Personal website: https://mkoshkina.github.io/

Linkedin: https://www.linkedin.com/in/kseniiabuzko/

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.

LinkedIn: www.linkedin.com/in/jerryhu-jt

Email: dklepach@uwaterloo.ca

Github: https://github.com/dklpp

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.

I am currently doing my master’s under the superivision of Prof. David Clausi and Prof. Yuhao Chen. My research falls in the intersection of classical machine learning, deep learning and computer vision.