Alan Sullivan
- Phone: 617-621-7596
- Email:
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Position:
Research / Technical Staff
Computer Vision Group Manager -
Education:
Ph.D., University of California at Berkeley, 1993 -
Research Areas:
Alan's Quick Links
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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.
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News & Events
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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, RoboticsBrief- 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, RoboticsBrief- 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.
See All News & Events for Alan -
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Awards
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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 Contacts: Alan Sullivan; Ziming Zhang
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- 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
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MERL Publications
- , "Analysis of the contribution and temporal dependency of LSTM layers for reinforcement learning tasks", IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Explanable AI Workshop, June 2019.
- , "Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics", IEEE International Conference on Robotics and Automation (ICRA), May 2019.
- , "Sem-GAN: Semantically-Consistent Image-to-Image Translation", IEEE Winter Conference on Applications of Computer Vision (WACV), DOI: 10.1109/WACV.2019.00196, January 2019.
- , "Learning Tasks in a Complex Circular Maze Environment", Modeling the Physical World: Perception, Learning, and Control, NIPS Workshop, December 2018.
- , "Simulation to Real Transfer Learning with Robustified Policies for Robot Tasks", arXiv, September 2018.BibTeX Download PDFAbout TR2018-144
- @techreport{MERL_TR2018-144,
- author = {van Baar, J. and Corcodel, R. and Sullivan, A. and Jha, D. and Romeres, D. and Nikovski, D.N.},
- title = {Simulation to Real Transfer Learning with Robustified Policies for Robot Tasks},
- institution = {MERL - Mitsubishi Electric Research Laboratories},
- address = {Cambridge, MA 02139},
- number = {TR2018-144},
- month = sep,
- year = 2018,
- url = {https://www.merl.com/publications/TR2018-144/}
- }
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Free Downloads
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Videos
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MERL Issued Patents
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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
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Title: "System and Method for Distributed Machining Simulation"
