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Events for April 30, 2018

  • Study Day

    Mon, Apr 30, 2018

    Viterbi School of Engineering Student Affairs

    University Calendar

    Audiences: Everyone Is Invited

    Contact: Sheryl Koutsis

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  • Rapid, Efficient, and Robust Neuroimage Analysis with Deep Neural Networks

    Rapid, Efficient, and Robust Neuroimage Analysis with  Deep Neural Networks

    Mon, Apr 30, 2018 @ 11:30 AM - 12:30 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Mert R. Sabuncu, Electrical and Computer Engineering, Cornell University

    Talk Title: Rapid, Efficient, and Robust Neuroimage Analysis with Deep Neural Networks

    Series: Medical Imaging Seminar Series

    Abstract: Abstract: Neuroimaging is entering a new era of unprecedented scale and complexity. Soon, we will have datasets including brain images from more than 100,000 individuals. The fundamental challenges in analyzing and exploiting these data are going to be computational. Today, widely-used traditional neuroimage analysis tools, such as FreeSurfer or FSL, are computationally demanding and offer limited flexibility, while cutting-edge tools based on modern machine learning techniques require large amounts of annotated training data, and/or are untested at scale. In this talk, I will present our recent work on two fundamental image analysis problems: registration and segmentation. In image registration, I will introduce a novel framework that allows us to train a neural network that rapidly computes a smooth and invertible nonlinear (diffeomorphic) deformation that aligns two input images, in an unsupervised fashion (i.e. without using ground-truth registrations). I will show experiments on 7000+ brain MRI scans with state-of-the-art results. In the second part, I will present a new segmentation framework that flexibly handles multiple labeling protocols, and generalizes well to new datasets and new segmentation labels, with little additional training.

    Biography: Mert R. Sabuncu is a faculty member of Cornel's School of Electrical Engineering and Computer Engineering.At Cornell, Mert directs a lab that focuses on biomedical image analysis for scientific (e.g. brain mapping) and clinical (e.g., computer-aided diagnosis) applications.Mert's research employs and contributes to the toolkits of machine learning, image processing, computer vision, and other modern computational methods.Mert has a Ph.D. from Princeton Electrical Engineering and was post-doc at MIT, where he worked with Polina Golland. Before joining Cornell, he was a faculty member at the A.A. Martinos Center for Biomedical Imaging (Harvard Medical School and Massachusetts General Hospital).

    Host: Professor Richard Leahy

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • PhD Defense - Stephanie Kemna

    Mon, Apr 30, 2018 @ 02:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Stephanie Kemna

    Committee: Gaurav Sukhatme (chair), Nora Ayanian, David Caron

    Title: Multi-Robot Strategies for Adaptive Sampling with Autonomous Underwater Vehicles
    Time & place: Monday April 30th, 2pm, RTH406
    Biologists and oceanographers are sampling lakes and oceans worldwide, to obtain data on the natural phenomena they are interested in. For example, measuring algae abundance to understand and explain potentially harmful algal blooms. Typical methods of sampling are (a) taking physical water samples and sensor measurements from boats, (b) deploying sensor packages off of buoys, docks or other static man-made structures, and more recently (c) running pre-programmed missions with aquatic robots. The use of robot teams could significantly improve cost- and time-efficiency of lake and ocean sampling, allowing persistent and efficient mapping of the water column in finer resolution. Additionally, these systems may be able to intelligently gather data without needing a lot of prior information. We envision a scenario where one or two groups of biologists or oceanographers come together for monitoring a lake, bringing their autonomous vehicles with biological sensors.
    Our focus is on improving sampling efficiency, and environmental modeling performance, through the addition of (decentralized) coordination approaches for multi-robot sampling systems. In this presentation, I will discuss adaptive informative sampling techniques for single- and multi-robot deployments. Adaptive informative sampling means that the robots adapt their trajectory online, based on sampled data, while incorporating information-theoretic metrics to seek out the most informative sampling locations. Through simulation studies we have shown the benefits that can be obtained from employing adaptive informative sampling approaches. We include field results to show the feasibility of running adaptive informative sampling on board an autonomous underwater vehicle (AUV).
    For the multi-robot case, we show the benefits that can be obtained from adding data sharing between vehicles, and we explore the trade-off of surface based (Wi-Fi) communications versus underwater (acoustic) communications. In terms of coordinating multiple vehicles, I will first discuss an explicit coordination approach, based on dynamic estimation of Voronoi partitions, which shows potential for improving modeling performance in the early stages of model creation. I then discuss a method we developed for how to best start adaptive sampling runs when no prior data is available. Finally, I will discuss the use of implicit coordination through asynchronous surfacing with a surface-based data hub. We showed that performance across surfacing strategies was similar, though some turned out to be less consistent in performance, and some methods showed potential for greatly reducing the number of surfacing events needed.
    Overall, I have developed several methods for adaptive informative sampling with AUVs, focusing on multi-robot coordination and field constraints. The results of my studies show the benefits and potential of incorporating data sharing and coordination strategies into adaptive sampling routines for multi-robot systems.

    Location: Ronald Tutor Hall of Engineering (RTH) - 406

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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  • Tumo Workshops Information Session

    Mon, Apr 30, 2018 @ 05:00 PM - 07:00 PM

    Information Technology Program (ITP)

    Workshops & Infosessions

    How does the opportunity to share the skills you've learned at the Information Technology Program sound? Now's your chance to design and propose a workshop of your choice to teach students this summer at the Tumo Center for Creative Technologies in Armenia; expenses, including airfare and accommodation, are covered by Tumo.

    Tumo is an innovative digital media studio where students are guided by skilled educators and media professionals in animation, digital media, video game design, and web development. Workshops are hands-on and result in a group or individual project that students can exhibit in a final presentation. Each workshop is taught in the afternoon and can include one or several groups of around 20 teens. Workshop leaders usually visit for a minimum of two weeks, depending on the workshop's topic.

    Ready to learn more? ITP will be hosting a representative from Tumo to discuss this summer opportunity on Monday, April 30 at 5p.m. in KAP 160. We hope you can join us to learn more about this opportunity.

    Location: Kaprielian Hall (KAP) - 160

    Audiences: Undergrad

    Contact: Tim Gotimer

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