Innovative Projects Realized

Enhancing Plasma Dynamics Modeling in RF Ion Sources Using Improved PIC Simulations and Refined Cross-Section Data

This study utilizes computational plasma modelling using a Particle-in-Cell computer program to simulate and compare hydrogen and deuterium in a volume-cusp ion source to ascertain why H¯ and D¯ output beams beam production ratio is ~3:1 [3-5]. To the best of our knowledge a comprehensive modelling study does not exist explaining this effect. This project will provide an important example regarding PIC code utility in shedding light on measurable plasma phenomena. Partner D-Pace develops, manufactures and sells ion sources. D-Pace has worked to experimentally improve the output of its D¯ beams (relative to H¯) with limited success. The challenge is to utilize the PIC/MCC computational plasma modelling code to simulate the H¯ and D¯ cases to: (i) better understand the underlying mechanisms in the plasma that contribute to the production difference, and (ii) to ascertain next experimental steps based on simulation that would yield increased relative D¯ production. The benefit to the partner company D-Pace would be improved H¯ and D¯ ion source products (particularly D¯) for medical cyclotrons used to produce radioisotopes for the diagnosis and therapy of cancer.

Large language models and other machine learning methods to advance AMD’s hardware and software capabilities

AMD is a leading innovator in high-performance computing, graphics, and visualization technologies, focusing on gaming, immersive platforms, and data center solutions. The company develops cutting-edge hardware
and software solutions to enhance computing performance across various applications, including artificial intelligence (AI), machine learning (ML), and edge computing. AMD is seeking to apply advances in AI and ML techniques, especially Large Language Models (LLMs) to address various technical development opportunities from internal process innovation to improving its software and hardware capabilities in gaming and video processing capabilities. The research areas AMD are seeking to address in the proposed project falls into the following research themes. Research Themes
1. Code optimization for asynchronous and synchronous parallel execution across its diverse array of processor types (such as CPUs and GPUs).
2. With the rapid advancement of LLMs, deploying state-of-the-art AI models presents significant memory and compute capacity challenges, especially on embedded and client platforms. While cloud-based AI inference is widely used, latency, privacy, and cost constraints make edge-based AI processing increasingly critical. AMD aims to optimize heterogeneous inference for LLMs by efficiently distributing workloads across CPUs, GPUs, and NPUs within its APUs. Increasing difficulty in optimizing code for its diverse array of processor types (such CPUs and GPUs), which presents significant challenges in code optimization for asynchronous and synchronous parallel execution. Deploying LLMs with long context prompts also poses significant challenges in terms of memory and compute. This issue is critical in applications like multimodal LLMs, which involve massive input token sizes.
3. AMD’s strategic goal of enhancing AI capabilities for game development, specifically focusing on intelligent and adaptive non-player character (NPC) behaviors. Traditionally, crafting engaging and realistic non-player character behaviour (NPC) requires extensive manual effort. This project leverages recent advancements in Large Language Models (LLMs) and reinforcement learning (RL) to automate NPC training and scenario creation, significantly reducing development costs and timelines while improving game realism and player engagement through AMD Schola.
4. The User Experience Group at AMD Canada is developing a local chatbot for answering customer questions about AMD’s products. In order to ensure that the chatbot accurately communicates product information, the chatbot must use Retrieval Augmented Generation (RAG) to ground its responses in product data. However, traditional methods that use text chunking and vector embeddings on structured and unstructured data struggle to find dynamic relationships between text chunks and extract context for complex queries. This project aims to explore innovative techniques to extract complex relationships and insights from AMD’s knowledge base to enhance chatbot intelligence. Doing so will improve customer satisfaction by allowing the chatbot to more accurately answer customer questions, potentially increasing sales and decreasing the need for customers to communicate with customer service.
5. In the video processing domain, AMD aims to enhance real-time video upscaling capabilities to improve user experience during video playback and streaming. Although existing solutions like Radeon Super Resolution and FidelityFX Super Resolution (FSR) are present, challenges persist in achieving highquality upscaling and super-resolution for compressed video content without introducing artifacts or latency. In addition, conventional high dynamic range (HDR) imaging, which merges multiple standard dynamic range (SDR) images, is impractical due to high computational costs, increased latency, power consumption, and motion artifacts. Meanwhile, the rising demand for video content necessitates more efficient processing techniques, as existing solutions fail to meet bandwidth requirements.
Successfully developing methods and solutions to the above research themes will advance the hardware and software capabilities in video processing and game development. The successful outcomes will also increase the general efficiency in deploying LLM with respect to memory usage and compute further increasing the capabilities of AMD’s hardware for video processing and game development. The economic benefits for AMD go beyond
gaining operational efficiency and promises broader impacts to the accessibility of high performance computing (HPC) by simplifying the process for creating optimized code for power gaming, immersive platforms and data centres, as well as retaining its leadership position in the semiconductor industry.

Efficient Signal Processing and Radio Resource Management for High-Throughput and Low-Latency Massive MIMO Cellular Systems – Year two

Future cellular systems must accommodate increasing demand for very high throughput and low latency data services.
Massive multiple-input multiple-output (MIMO) approach involving base stations equipped with much larger numbers
of antennas than the numbers of users served promises to significantly increase network capacity, while nonorthogonal
multi-carrier transmission is expected to dramatically reduce the latency. Integration of these techniques
will require novel efficient transceiver signal processing and radio resource management solutions, such as reducedcomplexity precoding and user scheduling algorithms. These algorithms will need to be robust to typical
imperfections, such as antenna coupling in large arrays of limited physical size and also possible non-reciprocity of
uplink and downlink hardware chains, resulting in inaccurate channel state information at transmitters and reduced
capacity. 3D beamforming in massive MIMO will also be investigated. TELUS Communications has expressed great
interest in the proposed work and will support it in the amount of $30k per year.

Enhancing Legal Document Processing with Large Language Models: A Specialized ChatGPT Model for Corporate Law

A key challenge in corporate law is the efficient indexing and retrieval of legal documents. This project focuses on enhancing the accuracy of AI-driven legal document processing, making it easier for law firms to manage client records. With improved document indexing and data extraction, legal professionals will have better access to structured, reliable legal information. This will streamline workflows, reduce errors, and improve decision-making, ultimately saving time and costs for businesses and law firms across Canada.
Beyond direct business benefits, the research also creates broader economic and societal advantages for Canada. The legal industry plays a vital role in the country’s economy, and improving its efficiency through AI will reduce costs, making legal services more affordable and accessible to Canadian businesses and individuals. By increasing the accuracy of legal document automation, this project can help prevent legal disputes and improve regulatory compliance, supporting fair and transparent business practices.
Furthermore, this research aligns with global advancements in AI-driven legal technology. By participating in such innovation, Canada strengthens its position as a leader in legal tech, fostering economic growth and making the country a hub for AI-driven solutions. The knowledge and tools developed through this project can be applied across various legal domains.

Validation of wearable device for use in predicting high and low levels of stress and anxiety in daily life

The project explores the application of physiological sensors in psychology, mental health, and mindfulness by integrating artificial intelligence for emotional classification. It aims to validate a wearable physiological monitoring system, using devices like Fitbit Versa 6 and Emotibit, to predict stress and anxiety levels through machine learning. This initiative is crucial given the prevalence of mental health issues in Canada, affecting 20% of Canadians annually, and demonstrating a desperate need for effective solutions.
By understanding emotional states and external influencing factors, the project seeks to improve mental health monitoring. Wearable technology can capture physiological signals—heart rate variability (HRV), heart rate (HR), skin conductance, and blood oxygenation—that correlate with emotional changes. Numerous studies have linked HRV to mental health conditions like anxiety and depression, establishing it as a reliable measure of the autonomic nervous system’s response to stress.

Emma Lake Environmental Research and Stewardship Internship

Emma Lake is one of many recreational lakes located in the boreal forest of west central Saskatchewan that has experienced increased cultural eutrophication due to increased lake development and urbanization. Cultural eutrophication refers to artificially enhanced nutrient loading associated with shoreline development, vegetation removal, and factors related to sedimentation, increased use of fertilizers, and proliferation of septic pump-out systems. Concern for the lake environment has been steadily increasing as the lake experiences increased algal and weed growth, reduced water clarity, and diminishing environmental quality. The objective of this research is to identify metrics that are relatable and indicative of aquatic ecosystem health especially as it pertains to cultural eutrophication.

Enhancing Grand Pre UNESCO World Heritage site visitor experience via interactive QR coded smart phone application

The primary goal of this “Landscape of Grand Pré ” research project, a UNESCO World Heritage Site (LGPI 2025), examines how the visitor experience can be enhanced by employing an interactive QR coded smart phone application (app). The second goal assesses how the app can raise awareness of the Landscape’s storied history in shaping Canada’s pre and post-colonial history. The third goal assesses how app development can engage local business, and the agricultural and residential communities in promoting adaptive measures to protect the landscape against sea level rise as well as create visitor awareness of appropriate visitor behaviours to reduce interference with agricultural operations. Using community engagement strategies, this applied research methodology will align with the community’s goals and best practices in visitor management.

Efficient Signal Processing and Radio Resource Management for High-Throughput and Low-Latency Massive MIMO Cellular Systems

Future cellular systems must accommodate increasing demand for very high throughput and low latency data services.
Massive multiple-input multiple-output (MIMO) approach involving base stations equipped with much larger numbers
of antennas than the numbers of users served promises to significantly increase network capacity, while nonorthogonal
multi-carrier transmission is expected to dramatically reduce the latency. Integration of these techniques
will require novel efficient transceiver signal processing and radio resource management solutions, such as reducedcomplexity precoding and user scheduling algorithms. These algorithms will need to be robust to typical
imperfections, such as antenna coupling in large arrays of limited physical size and also possible non-reciprocity of
uplink and downlink hardware chains, resulting in inaccurate channel state information at transmitters and reduced
capacity. 3D beamforming in massive MIMO will also be investigated. TELUS Communications has expressed great
interest in the proposed work and will support it in the amount of $30k per year.

Automated gated quantum-dot 3D model generation and tuning for technology-computer aided design of realistic spin-qubit systems

Spin qubits—nanoscale quantum bits made in semiconductor materials—are a promising technology for building future quantum computers. They are highly miniaturized, can work at relatively higher temperatures than competing technologies, and can be made using standard industrial semiconductor-chip fabrication techniques, which makes them ideal for scaling up. However, traditional chip design software is not adequate for these devices because they operate under extremely low-temperature conditions and follow quantum physics rules.

To solve this, Nanoacademic created QTCAD®, the first commercial software designed to simulate spin qubits. QTCAD® is already used by researchers and engineers around the world. As devices get more complex, users need more automation to save time. This internship project will develop tools that automatically create 3D models of devices from simpler 2D designs and tune settings to get the desired performance—tasks that currently take a lot of manual work.

The intern’s work will become part of QTCAD®, helping users work more efficiently and supporting Nanoacademic’s efforts to provide tools and training for both academic and industry partners. These improvements will not only benefit users but also support Nanoacademic’s services, such as contract research and training in quantum technology programs worldwide.

Forecasting Levodopa-Induced Dyskinesia in Human Subjects with Parkinson’s Disease

We have developed a novel data augmentation procedure that significantly enhances machine learning-based classification of different brain imaging scans. Having successfully demonstrated proof-of-concept in a rodent model, we are now expanding this approach to clinical applications in humans. Specifically, we aim to utilize this technology to identify early biomarkers of neurodegenerative diseases, enabling personalized treatment strategies based on individual disease progression rates.

As a starting point, we will focus on Parkinson’s disease, which affects approximately 100,000 Canadians and is the second most prevalent neurodegenerative disorder. Over half of patients develop levodopa-induced dyskinesia, a challenging motor side effect. Our previous research demonstrated that individuals who develop this side effect exhibit distinct brain activity patterns from those who do not—even on the first day of levodopa treatment.

With our advanced machine learning technology, we believe we can accurately identify “at-risk” patients before symptoms manifest. This capability will provide clinicians with a powerful tool to tailor treatment strategies, mitigate dyskinesia risk, and uncover novel therapeutic targets. Ultimately, this project aims to establish imaging-based classification as a clinically viable approach for early-stage disease detection and personalized intervention.

Medical Microrobots for Anti-cancer Drug Delivery

This project focuses on developing innovative, biocompatible microrobots for targeted drug delivery, particularly in cancer treatment. By integrating smart hydrogels with magneto-active properties, these tiny robots can navigate the body and deliver highly precise chemotherapy drugs, reducing side effects and improving patient outcomes. Advanced 3D microfabrication techniques, such as two-photon lithography and digital light processing, will be used to create these microrobots, while external stimuli like radiofrequency (RF) fields and infrared (IR) illumination will control their movement and drug release. The collaboration between institutions provides access to cutting-edge equipment and expertise, ensuring that these microrobots are effectively designed, fabricated, and tested. This research has the potential to revolutionize targeted therapy, making cancer treatment more effective and less invasive.

Evaluating Mutational Signatures from Circulating Tumor DNA: A Comparative Analysis with Whole-Genome Sequencing

Cancer leaves behind unique mutation patterns, known as mutational signatures, which provide insights into its origins and potential treatments. Traditionally, these signatures are identified using tumor tissue samples, but obtaining biopsies can be invasive and challenging. A promising alternative is circulating tumor DNA (ctDNA)—small fragments of tumor DNA found in the blood. Liquid biopsies offer a less invasive way to study cancer mutations, but it remains unclear whether they capture the same mutational signatures as traditional tissue sequencing.

This project aims to compare mutational signatures derived from ctDNA and whole-genome sequencing of tumor tissue to assess their concordance. By analyzing data from both sources, we will determine whether ctDNA can reliably replace tissue biopsies for mutational signature analysis. If successful, this approach could improve non-invasive cancer detection and monitoring, reducing the need for surgical biopsies. The findings may also enhance computational tools for analyzing ctDNA, making cancer diagnostics more accessible and precise. Ultimately, this research brings us closer to personalized and less invasive cancer care, using simple blood tests to decode tumor biology.