Ken Christofferson

👋 Hi, I'm Ken

I am a PhD student advised by Alex Mariakakis and Joseph Cafazzo who lead the Computational Health and Interaction (CHAI) lab at the University of Toronto Department of Computer Science and the Centre for E-Health Innovation at the University Health Network respectively.

My research focuses on using the sensors embedded in common devices (e.g., mobile phone, earbud, smartspeaker) and novel machine learning techniques to acquire information relevant to human health as well as exploring how that information is best used to improve health outcomes. I am interested in a broad array of sensing modalities and use cases, please check out my research and projects pages for more.

🏫 Education

  • _PhD Computer Science - Univeristy of Toronto (in progress)
  • _MS Engineering, Data Science, and Information Technology - Tsingua Univeristy (2021)
  • _MS Technology Innovation - Univeristy of Washington (2021)
  • _BA International Studies - American University (2011)

🧰 Mentoring and Outreach

📨 Get In Touch

kenc at cs dot toronto dot edu

Recent Updates

Ultrasonic Occluded Ear Canal Deformation Analysis for Hands-Free Silent Text Entry

Abstract: Speech recognition systems are widely used in mobile devices. These systems enable natural hands-free input mobile devices input. However, speech reconition systems can be awkward to use ...

Sleep Sound Classification Using ANC-Enabled Earbuds

Abstract: Standard sleep quality assessment methods require custom hardware and professional observation, limiting the diagnosis of sleep disorders to specialized sleep clinics. In this work, we l...

An AI Driven, Mechanistically-Grounded Geospatial Liquefaction Model for Rapid Response and Scenario Planning

Abstract: Geospatial models for predicting soil liquefaction infer subsurface traits via satellite remote sensing and mapped information, rather than directly measure them with subsurface tests. Fi...

Induced Acoustic Resonance for Noninvasive Bone Fracture Detection Using Digital Signal Processing and Machine Learning

Abstract: A bone fracture is a complete or incomplete discontinuity in a bone, often caused by an impact. While extreme fractures are sometimes obvious, most fractures require radiographic imaging ...