Meet the participants

Faculty of Business and Information Technology

• Mahshid Khatiriolyaee

  Program

  Computer Science (MSc)

  Supervisor

  Dr. Richard Pazzi

  3MT®  PRESENTATION

  Can Traffic Models Adapt to a Changing Future?

  Traffic prediction models are often evaluated on short time spans and clean data, while real-world traffic data experience long-term temporal changes and missing values. This research investigates the robustness and generalization of spatiotemporal traffic prediction models when trained on historical data and evaluated over extended periods. It also explores AutoML approaches to improve prediction performance and model robustness.

  About Mahshid

  From a young age, I have been interested in solving problems, which naturally led me to mathematics and computer science. I enjoy handmade crafts and spending time in nature. I am also passionate about cooking and preparing meals from scratch, and I’ve recently developed an interest in psychology.

• Tam Huynh

  Program

  Master of Business Analytics and AI

  3MT®  PRESENTATION

  Factors Affecting Carbon Emission in Southeast Asian Developing Countries

  The study analyzes the role of FDI as an interactive factor in the nexus of democracy, corruption, ICT, circular economy, and carbon emissions in eight Southeast Asian developing countries. The author employs the Method of Moments Quantile Regression technique, utilizing data from SEADCs spanning the period 1995-2021. The results reveal evidence that democracy, corruption, and ICT create a significantly negative association with carbon emissions across most (some) quantiles for the SSA countries. Conversely, the circular economy develops a significantly positive relationship with environmental quality. More results demonstrate that the interaction variables, corruption × FDI and ICT × FDI, also produce positive and significant links with carbon emissions. While the link between circular economy x FDI and emissions is significantly negative, democracy × FDI and emissions is insignificantly negative. In conclusion, these findings highlight the importance of fostering democratic governance, combating corruption, promoting ICT, and supporting circular economy practices to achieve environmental sustainability.

Faculty of Engineering and Applied Science

• Habiba Mahrin

  Program

  Software Engineering (MEng)

  Supervisor

  Dr. Akramul Azim

  3MT®  PRESENTATION

  Can Artificial Intelligence Help Doctors Detect Brain Tumors Earlier?

  Brain tumors are among the most life-threatening neurological conditions, where early and accurate diagnosis is critical for patient survival. Medical imaging techniques such as MRI scans generate large volumes of complex data, making diagnosis time-consuming and challenging for clinicians. This research explores how artificial intelligence can assist doctors by identifying patterns in brain MRI images that may indicate the presence of tumors. Rather than replacing medical experts, the goal is to develop AI systems that support faster and more reliable decision-making. By reducing diagnostic delays and supporting clinical judgment, this work aims to improve patient outcomes and demonstrate how AI can play a meaningful role in modern healthcare.

  About Habiba

  I am curious and enjoy finding patterns in research and everyday life. I grew up in Bangladesh, where creativity shaped my problem solving. I enjoy painting, spending time with kids, learning tools, and simplifying complex ideas. A fun fact is that I love explaining concepts to non technical audiences.

• Hameeda Onigemo

  Program

  Mechatronics Engineering (MEng)

  Supervisor

  Dr. Aaron Yurkewich

  3MT®  PRESENTATION

  A Personalized Framework for Real-Time Monitoring of Human Physical State During Physical Activity

  Injury, poor performance, and ineffective training rarely stem from a single mistake during physical activity. Instead, they tend to develop gradually as movement quality degrades, exertion accumulates, and patterns emerge without the individual being fully aware. While existing monitoring tools can capture isolated metrics such as heart rate or activity duration, they offer limited insight into how the body is actually moving, how internal load evolves, and when deviations become meaningful for a specific individual.

  This project aims to model and interpret an individual’s biomechanical and physiological state during physical activity. Using multimodal sensing, such as motion data and vital signals, the system continuously estimates posture, movement quality, exertion indicators, and symmetry patterns before, during, and after an activity session. Rather than relying on population-level thresholds, the system learns a personalized baseline for each user and evaluates deviations relative to their own movement patterns and physiological response.

  At the core of this work is the question of how much more effective a personalized, real-time digital twin can be compared to traditional threshold-based monitoring approaches. The project investigates whether personalization facilitates the earlier and more accurate detection of significant movement deviations, reduces false alarms associated with rigid thresholds, and provides clearer, more interpretable feedback through visual representations and corrective guidance. This work does not aim to diagnose injury or replace professional supervision, but to function as a tool that enhances self-awareness and informed action across athletic, rehabilitation, and general physical activity contexts.

  About Hameeda

  I’m curious by nature and enjoy building things at the intersection of humans, health, and technology, especially robotics and AI. Outside of that, I love dancing, cooking, and baking, and I’ve binged MasterChef Canada and Grey’s Anatomy more times than I can count.

• Katrina Mamenta

  Program

  Nuclear Engineering (MASc) - Nuclear Power

  Supervisors

  Dr. Akira Tokuhiro and Dr. Filippo Genco

  3MT®  PRESENTATION

  Powering the Global South: Unlocking Nuclear Finance for Emerging Economies

  Emerging markets face an impossible choice: rapid economic growth or climate action. Nuclear power offers both, but financing remains the critical barrier. Developing nations need hundreds of new reactors to replace coal while meeting growing electricity demand, yet they face higher capital costs (12-18% vs. 6-9% in developed markets), currency risks, and limited access to patient capital. My research analyzes how emerging markets like the Philippines and India are pioneering innovative financing structures—blending development bank support, sovereign guarantees, vendor financing, and climate funds—to overcome these barriers. By comparing successful projects across different economic contexts and using quantitative risk modeling, I'm identifying optimal financing frameworks that can reduce costs and accelerate deployment in capital-constrained environments. This research addresses the multi-trillion dollar financing gap preventing nuclear adoption in the Global South, while offering lessons that can reduce costs even in developed markets. The findings will provide actionable pathways for developing nations to leapfrog fossil fuels and achieve energy security through nuclear power.

  About Katrina

  Born and raised in Manila, I moved to Canada in September 2023. I’m an introverted homebody who enjoys walks in mild weather, loves traveling when possible, and is currently learning Russian, French, and Chinese. Curious, adaptable, and always growing—professionally and personally.

• Kushan Lulbadda Waduge

  Program

  Electrical and Computer Engineering (PhD)

  Supervisor

  Dr. Sheldon Williamson and Dr. Tarlochan Sidhu

  3MT®  PRESENTATION

  Protection of Solid-State Transformer Enabled EV Charging Stations in DC Microgrids

  Modern energy systems are changing rapidly as renewable energy, electric vehicles, and smart technologies become part of everyday life. To support these changes, traditional electrical equipment must advance. One promising innovation is the solid-state transformer (SST), which replaces bulky conventional transformers with faster, smarter, and more flexible electronic systems. SSTs can control power more precisely and efficiently, making them ideal for future energy networks and electric vehicle charging stations.

  While SSTs offer many benefits, they also introduce new challenges. Unlike traditional transformers, SSTs rely on many electronic components, which makes them more sensitive to faults such as unexpected failures, overheating, or short circuits. If these issues are not detected quickly, they can disrupt power delivery, damage equipment, or affect connected systems.
  My research focuses on improving the safety and reliability of SST-based energy systems. I study how faults can occur both inside the SST itself and in the surrounding power network. By understanding these problems, I develop fast and practical protection methods that can detect issues early and prevent them from spreading to healthy parts of the system.

  The main goal of this work is to create safer and more reliable energy systems for electric vehicle charging stations and future smart grids. By making SST technology more robust and dependable, this research supports the transition to cleaner energy, smarter transportation, and more resilient power systems that benefit communities and everyday users.

  About Kushan

  I’m Kushan, a PhD researcher in power electronics who enjoys turning complex energy challenges into practical solutions. Beyond research, I love exploring new places, capturing travel moments, and staying curious about emerging technologies. Originally from Sri Lanka, I enjoy learning from diverse cultures and experiences.

• Mitra Hekmat

  Program

  Electrical and Computer Engineering (MASc)

  Supervisor

  Dr. Min Dong

  3MT®  PRESENTATION

  Removing the Bottleneck of Wireless Cooperation

  For a long time, researchers have known that wireless cooperation is a powerful way to improve service quality. When base stations cooperate, users can receive stronger and more reliable signals. However, as wireless systems evolved, especially with the rise of massive MIMO in 5G and beyond, cooperation became increasingly difficult to realize and implement. The reason is simple: the amount of information required to coordinate base stations grows rapidly with the number of antennas, which is expected to reach the hundreds or thousands. This massive information exchange turned cooperation into a bottleneck, causing the model to be largely set aside rather than fully explored.

  The performance benefits of cooperation, including improved service quality and reduced power consumption, have been well understood for years. However, realizing these benefits in practice has been limited by high information-sharing requirements.

  In my research, I address this challenge by developing mathematical techniques that enable cooperative transmission with significantly reduced information sharing. This allows base stations to cooperate effectively without relying on extensive centralized data exchange, making cooperation practical and scalable under realistic system constraints. By enabling cooperation with low information overhead, the proposed approach allows these well-known advantages to be achieved in realistic deployments. Compared to simpler coordinated systems, cooperative transmission under the proposed framework results in fewer infeasible solutions and lower energy usage.

  By removing the information bottleneck, this work enables wireless cooperation and allows its full potential to be realized—both in performance and energy efficiency.

  About Mitra

  I was born in Arak, one of Iran’s most industrial cities. Academically, I enjoy working at the intersection of mathematics and real-world wireless systems. Outside research, my main “hobby” is my energetic 10-month-old baby girl, who happily leaves no time for anything else.

• Paul Oladapo

  Program

  Mechatronics Engineering (MASc)

  Supervisor

  Dr. Aaron Yurkewich

  3MT®  PRESENTATION

  KeyGloves: Wearable Keyboard & Mouse System

  KeyGloves is an innovative wearable device that transforms finger presses and hand gestures into keyboard and mouse commands. Based on traditional touch-typing principles, the system enables users to type using finger-mounted buttons, enabling fast, accurate typing without the need for a physical keyboard. When the user closes their fist, the glove activates mouse mode, utilizing a motion sensor similar to the optical sensor of a computer mouse to control the cursor.

  This project combines mechanical design, integrated electronics, haptic interaction, gesture recognition, and human–computer interaction through intuitive, ergonomic, and highly accessible input methods that operate on any solid surface and at a distance from traditional hardware. Conventional keyboards require a fixed posture, a rigid surface, and precise hand positioning. These restrictions can cause physical stress, limited mobility, and barriers for users with reduced motor control. KeyGloves introduces a new paradigm: the ability to type and control a computer directly from the hands, without depending on a physical keyboard or desk.

  This has the potential to:

  • Reduce arthritis caused by improper keyboard posture.
  • Reduce repetitive stress injuries.
  • Allow typing with natural and relaxed hand positions.
  • Avoid strain caused by prolonged desk use.

  The system is designed to support users with:

  • Visual impairments (keys are on their fingers).
  • Tremors or difficulty targeting keys.
  • Limited mobility.
  • Motor rehabilitation needs.
  • Difficulty coordinating traditional typing movements.

  About Paul

  I am a creative engineer that builds anything from a computer to a robot. I love to create devices to support people with disabilities. My interests are 3D designing, reading, creating, and teaching. I am a personal Tutor and CEO of my start-up called SearchEatt.

• Rafia Kouser

  Program

  Mechanical Engineering (MASc)

  Supervisor

  Dr. Brendan MacDonald

  3MT®  PRESENTATION

  Turning Steel Mill Smoke into Power: Quantifying Industrial Waste Heat for Energy Recovery

  Every day, steel mills release enormous amounts of heat straight into the atmosphere through their exhaust stacks. This wasted thermal energy represents both an environmental cost and a missed opportunity for power generation. But before we can capture that energy, we need to answer a fundamental question: how much recoverable heat is actually there?

  My research quantifies the waste heat available in the exhaust gases of a reheat furnace at Gerdau Steel Mill in Cambridge, Ontario, through a Mitacs industry partnership. Using real-time data collected from the mill's monitoring system, I developed combustion analysis methods to determine the actual thermodynamic properties of the exhaust gases—and found that they differ significantly from standard engineering assumptions. Properties like density and Prandtl number deviated by 5 to 22 percent from textbook air values, meaning conventional estimates of recoverable energy could be substantially off.

  By analyzing velocity and temperature data across multiple weeks of operation, I identified when the furnace produces the most recoverable heat and mapped optimal operating windows for deploying a Stirling engine—an external combustion engine that converts heat directly into mechanical work. Peak heat recovery rates reached 1.7 kW, with the strongest performance during weekday shifts when furnace activity is highest.

  This research bridges a critical gap between theoretical waste heat recovery models and the messy reality of industrial operations. The findings provide the data foundation needed to design and size clean energy recovery systems that could help heavy industry reduce emissions while generating useful power.

  About Rafia

  I'm a materials science graduate turned mechanical engineer and a former patent examiner with the Government of Canada. I love tackling complex problems and working toward solutions that support sustainability, make a positive economic impact, and promote lasting change.

Faculty of Health Sciences

• Amber Rizvi

  Program

  Health Science (MHSc) - Kinesiology

  Supervisor

  Dr. Bernadette Murphy

  3MT®  PRESENTATION

  Effects of Force Feedback on Brain Activity and Motor Performance in Virtual Reality Medical Training

  Virtual reality (VR) is increasingly being used to simulate healthcare scenarios for medical training, using advanced equipment to provide safe, controlled practice environments. A critical shortcoming of most VR systems is the limited force feedback. In VR, force feedback allows users to physically sense simulated forces, resistances, and interactions with objects, which increases realism and improves the effectiveness of training. Without force feedback, VR’s ability to fully replicate physical simulators becomes limited, especially in areas where tactile realism is crucial such as medical training. Electroencephalography (EEG) offers a promising means to investigate this area by measuring how force feedback influences brain adaptability during motor learning. My research aims to compare EEG signatures and motor performance recorded during medical training in VR, with and without force feedback, to those captured during training with a physical simulator. This research will significantly impact multiple fields that use VR for motor skill training, particularly industrial and emergency response fields. This unique work has the potential to make ground-breaking advancements in our understanding of the basic neuroscience of the role of force feedback in motor learning. This will be the first research to effectively incorporate and measure force feedback in motor learning in VR both at the behavioral and neural level.

  About Amber

  I am an active student leader, volunteer, and educator contributing through multiple roles at Ontario Tech University and within the broader community. In my spare time, I enjoy spending time with friends and family, painting and drawing, playing badminton, and spending time in nature.

• Maryam Ashraf

  Program

  Health Science (MHSc) - Community, Public, and Population Health

  Supervisor

  Dr. Caroline Barakat

  3MT®  PRESENTATION

  What’s in the Bottle? How Everyday Products May Be Disrupting Women’s Hormones

  Phthalates are synthetic chemicals widely used in plastics, cosmetics, and personal care products, particularly fragranced items marketed to girls and young women. These chemicals are recognized endocrine disruptors, meaning they can interfere with hormonal regulation. Exposure has been associated with menstrual irregularities, altered hormone levels, and reproductive health outcomes, yet the strength and consistency of evidence for premenopausal women remains unclear. At the same time, consumer awareness of phthalates and their health implications is limited, and product labels often make it difficult to identify potential sources of exposure, using general terms such as “fragrance” or “scented”.

  This research presents one of the first systematic reviews in Canada examining associations between phthalate exposure and hormonal or menstrual outcomes among premenopausal females. Regulatory and standards documents from international, national, and provincial contexts will also be reviewed to clarify how fragrance-related terminology is defined and applied in consumer products.

  The review synthesizes evidence to identify consistencies, limitations, and key knowledge gaps, while situating phthalate exposure within gendered and place-based consumer environments. In Ontario, exposure is shaped by retail spaces and digital marketing that influence product use among young women. Findings from this review will inform the development of an educational toolkit for university students, including a knowledge assessment to evaluate whether targeted education can influence consumer habits. This research aims to support evidence-informed policy discussions, clearer labeling practices, and practical interventions to promote women’s hormonal health.

  About Maryam

  I am a first-year master’s student in Community, Public, and Population Health at OTU. Beyond academia and research, I enjoy weightlifting, reading, baking, and learning new languages, shaped by having lived in three countries. I am also a proud cat mom to two naughty and mischievous tabby cats.

• Nathaniel Lamain

  Program

  Health Science (PhD) - Kinesiology

  Supervisor

  Dr. Meghann Lloyd

  3MT®  PRESENTATION

  'This is how I play': A qualitative think aloud study

  Active play is the developmentally appropriate and beneficial physical activity option for children. Children with ASD experience challenges with social communication, along with restricted patterns of interests and behaviours. In addition, children with ASD experience delays in motor skill development, which presents a double burden for active play. There is no qualitative research that asks children with ASD what they think about active play. A contextually relevant think-aloud method may be an option to support the communication of children with ASD. This study aims to explore the active play experiences, interests, and preferences of children with ASD as they engage in active play.

  A think-aloud interview protocol was designed to provide diverse opportunities for active play in the Motor Behaviour and Physical Activity Lab. Children aged 8-12 years with ASD (n = 16) participated in this study. The children were asked questions about active play as they engaged in active play. Interviews were recorded, transcribed, and coded for thematic analysis.

  Initial findings indicate that support from adults is critical to the successful active play participation of children with ASD. The findings also reflect high levels of participation termination across different active play domains. The preferences of the participating children varied. For children who self-identified as athletes, sports activities were preferred. For children who didn’t prefer sports, less structured games were preferred, such as tag. This think-aloud interview methodology was found to be a viable option to promote the inclusion of children with ASD in qualitative research.

  About Nathaniel

  I am a passionate advocate for the meaningful participation and inclusion of children with ASD in physical activity spaces. Originally from Brighton, Ontario, I can often be found playing my guitar, training at the local gym, or exploring local hiking trails.

• Sonia Mirza

  Program

  Health Science (MHSc) - Community, Public, and Population Health

  Supervisors

  Dr. Syed Qadri and Dr. Winnie Sun

  3MT®  PRESENTATION

  Code Red for Dementia: Can the quality of our blood play a role in cognitive impairment?

  Over the next 25 years, dementia cases in Canada are projected to increase by 160%, impacting 2 million adults. This surge will not only significantly reduce quality of life but will also place added strain on the healthcare system and caregiving personnel. To date, dementia does not have a cure, relying on preventive strategies to mitigate its impact. With this call to action, my study explores anemia as a potential modifiable risk factor for dementia.

  I am conducting a cross-sectional study at Lakeridge Health using patient charts to examine the correlation between the two conditions. I will describe the potential high-risk populations, whether by sex, ethnicity, age, comorbidities or a combination of factors. As the pilot study in Ontario, we also aim to set the foundation for larger-scale studies to be more representative of Canadians.

  Given the rapidly increasing dementia rates, understanding the extent to which our blood quality plays a role in the onset of dementia may be critical for improving early detection and intervention.

  About Sonia

  In my own time, I enjoy watching thrillers with my sister, going to the gym, and pretending I'm a professional barista (just take my word for it). A fun fact about me is that I can speak two languages and am learning three others.

Faculty of Science

• Cordelia Adams

  Program

  Applied Bioscience (MSc)

  Supervisor

  Dr. Olena Zenkina

  3MT®  PRESENTATION

  What's in This? Fluorometric and Colorimetric Detection of Toxic Heavy Metals

  Heavy metal contamination is a huge problem in today's world, whether in aquatic environments, industrial waste, or consumer goods. These contamination issues are where my research comes in, to detect and prevent this contamination from happening and harming people or animals. I test the sensing capabilities of novel terpyridine- or thiazole-based molecules for detection of toxic heavy metals such as mercury, lead, etc. This is done with analytical techniques such as using fluorescence emission spectroscopy (fluorometric) or UV-vis absorption spectroscopy (colorimetric) to detect and quantify unique signals from the presence of heavy metals. When the presence of a heavy metal provides a distinct signal that is different compared to the signal from the molecule alone, it tells us that this molecule can work as a sensor for this particular metal. This molecule and its interaction with this target metal are then extensively explored and characterized, leading to the development of a metal ion sensor specifically for that metal.

  About Cordelia

  On top of my love for heavy metals in the lab, heavy metal also happens to be my favourite genre of music. Music is a large part of my life. I have been playing the piano for 20 years and have since learned 9 other instruments.

• Dilan Mian

  Program

  Computer Science (MSc)

  Supervisor

  Dr. Cristiano Politowski

  3MT®  PRESENTATION

  How Smart Can NPCs Be Today?

  The use of large language models (LLMs) to craft realistic non-player characters (NPCs) has become an emerging topic in game AI research. This is a systematic literature review focused on the implications of LLM-driven NPCs, as both a development tool and source of complexity, and proposes future pathways for game developers and research enthusiasts exploring this rapidly changing landscape.

  About Dilan

  I am a research and gaming enthusiast from a humble Durham suburb. I love reading about history from articles and books. Along with that, gaming in expansive, imaginative environments is a great leisurely hobby for me. My main career focus is to be a researcher and engineer in AI.

• Janani Balasubramanian

  Program

  Materials Science (PhD)

  Supervisor

  Dr. Nisha Agarwal

  3MT®  PRESENTATION

  Listening to Small Molecules: Big Clues for Women’s Health

  Polycystic Ovary Syndrome (PCOS) is a common and complex metabolic condition affecting women of reproductive age, for which accessible diagnostic tools remain limited. This PhD project aims to develop a Raman spectroscopy–based sensing platform to support decentralized screening and monitoring of PCOS-related biomarkers and treatments. The research focuses on reliable sensing materials and analytical strategies for consistent, quantitative detection. By integrating nanomaterials, spectroscopy, and data analysis, this work bridges fundamental physical science with clinical needs, laying the groundwork for scalable diagnostic approaches for PCOS and broader biomolecular health screening.

  About Janani

  I am a PhD student and Vanier scholar who loves interdisciplinary science from biotech to nanomaterials, connecting the dots into real-world solutions. I’m an enthusiastic researcher at heart. Outside the lab, I enjoy traveling, yoga, fantasy movies, creative science communication, singing, and I absolutely love cats.

• Mosarrat Rumman

  Program

  Computer Science (MSc)

  Supervisor

  Dr. Mehran Ebrahimi

  3MT®  PRESENTATION

  Uncertainty-Aware Fusion of Foundation and Task-Specific Models for Cardiac MRI Segmentation

  Vision foundation models, such as the Segment Anything Model (SAM), demonstrate strong zero-shot generalization but lack precision with anatomically challenging objects. In contrast, convolutional neural network(CNN)-based models, such as nnU-Net, achieve high accuracy on domain-specific data but struggle to generalize on unseen data. To address these complementary limitations, we propose an uncertainty-aware fusion framework that integrates the generalizability of foundation models with the anatomical precision of task-specific models for cardiac MRI segmentation. The proposed approach combines Dempster-Shafer Theory (DST) with an entropy-guided fallback mechanism to perform voxel-wise fusion of calibrated probability maps. Unlike simple ensemble methods, the framework takes into consideration the inter-model agreement and conflict and the uncertainty of the models. DST fusion is applied where the models agree, while high-conflict regions are handled by an entropy-guided fallback mechanism that selects predictions from the more reliable model. Extensive evaluation on the M&Ms dataset (in-domain) and the ACDC dataset (cross-domain) demonstrates consistent improvements in Dice and IoU across model pairings of varying strengths. In-domain gains are modest, whereas cross-domain evaluations show substantially larger improvements. Notably, the nnU-Net+SAM2 pairing achieves relative gains of approximately 8% in Dice and 11% in IoU on the cross-domain dataset. Comparisons with simple averaging, ablation studies, and statistical analysis confirm the effectiveness of the proposed model. To our knowledge, this is the first application of voxel-wise DST-based fusion to combine vision foundation models with task-specific CNNs for cardiac MRI segmentation.

  About Mosarrat

  I am a Master’s student researching uncertainty in AI, passionate about simplifying complex problems. Outside research, I love crime and psychological thrillers—stories that question motives, trust, and hidden truths. My fascination with morally grey characters mirrors my research interest in uncertainty, where answers are rarely black and white.

Faculty of Social Science and Humanities

• Guadalupe Blanco-Velasco

  Program

  Forensic Psychology (PhD)

  Supervisor

  Dr. Kimberley Clow

  3MT®  PRESENTATION

  Uncovering Key Differences Between False and (Presumed) True Confessions

  Is it possible to differentiate false and (presumed) true confessions the same way we can differentiate classical from pop songs?

  For years, researchers have aimed to find reliable clues to distinguish between these types of confessions. However, their findings have shown that both confessions are similar in content (i.e., topic). Regarding the continent (i.e., communicative intent—what the utterance aims to do), it has received attention in both child and suspect interviewing, but it has not been applied systematically to false confession cases. Yet previous research has mainly focused on a single feature within a speaking turn rather than the whole turn, which is where patterns may emerge. Thus, this research aims to combine the field of investigative interviewing and linguistics to provide an answer to this question by focusing on smaller functional segments in police interview transcripts. For that, I have developed an artificial intelligence pipeline using large language models that divides speaking turns into smaller units and classifies them into categories based on a new codebook for content and continent. This pipeline has already revealed differences between false and (presumed) true confessions across the full exchange.

  With more confessions, it could help map the combinations of categories that comprise specific interview profiles and estimate the likelihood that a confession is false rather than (presumed) true. This opens a window to reduce wrongful convictions and provide a second opportunity to people who are innocent and still in prison.

  About Guadalupe

  I’m from Burgos, Spain. I’m passionate about music and sports, and I’ve travelled across Estonia and the Netherlands by bike.

• Jasmine Cossette

  Program

  Forensic Psychology (MSc)

  Supervisor

  Dr. Matthew Shane

  3MT®  PRESENTATION

  Assessment of Neural Network Activity During Rest and Sustained Attention in Psychopathy

  Recent neuroimaging work suggests that psychopathy is not linked to dysfunction in a single brain region, but rather to widespread differences in large-scale neural network interactions. Two networks in particular, the default mode network (DMN) and dorsal frontoparietal network (dFPN), appear to be hyperconnected.
  The DMN supports internal cognition (i.e. mind wandering, self-referential thinking) and typically deactivates when attentional resources are directed externally. The dFPN supports goal-directed sustained attention, becoming more active during task engagement and deactivating when attention is directed internally. The DMN and dFPN function in opposition with an anticorrelated pattern of activity. However, hyperconnectivity between these networks in psychopathy could disrupt typical activation-deactivation patterns.

  This study proposes that increased DMN-dFPN hyperconnectivity reflects impaired network switching ability, resulting in lingering DMN activity during task engagement. This study will examine DMN and dFPN activity during both rest and task-engaged states, and how these patterns relate to psychopathy severity as measured by the Psychopathy Checklist–Revised (PCL-R). Using functional near-infrared spectroscopy (fNIRS), neural network activity will be recorded from participants as they complete a task that alternates between rest blocks and sustained attention blocks. Connectivity will be quantified using network activity ratios and analyzed in relation to PCL-R scores across conditions. By identifying dynamic network-level mechanisms in psychopathy, this study aims to advance neurocognitive models of the disorder and inform future interventions targeting related deficits in cognitive control and self-regulation.

  About Jasmine

  I am a first year MSc Forensic Psychology student in CANdiLab studying forensic neuroscience! Before joining Ontario Tech, I attended Wilfrid Laurier University and studied criminology, forensic psychology, and forensic science. I am originally from Montreal and like to go home often to see my pets, friends, and family.

• Jessica Biasin

  Program

  Forensic Psychology (MSc)

  Supervisor

  Dr. Taylor Heffer

  3MT®  PRESENTATION

  Developmental Patterns of Honesty-Humility from Childhood to Adolescence: A Six-Year Longitudinal Investigation

  Honesty-Humility (HH), a personality trait characterized by sincerity, fairness, modesty, and greed avoidance, is important for promoting healthy social development. At low levels, however, HH can have maladaptive outcomes, including dishonest behaviour. Importantly, developmental research has identified adolescence as a period for heightened dishonesty. Much of this research, however, has relied on cross-sectional designs, categorized age into discrete, and treated age as a linear variable, limiting the ability to assess non-linear (e.g., peaks) and continuous trends across development. The current study addresses these limitations by leveraging a longitudinal design to examine the non-linear development of HH from childhood to adolescence.

  Children and adolescents’ (N = 1,141; M(age) = 10 years at baseline) HH was tracked over six years. Prior to modelling developmental trajectories, an exploratory factor analysis was conducted on HH items to examine their underlying structure. The analyses revealed a two factor-solution corresponding to distinct Honesty and Humility subcomponents. Consequently, subsequent analyses modelled age-related change separately for the Honesty and Humility subfactors. Generalized additive mixed-effects models were used to capture non-linear developmental trajectories.

  Results revealed distinct developmental patterns for Honesty and Humility. For Honesty, age was characterized by a linear decrease. For Humility, age exhibited a significant non-linear trajectory. A significant main effect of HH subfactors indicated that Humility scores were higher than Honesty scores. These findings aim to refine theoretical models of personality development by illustrating how Honesty and Humility differentially emerge and evolve over development.

  About Jessica

  As a graduate student at Ontario Tech University, my passions mainly lie in my area of research—forensic psychology and youth mental health. Outside of research, however, I am actively involved in the community, holding positions with Ontario Shores Centre for MH Sciences, Ontario Provincial Police, and more.

• Kailey Haskell

  Program

  Social Practice and Innovation (MA)

  Supervisor

  Dr. Zenia Kish

  3MT®  PRESENTATION

  Building Collaborative Pathways for Gen Z Youth Engagement in Community-Based Arts and Culture Programming

  My research will explore how Gen Z (ages 16-30) engage with local arts and culture, with a focus on relationships between municipalities, school boards, secondary and post-secondary institutions, and community arts/culture organizations. Through this project, I will examine where and how youth participate in arts and culture at the local level, identify community/peer leaders, and explore gaps in accessibility, awareness, and availability of programming. This work aims to build youths' knowledge and access to local arts initiatives, which I hope will foster civic participation, cultural citizenship, and further relationship building in the local community. There is a perceived lack of engagement of Gen Z youth in community programming, which was discussed by community leaders during the virtual public forum session I attended for Oshawa’s new culture plan on November 20, 2025. Upon conducting further research about youth's community-based participatory practices, a key distinction I found is that Gen Z tends to engage more online through digital media platforms, and may appear to be not involved or invested, because they feel unheard or that “their solutions are never taken seriously” (Asiyah Herrera, 2021). This was likely furthered by the lasting social effects of the pandemic, which also impacted the in-person capacity of many nonprofit and local arts organizations. For the purpose of this project, arts and cultural programming includes visual and performance arts. In addition to this, there are systemic barriers that impact access to participation, which include transportation, affordability, and communication gaps between institutions, organizers, and community members.

  About Kailey

  I am currently a graduate student at Ontario Tech University, with a background in creative and nonprofit work. I previously studied Music Business at Durham College and Communications & Digital Media at Ontario Tech, and I hope to pursue a career in community-facing or municipal cultural planning.

• Kristen McKinnon

  Program

  Social Practice and Innovation (MA)

  Supervisor

  Dr. Thomas McMorrow

  3MT®  PRESENTATION

  Advocating for Inclusion: Empowering Caregivers to Uphold Autistic Children's Legal Right to In-person Education in Ontario

  My topic summary explores how caregiver advocacy can challenge systemic barriers within Ontario’s public education system, particularly for autistic children who experience informal exclusion. These exclusions often take the form of shortened school days, repeated send-homes, or reduced access to in-person learning. While rarely documented, such practices undermine students’ legal rights under the Education Act and the Ontario Human Rights Code, and place significant emotional, financial, and advocacy burdens on caregivers.

  Using a mixed qualitative methodology, this research draws on key informant interviews, analysis of online public caregiver forums, and comprehensive literature, policy, and case law reviews. The project is grounded in access to justice theory, critical disability studies, and ethnographic approaches, centering caregiver experiences as a legitimate source of knowledge and expertise.
  My three-minute thesis will introduce my MRP that responds to these systemic issues through the development of a caregiver advocacy toolkit grounded in Public Legal Education. The toolkit translates complex legal frameworks into plain-language resources designed to support caregivers in understanding their rights and navigating exclusionary school practices.

  While valuable legal education toolkits already exist through organizations such as ARCH Disability Law Centre, Community Living Ontario, and Inclusion Action Ontario, this project offers a distinct contribution. It is written through the lens of a fellow caregiver, combining practical legal knowledge with empathy, validation, and encouragement. By bridging legal literacy with lived experience, the project demonstrates how PLE can function not only as an informational tool, but as a mechanism for collective resistance and systemic change.

  About Kristen

  I am an outgoing mother of five who somewhat fell into post-secondary education later in life. I love nothing more than meeting a good friend at a local café for strong coffee, deep conversation, and thought-provoking debates that stretch on for hours, when childcare can be secured :)

• Melissa Handford

  Program

  Forensic Psychology (MSc)

  Supervisor

  Dr. Leigh Harkins

  3MT®  PRESENTATION

  Does Context Matter? Assessing Victim Decisions to Report Non-Consensual Intimate Image Distribution

  Non-consensual intimate image distribution (NCIID) is a form of sexual violence which involves the creation, distribution, and threatening to create or distribute nude or sexual images of another individual without their permission. Images can be taken secretly of the victim, created synthetically using photoshop or AI, or created and sent to the perpetrator with the victim's consent, only to then be shared further without consent. Regardless of how the image was created, victims can experience long-lasting negative consequences such as reduced self-esteem, social ostracization, and educational and employment impacts. In spite of these impacts, it is rare for victims to report these crimes to police.

  This research assesses why victims of NCIID decide to report these crimes to police, when accounting for factors such as image creation method and personality characteristics. We will also assess data from Canadian legal cases, to better understand how participant perceptions and decisions compare to the characteristics of NCIID cases which make it to trial, and result in convictions and more severe sentencing.

  This research is one of a few works to use an experimental design to understand victim behaviours regarding NCIID. Understanding victim decisions to report such crimes will help us understand the perceived effectiveness of NCIID laws, resulting in improved legal options and supports for victims. Additionally, the findings of this research will contribute to the rapidly growing literature around NCIID, and inform legal practices so that prospective perpetrators will be more effectively deterred and prosecuted.

  About Melissa

  I am a second-year MSc student interested in the intersection between sexual violence and technology. Outside of research, I enjoy reading, watching movies, and baking!

• Taylor Anthony

  Program

  Forensic Psychology (PhD)

  Supervisor

  Dr. Taylor Heffer

  3MT®  PRESENTATION

  When Maybe Feels Like Too Much: Intolerance of Uncertainty in Adolescence

  Uncertainty is a pervasive feature of everyday life, arising in areas such as academic performance and peer relationships, and it can trigger a wide range of emotions and behaviours. Adolescence is a developmental stage during which the effects of uncertainty may be particularly salient, as young people navigate challenges related to peer acceptance, academic success, and future planning. While some adolescents may respond to uncertainty with curiosity and motivation, others experience it as distressing. This distress associated with the unknown is called intolerance of uncertainty (IU). IU describes a tendency to perceive uncertain situations negatively, and individuals high in IU interpret ambiguous situations as threatening and overestimate the likelihood of negative outcomes. Although research indicates that adults with high IU often rely on strategies (e.g., excessive reassurance seeking) to manage uncertainty, less is known about how adolescents use similar approaches. Adolescence, marked by rapid cognitive, emotional, and social changes, is a key period for studying IU, as adolescents, with more autonomy than children but less experience than adults, may use distinct strategies (e.g., peer support) to manage uncertainty. Past research in adults has found that when applied rigidly, these strategies increase distress and heighten vulnerability to adjustment difficulties. Together, my dissertation will provide a comprehensive overview of IU during adolescence. Although uncertainty is a normal part of life, extreme levels of IU are associated with mental health problems, many of which emerge during adolescence, making it important to understand IU during this developmental stage.

  About Taylor

  I am a first-year PhD student in Forensic Psychology. Outside of academia, I enjoy pilates, spin, reading, listening to podcasts, and embroidering. I also have a cat named Tinkerbell that loves to keep me company while I do my work.