Events

Presented by the Technology for Independent Living Program

The Technology for Independent Living Program (housed at Technology for Living) is excited to present the 10th Simon Cox Student Design Competition! This multidisciplinary and collaborative project challenges students to design innovative solutions that improve the quality of life for people with physical disabilities.

💰 $10,000 in prizes are up for grabs! From fashionable clothing to a wheelchair sidecar for a therapy dog, there are many exciting ideas waiting to be developed.

About the Competition

Students will work directly with individuals with physical disabilities to better understand the challenges they face in daily life. The competition is open to any student currently enrolled in a BC technical college or university, and entries will be judged based on design merit and functionality in helping people live as independently as possible.

This is a tremendous opportunity to:
✅ Make a real impact on people's lives
✅ Gain recognition for your work
✅ Showcase your skills to potential employers
✅ Win prize money!

Competition Details

📅 Final Showcase & Judging: April 26, 2025
📍 Location: Blusson Centre, Vancouver

📅 Application Deadline: February 13, 2025
🔗 Apply Here: Simon Cox Student Design Competition

Project Ideas from the Community

Here are some real-world challenges that people with physical disabilities are hoping students will help solve:

Assistive Devices & Mobility Solutions

• Sip ‘n Puff Fishing Rod
• Motorized Casting System for Fishing Rods
• Proximity Warning for Wheelchair Vans
• Heated Walker Handles
• Portable Lift for Power Wheelchairs
• Cushion Pressure Gauge
• Portable Taller Headrest for Air Travel
• Bike Pedal-Inspired Wheelchair Footrests Controlled by Joystick
• Hovercraft Wheelchair
• Wheelchair Sidecar for Therapy Dog

Smart Home & Daily Living Aids

• Smart Window Opener
• Adjustable Table Top
• Adjustable Blanket
• RV Transfer Aid
• Motorized Mobile Table Top
• Automatic Cabinet Opening Device
• Elevator Control via Phone
• Cabinet with Adjustable Height

Wearable & Adaptive Clothing

• Heated Socks Powered by a Wheelchair Battery
• Leg Warmer
• Poncho 2.0
• Modified Warm Clothing for Tracheostomy Users
• Heated Backrest
• Modified Thick Winter Pants
• Specialized Floormat
• Modified Boots

Technology & Communication Aids

• Touch-Activated Call Bell
• Ventilator Humidifier Water Level Sensor
• Page-Turning Device
• Extendable Telescoping Pole
• Bluetooth Headphones Integrated with Pillow or Wheelchair Headrest
• Motorized Drink Holder
• Wheelchair-Mounted Drink Warmer
• Small Form-Factor Door Opener
• 6-Output Multiplexer

Maybe you and your student team would like to take on one of these challenges!

Last Year's Winners

🏆 1st Place – Simon Cox Principal Award ($3,000)
Wheelchair Obstacle Detection System – UBC Enable

🥈 2nd Place – Achievement Award in Honour of Jim Watson ($2,000)
UBC Multiple Sclerosis to Movement (M2M) – UBC M2M

🥉 3rd Place – Innovation Award in Honour of Dr. Jeremy Road ($1,000)
Assistive Knitting Device – University of Victoria

🎖️ Peer’s Choice Award in Honour of Ken Kramer ($1,500)
Wheelchair Obstacle Detection System – UBC Enable

Name: Hao Chen

Date: April 10, 2025

Time: 12:00 pm

Location: ICCS 202

Supervisor: Prof. Chen Greif

Title: Field of Values Analysis that Includes the Origin for Preconditioned Nonsymmetric Saddle-Point Systems

Abstract:

Nonsymmetric saddle-point systems arise in numerous significant physical applications, including the Navier-Stokes equations and Stokes-Darcy coupling problems. The solution of these systems frequently employs preconditioned Generalized Minimal Residual Method (GMRES). To analyze convergence speed using preconditioners, field-of-values (FOV) analysis serves as a useful theoretical tool. While existing FOV analysis effectively characterizes the convergence of preconditioned GMRES when zero is not included in the field of values of the preconditioned matrix, it cannot be applied when zero is contained in the FOV.

Recent theoretical advances by Crouzeix and Greenbaum [Spectral sets: numerical range and beyond. SIAM Journal on Matrix Analysis and Applications, 40(3):1087–1101, 2019] show that a convex region with a circular hole is a spectral set. We apply this result to nonsymmetric systems and derive sufficient conditions for convergence of preconditioned GMRES independent of the matrix dimensions. We apply our results to preconditioned nonsymmetric saddle-point systems and demonstrate applicability to previously unaddressed families of block preconditioners: specifically, a block diagonal preconditioner and a block upper triangular preconditioner for left conditioning.

A limitation of our theory is that the  preconditioned matrix is required to have a small skew-symmetric part in norm. Consequently, our analysis may not be applicable, for example, to fluid flow problems characterized by a small viscosity number.

To demonstrate the practical effectiveness of our theoretical analysis, we conduct numerical experiments on Navier-Stokes and Stokes-Darcy systems, which validate the performance of the proposed preconditioners and show that the iteration counts are independent of the matrix dimensions.

Name: Vasileios Lioutas 

Date: Thursday, April 10, 2025

Time: 9:00 AM

Location: X836

Supervisor: Frank Wood

Thesis title: Towards Realistic Controllable Driving Simulators

Abstract:
The development of autonomous vehicles requires extensive testing in simulated environments before deployment; however, current simulation approaches often fail to capture the complex, interactive nature of human driving behavior, relying instead on simplified or scripted agent behaviors. This thesis presents novel methods for generating realistic multi-agent driving behavior in simulated environments. We introduce ITRA (Imagining The Road Ahead), a generative model based on recurrent variational neural networks that captures complex spatial and temporal dependencies in driving behavior. To address ITRA's tendency to generate unsafe trajectories, we develop TITRATED, which combines amortized rejection sampling with differentiable infraction losses, and CriticSMC, a novel algorithm that enhances planning efficiency through learned value function heuristics. We then present Control-ITRA, enabling precise control over agent behavior through waypoint specification and target speeds while maintaining behavioral realism. Our extensive experimental evaluation demonstrates that these methods improve the realism and safety of simulated driving behavior while providing flexible control mechanisms for scenario generation, advancing the state-of-the-art in autonomous vehicle simulation and testing.

Name: Merry Shirvani

Date: April 9, 2025 (Wed)

Time: 3:00 pm

Location: ICCS 238

Zoom link: https://ubc.zoom.us/j/65528004771?pwd=U05zpKVAbWFrUGQUNC9VyaxmbmbyPr.1 

Supervisor: Prof. Dongwook Yoon

Title: Talking to an AI Mirror: Designing Self-Clone Chatbots for Enhanced Engagement in Digital Mental Health Support

Abstract:
As mental health support becomes increasingly digitized, chatbots have emerged as promising tools for immediate, cost-effective remedies. These conversational agents have the potential to deliver valuable therapeutic impact, but low user engagement remains a critical barrier hindering their efficacy. Existing therapeutic approaches have leveraged clients’ internal dialogues (e.g., journaling, talking to an empty chair) to enhance engagement through accountable, self-sourced support. Inspired by these, we aimed to design, implement, and evaluate novel AI-driven self-clone chatbots replicating users’ support strategies and conversational patterns to improve therapeutic engagement through externalized meaningful self-conversation. To ensure the therapeutic safety and effectiveness of self-clone chatbots, we analyzed insights from 16 expert interviews with mental health professionals to identify key design considerations that informed our final chatbot principles. Validated through a controlled experiment (N=180), significantly higher emotional and cognitive engagement was demonstrated with self-clone chatbots than with a chatbot with a generic counselor persona. This finding was consistent in a follow-up study with a subgroup of participants (N=66) conducted after a 10-week period to mitigate any novelty effects. Our findings highlight self-clone believability as a mediator and emphasize the balance required in maintaining convincing self-representation while creating positive interactions. This work contributes to AI-based mental health interventions by introducing and evaluating self-clones as a promising approach to increasing the efficacy of conversational agents while offering insights into their design and exploring implications for their application in mental health care.

Name: Nicholas Ioannidis

Date: April 9, 2025 (Wed)

Time: 11:00 am

Location: ICCS 238

Supervisor: Prof. Michiel van de Panne

Title: Viability Estimation for Diffusion-Based Planning

Abstract:

Diffusion models can be used as a motion planner by sampling from a distribution of possible futures. However, the samples may not satisfy hard constraints that exist only implicitly in the training data, e.g., avoiding falls or not colliding with a wall. We propose learned viability filters that efficiently predict the future success of any given plan, i.e., diffusion sample, and thereby enforce an implicit future-success constraint. This serves to identify samples from the output of a diffusion-based motion planner with respect to implicit constraints and takes the general form of a learned Q-function.

This allows for efficient online planning with diffusion models, including for situations where the diffusion model and viability filter have asymmetric access to environment observations.

Multiple viability filters can also be composed together so that they are each taken into consideration. We demonstrate the approach on detailed footstep planning for challenging 3D human locomotion tasks, showing the effectiveness of viability filters in performing online planning and control for box-climbing, step-over walls, and obstacle avoidance. We further show that using viability filters is significantly faster than guidance-based diffusion prediction.