Dean’s Research Awards - Faculty of Engineering - University of Alberta

Advances in medical sciences have identified the genetic roots of many diseases, but this knowledge hasn't fully translated into treatments for these defects. The two major classes of drugs are small molecules and proteins. Small molecules deactivate or inhibit target proteins through competitive binding, but only about 2-5% of proteins have such binding capabilities, limiting their targets. Proteins can bind specifically to various targets or replace mutated or missing proteins, but their size and stability are limiting factors. Thus, precision medicine needs new treatment options.

RNA-based therapeutics can selectively target undruggable human and viral genomes, knock down gene expression, alter mRNA splicing, target regulatory non-coding RNAs, express genes, and edit the genome. We aim to use RNA bioinformatics for personalized medicine, precise diagnostics, and tailored treatments based on a patient’s genetics to reduce adverse effects.

Advances in medical sciences have identified the genetic roots of many diseases, but this knowledge hasn't fully translated into treatments for these defects. The two major classes of drugs are small molecules and proteins. Small molecules deactivate or inhibit target proteins through competitive binding, but only about 2-5% of proteins have such binding capabilities, limiting their targets. Proteins can bind specifically to various targets or replace mutated or missing proteins, but their size and stability are limiting factors. Thus, precision medicine needs new treatment options.

RNA-based therapeutics can selectively target undruggable human and viral genomes, knock down gene expression, alter mRNA splicing, target regulatory non-coding RNAs, express genes, and edit the genome. We aim to use RNA bioinformatics for personalized medicine, precise diagnostics, and tailored treatments based on a patient’s genetics to reduce adverse effects.

Advances in medical sciences have identified the genetic roots of many diseases, but this knowledge hasn't fully translated into treatments for these defects. The two major classes of drugs are small molecules and proteins. Small molecules deactivate or inhibit target proteins through competitive binding, but only about 2-5% of proteins have such binding capabilities, limiting their targets. Proteins can bind specifically to various targets or replace mutated or missing proteins, but their size and stability are limiting factors. Thus, precision medicine needs new treatment options.

RNA-based therapeutics can selectively target undruggable human and viral genomes, knock down gene expression, alter mRNA splicing, target regulatory non-coding RNAs, express genes, and edit the genome. We aim to use RNA bioinformatics for personalized medicine, precise diagnostics, and tailored treatments based on a patient’s genetics to reduce adverse effects.

Additive manufacturing, or creating an object layer by layer, is an emerging technology in various industries such as oil and gas, aerospace, automotive, and many others. This project focuses on robotic welding technology and the 3D printing of metal components. During the term, you will become proficient in operating a welding robotic machine designed specifically for this purpose. You will have the opportunity to select and modify various printing parameters to observe and analyze their effects on the final printed components for application in these industries. These skills are crucial for advancing material properties, optimizing processes, and improving component design and performance in various industries.

Additive manufacturing, or creating an object layer by layer, is an emerging technology in various structural industries and materials. In civil engineering, AM is revolutionizing the way structural components are designed and constructed. This technology enables the production of complex geometries and customized parts that traditional methods struggle to achieve. For example, AM is used in the construction of bridges, custom building components, and infrastructure repairs, offering enhanced performance and unique design possibilities. It also facilitates rapid prototyping and the creation of lightweight yet strong metal frameworks, thereby reducing material waste and improving overall efficiency in structural projects. As a civil engineering student, exploring AM's applications in metal 3D printing provides a valuable opportunity to innovate and advance structural design and construction techniques.

High Entropy alloys (HEAs) and near stoichiometric HEAs (all referred as HEAs) are a new generation of materials that show promise to provide a unique combination of properties. Given the enormous possibilities in tuning the structures and compositions of HEAs, this project aims to test our model for predicting new HEA alloys. Thus, a novel high throughout method has been developed to evaluate candidate HEAs.

Active particles constitute a novel class of soft materials called active matter that are intrinsically out of thermal equilibrium. Due to their continuous consumption of stored or ambient energy, active matter such as motile bacteria and chemically active colloids exhibit fascinating properties that are highly desirable for applications in chemical sensing, cargo transport, and self-assemblying materials. In natural and engineered systems, active particles often exist in confined and structured environments. Understanding the interplay between geometric confinement and activity is critical towards the development of a theoretical and computational framework to characterize the dynamics of active materials. Using computer simulations, in this project we will consider the transport and dispersion process of active particles in a periodic porous media. We will consider the effect of an external biasing force on the transport dynamics.

As computer vision technology rapidly advances, it becomes an increasingly powerful tool for process monitoring and research, especially in the welding industry. This project aims to apply the power of deep learning to analyze high-speed videography of gas metal arc welding (GMAW)—a critical and widely adopted welding process, included in new applications like additive manufacturing. Throughout this project, you will gain domain knowledge in the field of welding, while gaining insight into how deep learning is transforming our understanding of the complex physical phenomena within the welding arc. You will have the opportunity to refine and enhance the training of the algorithm (written in python for machine learning based on PyTorch) designed to segment images and extract precise, quantifiable data from welding videos. This hands-on experience will not only strengthen your skills in deep learning and computer vision but also provide you with a unique perspective on how these technologies are driving innovation in industrial applications.

Pseudoexfoliation syndrome (a.k.a. exfoliation syndrome) involves the deposition of insoluble, fibrillar protein aggregates (i.e., exfoliation materials, XFM) in the anterior segment of the eye. The accumulation of XFM in the trabecular meshwork is of particular concern as it increases resistance to fluid outflow from the eye, resulting in increased backpressure that greatly increases the risk for glaucoma. Pseudoexfoliation glaucoma accounts for 20 – 25% of open-angle glaucoma cases and is the most common form of secondary open-angle glaucoma worldwide.

Unsworth Labs is uniquely positioned to study XFM, being the only group worldwide to
evaluate the structure of XFM fibrils at high resolution, develop targeting peptides, and develop peptide-conjugated MNPs for controlled delamination and collection of XFM.

Come join our team and test different nanoparticles and magnetic field properties in an effort to destroy human XFM fibrils safely.

As more industries move to capitalize on the technological benefits of additive manufacturing, researchers are exploring ways to design new alloys with properties that cannot be achieved through traditional manufacturing methods. One approach is to tailor the solidification microstructures of lightweight components using dense materials such as eutectics. Eutectics are natural composite materials that are composed of a ductile phase and a brittle phase. This study examines the microstructures and mechanical properties of near eutectics under different thermal histories found in various additive manufacturing techniques. Rapidly solidified powders of various sizes are generated by atomization. Microstructural analysis will reveal the different eutectic morphologies and spacing depending on the cooling rate. The aim of this study is to develop Solidification Microstructure Maps of eutectics as well as Continuous Heating Transformation Diagrams. These will serve as a microstructure digital library of additive manufacturing processes.

This project primarily aims to develop a universally applicable, ex-ovo virus propagation platform for influenza vaccine production. Our very own strategy to achieve this goal is to automate the process with robotic motion control using machine learning techniques to pinpoint virus inoculation. The findings of this research will contribute to resolving the long-lasting problems of egg-based influenza vaccine production technologies and facilitate protection for the whole world against current and future infectious diseases.

To meet the demand for effective inactivation of biologically contaminated metal, plastic, and glass surfaces, we aim to develop the salt-incorporated alcohol solution for universal pathogen inactivation. Our initial study demonstrated the concept of an alcohol-salt disinfectant using enveloped pathogens. In this study, we plan to find optimal alcohol-salt disinfectant formulations to improve their biocidal effects against spore-forming bacteria.

Food preservation and safety emerge as significant concerns in the Canadian food industry. Current industry practices for antibacterial food applications rely on chemical or preservative-based methods. Addressing this need, our group develops an innovative solution for fabricating antimicrobial food packaging antimicrobial surfaces based on micro-CNC techniques. This solution is applicable not only to the food industry but also to other sectors such as healthcare and public health.

Dialysis is a life-saving therapy. However, life expectancy upon kidney failure has not improved for ~30 yrs. This situation is intolerable, and Unsworth Labs is trying to understand why this situation has persisted.

Come join our team to work on metabolite identification and how their concentration in blood affects biofouling in an attempt to build better systems for cleaning blood.

The widespread use of additives in plastics poses a significant challenge to global recycling efforts. Traditional mechanical recycling struggles with persistent contaminants and additives, as well as issues like thermal–mechanical degradation and polymer immiscibility. Chemical recycling, which involves breaking of the polymer chains, can process plastic waste that is difficult to treat mechanically, yet faces operational problems such as coking, catalyst poisoning, and corrosion due to impurities.

To address these issues, this project explores adsorption in non-aqueous environments for recycling plastic waste, with a focus on polyvinyl chloride and polystyrene. You will design and conduct adsorption-isotherm experiments, analyze results through modeling, and gain hands-on experience with characterization techniques including BET analysis, UV-vis spectroscopy, FT-IR spectroscopy, and elemental and morphological analysis.

In recent years, 3D printing has become an excellent alternative to casting, especially for making complex shape components that are traditionally, manufactured by investment casting. However, 3D printing, despite covering a wide range of metals and alloys is relatively expensive for manufacturing complex shapes, and the surface finish of the printed part does not always meet the quality specifications. In this work, an economic manufacturing process, termed the hybrid investment casting, is proposed. It combines the traditional investment casting with Stereolithography (SLA) 3D printing. The process consists in creating a 3D model of the part to be manufactured, by selectively curing a polymer resin layer-by-layer using an ultraviolet (UV) laser beam. The model is then used as a pattern for the investment casting of the part.

The increasing use of plastics has heightened waste management challenges, highlighting the need for effective recycling solutions. Polyethylene terephthalate (PET), commonly found in bottles, trays, packaging, and textiles, struggles with quality issues in mechanical recycling due to impurities like pigments and dyes.

This project addresses these challenges through chemical recycling, aiming to convert PET into pure monomers. You will conduct solvent-uptake experiments on waste PET samples and analyze the polymer's swelling behavior in various solvents. Additionally, you will gain hands-on experience with characterization techniques such as thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and microscopy. The experimental data will help determine the optimal kinetic model for PET dissolution using different co-solvents.

“GeoCloud” is a research program that focus on developing a multi-scale, multi-physics and data-driven decision makings platform for carbon capture, utilization and storage (CCUS), hydrogen storage and enhanced geothermal system. As a researcher in this project, you will join a multi-disciplinary and vibrant research group, collaborate with our industrial and academic partners to achieve a variety of milestones, and cultivate your skillset to prepare yourself for future challenges during energy transition. You can join one of the three themes that is led by postdocs, PhD students and research associates.
Theme 1 is creating a data-driven site screening and evaluation algorithm for CO2 storage in deep saline aquifer and depleted oil reservoir.
Theme 2 is evaluating the feasibility for CO2-Plume-Geothermal (CPG) in heterogeneous geological formations.
Theme 3 is regional-scale geological modeling at Edmonton Heartland region and investigating the viability of hydrogen storage.

Two positions are available to support the research conducted by Dr. Mustafa Gül and his team on sustainable and smart cities and communities, with a focus on infrastructure and energy systems. The selected candidates will engage in advanced data analytics, image/video processing using Artificial Intelligence (AI) and deep learning, computer modeling, and software development. Successful candidates must demonstrate a strong ability to integrate these skills effectively into practical applications.

The project will focus on two main areas:

Crowdsensing Technologies for Monitoring Built and Natural Environments: This research involves developing advanced image and signal processing methods to analyze data and images collected from sensors and cameras mounted on mobile vehicles. The goal is to monitor and assess the condition of bridges, roads, and the natural environment-urban interface, particularly for wildfire risk assessment.

IoT for Solar PV Integration into Energy-Efficient Buildings: This research focuses on applying IoT (Internet of Things) technologies to energy-efficient buildings equipped with solar PV systems. The main responsibilities will include developing and optimizing IoT-based solutions to enhance the efficiency and integration of solar energy within these buildings.

Two positions are available to support the research conducted by Dr. Mustafa Gül and his team on sustainable and smart cities and communities, with a focus on infrastructure and energy systems. The selected candidates will engage in advanced data analytics, image/video processing using Artificial Intelligence (AI) and deep learning, computer modeling, and software development. Successful candidates must demonstrate a strong ability to integrate these skills effectively into practical applications.

The project will focus on two main areas:

Crowdsensing Technologies for Monitoring Built and Natural Environments: This research involves developing advanced image and signal processing methods to analyze data and images collected from sensors and cameras mounted on mobile vehicles. The goal is to monitor and assess the condition of bridges, roads, and the natural environment-urban interface, particularly for wildfire risk assessment.

IoT for Solar PV Integration into Energy-Efficient Buildings: This research focuses on applying IoT (Internet of Things) technologies to energy-efficient buildings equipped with solar PV systems. The main responsibilities will include developing and optimizing IoT-based solutions to enhance the efficiency and integration of solar energy within these buildings.

The research will concentrate on conducting advanced laboratory investigations on asphalt materials, specifically studying the performance of asphalt mixes in cold climates. This will involve utilizing sophisticated laboratory testing equipment tailored to cold climate conditions. DRA students will have the opportunity to collaborate with graduate students, gaining insight into these tests, data collection, analysis, and contributing to publications.

In hydrological analysis, identifying individual rainfall-runoff events is important. For example, it is essential to study flood generating mechanisms or changes in flood events. Traditional methods for identifying these events often rely on baseflow separation, which involves subjective decision-making and parameter adjustments when applied to different basins. This project offers an exciting opportunity to test and validate a novel, objective rainfall-runoff event identification method recently published in Water Resources Research (Giani et al., 2022).

Focusing on the Bow River at Banff basin, you will apply this method and evaluate its performance by comparing identified events with observed data. This involves processing hydrological and meteorological data, implementing the method using programming tools like MATLAB, R, or Python, and analyzing the results against existing records. Through this project, you will gain hands-on experience with advanced hydrological techniques.

Accurate traffic volume estimation is essential for effective urban planning and transportation management in Edmonton. By leveraging key metrics such as Annual Average Daily Traffic (AADT) and Annual Average Weekday Traffic (AAWDT), the city aims to optimize road safety and enhance traffic flow. Currently, Edmonton is transitioning from traditional manual calculations to more advanced analytical methods. These methods are specifically designed to maximize data utility and improve both road network analysis and strategic planning. As part of a multi-year initiative, students will be actively involved in comprehensive research activities including data collection, literature review, and model development, thereby improving traffic management practices within the city.

This project focuses on developing advanced generative AI models to enhance natural language processing (NLP) specifically for smart city applications. The primary goal is to create a model capable of efficiently extracting and synthesizing critical information from complex, unstructured text data generated in urban environments. This includes analyzing data from sources such as social media, public service reports, sensor networks, and urban planning documents. By leveraging state-of-the-art machine learning and deep learning techniques, the model will improve the accuracy and responsiveness of smart city systems, enabling better decision-making in areas like infrastructure management, public safety, and environmental monitoring. The project aims to contribute to the development of intelligent, data-driven solutions that enhance the sustainability and livability of urban areas.

This project focuses on developing advanced generative AI models to enhance natural language processing (NLP) specifically for smart city applications. The primary goal is to create a model capable of efficiently extracting and synthesizing critical information from complex, unstructured text data generated in urban environments. This includes analyzing data from sources such as social media, public service reports, sensor networks, and urban planning documents. By leveraging state-of-the-art machine learning and deep learning techniques, the model will improve the accuracy and responsiveness of smart city systems, enabling better decision-making in areas like infrastructure management, public safety, and environmental monitoring. The project aims to contribute to the development of intelligent, data-driven solutions that enhance the sustainability and livability of urban areas.

This project focuses on enhancing semantic segmentation of LiDAR-acquired data by integrating text- based scene description tools. The project involves developing machine learning models for precise object detection and integrating natural language processing techniques to create comprehensive textual descriptions of the LiDAR-mapped environments. The outcome is expected to automate report generation for infrastructure analysis, with significant applications in transportation engineering. Additionally, the project explores advanced image processing algorithms for object detection, requiring strong coding skills and a deep understanding of machine learning and computer vision principles. Responsibilities include improving model accuracy through data enhancement and extracting quantitative information from labeled point clouds to assess highway conditions.

Imagine a possible scenario: In an auto accident, an oil truck flipped, and the oil leaked to a nearby river where a drinking water treatment plant’s water intake is located downstream. The microwave hydrocarbon sensors, which were deployed strategically in the river for 24/7 monitoring, detect sudden water quality changes in its hydrocarbon content and send real-time signal to the control room of the water treatment plant, allowing decision-makers valuable time to shut down the water intake to secure the safety of drinking water. There is currently no such sensor, however, microwave sensors can be developed for such purposes.

This novel research exploration is supported by a cross-disciplinary approach, merging two separate disciplines in electrical engineering and environmental engineering, respectively, during the research project. The Dean’s Research Award student will participate in the environmental engineering side of the research project.

The success stories from Mali, South Australia, and preliminary findings in Ontario, Canada (Geoscientist 2022) provide a promising outlook for natural (white/gold) hydrogen exploration. Depending on the deposit’s depth and purity, the cost for producing natural hydrogen is $0.5-1.0/kg (Mining.com 2024), which is more competitive than the grey hydrogen produced from fossil fuels ($1-3/kg) and green hydrogen produced by electrolysis ($4-7/kg). Continued research, technological advancements and increased investment in exploration activities are likely to detect more high-purity hydrogen sources in Canada.
In this project, we will conduct an integrated analysis using deep-learning techniques leveraging on geological, satellite and remote sensing data for natural hydrogen exploration. Our goal is to develop a more accurate, efficient and cost-effective exploration techniques than state-of-art methods to unlock the untapped potential for natural hydrogen production in Canada.

This research investigates innovative strategies to optimize curbside space management and on-street parking efficiency in Edmonton. To achieve this, we employ a cutting-edge approach that combines AI-powered stereo cameras and satellite imagery within a Geographic Information System (GIS) framework. By integrating these data sources within a GIS, we can accurately analyze parking utilization patterns across various timeframes, including weekdays, weekends, and different times of the day. This data-driven approach will inform the development of new, effective parking policies and reevaluating the old ones to maximize their benefits, identify opportunities for curbside space reallocation, and support Edmonton’s broader goals of creating safer, more sustainable urban environments. This research offers significant opportunities for students to engage in cutting-edge data analysis, urban planning, and transportation engineering, contributing to the development of innovative solutions for urban challenges.

This is another focus area of the project: Yukon river ice study, which aims to investigate river ice processes in a small-steep-regulated river in Yukon during the 2023-2024 winter season. Dynamic ice processes and flow variations can lead to frequent overflow onto existing ice and floodplains, which presents significant flood risks.

Detection of Overflow Events: Frequent overflow of water onto the ground and/or ice surface is observed in this river basin. This component aims to enhance the image analysis capabilities to identify and characterize overflow events on both the riverbank and floodplains. This will like require the implementation of machine learning techniques, particularly deep learning models, to improve detection accuracy and efficiency. This research is vital for understanding the characteristics of overflow events, which are critical for assessing flood risks and ecological impacts.

This research project aims to investigate river ice processes in a small-steep-regulated river in Yukon during the 2023-2024 winter season. Dynamic ice processes and flow variations can lead to frequent overflow onto existing ice and floodplains, which presents significant flood risks. The study will employ digital image processing techniques, with potential applications of machine learning algorithms, to analyze time-lapse images collected from riverbank and floodplain locations.

Quantitative Ice Thickness Measurement is one of the primary focus areas. This component focuses on developing and applying an image processing program to accurately measure ice thickness from images under varied lighting and environmental conditions. The ice evolution, including both formation and decay, throughout the winter season will be studied, providing valuable insights into river ice models and winter flow management strategies.

The emergence of new wireless communication technologies and systems creates an urgent need for intelligent planning of a plethora of existing and emerging B5G/6G wireless services. To this end, radio wave propagation models are a necessary prerequisite, as they can predict the signal levels created by a system of transmitters in a given environment. Such models can be used to optimize the position of network access points, assess interference from and towards neighbouring systems and perform network-level performance evaluation studies. Radio wave propagation models can be derived by physics-based methods (e.g., ray-tracing, full-wave electromagnetic modeling techniques). However, the development of physics-based models demands a high level of relevant expertise and computational resources that can, in practice, be prohibitive for real-time B5G/6G wireless applications. This project aims to explore a data-driven approach that can lead to computationally efficient, high-fidelity radio wave propagation models, without performing a channel simulation, by leveraging advances in deep learning (e.g., physics-informed neural network, PINN). The goal is to build a learning-centred methodology that can recognize the signal propagation and fading characteristics of a channel over a frequency bandwidth, by processing the channel geometry.

The relative permittivity (also known as dielectric constant) of a material is a complex function of frequency with both real and imaginary components at each frequency. If the relative permittivities of thin semiconductor films are quantified over the entire ultraviolet, visible and near-infrared spectral range, the optical and electronic properties of the semiconductor are determined to a large extent. Thus, while detailed information about the complex permittivity function of conventional semiconductors such as silicon, germanium, gallium arsenide and indium phosphide is widely available, the same is not true for new and emerging semiconductors. This project is primarily an in-person experimental project with data analysis tasks that can be conducted remotely. This project can accommodate multiple students who collect & anayze data from dfferent semiconductors.

Industrial robots, such as those used in welding applications, typically rely on internal controllers for precise operation. However, higher accuracy and adaptability can be achieved through computer-based control, overcoming the limitations of traditional teach pendant programming. This project aims to develop and implement computer-based programming for an industrial FANUC welding robot, focusing on enhancing trajectory tracking and precision through real-time arc-sensing in a closed-loop control system.

The Shankar Group is using DC, pulsed and microwave measurements for sensing and diagnostics. On the one hand, customized tests need to be built in a programming language (Labview is ideal) to communicate with instruments, set-up measurements and receive data. On the other hand, a tremendous amount of data is being generated which needs to processed. ECE students are typically aware of Z-parameters, Y-parameters, h-parameters and ABCD parameters used to describe the behavior of low frequency circuits and systems. At GHz and higher frequencies, S-parameters are used. Interested students will be proficient users of python and/or MATLAB and/or LABVIEW, which will be needed to acquire data, process the data and convert it into scientifically meaningful graphs/plots followed by curve fitting. Some knowledge of machine learning is desirable but not essential. This project provides great introductory hands-on experience in microwave engineering and handling Big Data.

This project focuses on developing deep learning techniques to interpret images/videos. You are expected to work with a graduate student/researcher, get familiar with state-of-the-art deep learning techniques, and gain hands-on research experience on benchmark and home-grown datasets. It is also a good opportunity to have a taste of & participate into the computer vision and machine learning related research projects carried out in our lab.

This project aims to develop advanced deep learning techniques for efficient and accurate wireless channel characterization. By leveraging state-of-the-art machine learning models, the project will address the complexities of dynamic wireless environments to enable more robust and adaptive communication systems. The primary focus will be on designing neural network architectures that can predict channel conditions in real-time, optimize signal quality, and reduce computational overhead. This initiative will bridge the gap between theoretical channel models and practical implementations, facilitating higher data rates and more reliable connections in next-generation wireless networks.

A p-n junction, n-n junction or p-p junction between dissimilar semiconducting materials is called a heterojunction (HJ). Molecules belonging to the acene family (anthracene, tetracene, pentacene, etc) are among the best performing organic semiconductors in electronic applications such as organic thin film transistors (OTFTs), organic photodetectors (OPDs) and organic photovoltaics (OPV). This project involves forming two-terminal heterojunctions of thin films of oligoacenes with other semiconductors and metals, and then testing the electrical properties of the resulting heterojunctions. Analysis of the electrical properties will reveal resistive and/or diode-like behavior exhibited by the heterojunctions.

This research project aims to enhance the performance of Radio Frequency (RF) distributed amplifiers by leveraging innovative circuit techniques/structures. RF distributed amplifiers are crucial in various communication systems, radar applications, and signal processing devices due to their wide bandwidth and high gain characteristics.

This project aims to optimize Switching Mode Power Supplies (SMPS) by focusing on reducing output noise and improving response time through the application of machine learning techniques. Given the widespread use of SMPS in various electronic systems across industries, enhancing these aspects can lead to significant energy savings and superior performance in numerous applications.

This project aims to explore a data-driven approach that can lead to computationally efficient, high-fidelity radio wave propagation models, without performing a channel simulation, by leveraging advances in deep learning (e.g., physics-informed neural network). The goal is to build a learning-centred methodology that can recognize the signal propagation and fading characteristics of a channel over a frequency bandwidth, by processing the channel geometry.

Magnetic actuation has been increasingly utilized in robotic systems, especially in medical and surgical devices. This type of actuation involves an external magnet that induces movement in small internal magnets embedded within the robot’s structure. This project aims to model and simulate the magnetic field and its gradients generated by a dipole external magnet, as well as the forces applied to an internal magnet, considering the 3D motion and rotation of the external magnet.

This project demands a prior familiarity with the Labview programming environment. The first objective of this project is to use Labview to obtain a sinusoidally modulated output from a high power LED. The next objective is to use Labview to control a lock-in amplifier (LIA) and acquire data from it. The third and final objective consists of synchronized optical and electrical tests wherein a diode is illuminated by the LED and its electrical response is measured using the LIA. Graduate students in the Shankar Lab will assist the intern in experimental set-ups and measurements.

This project focuses on analyzing visual motions of human and animals from video feed, a problem that plays a crucial role in many real-life applications ranging from natural user interface to autonomous driving. You are expected to work with a graduate student/researcher, get familiar with state-of-the-art deep learning techniques, and gain hands-on research experience on benchmark and home-grown datasets.

A recently published work by the IMPACT lab has demonstrated the integration of a needle valve with a heated hose feed mechanism for large flow FDM 3D printing. The purpose of this project is to redesign the basic system to be as lightweight and as aesthetically pleasing as possible for future commercialization or academic work. The system should be designed to work with a Wham Bam Mutant quick connection system and integrated on large format 3D printers like a MODIX Big 60 in the future. Needle valve paper can be found here: https://www.sciencedirect.com/science/article/pii/S2214860424000915

In this DRA project, the student will use MATLAB or ImageJ to analyze the droplet diameter, impact velocity, and spreading diameter from the snapshot experimentally recorded. The influence of Weber number, Reynolds number, and surface temperature on the maximum spreading will be investigated. The skills acquired include image analysis and empirical modeling.

We have developed a thermal IR pixel that can be used to change apparent temperatures by varying thermal emissivity. We are looking to design a control system that can sense system temperature, and external thermal inputs to change the degree of which the pixel emits or reflects thermal IR. Actuation schemes, sensor input and software design for Arduino are desired.

This project aims to explore and analyze various machine learning methods (especially deep learning methods) for estimating human poses from videos. It focuses on comparing different algorithms to understand which technique provides the most accurate and efficient human pose estimation. This is significant for applications in areas like augmented reality, healthcare, and motion analysis. The project involves implementing and testing different machine learning models, evaluating their performance, and determining the most effective approach for accurately capturing and interpreting human body positions and movements.

We will experimentally investigate how to control viscous fingering instability of complex fluids using a special fluidic cell of varying permeability. The student will record the dynamics of interface profile between fluids during fluid-fluid displacement, when a less viscous fluid pushes another immiscible one. Valuable skillsets that the student will acquire include basic experimental techniques, image analysis, and data acquisition and analysis.

In this DRA project, the student will use a high-speed camera to record drop dynamics impacting surfaces to investigate the effect of porous substrates on drop impact outcomes. The skills gained include basic instrumentation, image analysis, and hands-on experimental experience.

In this DRA project, the student will utilize a high-speed camera to record drop dynamics impacting onto freezing surfaces to explore the effect of surface temperature on impact events and the maximum spreading diameter. The skills gained include basic instrumentation, image analysis, and hands-on experimental experience.

We are looking for one motivated student to help design and develop heat exchangers for HVAC applications. This project aims to improve energy efficiency and sustainability in building systems by analyzing the working mechanism for diverse heat exchangers. You'll get hands-on experience with real-world equipment (shell and tube heat exchanger, air-to-water heat exchanger, expansion tank, tankless hot water heater, and more), following ASHRAE guidelines and contributing to innovative designs that reduce energy consumption and environmental impact.
This project is a great opportunity to explore thermodynamics, fluid mechanics, and the HVAC industry more deeply while building valuable skills for a future career in engineering and sustainable technology. If you're passionate about sustainability and want to make a real impact, we’d love to hear from you!

Modular concentrated solar thermal systems can effectively address the challenge of industrial heat, while also co-generating hydrogen via steam electrolysis, targeting 38% of on-site GHG emissions in sectors such as oil and gas and mining. We aim to predict the response of high-temperature modular concentrated solar thermal systems by building machine learning-based models. Our group has previously developed experimentally backed semi-analytical models capable of predicting the thermofluid response of molten salt-based concentrated solar thermal systems. However, these models are specific to certain fluid types and lack generality. By utilizing experimental data from lab-scale prototypes, we plan to apply machine learning techniques to extend our previous models and create more generic versions. We will experimentally validate the machine learning models by replicating various real-life scenarios in the lab.

I am seeking motivated students to explore the application of machine learning methods to analyze the relationship between human cadaver skull information and its biomechanical properties. This research holds potential contributions to fields such as forensic science, anthropology, and medical research by enhancing the understanding of cranial biomechanics.

The project steps include machine learning-based processing of micro-CT scan data of human skulls with critical information (age, sex, porosity). Next, the scanned data will be reconstructed into 3D models, allowing for finite element analysis using Abaqus to simulate mechanical behaviors under various conditions. Finally, machine learning-based methods will be used to analyze the relationships how the human skull changes with age and other factors.

Proficiency in Matlab or Python and Abaqus is encouraged.

This project aims at the generation and application of microfluidic, monodisperse micro/nano-bubbles. The students will gain essential experimental techniques of high-speed imaging and Microfluidic operations. The size and frequency of bubble generations will be studied under various fluid conditions, such as gas pressure and flow rate. The response of bubbles under ultrasonic drive will be studied experimentally.

The IMPACT lab has developed several prototypes of a growing robot that can be used to potentially clear minefields. To reduce complexity of the design, we are looking for solutions that can reduce internal friction of the robot so that the growth length can be extended as far as possible - sources of friction and their mitigation will come from multiple mechanisms. The project will involve testing different everting systems and mechanisms to reduce friction and required deployment pressure for everting tube robots. Some other aspects like directional control post deployment and inversion without buckling may be part of the project if initial goals are achieved.

Create a mathematical model of the transient cooling of land mines after sunset. Implement the model into a computer program to predict conditions for thermal imaging detection of land mines in the field.

This DRA project investigates the light propagation through bending soft materials under a load shear force, targeting a bio-mechanical application. The research skills learned will include basic numerical simulation concept and experience as well as using a common commercial numerical simulation package.

We have developed several manufacturing processes for encapsulated microelectronic systems based on liquid metal cores in microfliaments. We are looking for a student to help improve a test system for measuring resistance vs. strain over repeated cycles to examine fatigue performance, durability and stress/strain of fibers produced from 3D printed pre-forms. Development of Arduino code to work with a linear stage, resistance measurements and force probes is required. Some details on the fibre manufacturing can be found at www.sameoto.com

This project aims to develop a system that can assess stress levels and cognitive load through the analysis of biosignals. Using machine learning techniques, this project will analyze data from sources like heart rate and skin conductance to identify patterns that indicate varying levels of stress and mental workload. The goal is to create an automated tool that could be used in health monitoring, workplace optimization, or psychological studies. This project provides an opportunity to explore the intersection of physiology, psychology, and artificial intelligence in a practical, real-world context.

Develop a model of the radiative cooling of land mines after sunset.
Use the new model to develop spectral profiles and patterns for remote sensing and pattern recognition of land mines via drones.