Seminar

Seminar and roundtable with Prof. Robert Soden on HCI and data science research in climate action

Amanda Brown — Wed, 11 Mar 2026 14:15:35 +0000

Seminar and roundtable with Prof. Robert Soden on HCI and data science research in climate action Amanda Brown Wed, 2026-03-11 - 10:15

HCI and Data Science Research Towards Development of a Public Climate Observatory: The View From Toronto.

What: SYDE Seminar

Who: Prof. Robert Soden, UofT

When: Tuesday, March 24th, 1PM - 2PM

Where: E5 - 6127

Join Professor Soden on March 24 at 1:00 p.m. in E5 6127 as he examines why three decades of rapid technological advancement, spanning satellite imagery, urban sensors, GIS, and machine learning, have yet to translate into effective implementations of climate action. He will draw on human-computer interaction and science & technology studies to rethink how we use climate data, as well as discuss his work on the development of a public climate observatory for the City of Toronto.

Bio: Professor Robert Soden is an assistant professor in the Department of Computer Science and School of Environment at the University of Toronto, whose work is improving the data and information that experts, governments, and communities use to respond to climate change and disaster. His current projects include redesigning the data and indicators used to support monitoring progress towards urban resilience, ethnography and design research into improving the accessibility of severe weather risk communication, and the development of equitable climate and disaster risk models.

Follow-up Student Round Table Discussion with Prof. Robert Soden

Following the seminar, students are invited to E5 6111 to engage in a small-group discussion with Professor Soden about his work and the role of HCI and data science in climate change risk management. Sign-up here: https://forms.gle/cjdRAAMMUbpX1r887

SYDE Graduate Symposium 2026

Amanda Brown — Tue, 20 Jan 2026 16:20:01 +0000

SYDE Graduate Symposium 2026 Amanda Brown Tue, 2026-01-20 - 11:20

Grad Seminar: Advancing Semi-Supervised Domain Adaptive Semantic Segmentation Through Effective Source Integration Strategies

Amanda Brown — Thu, 04 Dec 2025 20:25:06 +0000

Grad Seminar: Advancing Semi-Supervised Domain Adaptive Semantic Segmentation Through Effective Source Integration Strategies Amanda Brown Thu, 2025-12-04 - 15:25

Presenter

Joshua Kurien, MASc candidate in Systems Design Engineering

Abstract

Semantic segmentation is a key visual recognition task with applications in medical imaging, remote sensing, and manufacturing, but developing high-performance models requires costly, dense pixel-level labels. Recent semi-supervised learning (SSL) methods built on vision foundation models offer strong segmentation performance with limited labeled data, motivating the question of how best to incorporate labeled source-domain data, like synthetic data, when available. This seminar presents a series of strategies for integrating source data into SSL pipelines, starting with simple source transfer and ensembling approaches that improve accuracy in an effective yet straightforward manner. While these methods introduce some training inefficiencies, we address them with DuCuMix, a dual-curriculum method that progressively samples source examples from easy to hard and applies a progressive mixing strategy to enhance diversity. Across synthetic-to-real and real-to-real benchmarks, this suite of methods consistently improves segmentation performance, demonstrating the value of structured source integration for label-efficient semantic segmentation.

Join on Teams or in-person in E5 6111.

This seminar counts towards the graduate student seminar attendance milestone!

Grad Seminar: Towards Adaptable and Deployable Wildlife Detection from Aerial Imagery

Amanda Brown — Tue, 25 Nov 2025 15:22:09 +0000

Grad Seminar: Towards Adaptable and Deployable Wildlife Detection from Aerial Imagery Amanda Brown Tue, 2025-11-25 - 10:22

Presenter

Jayden Hsiao, MASc candidate in Systems Design Engineering

Abstract

Accurate wildlife monitoring is critical for understanding ecosystem change, yet most conservation agencies still rely on manual interpretation of aerial imagery, a process that is slow, inconsistent, and difficult to scale. This talk presents two complementary advances aimed at making species detection from aerial platforms more adaptable and directly usable in real conservation workflows.

First, I introduce OpenWildlife, a multi-species open vocabulary detector trained across diverse aerial wildlife datasets. By using language guided grounding rather than fixed class labels, the model can identify a wide range of species including those not present in its training set and can be efficiently fine tuned with only a small number of annotated examples, giving conservationists a strong and broadly transferable starting point rather than requiring them to train a new model from scratch for each survey.

Second, I describe a deployed human in the loop annotation system built around this model and used operationally by the Arctic Eider Society for demographic analysis of eider duck populations in Hudson Bay. The workflow integrates automated predictions, regional correction tools, incremental fine tuning, and interface features designed for dense small object scenes typical of eider colonies, and in field use it substantially reduced annotation effort while enabling continuous model refinement as new imagery is collected.

Together, these components demonstrate a path toward wildlife detection systems that are both technically robust and practically deployable, supporting conservation programs that require adaptable and scalable tools rather than one off research models.

This seminar counts towards the graduate student seminar attendance milestone!

Grad Seminar: Pan-Arctic Weekly Sea-Ice Forecasts: A Large-Scale Baseline Study and Meta-Learning Ensemble Model

Amanda Brown — Tue, 25 Nov 2025 15:16:13 +0000

Grad Seminar: Pan-Arctic Weekly Sea-Ice Forecasts: A Large-Scale Baseline Study and Meta-Learning Ensemble Model Amanda Brown Tue, 2025-11-25 - 10:16

Presenter

Kiernan McGuigan, MASc candidate in Systems Design Engineering

Abstract

This thesis introduces a standardized benchmark for a diverse set of architectures for weekly Pan-Arctic sea-ice forecasts with a 13-week (91-day) horizon. This benchmark is introduced in combination with a meta-learning ensemble model which consistently produces improved ice forecasts. The diverse suite of deep learning baselines is trained and evaluated under a common protocol to predict sea-ice concentration (SIC), sea-ice thickness (SIT), and sea-ice presence (SIP) on a Pan-Arctic grid. Building on these baselines, the proposed Meta-Learner employs stacked generalization within a sequence-to-sequence forecasting setup, learning to fuse model outputs with spatio-temporal and lead-time context to improve robustness and reliability. Utilizing atmospheric reanalysis from ERA5 and oceanographic reanalysis from GLORYS12, the study assesses skill across short-, medium-, and long-lead regimes up to the 13 week horizon. Results demonstrate that the Meta-Learner outperforms the best individual baseline, with reductions in ice concentration MAE of ~11% and a reduction in ice thickness MAE of ~21% while reducing the cross-entropy in ice presence classification by ~5%. Improvements are most pronounced in lower variance across random initializations for all tracked metrics, indicating enhanced stability. The benchmark and ensemble framework provide a reproducible foundation for Pan-Arctic weekly forecasting and highlight learned ensembling as a practical pathway to more accurate and dependable SIC/SIT/SIP predictions at operationally relevant horizons.

This seminar counts towards the graduate student seminar attendance milestone!

Grad Seminar: Studying the Biomechanics of a Wheelchair Basketball Free Throw using Pose Estimation

Amanda Brown — Tue, 23 Sep 2025 16:07:25 +0000

Grad Seminar: Studying the Biomechanics of a Wheelchair Basketball Free Throw using Pose Estimation Amanda Brown Tue, 2025-09-23 - 12:07

Presenter

Hisham Mohammad, MASc candidate in Systems Design Engineering

Abstract

Wheelchair basketball is a popular Paralympic sport where athletes with varying disabilities compete under a point-based classification system. Lower-class athletes (1.0–2.5), with higher levels of disability, often struggle to engage their trunk and core muscles, while higher-class athletes (3.0–4.5) have greater functional ability and utilize their trunk extensively. Coaches must consider these functional disparities when formulating strategies and designing individualized training regimens.

Consistent free-throw shooting is critical in wheelchair basketball, as it offers an uncontested scoring opportunity. Higher-class athletes, who incorporate trunk motion, rely less on their arms for force generation, resulting in distinct shooting mechanics. Given the biomechanical variability arising from these physical differences, understanding individual shooting techniques is vital for optimizing performance.

Motion capture technologies are widely employed to analyze and improve athletic movements. However, traditional systems, such as wearable sensors and marker-based motion tracking, are often costly, time-intensive, and restrictive to mobility. Markerless motion capture systems address these limitations using computer vision techniques like pose estimation. Convolutional neural networks (CNNs) trained on large human image datasets can accurately detect joints and limbs, enabling real-time analysis. Commercial systems typically require multiple cameras, but deploying pose estimation CNNs on mobile devices allows motion analysis using only a built-in camera, enhancing portability and accessibility for sports training and biomechanical research.

This research focuses on designing and deploying pose estimation models within a mobile application to analyze the shooting arm's motion during a basketball free throw, with specific considerations for wheelchair basketball players. The pose estimation models, trained on a COCO-WholeBodyTM dataset to detect fingertip positions, were deployed on an iPhone and tested for accuracy and computational performance, particularly real-time motion analysis. The derived joint positions are used to calculate kinematic and dynamic metrics, including joint angles and torques.

The system's joint angle calculations were compared against the Vicon motion capture system. While upper arm and elbow angle errors had a root mean squared error (RMSE) within an acceptable range (less than 20◦), wrist angle errors exceeded 65◦ due to limitations in pose estimation accuracy and the iPhone camera's frame rate. To demonstrate the system's utility, two shooting studies were conducted: (1) a comparison of biomechanics between one-motion and two-motion shooting techniques and (2) a biomechanical analysis of the shooting arm contrasting a national-level class 1 wheelchair basketball athlete with class 4.5 able-bodied participants shooting from a basketball wheelchair.

Attend on Teams to count this seminar towards the graduate student seminar attendance milestone!

Grad Seminar: Analysis of Limitations of AI Tools for Pediatric Speech Language Pathology Documentation and Mitigation Strategies

Amanda Brown — Tue, 16 Sep 2025 18:09:34 +0000

Grad Seminar: Analysis of Limitations of AI Tools for Pediatric Speech Language Pathology Documentation and Mitigation Strategies Amanda Brown Tue, 2025-09-16 - 14:09

Presenter

Tia Tuinstra, MASc candidate in Systems Design Engineering

Abstract

Speech Language Pathology (SLP) is a therapy discipline offered by KidsAbility, a pediatric rehabilitation clinic in Southern Ontario. Documentation is a key part of SLP and other therapy practice guidelines and can take up significant portions of a therapist’s time. AI-based clinical documentation aids have been developed to help reduce this burden, and one such tool - MutuoHealth’s AutoScribe - has been piloted by KidsAbility. Though this AI tool has been beneficial to some therapy disciplines, the SLP clinicians face unique challenges when using these tools. This thesis seeks to explore the issues SLPs encounter with AI documentation tools and propose potential approaches to mitigate these issues.

Attend on Teams to count this seminar towards the graduate student seminar attendance milestone!

Grad Seminar: Label-free optical microscopy: Photon Absorption Remote Sensing (PARS) for histopathological imaging of tissues

Amanda Brown — Wed, 10 Sep 2025 13:52:02 +0000

Grad Seminar: Label-free optical microscopy: Photon Absorption Remote Sensing (PARS) for histopathological imaging of tissues Amanda Brown Wed, 2025-09-10 - 09:52

Presenter

Ben Ecclestone, PhD candidate in Systems Design Engineering

Abstract

Emerging label-free microscopy methods offer promising new avenues to view cells and tissues in their native environment while minimizing external influences. This is an exciting departure from gold standard chemical staining methods for visualizing microscopic cellular and tissue structures. Chemical labelling can unavoidably interfere with specimens’ physical and biochemical integrity. As a result, samples are effectively consumed by staining with only a single stain set normally applied to each sample. This limitation is impactful in applications such as clinical oncology and medical histopathology.

This seminar explores new methods for label-free histopathology. The goal of this research is to develop a label-free analog to chemical hematoxylin and eosin (H&E) staining, which is the gold standard contrast used in clinical diagnostics. Developments cover three main accomplishments: (1) developing label-free microscopy methods for H&E-like imaging, (2) exploring the biomolecular specificity of developed methods to validate the label-free H&E-like contrast, and (3) producing a label-free microscopy architecture capable of meeting the imaging requirements necessary for clinical adoption.

Presented research resulted in the development of a new optical absorption microscopy modality, Photon Absorption Remote Sensing (PARS). This comprehensive technique provides biomolecule-specific visualizations characterizing the dominant photophysical effects caused when photons are absorbed by a biomolecule, which may offer unique views into biomolecules’ excited state dynamics. By specifically probing specimens’ response to the absorption of deep ultraviolet light, PARS was shown to provide label-free contrast directly reminiscent of gold standard chemical H&E staining methods. In combination with deep learning-based image-to-image transforms, PARS was shown to produce emulated H&E images indistinguishable from chemically stained representations. This PARS-based H&E-like contrast was validated through established statistical methods. In addition, PARS emulated H&E-like visualizations were validated against chemical H&E staining through a clinical concordance study. Finally, a novel PARS architecture is presented which achieved pragmatic imaging performance nearing the requirements for clinical diagnostic settings. This microscope features a hybrid opto-mechanical scanning architecture which allows for high-speed MHz rate imaging (more than an order of magnitude faster than earlier PARS embodiments).

In total, the findings presented across this thesis encompass the development of a new variation of microscopy technique (PARS). This method provides unique views into the absorption and scattering characteristics of specimens opening a new avenue of label-free contrast. For the presented histopathology application, PARS can provide powerful H&E-like images which may circumvent key challenges of chemical staining. In clinical histopathology, this method could enhance the diagnostic utility of tissue specimens directly improving patient outcomes. Beyond histopathology, the principles of PARS may be directly applicable to a wide range of imaging applications spanning material science, biological research, and clinical diagnostics.

Attending this seminar will count towards the graduate student seminar attendance milestone!

Grad Seminar: Experimental Study on the Vibration Response of a Jackleg Hammer Drill

Amanda Brown — Tue, 19 Aug 2025 20:05:56 +0000

Grad Seminar: Experimental Study on the Vibration Response of a Jackleg Hammer Drill Amanda Brown Tue, 2025-08-19 - 16:05

Presenter

Srividya Kuppa, MASc candidate in Systems Design Engineering

Abstract

This seminar presents an experimental study on vibration response in jackleg hammer drills, which are widely used in underground mining. Unlike previous research that focused mainly on vibration at the handle, this work examines how vibration is transmitted through the full drill structure under real operating conditions. Measurements were collected with accelerometers mounted on the fronthead, main cylinder, backhead, and handle during different drilling phases (collaring, sustained drilling, and retraction). Analysis using time and frequency domain methods, including FFT and RMS, revealed that axial vibrations dominate during drilling and that vibration increases across all axes during collaring. A resonance condition was identified around 142 Hz in the handle assembly, highlighting a key source of amplification. These findings provide a system-level understanding of vibration propagation in jackleg drills and lay the foundation for improved design and mitigation strategies to reduce operator exposure.

Attending this seminar will count towards the graduate student seminar attendance milestone!

Join on Teams

Grad Seminar: Towards a Novel Optical Spectroscopy Technique Using Photon Absorption Remote Sensing

Amanda Brown — Mon, 18 Aug 2025 17:59:04 +0000

Grad Seminar: Towards a Novel Optical Spectroscopy Technique Using Photon Absorption Remote Sensing Amanda Brown Mon, 2025-08-18 - 13:59

Presenter

Jodh Singh Dhillon, MASc candidate in Systems Design Engineering

Abstract

We present an optical spectroscopy system based on photon absorption remote sensing (PARS) that simultaneously captures radiative and non-radiative sample relaxation following UV-excitation. Non-radiative relaxations are measured through probing the thermal and pressure induced refractive index changes of an excited sample, while a spectrometer is used to record radiative sample relaxation, which occurs in the form of fluorescence. We benchmark the generated non-radiative, radiative and combined total absorption PARS absorbance spectra of liquid eumelanin, NADH, DMSO and methylene blue samples against data collected from a UV-visible NanoDrop spectrophotometer. Finally, we leverage the absorption and fluorescence data collected from the PARS system to accurately determine the make-up of mixed craft ink samples. The PARS system overcomes the limitations of traditional optical spectroscopy techniques by broadening the range of samples which can be analyzed and providing a more detailed level of sample characterization.

Seminar

Seminar and roundtable with Prof. Robert Soden on HCI and data science research in climate action

HCI and Data Science Research Towards Development of a Public Climate Observatory: The View From Toronto.

Follow-up Student Round Table Discussion with Prof. Robert Soden

SYDE Graduate Symposium 2026

Save the date for the Systems Design Engineering (SYDE) Graduate Symposium on Tuesday, April 7, 2026 in Faculty Hall (PSE-7303/7363).

Abstract submission form

Symposium registration

Grad Seminar: Advancing Semi-Supervised Domain Adaptive Semantic Segmentation Through Effective Source Integration Strategies

Presenter

Abstract

Join on Teams or in-person in E5 6111.

This seminar counts towards the graduate student seminar attendance milestone!

Grad Seminar: Towards Adaptable and Deployable Wildlife Detection from Aerial Imagery

Presenter

Abstract

This seminar counts towards the graduate student seminar attendance milestone!

Grad Seminar: Pan-Arctic Weekly Sea-Ice Forecasts: A Large-Scale Baseline Study and Meta-Learning Ensemble Model

Presenter

Abstract

This seminar counts towards the graduate student seminar attendance milestone!

Grad Seminar: Studying the Biomechanics of a Wheelchair Basketball Free Throw using Pose Estimation

Presenter

Abstract

Attend on Teams to count this seminar towards the graduate student seminar attendance milestone!

Grad Seminar: Analysis of Limitations of AI Tools for Pediatric Speech Language Pathology Documentation and Mitigation Strategies

Presenter

Abstract

Attend on Teams to count this seminar towards the graduate student seminar attendance milestone!

Grad Seminar: Label-free optical microscopy: Photon Absorption Remote Sensing (PARS) for histopathological imaging of tissues

Presenter

Abstract

Attending this seminar will count towards the graduate student seminar attendance milestone!

Grad Seminar: Experimental Study on the Vibration Response of a Jackleg Hammer Drill

Presenter

Abstract

Attending this seminar will count towards the graduate student seminar attendance milestone!

Join on Teams

Grad Seminar: Towards a Novel Optical Spectroscopy Technique Using Photon Absorption Remote Sensing

Presenter

Abstract

Attending this seminar will count towards the graduate student seminar attendance milestone!