particular focus on studying different embedded platforms and tool chains for computer vision automotive applications. The ideal candidate should have a Master’s degree related to automotive technology, experience working with high-performance embedded devices, and NOT be enrolled in a PhD program. Some knowledge of deep learning and computer vision would be highly valued, as is experience with sensor fusion algorithms. Proficiency in software development in C++ and Python is required as are strong communication and writing skills. The intern will collaborate closely with both researchers at MERL as well as engineers in our applied research team working on ADAS and autonomy in Detroit. The internship is for 6 months with a flexible start date. There is also an opportunity for full-time employment in our applied research team after successful completion of the internship.

    See All Internships at MERL

  • MERL Publications

    See All Publications for Alan

  • Free Downloads

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Distributed Machining Simulation"
      Inventors: Sullivan, Alan; Lee, Teng-Yok; Thornton, Jay E.
      Patent No.: 10,353,352
      Issue Date: Jul 16, 2019
    • Title: "System and Method for Determining Feedrates of Machining Tools"
      Inventors: Erdim, Huseyin; Sullivan, Alan
      Patent No.: 9,892,215
      Issue Date: Feb 13, 2018
    • Title: "Method and System for Rendering 3D Distance Fields"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 9,336,624
      Issue Date: May 10, 2016
    • Title: "System and Method for Performing Undo and Redo Operations during Machining Simulation"
      Inventors: Sullivan, Alan; Konobrytskyi, Dmytro
      Patent No.: 9,304,508
      Issue Date: Apr 5, 2016
    • Title: "Hybrid Adaptively Sampled Distance Fields"
      Inventors: Sullivan, Alan
      Patent No.: 9,122,270
      Issue Date: Sep 1, 2015
    • Title: "Analyzing Volume Removed During Machining Simulation"
      Inventors: Erdim, Huseyin; Sullivan, Alan
      Patent No.: 8,935,138
      Issue Date: Jan 13, 2015
    • Title: "System and Method for Simulating Machining Objects"
      Inventors: Sullivan, Alan; Manukyan, Liana
      Patent No.: 8,838,419
      Issue Date: Sep 16, 2014
    • Title: "System and Method for Identifying Defects of Surfaces Due to Machining Processes"
      Inventors: Sullivan, Alan; Yoganandan, Arun R
      Patent No.: 8,532,812
      Issue Date: Sep 10, 2013
    • Title: "System and Method for Optimizing Machining Simulation"
      Inventors: Sullivan, Alan; Yerazunis, William S.
      Patent No.: 8,483,858
      Issue Date: Jul 9, 2013
    • Title: "Volume-Based Coverage Analysis for Sensor Placement in 3D Environments"
      Inventors: Sullivan, Alan; Garaas, Tyler W
      Patent No.: 8,442,306
      Issue Date: May 14, 2013
    • Title: "A Method for Reconstructing a Distance Field of a Swept Volume at a Sample Point"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 8,265,909
      Issue Date: Sep 11, 2012
    • Title: "A Method for Simulating Numerically Controlled Milling Using Adaptively Sampled Distance Fields"
      Inventors: Frisken, Sarah F.; Perry, Ronald N.; Sullivan, Alan
      Patent No.: 8,010,328
      Issue Date: Aug 30, 2011
    See All Patents for MERL