Events
HCIL BBL Speaker Series: (Re)presented Talks: Maxine Perroni-Scharf and Haozhe Zhou
Event Start Date: Thursday, February 26, 2026 - 12:30 pm
Event End Date: Thursday, February 26, 2026 - 1:30 pm
Location: In Person: HBK 2105 and Virtual/Zoom
This week we’ll hear from two researchers about their UIST papers!
Talk Title: SustainaPrint: Making the Most of Eco-Friendly Filaments
Abstract: 3D printing is widely used for prototyping and everyday objects, but it also generates significant plastic waste. Although biodegradable and recycled filaments offer a more sustainable alternative, their reduced and inconsistent mechanical strength often limits their use in functional prints. As a result, makers frequently rely on virgin plastics when durability is required. This talk situates sustainable 3D printing within the practical constraints of personal fabrication and examines how design tools can help makers better use eco-friendly materials.
In this talk, I will present SustainaPrint, an interactive system that supports multi-material 3D printing by assigning stronger filament to high-stress regions while maximizing the use of eco-friendly filament elsewhere. The system combines stress simulation, material-aware segmentation, and a low-cost mechanical testing toolkit that allows users to evaluate filament strength using simple known loads. Through printed examples of everyday load-bearing objects, I will discuss how these tools enable more reliable and accessible sustainable printing, and reflect on broader directions for material-aware interfaces in digital fabrication.
Bio: Maxine Perroni-Scharf is a Ph.D. student in Electrical Engineering and Computer Science at MIT, advised by Stefanie Mueller in the HCI Engineering Group at CSAIL. Her research focuses on human-computer interaction, computer graphics, and computational fabrication, with an emphasis on machine-learning-driven design tools and material-aware 3D printing workflows for sustainable digital fabrication. Her work has appeared at CHI, SIGGRAPH, UIST, ICML, RA-L, and MobiSys. Maxine has been supported by the Adobe Women in Technology Scholarship, the MathWorks Fellowship, the MIT Morningside Academy for Design Fellowship, and the MIT Andrew and Erna Viterbi Fellowship. She was previously a research intern at Adobe and Google, and actively serves on the Executive Committee of ACM Women in Graphics (WiGRAPH) and the MIT Graduate Application Assistance Program.
Talk Title: Towards Human Motion Sensing with Flexible IMU Placement
Abstract: Abstract: IMUs are regularly used to sense human motion, recognize activities, and estimate full-body pose. Users are typically required to place sensors in predefined locations that are often dictated by common wearable form factors and the machine learning model’s training process. Consequently, despite the increasing number of everyday devices equipped with IMUs, the limited adaptability has significantly constrained the user experience to only using a few well-explored device placements (e.g., wrist and ears). In this paper, we rethink IMU-based motion sensing by acknowledging that signals can be captured from any point on the human body. We introduce IMU over Continuous Coordinates (IMUCoCo), a novel framework that maps signals from a variable number of IMUs placed on the body surface into a unified feature space based on their spatial coordinates. IMUCoCo supports pose estimation and activity recognition across both typical and atypical sensor placements. Overall, IMUCoCo supports significantly more flexible use of IMUs for motion sensing than the state-of-the-art, allowing users to place their sensor-laden devices according to their needs and preferences. The framework also supports the ability to change device locations depending on the context and suggests placement depending on the use case.
Haozhe Zhou
Bio: Haozhe Zhou is a PhD student in Societal Computing at Carnegie Mellon University’s School of Computer Science, where he is co-advised by Professors Yuvraj Agarwal and Mayank Goel within Synergy Labs and the SmaSH Lab. His research focuses on developing adaptive and generalizable human sensing systems that operate reliably in heterogeneous real-world environments. By building new machine learning and system foundations, Haozhe enables sensing platforms to adapt dynamically to diverse device placements, sensor types, and contexts.
Additional Information:
Please contact infoevents@umd.edu at least one week prior to the event to request disability accommodations. In all situations, a good faith effort (up until the time of the event) will be made to provide accommodations.
For parking information and directions, please visit the Transportation Services website or view the campus map.
Speaker(s): Maxine Perroni-Scharf, Ph.D. student, Electrical Engineering and Computer Science, MIT ; Haozhe Zhou, PhD student, Societal Computing, Carnegie Mellon University’s School of Computer Science