Alex Mariakakis

Zahra Hassanzadeh, Anne Hsu, Rachel Kornfield et al.

This paper explores the design space for one-minute digital interventions that prompt immediate action without onboarding or sensing. By embracing Fogg's Behavior Model and four design principles informed by literature, the goal of these interventions was to provide triggers that encourage actions so simple that even people with low motivation would be willing to complete them. We examined the utility of these prompts by conducting a 14-day study with 22 participants interested in making small lifestyle improvements in at least one of three domains: physical activity, healthy eating, and mental well-being. When combined with insights drawn from participants' rewrites of our prompts, our findings suggest that intentional personalization through co-authorship could be a lightweight personalization mechanism that balances relevance with low friction.

Camellia Zakaria, Aryan Sadeghi, Weaam Jaafar et al.

Black carbon (BC) emissions in urban areas are primarily driven by traffic, with hotspots near major roads disproportionately affecting marginalized communities. Because BC monitoring is typically performed using costly and specialized instruments. there is little to no available data on BC from local traffic sources that could help inform policy interventions targeting local factors. By contrast, traffic monitoring systems are widely deployed in cities around the world, highlighting the imbalance between what we know about traffic conditions and what do not know about their environmental consequences. To bridge this gap, we propose a machine learning-driven system that extracts visual information from traffic video to capture vehicles behaviors and conditions. Combining these features with weather data, our model estimates BC at street level, achieving an R-squared value of 0.72 and RMSE of 129.42 ng/m3 (nanogram per cubic meter). From a sustainability perspective, this work leverages resources already supported by urban infrastructure and established modeling techniques to generate information relevant to traffic emission. Obtaining BC concentration data provides actionable insights to support pollution reduction, urban planning, public health, and environmental justice at the local municipal level.

Dhruv Verma, Andrew Qiu, Roberto Rangel et al.

We present Lumosaic, a compact active hyperspectral video system designed for real-time capture of dynamic scenes. Our approach combines a narrowband LED array with a coded-exposure-pixel (CEP) camera capable of high-speed, per-pixel exposure control, enabling joint encoding of scene information across space, time, and wavelength within each video frame. Unlike passive snapshot systems that divide light across multiple spectral channels simultaneously and assume no motion during a frame's exposure, Lumosaic actively synchronizes illumination and pixel-wise exposure, improving photon utilization and preserving spectral fidelity under motion. A learning-based reconstruction pipeline then recovers 31-channel hyperspectral (400-700 nm) video at 30 fps and VGA resolution, producing temporally coherent and spectrally accurate reconstructions. Experiments on synthetic and real data demonstrate that Lumosaic significantly improves reconstruction fidelity and temporal stability over existing snapshot hyperspectral imaging systems, enabling robust hyperspectral video across diverse materials and motion conditions.

Ananya Bhattacharjee, Pan Chen, Linjia Zhou et al.

We investigate users' perspectives on an online reflective question activity (RQA) that prompts people to externalize their underlying emotions on a troubling situation. Inspired by principles of cognitive behavioral therapy, our 15-minute activity encourages self-reflection without a human or automated conversational partner. A deployment of our RQA on Amazon Mechanical Turk suggests that people perceive several benefits from our RQA, including structured awareness of their thoughts and problem-solving around managing their emotions. Quantitative evidence from a randomized experiment suggests people find that our RQA makes them feel less worried by their selected situation and worth the minimal time investment. A further two-week technology probe deployment with 11 participants indicates that people see benefits to doing this activity repeatedly, although the activity may get monotonous over time. In summary, this work demonstrates the promise of online reflection activities that carefully leverage principles of psychology in their design.

Chunjong Park, Morelle Arian, Xin Liu et al.

Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good night's sleep is important for job performance. However, both real-world sleep behavior and job performance are hard to measure at scale. In this work, we show that people's everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. We present an observational study in which we objectively tracked the sleep behavior and job performance of salespeople (N = 15) and athletes (N = 19) for 18 months, using a mattress sensor and online mobile app. We first demonstrate that cumulative sleep measures are correlated with job performance metrics, showing that an hour of daily sleep loss for a week was associated with a 9.0% and 9.5% reduction in performance of salespeople and athletes, respectively. We then examine the utility of online app interaction time as a passively collectible and scalable performance indicator. We show that app interaction time is correlated with the performance of the athletes, but not the salespeople. To support that our app-based performance indicator captures meaningful variation in psychomotor function and is robust against potential confounds, we conducted a second study to evaluate the relationship between sleep behavior and app interaction time in a cohort of 274 participants. Using a generalized additive model to control for per-participant random effects, we demonstrate that participants who lost one hour of daily sleep for a week exhibited 5.0% slower app interaction times. We also find that app interaction time exhibits meaningful chronobiologically consistent correlations with sleep history, time awake, and circadian rhythms. Our findings reveal an opportunity for online app developers to generate new insights regarding cognition and productivity.

Xin Liu, Yuang Li, Josh Fromm et al.

Super-resolution (SR) is a coveted image processing technique for mobile apps ranging from the basic camera apps to mobile health. Existing SR algorithms rely on deep learning models with significant memory requirements, so they have yet to be deployed on mobile devices and instead operate in the cloud to achieve feasible inference time. This shortcoming prevents existing SR methods from being used in applications that require near real-time latency. In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock. The SplitSRBlock supports channel-splitting, allowing the residual blocks to retain spatial information while reducing the computation in the channel dimension. SplitSR has a hybrid design consisting of standard convolutional blocks and lightweight residual blocks, allowing people to tune SplitSR for their computational budget. We evaluate our system on a low-end ARM CPU, demonstrating both higher accuracy and up to 5 times faster inference than previous approaches. We then deploy our model onto a smartphone in an app called ZoomSR to demonstrate the first-ever instance of on-device, deep learning-based SR. We conducted a user study with 15 participants to have them assess the perceived quality of images that were post-processed by SplitSR. Relative to bilinear interpolation -- the existing standard for on-device SR -- participants showed a statistically significant preference when looking at both images (Z=-9.270, p<0.01) and text (Z=-6.486, p<0.01).

Alex Mariakakis, Sifang Chen, Bichlien Nguyen et al.

Environmental hazards often go unnoticed because they are invisible to the naked eye, posing risks to our health over time. Project Calico aims to raise awareness of these risks by augmenting everyday fashion with color-changing chemical sensors that can be observed at a glance or captured by a smartphone camera. Project Calico leverages existing cosmetic and fabrication processes to democratize environmental sensing, enabling creators to make their own accessories. We present two fashionable instantiations of Project Calico involving UV irradiation. EcoHair, created by hair treatment, is UV-sensitive hair that intensifies in color saturation depending on the UV intensity. EcoPatches, created by inkjet printing, can be worn as temporary tattoos that change their color to reflect cumulative UV exposure over time. We present findings from two focus groups regarding the Project Calico vision and gathered insights from their overall impressions and projected use patterns.