News & Events

30 MERL Talks found.

  •  TALK    [MERL Seminar Series 2021] Prof. Marco Di Renzo presents talk at MERL entitled Reconfigurable Intelligent Surfaces for Wireless Communications
    Date & Time: Tuesday, November 9, 2021; 1:00 PM EST
    Speaker: Prof. Marco Di Renzo, CNRS & Paris-Saclay University
    Research Areas: Communications, Electronic and Photonic Devices, Signal Processing
    • A Reconfigurable Intelligent Surface (RIS) is a planar structure that is engineered to have properties that enable the dynamic control of the electromagnetic waves. In wireless communications and networks, RISs are an emerging technology for realizing programmable and reconfigurable wireless propagation environments through nearly passive and tunable signal transformations. RIS-assisted programmable wireless environments are a multidisciplinary research endeavor. This presentation is aimed to report the latest research advances on modeling, analyzing, and optimizing RISs for wireless communications with focus on electromagnetically consistent models, analytical frameworks, and optimization algorithms.
  •  TALK    [MERL Seminar Series 2021] Dr. Hsiao-Yu (Fish) Tung presents talk at MERL entitled Learning to See by Moving: Self-supervising 3D scene representations for perception, control, and visual reasoning
    Date & Time: Tuesday, November 2, 2021; 1:00 PM EST
    Speaker: Dr. Hsiao-Yu (Fish) Tung, MIT BCS
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Robotics
    • Current state-of-the-art CNNs can localize and name objects in internet photos, yet, they miss the basic knowledge that a two-year-old toddler has possessed: objects persist over time despite changes in the observer’s viewpoint or during cross-object occlusions; objects have 3D extent; solid objects do not pass through each other. In this talk, I will introduce neural architectures that learn to parse video streams of a static scene into world-centric 3D feature maps by disentangling camera motion from scene appearance. I will show the proposed architectures learn object permanence, can imagine RGB views from novel viewpoints in truly novel scenes, can conduct basic spatial reasoning and planning, can infer affordability in sentences, and can learn geometry-aware 3D concepts that allow pose-aware object recognition to happen with weak/sparse labels. Our experiments suggest that the proposed architectures are essential for the models to generalize across objects and locations, and it overcomes many limitations of 2D CNNs. I will show how we can use the proposed 3D representations to build machine perception and physical understanding more close to humans.
  •  TALK    [MERL Seminar Series 2021] Prof. Greg Ongie presents talk at MERL entitled Learning to Solve Inverse Problems in Computational Imaging: Recent Innovations
    Date & Time: Tuesday, October 12, 2021; 1:00 PM EST
    Speaker: Prof. Greg Ongie, Marquette University
    MERL Host: Hassan Mansour
    Research Areas: Computational Sensing, Machine Learning, Signal Processing
    • Deep learning is emerging as powerful tool to solve challenging inverse problems in computational imaging, including basic image restoration tasks like denoising and deblurring, as well as image reconstruction problems in medical imaging. This talk will give an overview of the state-of-the-art supervised learning techniques in this area and discuss two recent innovations: deep equilibrium architectures, which allows one to train an effectively infinite-depth reconstruction network; and model adaptation methods, that allow one to adapt a pre-trained reconstruction network to changes in the imaging forward model at test time.
  •  TALK    [MERL Seminar Series 2021] Dr. Ruohan Gao presents talk at MERL entitled Look and Listen: From Semantic to Spatial Audio-Visual Perception
    Date & Time: Tuesday, September 28, 2021; 1:00 PM EST
    Speaker: Dr. Ruohan Gao, Stanford University
    MERL Host: Gordon Wichern
    Research Areas: Computer Vision, Machine Learning, Speech & Audio
    • While computer vision has made significant progress by "looking" — detecting objects, actions, or people based on their appearance — it often does not listen. Yet cognitive science tells us that perception develops by making use of all our senses without intensive supervision. Towards this goal, in this talk I will present my research on audio-visual learning — We disentangle object sounds from unlabeled video, use audio as an efficient preview for action recognition in untrimmed video, decode the monaural soundtrack into its binaural counterpart by injecting visual spatial information, and use echoes to interact with the environment for spatial image representation learning. Together, these are steps towards multimodal understanding of the visual world, where audio serves as both the semantic and spatial signals. In the end, I will also briefly talk about our latest work on multisensory learning for robotics.
  •  TALK    [MERL Seminar Series 2021] Prof. David Bergman presents talk in MERL Seminar Series titled, Integration of Analytics Techniques for Algorithmic Sports Betting
    Date & Time: Tuesday, September 14, 2021; 1:00 PM EST
    Speaker: Prof. David Bergman, University of Connecticut
    MERL Host: Arvind Raghunathan
    Research Areas: Data Analytics, Machine Learning, Optimization
    • The integration of machine learning and optimization opens the door to new modeling paradigms that have already proven successful across a broad range of industries. Sports betting is a particularly exciting application area, where recent advances in both analytics and optimization can provide a lucrative edge. In this talk we will discuss three algorithmic sports betting games where combinations of machine learning and optimization have netted me significant winnings.