By Elizabeth Kane
Once the domain of early science fiction, Artificial Intelligence (AI) is now a major technological advance that has the potential to impact our everyday lives in countless ways.
To keep up with the breakneck pace of the breakthroughs, the race is on to understand how these developments can change our reality.
Carleton University is home to many graduate students working with faculty to advance our world’s knowledge on AI’s potential applications.
Below, we share the stories of five inspiring Carleton graduate students who are exploring cutting-edge advancements in AI and machine learning.
Elmira Amooei
Using AI to Personalize Transplant Drug Dosing
Following organ transplant surgery, patients may be prescribed a strong immunosuppressant drug called tacrolimus. The blood levels of the medication can be highly variable among patients which can lead to increased risks.
Elmira Amooei
Computer Science PhD student Elmira Amooei is developing AI-based models to personalize the dosing of the drug.
“Currently, dosing decisions are made based on trial-and-error and generalized guidelines, which do not capture the individual variations in patients and often result in increasing organ rejection or toxicity risks,” says Amooei.
By leveraging AI methods, including machine learning and reinforcement learning techniques, the research team is developing models to predict an individual’s drug response to optimize dosing.
“Using AI to personalize drug dosing can enhance patient safety and treatment effectiveness,” says Amooei. “This field of research can potentially serve as a foundation for similar personalized dosing practices.”
Asit Kaul
Exploring Consumer Adoption of AI Personalization
As AI technologies transform online retail by enabling personalized shopping experiences, Sprott School of Business PhD candidate Asit Kaul is investigating the factors that influence consumer adoption and why many users are cautiously hitting the brakes on personalization algorithms.
Asit Kaul
“Consumers often approach AI personalization with hesitation when there is uncertainty around how their data is managed or when the benefits are not clearly communicated,” says Kaul.
His research examines the psychological, technological, and behavioural factors that shape these perceptions and looks at what builds or erodes consumer confidence in AI technologies.
The findings will provide practical guidance to businesses on how to design responsible personalization strategies that align with consumer expectations and concerns, communicate value effectively, and foster long-term user engagement. The research also contributes to broader public policy by offering a consumer-centric perspective grounded in empirical findings on how AI technologies are reshaping digital commerce.
“The future of AI won’t be decided by algorithms alone,” says Kaul. “It will be shaped by how well we understand the people who use them and by designing systems that are personal, principled, and deeply human. This research moves us closer to that future.”
Mai Abourobea
Leveraging AI to Improve Radar-Based Road Detection
Canadian drivers must contend with a host of adverse weather conditions, including heavy snowfall, rain and fog. While precipitation can hinder the performance of traditional road sensors, radar systems operate well in challenging weather, making them a valuable tool for reliable road user detection.
Systems and Computer Engineering PhD student Mai Abourobea is using AI to improve radar-only road user detection and classification, resulting in safer, smarter and more sustainable urban mobility.
“AI offers transformative capabilities for enhancing road safety, particularly through its proficiency in processing complex and noisy sensor data in real time,” says Abourobea.
“AI enables highly accurate classification of road users based on radar data, even under adverse weather conditions.”
In addition to its ability to operate effectively in challenging environments, radar technology requires less power consumption making it a more environmentally sustainable approach to intelligent transportation systems.
“By enabling fixed radar systems, municipalities and transportation agencies can gain deeper insights into traffic behavior, improve decision-making and implement safety interventions.”
Ryan Dempsey
Predicting Wireless Signal Loss with AI
Our modern world is deeply reliant on internet enabled devices, making wireless signal strength critically important.
Ryan Dempsey
Electrical and Computer Engineering masters student Ryan Dempsey is exploring a new method to predict how buildings, trees and terrain affect wireless signal strength. Dempsey designs and trains an AI model known as convolutional neural networks to analyze maps showing heights of physical obstructions and directly predict loss of signal strength—a critical factor in network planning.
“Future communications networks will leverage AI in countless areas,” says Dempsey.
“This extends beyond radio propagation prediction, as AI will be integrated throughout communications networks.”
Unlike traditional methods, the networks automatically identify complex patterns in the data, leading to streamlined, accurate predictions. The new method consistently outperforms industry-standard techniques while requiring less manual effort. Better signal predictions help service providers and regulators to optimize network performance, improve coverage in challenging environments and more efficiently plan spectrum usage—ultimately leading to better service for Canadians.
“AI will provide powerful capabilities to make efficient use of the radio frequency spectrum and ensure users have seamless access to wireless services.”
Adnan Khan
Creating Tactile Graphics with Machine Learning
For the 43 million people living with vision loss, tactile graphics are essential tool for providing access to visual information. However, the creation process is labor-intensive and demand outpaces supply.
Adnan Khan
Computer Science PhD student Adnan Khan is exploring a novel framework that harnesses AI to create tactile graphics from digital images.
“Utilizing AI in tactile graphic creation opens up a world of possibilities—from rapid, personalized conversion of visual data into touchable art to empowering individuals with vision impairment to independently access complex information,” says Khan.
By integrating advanced machine learning techniques with computer vision, the system identifies critical visual information and transforms it into tactile representations suitable for embossing or 3D printing.
The approach automates the production process and offers adaptability, allowing the tactile graphic to be refined to meet individual sensory requirements.
“This research holds the promise of broadening individual’s educational, employment and everyday opportunities – ultimately enhancing inclusivity and quality of life.”
Tuesday, May 6, 2025 in Artificial Intelligence (AI), Innovation, Research, Technology
Share: Twitter, Facebook
