WristSense / EuroViz

[March 30th, 2020] Two updates with regards to papers.  Our short paper for EuroViz 2020 entitled “Characterizing Exploratory Behaviors on a Personal Visualization Interface Using Interaction Logs” was accepted to appear.  Congrats to Poorna on her hard work in driving the paper through.

Our paper presented last week at WristSense 2020 entitled “Improved Sleep Detection Through the Fusion of Phone Agent and Wearable Data Streams” won Honorable Mention for the Apple Best Paper Award.  Congrats to Stephen on the presentation and Gonzalo on their work for the paper.

Two new publications @ WristSense

[January 16th, 2020] Very good news for my student Gonzalo Martinez and post-doc Steve Mattingly who had their papers accepted for WristSense 2020.

  • The first paper entitled “Improved Sleep Detection Through the Fusion of Phone Agent and Wearable Data Streams” focuses on the extent to which phone agent data, specifically the screen on / off state, impacts the accuracy of time to bed and wake time as observed by the wearable.
  • The second paper entitled “On the Quality of Real-world Wearable Data in a Longitudinal Study of Information Workers” explores the quality of wearable data in terms of data consistency, interruptions / missing data, and charging behavior from the Garmin vivoSmart 3.

Both of these efforts are drawn from our data for Tesserae with one presented by Gonzalo and the other presented by Steve at the WristSense workshop at IEEE PerCom this year. Congrats again to Gonzalo and Steve on a job well done in driving these papers!

Back from Sabbatical

[January 10th, 2020] Alas, all good things must come to an end and my sabbatical is officially wrapped up as of this Friday with the start of the spring semester this coming week.  This spring, I will be teaching the second iteration of my course on Advanced Wireless Networks and will officially be back on campus full-time at my office in 211B Cushing Hall.

Continue reading “Back from Sabbatical”

Sabbatical Update

[November 11th, 2019] Brief update as I am well over halfway through my sabbatical this fall.  Some interesting new projects in the hopper that we will highlight the projects bake a bit more but I can give a bit of a preview of some of the efforts.

  • WiFi Leaf Detection: We are looking how WiFi signals can be used to detect the presence (or lack thereof) leaves on the various trees to help optimize leaf pickup for St. Joseph County (and many other municipalities with burn bans).  Some interesting early work taking our monster WiFi capture rig (all 2.4 GHz channels, all 5 GHz channels) done by Al-Amin Mohammed, the lead graduate student on the project.  Many thanks to our undergraduate REU student Alexandra Berjarano who kicked off this effort this past summer as part of our Wireless Institute REU site.
  • QUIC-enabled FMNC / PASS: We are working on porting our efforts for Fast Mobile Network Characterization (FMNC) and Provider-Assisted Storage Sub-system (PASS) into a unified library riding on QUIC.  This should be an interesting adventure and if successful, a very cool unified platform for measurement that leverage QUIC with legacy support for TCP fallback taking advantage of our older work.
  • Tesserae: Our Tesserae project continues.  Look for some interesting paper updates now that the vast majority of our data collection has wrapped for the effort.

Papers at CHI 2019

[May 14, 2019] We had two papers appear at CHI in the Case Studies track.  One paper was a general overview of the Tesserae study and the other was an overview of our publicly accessible social media corpus related to Tesserae (all participants opted in who agreed to share data).  Both papers are accessible via the CHI website.

Major Grant Award for $7.9M

[September 7, 2017] Under the direction of ND CSE Profs. Aaron Striegel (PI), Nitesh Chawla (co-PI), and Dong Wang (Senior Personnel), the University of Notre Dame has been awarded a $7.9M contract from the Intelligence Advanced Research Projects Agency (IARPA). The research will involve advances in data fusion from wearables, smartphones, and social media to better understand performance in cognitively demanding workplaces. The work will involve instrumenting 750 working professionals over an entire year to create a landmark dataset for studying the myriad of factors that impact workplace performance.