June 19th, 2026 – 12:00-1:00 pm, EC4-2101A

This presentation describes the current state of the interdisciplinary Living Lab being developed on campus. The goal is to connect researchers across disciplines and make it easier to bring real environmental data into classes, undergraduate design projects, and research.

We will give a quick overview of the existing sensor setups in E2, RCH, and CPH, which have been collecting data for about 1 to 2 years. We will also discuss the planned expansion of the network, including new sensors to measure salt concentration in Laurel Creek, which is scheduled for deployment this summer.

Finally, we will introduce our new platform, DataHub.uwaterloo.ca, which brings together all data collected from campus sensors and citizen science into a single easy-to-use resource for both research and teaching. 
 

June 12th, 2026 – 12:00-1:00 pm, EC4-2101A

3D Gaussian Splatting (3DGS) has emerged as a leading method for photorealistic scene reconstruction from multi-view images, yet existing approaches treat reconstruction as a purely visual optimization problem, ignoring the physical laws that govern real-world scenes. This talk explores a bidirectional relationship between physics and vision within the 3DGS framework. In the forward direction, physical and geometric priors are introduced at each stage of the pipeline: upsampled point cloud initialization improves reconstruction quality without architectural changes, mesh-coupled Gaussian representations enable physics simulation of dynamic scenes, and differentiable rigid-body simulation provides trajectory supervision during object occlusion in 4D reconstruction. In the inverse direction, we examine how observed fracture behaviour in food materials can be used to recover latent material parameters via surrogate modelling and reinforcement learning, enabling novel simulation of physically plausible fracture dynamics. Together, these contributions demonstrate that neither physics nor vision alone is sufficient for faithful dynamic reconstruction and simulation, and that integrating the two disciplines yields measurable and qualitatively meaningful improvements across all stages of the pipeline.
 

June 5th, 2026 – 12:00-1:00 pm, EC4-2101A

Previous eye tracking research in sports has relied on hand annotated areas of interest to label data. This bottlenecked the number of participants and trials that could be used for research leading to small sample sizes and expensive experimental set ups. Using RF-DETR and controlled environments, it is possible to automate ball detection with reasonable accuracy speeding up the analysis and increasing the sample size. The presentation will show how I automated data collection and how the automated ball detection helps with analyzing data in baseball and volleyball.
 

May 29th, 2026 – 12:00-1:00 pm, EC4-2101A

The recent advancements in artificial intelligence (AI) have been changing how we live, work, and dream, revolutionizing many industries through its versatile capabilities. Despite its many benefits, numerous challenges remain when integrating AI agents into our current workflows. No matter how advanced AI becomes, humans will remain in the loop at all times—focused more on goals, operating at higher levels of abstraction, and analyzing domain-specific circumstances. A major barrier to effective collaboration lies in the communication between humans and AI agents. In this talk, I will introduce my research, which takes a step toward addressing this gap using advanced interaction and visualization techniques to create harmony between humans and AI. I will share my perspective on the factors that influence human-AI communication, as well as showcase a range of example projects situated in various applications such as programming, information-seeking, and design.
 

May 22th, 2026 – 12:00-1:00 pm, EC4-2101A

My seminar focuses on 3D reconstruction of thin and strand-like structures such as hair, fur, fibres, and occluders. I first discuss direction-aware kernel support for neural implicit reconstruction of fuzzy geometry, then present a controlled 3D Gaussian Splatting study where thin occluders partially hide a target object. The goal is to understand how directional structure affects both representation quality and reconstruction capacity under occlusion.

March 27th, 2026 – 12:00-1:00 pm, EC4-2101A

Traditionally, most studies of microbes involve bulk population measurements of, e.g. well-mixed populations grown in culture tubes. However, spatial distribution is known to play an important role in microbial ecology. Our group has developed a pipeline to characterize ecological interactions among microbial populations from time-lapse microscopy at single cell resolution. We collect images of mixed populations of ~1000 cells constrained to two dimensions, at a frequency of 3-5 minutes. Image processing (segmentation and object tracking) is implemented with CellProfiler and our custom software package TrackRefiner. We then use processed image data to calibrate agent-based models of cellular activity. These models allow us to assess hypotheses regarding cellular behaviour and build toward predictive model-based design of interventions in natural and engineered microbial systems.

March 20th, 2026 – 12:00-1:00pm, EC4-2101A

Understanding complex physical systems—from engineered materials to human movement—requires models that capture structure, variability, and cross-scale interaction. In this talk, I will present our group’s work on data-driven modeling across manufacturing, biomechanics, and human–robot interaction. I will first highlight the use of generative and image-based models to synthesize realistic microstructures and predict material properties, enabling exploration and optimization. I will then present approaches for modeling and predicting human gait, including detection and prediction of pathological behaviors such as freezing of gait in Parkinson’s disease. Finally, I will describe learning-based methods for real-time movement estimation and human–robot interaction in wearable robotics, illustrating how these models support adaptive and personalized control.

March 13th, 2026 – 12:00-1:00pm, EC4-2101A

ENVGO is a Waterloo-based startup developing electric hydrofoiling boats that “fly” above the water to dramatically reduce drag and enable efficient, zero-emission marine transportation. In this talk, ENVGO staff will discuss how computer vision and machine learning are being integrated into their development. Presenter April Blaylock (UW Mechanical Engineering B04, M07) is a co-Founder who leads the AI, computer vision, and autonomy development.

March 6th, 2026 – 12:00-1:00pm, EC4-2101A

 GeoMate is an AI-driven mapping company transforming how cities and autonomous systems see the world. Our vision pipeline automatically extracts roads, lanes, and urban features from aerial and satellite imagery to create simulation-ready HD maps and dynamic mobility environments. These maps power applications in autonomous driving, delivery robotics, and smart-city planning, enabling large-scale, accurate, and continuously updated digital twins of the real world.  This presentation will introduce GeoMate’s problem space and demo our RealSimE platform, which delivers simulation-ready maps for autonomous driving and advanced driver assistance system (ADAS) validation. Additionally, we’ll outline real-world challenges and approaches to using remote sensing data for computer vision applications.

February 27th, 2026 – 12:00-1:00pm, EC4-2101A

Rapid bone loss in older adults is associated with calcification of the medial layer of large arteries. Does the same process happen in astronauts? Nine astronauts in the Vascular Calcium study from the University of Waterloo were imaged with coronary CT scans and high resolution peripheral computed tomography (HR pQCT) of the wrist and ankle before and after ~6 months in space. Our group is in the early stages of analysis and looking for insights into the best methods to analyze these images.