Jen Rose Smith

About

Jen Rose Smith is an assistant professor in the Department of Geography at the University of Washington. She is a dAXunhyuu (Eyak, Alaska Native) geographer whose research interests include the intersections of coloniality, race, and indigeneity, as examined through aesthetic and literary contributions, archival evidences, and experiential embodied knowledges. She holds a Ph.D. in Comparative Ethnic Studies from UC Berkeley, where she also earned her Master's Degree, and a BA in English Literature and the Environment from the University of Alaska, Southeast. Smith has published in several academic journals, including Environment and Planning D: Society and Space, Transactions of the Institute of British Geographers, and The Geographical Journal. Her first book, 'Ice Geographies: The Colonial Politics of Race and Indigeneity in the Arctic,' was released by Duke University Press in May 2025.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Physical medicine and rehabilitation
• Engineering
• Medicine
• Human–computer interaction
• Neuroscience

Selected publications

• Proximity Perception in Human-Centered Robotics: A Survey on Sensing Systems and Applications

  IEEE Transactions on Robotics · 2021 · 138 citations

  Senior authorCorresponding

  Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey article, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. In this domain, proximity sensors are typically deployed in two scenari…

  DOI

• Brain-Computer-Spinal Interface Restores Upper Limb Function After Spinal Cord Injury

  IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2021 · 53 citations

  Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and require frequent calibration and high computational complexity. Furthermore, most applications of closed-loop stimulation act on peripheral nerves or muscles, resulting in rapid muscle fatigue. Here w…

  DOI

• Acoustic Balance: Weighing in Ultrasonic Non-Contact Manipulators

  IEEE Robotics and Automation Letters · 2022-07-12 · 19 citations

  articleSenior author

  Acoustic traps and levitation systems can lift, translate and manipulate a wide range of objects and materials without contact. This enables new manipulation capabilities for robots that may not be possible otherwise. This paper presents an acoustic balance, a contactless method for weighing acoustically trapped objects in air. The method works by measuring a step response: the system commands a change in the phase of the acoustic emitters, which results in a sudden change in the equilibrium pos…

  PublisherDOI

• Lessons for Robotics From the Control Architecture of the Octopus

  Frontiers in Robotics and AI · 2022-07-18 · 16 citations

  articleOpen access

  Biological and artificial agents are faced with many of the same computational and mechanical problems, thus strategies evolved in the biological realm can serve as inspiration for robotic development. The octopus in particular represents an attractive model for biologically-inspired robotic design, as has been recognized for the emerging field of soft robotics. Conventional global planning-based approaches to controlling the large number of degrees of freedom in an octopus arm would be computat…

  PublisherOA PDFDOI

• NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras

  2023-09-30 · 13 citations

  articleOpen access

  We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low power (1.1 mW) but only outputs grey-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second…

  PublisherOA PDFDOI

Recent grants

• CI-ADDO-EN: Infrastructure for the RF-Powered Computing Community

  NSF · $988k · 2013–2018

• CRI:CI:SUSTAIN: Next-Generation, Sustainable Infrastructure for the RF-Powered Computing Community

  NSF · $980k · 2018–2023

Frequent coauthors

• Alanson P. Sample

  University of Michigan–Ann Arbor

  56 shared

• Aaron Parks

  52 shared

• Vamsi Talla

  37 shared

• Yi Zhao

  Shenyang Institute of Engineering

  30 shared

• Shyamnath Gollakota

  University of Washington

  29 shared

• Saman Naderiparizi

  27 shared

• Benjamin H. Waters

  24 shared

• Zerina Kapetanovic

  Stanford University

  20 shared

Education

• Ph.D., Comparative Ethnic Studies

  UC Berkeley

• M.S., Comparative Ethnic Studies

  UC Berkeley

• B.A., English Literature and the Environment

  University of Alaska, Southeast

Awards & honors

• On the Brinck Award Winner (February 13, 2026)

Similar researchers at University of Washington

• Sarah Elwoodh-49
• Michael Brownh-39
• Mark Ellish-37