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Naren Dasan

My interests are in Artifical Intellegence, Robotics, and Tangible Interaction Design mostly surrounding systems with multiple agents. I've done other work in the past on distributed and embedded system, the web and high performace. Right now I am working on the developement of Autonomous Vehicles at NVIDIA. I am a Colorado native and completed my undergrad at the University of Illinois at Urbana-Champaign in Computer Engineering. In my free time I work on / advocate for open source, play rugby, soccer and tennis, race bikes and from 2016 to 2017 I served as the Chair of the UIUC Chapter of the ACM.

Academic and Professional Timeline

  • 2018 / NVIDIA / Automotive Solution Architect
  • 2017 / NVIDIA / Automotive Solution Architect Intern
  • 2016 / University of Illinois Urbana-Champaign - Bretl Research Group / Undergraduate Research Assistant under Professor Tim Bretl
  • 2016 / Nest Labs / Embedded Software Engineering Intern
  • 2014 - 2018 / University of Illinois Urbana-Champaign - Vision Group / Undergraduate Research Assistant under Professor Derek Hoiem
  • 2013 - 2014 / University of Colorado Boulder - Correll Lab / High School Research Assistant under Professor Nikolaus Correll

Publications, Preprints and Technical Reports

  • [arXiv:1606.05002] 3DFS: deformable dense depth fusion and segmentation for object reconstruction from a handheld camera

    We propose an approach for 3D reconstruction and segmentation of a single object placed on a flat surface from an input video. Our approach is to perform dense depth map estimation for multiple views using a proposed objective function that preserves detail. The resulting depth maps are then fused using a proposed implicit surface function that is robust to estimation error, producing a smooth surface reconstruction of the entire scene. Finally, the object is segmented from the remaining scene using a proposed 2D-3D segmentation that incorporates image and depth cues with priors and regularization over the 3D volume and 2D segmentations. We evaluate 3D reconstructions qualitatively on our Object-Videos dataset, comparing to fusion, multiview stereo, and segmentation baselines. We also quantitatively evaluate the dense depth estimation using the RGBD Scenes V2 dataset [Henry et al. 2013] and the segmentation using keyframe annotations of the Object-Videos dataset.

    June 15, 2016 -
    Tanmay Gupta Daeyun Shin Naren Sivagnanadasan Derek Hoiem
  • [TEI '14] Gesture based distributed user interaction system for a reconfigurable self-organizing smart wall

    We describe user interactions with the self-organized amorphous wall, a modular, fully distributed system of computational building blocks that communicate locally for creating smart surfaces and functional room dividers. We describe a menu and a widget-based approach in which functions are color-coded and can be selected by dragging them from module to module on the surface of the wall. We also propose an on-off switch gesture and a dial gesture each spanning multiple units as canonical input mechanisms that are realized in a fully distributed way.

    February 16, 2014 -
    Nicholas Farrow Naren Sivagnanadasan Nikolaus Correll


  • Lecture: Intro to Convolutional Neural Nets

    "A quick crash course in using neural nets for Computer Vision. Builds up from logistic regression to CNNs with implementations in PyTorch"

    April 7, 2018 -
    University of Illinois Urbana-Champaign - [email protected] SAIL
  • Intro to Convolutional Neural Nets and Implications of Deep Learning and AI

    "Artificial Intelligence has entered a great age of productivity, with massive strides in Computer Vision, Natural Language Processing and Task Learning being enabled by the exponential growth in data availability and the computing power enabled by General Purpose GPU (GPGPU) computing. Developers can now create near state of the art AI applications on their laptops. This talk will cover one of the main tools in deep learning and AI: Convolutional Neural Networks (CNN), how to build one, and how to apply it to a problem like handwriting recognition. It will then explore some of the current problems and approaches in the field of AI such as self driving cars, machine translation, and robotics."

    August 23, 2017 -
    University of Colorado - OIT Tech Talk
  • Navigating Learning in the Multidisciplinary World

    "What skills are necessary to succeed in a world that's becoming more and more complex? Is it better to specialize, focusing on one subject area? Or is it better to have an inter-disciplinary approach? Why the future of work lies not within specialization, but in the ability to draw on design thinking and immediate problem solving to solve the world's big challenges."

    April 16, 2014 -
    TEDxMileHigh - Emergence


  • CS 196 - CS Projects

    First and Second year projects course teaching prototyping, ideation and introduction to sofware engineering;

    Spring 2015 - Fall 2015 -
    Dept. of Computer Science - University of Illinois Urbana-Champaign

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