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Meet the participants

3MT participants

All participants will present at the university's 3MT® heats on Wednesday, March 20. The top six will advance to the finals to be held on Thursday, March 21. The heats and finals will be held on campus in-person and will also be livestreamed.

Faculty of Health Sciences

  • Adrianna Trifunovski

    Program3mt participant headshot

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


    Caroline Barakat, PhD


    Hidden Hazards in Everyday Products: Women's Awareness and Attitudes Towards Toxins in Personal Care and Household Products

    Many personal care and household (PCH) products contain harmful ingredients, leading to increased exposure to toxins that have negative health impacts. For instance, parabens - often found in shampoo - are classified as endocrine disrupters, elevating the risk of hormonal and reproductive disorders, specifically among women and a growing fetus. Despite the existence of safer alternatives, research underscores women's lack of knowledge in exploring these options. Often, consumers presume that most PCH products are safe and tested for potential negative impacts prior to release into the market, leading to delayed discovery of the additive, synergistic and cumulative chemical health impacts until years post-market release.

    Using a mixed-methods approach, this study examined women's knowledge, health risk perceptions, beliefs, and avoidance behaviour, in relation to toxins commonly found in PCH products, namely BPA, lead, parabens, phthalates, PERC, and triclosan. Guided by the Health Belief Model, a questionnaire was developed and administered to 200 women (18-35 years) to explore influences on women's avoidance behaviour when it comes to choosing chemical toxin-free PCH products. Following after, 10 participants were interviewed to explore factors related to decision making when purchasing PCH products. This study underscores the necessity for effective education methods to raise awareness about the health risks of PCH chemical toxins and to promote healthier alternatives to women in reproductive and reproductive ages.

    About Adrianna

    Aside from being a student, I am a personal trainer, working with women. I like to travel on my downtime, and learn new things. One random fact is I have a black belt in Taekwondo!

  • Grace Adie-Berube

    Program3mt participant headshot

    Health Sciences - Kinesiology (MHSc)


    Bernadette Murphy, PhD


    Subclinical Neck Pain Affects Oculomotor Performance During an Upper Limb Goal-Directed Aiming Task

    The rise of technology has caused an increase in non-neutral neck postures that may lead to neck pain. Subclinical neck pain (SCNP) is a subset of mild-to-moderate neck pain in which the individual has not sought treatment and still experiences some pain-free days. This makes them an interesting population as they can be observed on pain-free days to determine if neuroplastic changes due to neck pain have occurred and are still present when pain has subsided. Individuals with SCNP have been shown to have impaired cerebellar processing due to altered sensory input from the neck to the cerebellum.

    The cerebellum is essential in the planning and execution of movement. To get a better idea of how SCNP may be affecting these processes we created an upper limb goal-directed aiming task where the individuals aim from a home target to an appearing target on a display monitor. Since, vision is imperative in locating the target, we also fitted each participant with an eye tracker so we can observe visuomotor changes. By observing visual and upper limb performance markers of participants with and without SCNP, we can see how activities of daily living, such as reaching towards objects, may be impaired without the individual being aware of it. Thus, treatments to improve the neck pain and associated impairments are vital!

    About Grace

    I love playing sports. I coach hockey in the winter and rugby in the summer. I live in my small town of Lindsay with my mom and our four dogs. I enjoy painting, drawing and browsing Facebook marketplace for unique clothing and furniture. I recently taught myself how to crochet!

Faculty of Engineering and Applied Science

  • Abdalrahman Alaska


    Mechanical Engineering (MASc)


    Atef Mohany, PhD


    Manipulating Flutes for Nuclear Plants Integrity

    Exploring the application of acoustic resonance principles, akin to those found in musical instruments like flutes, this research delves into a simplified representation closely mirroring the CANDU fuel channel in the form of a pipeline replica. The objective is to alleviate pressure pulsations, a historical concern for the integrity of system components in the CANDU reactor. Various acoustic devices and pipeline geometry manipulations, some of which are as simple as connecting specific flute holes together with special length, are being tested to target the suppression of amplified tones.

    The study focuses on evaluating the efficacy of three easily installable devices in a water pipeline system through a specialized measurement technique, aiming to generalize their performance parameters for practical application in the CANDU fuel channel.

    About Abdalrahman

    I love speedy things, racing games in general, haven't tried it in real life (YET!). I love creative teaching to simplify science and making it more interesting, like being a mini V-sauce. Hopefully my academic career will help be in that path.

  • Abdelrahman Elewah

    3MT Participant HeadshotProgram

    Electrical and Computer Engineering (PhD)


    Khalid Elgazzar, PhD



    Have you ever used your smartphone while using your social media and scrolling with no goal? So, you lose control of yourself endlessly watching these contents. That's happened due to the exploitation of your data. We have committed a terrible mistake when we take the right to exploit user data. Without going into the details of the algorithms behind these applications, these applications learn from your history of watching, listening to,and browsing content and present content that matches your interest to keep you holding your phone longer. Consequently, it has changed our lives; people walk on the streets or ride buses holding phones. In our research, SensorsConnect, we mitigate reliance on exploiting user data by deploying infrastructure-based sensing.

    As an example of how this can work, Google Maps uses user data to calculate the traffic status of each road segment based on users' smartphones on the roads. SensorsConnect can achieve the same result by using the installed traffic cameras at the intersections to sense the traffic volume and speed. Scientists described data as the new oil, and we claim its dark side started rising, causing another type of climate change that could worsen the climate changes the oil creates. It takes time to convince the decision-makers with points of view that may affect industries or economies in the short term. However, taking action now is a must before being impacted by the long-term effects, like what we are currently experiencing with the first climate change wave created by traditional oil.

    About Abdelrahman

    Scrolling through Facebook, Instagram and YouTube. 

  • Bita Porrang

    Program3mt participant headshot

    Applied Science (MASc)


    Sayyeh Ali Hosseini, PhD


    Analyzing Porosity and Modeling Material Behavior in 3D Printed Metals

    Additive manufacturing (AM) or 3D printing is a novel technology attracting more interest in different industries everyday. Metal additive manufacturing (AM) is a relatively new technology in the industry for producing metallic parts. Among the metal AM techniques is metal fused filament fabrication (FFF), which is rapidly growing due to its cost-effectiveness compared to other metal AM methods. This technique involves depositing a filament, composed of metal powder and polymeric binder, layer by layer to create the desired geometry. The printing step is then followed by debinding and sintering to produce the final part. However, fused filament fabrication of metal has its own inherent challenges such as the presence of microstructural defects in the final part. One of the most significant microstructural defects in the FFF metallic parts is porosity (void), which drastically affects the material properties. Therefore, accurate measurement of porosity in FFF metals is a critical primary step in ensuring the quality of the final part. In addition, there seems to be no available model capable of simulating/predicting the material behavior of FFF metals considering their porosity. One of the most renowned material models taking the porosity of metals into consideration is GTN model. The goal in my research is to analyze the porosity and identify the parameters needed for this model experimentally to investigate the application of GTN model for FFF metals.


    About Bita

    Bita is a creative soul and is obsessed with the art of mixing media for innovative creations. Bita is a nature lover at heart, finding peace walking barefoot on grass or sand, feeling the earth's energy.

  • Farimah Hosseinnouri

    Program3mt participant headshot

    Mechanical Engineering (MASc)


    Horia Hangan, PhD


    Chasing Winter Indoors

    The research subject is one of the scopes of the Weather on Wheels (WoW) project at Ontario Tech University, aimed at understanding and mitigating the impact of harsh weather on the Advanced Driver Assistance Systems (ADAS) of autonomous vehicles (AVs). Several experiments were designed and conducted at the Large Climatic Chamber (LCC) of the Automotive Center of Excellence (ACE) to understand snow guns' capabilities and limitations in producing artificial snow and comprehend the characteristics of man-made snow.

    The snowfall and environmental conditions data of outdoor tests (undergoing study from 2021) with indoor tests (inside the LCC) are compared. The initial purpose is to set parameters for the ACE's climatic wind tunnel snow simulations that can represent realistic natural conditions. A Dimensional Analysis of the problem is used to determine the configuration of future experimental studies. In addition, based on the obtained results, the main objective consists of creating a general snow accumulation model for inclined flat fixed surfaces without wind. The final step is to generalize the model and indoor test conditions for the driven surface (which resembles the car surface while driving) exposed to the wind.

    About Farimah

    I've followed my desire for self-improvement and made my aspirations come true by choosing Canada, where there are enough opportunities for women to pursue engineering careers. In Canada, life's balance comes easily, inspired by the stunning natural beauty and breathtaking landscapes I explore.

  • Haniyeh Fathi


    Automotive Engineering (MASc)


    Zeinab El-Sayegh, PhD


    Challenges in Tire Modeling

    This presentation focuses on the modeling and analysis of a four-groove passenger car tire, size 235/55R19, using Finite Element Analysis. The tire rim is modeled as a rigid body using aluminum alloy material, and the beads are modeled as beam elements using steel material. The tire model is validated in both static and dynamic domains through several simulations and is compared to published measured data. The tire is validated using footprint and vertical stiffness tests in the static domain. In the static footprint test, a steady-state vertical load is applied, and the tire-road contact area is computed.

    In the dynamic domain, the tire is validated using drum-cleat, and cornering tests. In the drum-cleat test, a drum with a 2.5 m diameter and a cleat with a 15 mm radius is used to excite the tire structure and obtain the frequency of the vertical and longitudinal first modes of vibration. That is by applying the Fast Fourier Transformation (FFT) of the reaction vertical and longitudinal forces at the tire center. In addition to this test, the tire model is presteered on a flat surface with a 2-degree slip angle and subjected to a steady state linear speed of 10 km/h to predict the cornering force and compute the cornering stiffness. The findings of this research work provide insights into passenger car tire-road interaction analysis and will be further used to perform tire rubber compound material model sensitivity analysis.

    About Haniyeh

    Interested in tire modeling & yoga!

  • Mehrdad Sadeghieh


    Mechanical Engineering (PhD)


    Sayyed Ali Hosseini, PhD


    Look ahead stress-based trajectory planning for fused filament fabrication additive manufacturing

    This paper introduces a multi-step look ahead trajectory planning algorithm (LATPA) designed to enhance the tensile properties of components manufactured through extrusion-based additive manufacturing processes. The proposed algorithm aims to optimize the trajectory for fused filament fabrication by following the direction of principal stress throughout the entire geometry. Numerous researches supports that closely aligning the trajectory with the direction of principal stress results in improved ultimate tensile strength of the additively manufactured parts. Comparative analysis with single-step trajectory planning reveal that the proposed multi-step LATPA significantly reduces the deviation from the direction of principal stress on a global scale. In order to verify the validity of the proposed model, Tensile testing has been performed on stainless steel 17-4 PH and Polylactic Acid (PLA) specimens.

    About Mehrdad

    As a mechanical engineering student, my passion exceeds gears and equations to theatre, cinema, and anime. Drawing from my acting experience, I've embraced the motto "the show must go on" in my research. This philosophy seamlessly intertwines the resilience learned on stage with determination required in scientific exploration.

  • Seyed Mahmoud Seyedi Sahebari

    3MT Participant HeadshotProgram

    Mechanical Engineering (PhD)


    Jana Abou-Ziki, PhD and Ahmad Barari. PhD


    Spark-Assisted Chemical Engraving (SACE) Real-Time Process Control with Data-Driven Modeling

    Glass, known for its biocompatibility, optical transparency, mechanical rigidity and thermal stability, is essential in micro applications in medical, optical, microelectronics and metrology sectors. This thesis focuses on Spark-Assisted Chemical Engraving (SACE), a glass hybrid micromachining technique based on electrochemical principles, using high-energy discharges from voltage applications to thermally accelerate glass chemical etching. SACE facilitates the creation of microstructures such as microholes and microchannels, offering high aspect ratios, efficient machining rates, and surface roughness comparable to thermal processes. Unlike chemical techniques, SACE is cost-effective and does not require a cleanroom environment.

    The inherent uncertainties and instability of SACE causes significant challenges. This research overcomes these through a machine learning-based, data-driven model for real-time SACE process control. The model's cornerstone is the real-time analysis of current signals to extract process signatures, like discharge energy and frequency, enabling precise parameter adjustments to mitigate uncertainties.

    A Temporal Convolutional Network (TCN) is employed for process signatures online monitoring and real-time prediction of microchannel surface roughness and depth, crucial for various applications. Implemented on a dSPACE control panel, this model substantially enhances productivity and reduces machining time. It is uniquely designed to rapidly detect high-speed sparking events, thereby preventing modeling overrun errors common in real-time applications.

    This research innovatively integrates real-time process control with the SACE technique, potentially enhancing the technology and offering a new perspective on this emerging method.

    About Seyed 

    My Ph.D. journey unexpectedly fueled my research passion. Embracing its budgeted lifestyle, I find joy in simplicity. Once a mountain climber, I now savour Ontario's flat landscapes. I am aiming to grow my self-confidence through 3MT participation.

  • Vida Gharavian

    Program3MT Contestant

    Electrical and Computer Engineering


    Masoud Makrehchi, PhD


    A Pairwise Surrogate Model using GNN for Evolutionary Optimization 

    Optimization problems widely arise in various science and engineering fields and can be computationally expensive in many real-world applications. Evaluation of the fitness function to assess a candidate solution is the main operation in all optimization procedures which can be heavily compute-intensive.

    Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to consequently reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison despite the fact that most of the surrogate models try to predict the exact fitness value. The proposed idea can significantly help the optimizer to reach better results in a shorter period of time. It seems comparing two candidate solutions for a greedy selection is much easier than approximating fitness values for both. We demonstrated Graph Neural Network (GNN) for this purpose to be trained on a limited number of pairwise ranks and then utilized to compare a pair of candidate solutions.

    About Vida

    I'm Vida Gharavian and I'm thrilled to share a bit about myself. When it comes to my downtime, I'm all about sports! Winter is my playground for skiing, while summer sees me hitting the tennis courts. Originally from Iran, I've been living the Canadian dream since 2023.

  • Wing Yi Pao

    Program3mt participant headshot

    Mechanical Engineering (PhD)


    Martin Agelin-Chaab, PhD 


    Get home safe everyday

    Driving in adverse weather is dangerous, rain in particular that occurs globally contributes to about 30% of road accidents. Although modern vehicles are equipped with sensors to assist the driver, the performance of these sensors in rain is uncertain. In recent years, Light Detection and Ranging (LiDAR) sensor is increasingly being employed in both modern and prototype autonomous vehicles for mapping of the vehicle surrounding environment and performing collision avoidance. Testing for LiDAR perception in rain is important because LiDAR performance degradation due to raindrops adhering on the surface poses safety concerns. It is crucial for the development of autonomous features to deploy adequate strategies to maintain vision as well as functions.

    The thesis work is a multi-disciplinary topic and is a complex multi-phase problem as weather impact on vehicle sensor performance is related to meteorology, aerodynamics, materials, and optics. The objectives of the thesis include developing a realistic, repeatable, controlled, and rapid weather testing methodology for autonomous vehicle sensor applications; then propose LiDAR signal enhancement solutions. It is an emerging topic that has limited work in the open literature, there is no standardized testing method and most existing work is unrealistic. Therefore, a novel artificial rain simulation system has been designed and implemented into a full-scale climatic wind tunnel, the target rain conditions have been theoretically derived and validated with outdoor measurements on a vehicle. Lastly, raindrop dynamics such as size, shape, and impact force are extensively investigated to provide guidelines on material selection for LiDAR sensing lens.

    About Wing Yi

    I am a dog mom of 3, one of them ran 5km with me in the Chancellor Challenge twice. I have many too many hobbies, if I have to pick, it will be triathlon. I have completed a Half-Ironman race so far - 1.5km swim, 90km bike, 21km run.

Faculty of Science

  • Janani Balasubramanian

    Program3mt participant

    Materials Science (MSc)


    Nisha Rani Agarwal, PhD


    SERS-Driven Nano-Biosensors: Advancing Precision for Hemoglobin Analysis

    Hemoglobin (Hb) is pivotal in blood composition, crucial for diagnosing disorders like β-Thalassemia and sickle cell anemia. A precise Hb sensor is essential to overcome delays and expenses in conventional blood assays. Our study introduces a nano-biosensor utilizing surface-enhanced Raman spectroscopy (SERS) for rapid, real-time Hb analysis with a small sample and high sensitivity. SERS provides unique "molecular fingerprints," aiding accurate Hb variant detection.

    To construct the sensor, we deposited gold and silver thin film substrates using the pulsed laser ablation technique and electrochemical deposition methods, suitable of resonance with 633 and 532 nm Raman lasers. The nanofilm were functionalized with a novel heteroaromatic ligand L, a derivative of alpha-lipoic acid and 2-(2-pyridine)imidazo[4,5,f]-1,10-phenanthroline. The molecular unit's grafting to nanofilm via the lipoic acid unit created a selective sensor for Hb. The sensing mechanism relies on the phenanthroline unit of L forming strong coordination bonds with the iron center in the heme unit of the Hb protein.

    The sensor chip demonstrated week-long stability, maintaining Hb sensitivity. Using the SERS band at 1390 cm-1, we quantified normal Hb, assessed iron levels, and distinguished oxy and deoxy forms, impacting oxygen-carrying capacity. Results were verified through electrochemical SERS, enabling early diagnosis of issues like carboxyhemoglobinemia. Furthermore, variations in SERS characteristic bands in the "fingerprints region" could facilitate the differentiation of normal Hb from abnormal Hb variants. This study lays the foundation for a lateral flow assay, advancing rapid and accurate Hb disorder diagnosis.


    About Janani

    Hailing from the pleasant city of Coimbatore, India, I'm an active enthusiast engaged in a symphony of interests—from singing, martial arts, yoga, painting, shuttle sports, editing photos and scouting. As a Karate Shodan rank holder, I harmonize discipline with creativity. Fun fact: I relish India's street food and chai.

  • Melika Abdollahi