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

Massive computations (such as those needed for generative AI) cannot be performed on a single computing device. Instead, they are broken into many smaller computations and distributed over many computing nodes. While parallelism enables the execution of massive tasks, it also brings a challenge. A single node failure (or delay) can jeopardize (or delay) the whole computation.
Information theory has solutions for robust distributed computing by proactively adding redundancy to the computations.
The DRA winner will work closely with a PhD student on the implementation of robust distributed computing solutions.

Production of polymeric shape memory polymers using 4D printing is a critical technique used in smart polymers.

This research will investigate printing and characterization of 4D printed materials and conduct shape memory recovery, shape memory speed, and shape memory force to produce a design curve.

Brief Description:
Braiding of smart materials is a hot topic both in biomedical fields as well as e-textiles. This project focuses on the production and characterization of miniature braiding machine to be used for e-textiles.

We have collaborated with biologists and clinicians on analyzing cellular or medical images for answering important scientific questions. In this project, you are expected to conduct 2D/3D biomedical image Understanding work. 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.

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 focuses on analyzing the visual motion of livestock animals from video feeds, in collaboration with our animal science collaborators in the Faculty of Agricultural, Life, and Environmental Sciences. The work has many exciting downstream applications—for example, predicting the timing of calf delivery based on the motion patterns of the mother cow. As part of this project, you will work closely with a graduate student or researcher, become familiar with state-of-the-art deep learning and computer vision methods, and gain hands-on research experience in this interdisciplinary area.

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.

This project focuses on the market assessment and development of design specifications for a new pipe-flow manipulation system aimed at reducing structural impacts, such as fatigue and stresses, in pipelines downstream of pumping stations or pressurization systems.

I am seeking motivated students to explore how advanced manufacturing can be applied to reproduce and improve components used in defence applications. This project will advance understanding of how digital design, scanning, and additive manufacturing workflows can be combined to fabricate high-performance parts for demanding environments.

The project steps include 3D scanning of existing polymer defence components to capture geometry and structural details. The scanned data will then be reconstructed into digital models, which can be optimized for manufacturing. Next, students will fabricate parts using polymer-based additive manufacturing techniques, such as extrusion-based 3D printing. Printed components will be characterized through mechanical testing and compared to original parts in terms of accuracy, strength, and durability.

Proficiency in Matlab is an asset.

Laser-induced breakdown spectroscopy (LIBS) is an optical spectroscopy technique used for elemental analysis of diverse materials. Recent advances in machine learning and artificial intelligence models enable the use of LIBS in heterogeneous materials analysis, such as soil, wheat, and animal feed, by overcoming physical and chemical matrix effects. However, machine learning-based calibration of LIBS requires a large calibration dataset. We aim to address this critical challenge by developing calibration-free LIBS (CF-LIBS) for diverse applications. CF-LIBS is based on the calculation of plasma electron temperature and number density, and it has the advantage of not requiring any reference materials from a specific sample to generate calibration curves. The success of CF-LIBS is dependent on developing a precise description and understanding of the physical states of laser-induced plasmas using theoretical modelling. In this research project, we will first experiment with metal and alloy samples such as aluminium (Al) and brass to develop a CF-LIBS model, as metals and alloys have well-known physical and atomic properties. Following the successful creation of the CF-LIBS model for metal/alloys, we will create CF-LIBS models for more complex heterogeneous material analysis, such as animal feed and soil.

Nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) is a modified LIBS approach in which nanoparticles deposited on a sample surface enhance the laser–sample interaction and increase the resulting plasma emission. This project will use electromagnetic modeling, for example with COMSOL Multiphysics or a similar simulation platform, to investigate how the plasmonic response of Au nanoparticles changes after deposition and assembly on a solid surface. The project will examine how particle–particle and particle–substrate coupling in the dried nanoparticle layer alters the plasmonic absorption spectrum, including possible changes in resonance position, bandwidth, and near-infrared response.

The student will build an optical setup for temporal compression of high-power laser pulses, enabling particle acceleration using laser Wakefield acceleration. This work will involve hands on optical design, analysis of pulse properties following transmission and reflection with components, and integration of the optical pulse compression setup with the kilohertz laser system in Dr. Hussein's laboratory. This work will directly support the development of a compact beam line for laser Wakefield acceleration.

Monitoring concrete as it sets is critical for evaluating early-age strength development and long-term durability. However, tracking internal properties can be difficult or cost-prohibitive using conventional external monitors or wired sensors. This project proposes embedding miniature, batteryless, RFID-based temperature sensing tags directly into concrete during the pour. These tags remain indefinitely, providing on-demand structural health data when scanned wirelessly. While theoretical framework development and tag creation will be completed by a graduate student, an undergraduate student is sought to conduct experiments with concrete. The student will characterize dielectric and thermal properties of the mix to assist tag antenna design, cast concrete specimens with embedded tags using standard industry techniques, and test tag readability. Finally, the student will correlate wireless sensor data with the physical characteristics and maturity of the curing concrete.

The growing demand for compact wireless systems operating across multiple frequency bands necessitates low-profile antennas that preserve radiation performance within constrained platforms. Conventional patch antennas offer a simple, lightweight, readily fabricated solution; however, their narrow bandwidth and single-resonant response can restrict multi-band communication and sensing applications. This project will investigate a conformal, wideband, multi-resonant patch antenna operating near 1.5 GHz and 2.4 GHz on conductive cylinders with diameters of 10–20 cm. The student researcher will develop and optimize antenna geometries using full-wave electromagnetic simulation software, emphasizing impedance matching, bandwidth, radiation characteristics, and signal integrity under curvature. Selected designs will be fabricated and integrated onto conductive tubes. Experimental measurements will validate simulated performance and quantify conformal-mounting effects on antenna operation.

UHF RFID traditionally operates with a frequency range of 902-928 MHz, and commercial readers are not designed to work outside this range. However, applications such as RFID-based sensing and communication benefit from operating at other frequencies such as the 2.4 GHz or 5.8 GHz bands, as chips themselves may be able to communicate in these ranges. This project proposes the design and implementation of a custom, wideband RFID reader on a software-defined radio (SDR) platform capable of agile communication with tags at frequencies up to 6 GHz. An undergraduate student researcher is sought to program and validate this architecture experimentally. The student will first program the SDR to replicate the protocols of a standard UHF RFID reader to establish a performance baseline, before expanding the software architecture to enable wideband communication across a broader frequency range.

This project aims to enable a robot to adapt its grasp using feedback from a GelSight tactile sensor. During grasping, uncertainty in object properties and contact conditions can lead to unstable grasps or unsuccessful lifts. The robot will use tactile observations to assess the current grasp and determine when and how to adjust its actions.

The project will develop a tactile-guided grasp adaptation framework that connects contact sensing with corrective actions, such as adjusting grip force, repositioning the fingers, or changing the grasp configuration. By observing how these actions affect contact and grasp outcomes, the robot will learn to select adjustments that improve grasp stability across different objects and conditions.

Ophthalmic conditions like diabetic retinopathy disproportionately affect remote communities where specialists are scarce. Automated retinal imaging is changing how we approach these barriers. In this project, you will learn about medical imaging and dive into the underlying science and how we can use AI.
You will learn about the anatomy of the eye, specifically the retinal layers and the pathology of conditions that threaten sight. You will build and train deep neural networks, learning to map these complex biological structures. Instead of just running a model, you will create custom segmentation algorithms that can interpret raw scan data with clinical precision.
As you gain mastery over these neural architectures, you will move from understanding the theory to applying real-world science. By the end, you will have built tools that empower ophthalmologists to identify disease earlier, directly bridging the gap between advanced engineering and patient care.

Deep learning models excel at segmenting brain tumors, but they suffer from structural blindness: they predict boundaries voxel-by-voxel without factoring in anatomical continuity. In an evidential framework, this independence assumption causes uncertainty maps to fracture into noisy, isolated spikes. This project bridges that gap by introducing spatial regularization into a Bayesian neural network's loss function, using Total Variation to force neighboring regions to communicate during training.

If you are deep into math and stats, this project lets you control the tension between local evidence and global coherence by modifying the loss to $L = L_{\text{edl}} + \lambda L_{\text{spatial}}$. You will trace how $\lambda$ alters Dirichlet parameters, mathematically untangling aleatoric noise from epistemic model doubt. You will implement this in PyTorch using clinical datasets to see if spatial awareness prevents high-confidence errors.

This industry-partnered project focuses on preparing mobile winter road data for AI-based monitoring technologies and future operational deployment. The student will work with large volumes of geotagged roadway imagery, vehicle-location records, and mobile road weather and friction measurements. Activities will include image labelling, data cleaning and synchronization, quality control, dataset organization, exploratory analysis, and support for model testing. The work will contribute to the development and commercialization pathway of a winter road monitoring solution for transportation agencies. Experience with Python, image processing, GIS, machine learning, or data management is an asset.

Accurate traffic volume estimates are essential for transportation planning, yet direct traffic counts are available at only a limited number of locations. This project, conducted in partnership with the City of Edmonton, will explore whether large language models (LLMs) and vision-language models (VLMs) can provide additional contextual variables to improve network-wide traffic volume estimation. The student will investigate methods for extracting semantic and visual information from roadway descriptions and street-level imagery, and evaluate how these features can complement conventional roadway and spatial variables in predictive models. Tasks will include data preparation, feature generation, model integration, and performance evaluation. Experience with Python, data analytics, or machine learning is preferred, but highly motivated students with strong analytical skills and a willingness to learn are encouraged to apply.

Winter cities need timely, network-wide information on changing road surface conditions to support safe and efficient snow and ice control. In partnership with the City of Edmonton, this project will use mobile roadway imagery and other transportation and weather datasets to improve methods for estimating winter road conditions across the urban network. The student will assist with data processing and integration, model development and evaluation, spatial analysis, and interpretation of results for operational use. The project offers hands-on experience with real-world municipal data and applied AI for winter transportation. Experience with Python, machine learning, computer vision, GIS, or data analytics is an asset, but highly motivated students with strong analytical skills and a willingness to learn are also encouraged to apply.

IOSI and Imperial sponsored project on welding repair of large shovels for reduced residual stresses and enhanced fatigue performance

MITACS-InterPro funded project on ERW of X70 Pipeline Steels, focusing on Nb behaviour during welding and post-weld normalizing.

Adolescent idiopathic scoliosis is a 3D spinal deformity involving abnormal spinal curvature, alignment, vertebral rotation, and external trunk asymmetry. In this project, the student will analyze existing ultrasound, radiographic, and 3D surface-topography data. The student will measure clinically relevant parameters, such as Cobb angle, thoracic kyphosis, lumbar lordosis, and vertebral rotation, and extract additional surface-topography measurements using semi-automatic software. Measurement reliability and agreement among the three imaging methods will be evaluated. The project will provide experience in biomedical imaging, quantitative measurement, statistical analysis, and scoliosis research.

Research Question

How well do ultrasound and surface-topography measurements agree with radiographic measures of spinal deformity in adolescent idiopathic scoliosis?

Adolescent idiopathic scoliosis is a 3D spinal deformity involving abnormal curvature, alignment, and vertebral rotation. Surface topography provides a radiation-free 3D representation of the back surface. In this project, the student will process existing 3D surface-topography scans and extract geometric or machine-learned features directly from the surface data. Machine-learning models will be developed to estimate clinically relevant radiographic measures, such as Cobb angle, thoracic kyphosis, lumbar lordosis, and vertebral rotation. The student will gain experience in biomedical imaging, 3D data processing, Python, machine learning, and model evaluation.

Research Question

Can features extracted directly from 3D surface-topography scans be used to estimate clinically relevant radiographic measures of spinal deformity?

Structural design is a process of designing structures such as buildings and bridges to resist the applied load effects in a resource-effective manner. In current practice, this have to be done by professional structural engineers. It is known that the artificial intelligence technologies have evolved significantly these years. This project will investigate the feasibility of applying the artificial intelligence technologies to assist structural design in order to improve the efficiency and reduce the life-cycle cost of the project.

This project adapts a transformer-based segmentation model with temporal memory to analyze metal transfer in high-speed GMAW videos. The pipeline segments and tracks droplets to quantify transfer frequency, projected area, volume, and post-detachment kinematics. These measurements can support the analysis of how current and voltage influence metal transfer behavior.

You will learn how computer vision models are trained to identify and segment features in images, how high-speed video data are processed and objects are tracked across frames, and how these results can be used to extract kinematic and other physical information from experiments. You will also see how a segmentation model can be integrated into software to automate parts of the analysis and turn model predictions into useful engineering measurements. Through this process, you will gain a practical view of how deep learning, image processing, software integration, and physics come together in a real research application.

Worn and damaged metal components are often scrapped rather than repaired, even when the underlying part could be restored to like-new condition. Laser cladding is changing this situation, using a focused laser beam to deposit a new layer of metal onto a damaged surface. In this project, you will learn about robotic programming and dive into the underlying science of how lasers, robots, and metallurgy come together to repair metal parts.

You will learn how to program industry-level KUKA robotic arms, translating a part's geometry into precise toolpaths the robot can execute, and work with the PLC-driven systems that control the laser cladding cell. You will also refine cladding parameters and toolpaths, then characterize the microstructure and mechanical performance of the resulting clad layers. At the end, you will have contributed to processes that let industry repair and reinforce critical components rather than replace them, directly bridging the gap between advanced manufacturing and materials science.

Critical minerals are essential for batteries, clean energy technologies, and advanced manufacturing, yet their extraction and separation are often energy- and chemical-intensive. Electrochemical processing offers a promising route to recover valuable metals with greater selectivity and potentially lower environmental impact.

The goal of this project is to build and test a simple electrochemical system for critical mineral separation and recovery. The student will conduct a literature review, assemble a bench-scale electrochemical cell, and investigate how operating conditions such as voltage, current, electrode materials, and solution composition affect metal recovery and separation.

The Autonomous Water Refinery in a Backpack (AWR) is a portable, low-energy water-treatment platform designed to convert diverse and potentially contaminated water sources into potable water with minimal external infrastructure. The system integrates gravity-driven membranes, solar-assisted regeneration, catalytic treatment, passive hydraulic control, and AI-assisted fouling prediction to adapt its operation according to feed-water conditions.

Unlike conventional portable filters, the AWR is envisioned as a multifunctional “micro water refinery” capable of simultaneously producing drinking water, degrading organic contaminants, removing heavy metals and PFAS, reducing pathogen loads, and selectively recovering useful minerals where appropriate. The project will progress from membrane chemistry optimization and field validation across challenging water sources to development of an integrated backpack-scale prototype, advancing the technology from approximately TRL 3 to TRL 6.

The platform has strong potential for defence and emergency applications, including forward operating bases, Arctic patrols, disaster response, and humanitarian missions, while also offering civilian value for remote communities, mining operations, emergency preparedness, and outdoor use. Key intellectual property opportunities lie in the adaptive hydraulic architecture and multifunctional catalytic membrane system.

This project aims to transform Canadian lignin, forestry residues, and agricultural biomass into high-value functional carbon materials using a continuous and tunable manufacturing platform. Rather than producing conventional biochar, the process will engineer carbon materials with controlled pore structure, electrical conductivity, catalytic activity, and surface chemistry for specific end uses. Target products include conductive and porous carbons, catalytic carbons, EMI-shielding materials, membrane additives, and battery-grade carbons. The project will optimize pyrolysis, activation, and functionalization pathways and validate the resulting materials in membranes, composites, and sensors. By converting abundant domestic biomass into strategic carbon materials, the technology could reduce reliance on vulnerable international supply chains while supporting defence applications such as EMI shielding, lightweight composites, batteries, camouflage systems, and water purification, as well as commercial applications in EV batteries, hydrogen technologies, filtration, and aerospace.

Electrochromic devices change their color or more generally their complex refractive index in response to an applied bias. Plasmonics is the science and engineering of the optically stimulated coherent and collective oscillations of conduction band electrons in coinage metals such as Au, Ag and Cu. Plasmochromism exploits the interaction of plasmonics and electrochromism to achieve "active control" of the surface plasmon resonance phenomenon. This project involves forming films of colloidal gold and silver nanoparticles coated with thin layers of electrochromic organic semiconductors. The goal of the project is to actively control the plasmon resonance frequency, resonance amplitude and response time using plasmochromism. The DRA intern will work closely with graduate students in the Shankar Research group. Prof. Shankar will pay the nanoFAB user fees for the DRA intern's use of instrumentation specific to this project.

The Shankar Lab has strong expertise in the synthesis & application of nanoparticles and nanorods of inorganic chalcogenides such as CdS, CdSe, PbS and CdTe. The core-shell structure (e.g. CdSe@CdS, CdTe@Au)) passivates defect states and allows excellent control over the optoelectronic properties. The objectives here are two-fold. First, to use sustainable synthetic protocols to achieve core-shell nanorods with a narrow size dispersion. Second, to perform time-resolved spectroscopic and electrical characterization of the nanorods to obtain insight into carrier dynamics. A portion of this research may be conducted in the Faculty of Science.

Time-resolved microwave conductivity (TRMC) is a less used but well-established experimental method to study the dynamics of electrons and holes. The Shankar Lab is using high frequency (i.e. microwave) devices and circuits for sensing and diagnostics; 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, which will be needed to process the data and convert them into scientifically meaningful graphs/plots followed by curve fitting. Some knowledge of machine learning is desirable but not essential. The goal is to collect, analyze and interpret the microwave response of nanostructured semiconductors. This project provides great introductory hands-on experience in microwave engineering.

The dynamics of electrons and holes in organic semiconductors (OSCs) are significantly different from those in inorganic semiconductors. The Shankar Group is using high frequency (i.e. microwave) devices and circuits for sensing and diagnostics; 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, which will be needed to process the data and convert them into scientifically meaningful graphs/plots followed by curve fitting. Some knowledge of machine learning is desirable but not essential. The goal is to collect, analyze and interpret the microwave response of OSC films. This project provides great introductory hands-on experience in microwave engineering and handling Big Data.

When organic molecules or macromolecules have alternating single and double bonds, the resulting molecule is said to be conjugated. Conjugation results in the delocalization of electrons and/or holes, and renders the organic molecule semiconducting. Many organic semiconductors have unpaired electrons in their outermost orbitals resulting in a net molecular spin. In certain organic semiconductors, light illumination results in correlated triplet pairs with entangled spin states. Such molecular spins (entangled or non-entangled) are used as fundamental units of computing and sensing in the emerging fields of (i) Spintronics and (ii) Quantum Information Science and Technology (QIST). Molecular spins can oscillate or precess in a magnetic field. Microwave fields can flip the spin states (up to down and vice versa). For specific frequencies, the interaction of spins with the high frequency alternating electric field is resonant. We seek to electrically detect such magnetic resonances.

Electrochromic devices change their color or more generally their complex refractive index in response to an applied bias. Piezoelectricity is the phenomenon involving the generation of an electric potential due to material deformation while electrostriction (the converse of piezoelectricity) is the generation of strain through application of an electrical bias. Piezochromism exploits the interaction of piezoelectricity and electrochromism to achieve "active control" of the optical transmission using strain and electrical bias. This project involves forming layered or blended films of a piezoelectric polymer and electrochromic organic semiconductors. The goal of the project is to actively control the optical resonance peak, resonance amplitude and response time using piezochromism. The DRA intern will work closely with graduate students in the Shankar Research group. Prof. Shankar will pay the nanoFAB user fees for the DRA intern's use of instrumentation specific to this project.

The Shankar Lab is collaborating with Canadian industry to fabricate and test optoelectronic devices based on new organic semiconductors (OSCs) which have applications in displays (e.g. OLEDs), solar energy conversion (OPV) and flexible/wearable electronics. The solution processability of OSC films are a key attraction. Most organic semiconductors are p-type. The major type of OSCs which are n-type are fullerenes (C60/C70 or buckyballs) which have huge ambient stability and photodegradation problems. The Shankar Lab has recently formed non-fullerene n-type OSCs and is eager to test them out. This project seeks to fabricate bulk heterojunctions consisting of blended thin films of p- and n-type OSCs, and characterize their optoelectronic properties using ultrafast transient absorption spectroscopy, transient photoluminescence, transient photoconductivity, etc. Electrical testing of diode, photodiode and solar cell behavior is an additional goal if time permits.

Organic semiconductors (OSCs) are strong competitors to inorganic semiconductors as evidenced by the recent commercial success of OLED, OPV, OFET technologies and the emergence of flexible electronics. A new area where OSCs find application is Quantum Information Science and Technology (QIST). The QIST applications rely on the manipulation of molecular spins in OSCs. OSCs are typically brightly fluorescent or phosphorescent. When quasiparticle excited states (excitons) with unpaired spins or entangled spins are present, the fluorescence and/or phosphorescence will be impacted (increased or decreased or wavelength-shifted) by the presence of strong magnetic fields, a phenomenon known as magnetophotoluminescence (MPL). This project seeks to measure MPL in OSC crystals and films. The DRA intern is expected to have good troubleshooting skills, be able to read & understand equipment manuals quickly, and be handy with tools. This project is well-suited to EngPhys, MecE and Mechatronics UGs.

Organic-Inorganic hybrid electronic materials and devices aim to combine the best features of both organic and inorganic semiconductors while minimizing the limitations of each. A new area where such hybrids find application is Quantum Information Science and Technology (QIST). The QIST applications rely on the manipulation of molecular spins in OSCs. OSCs are typically brightly fluorescent or phosphorescent. When quasiparticle excited states (excitons) with unpaired spins or entangled spins are present, the fluorescence and/or phosphorescence will be impacted (increased or decreased or wavelength-shifted) by the presence of strong magnetic fields, a phenomenon known as magnetophotoluminescence (MPL). This project seeks to measure MPL in organic-inorganic hybrids. The DRA intern is expected to have good troubleshooting skills, be able to read & understand equipment manuals quickly, and be handy with tools. This project is well-suited to EngPhys, MecE and Mechatronics UGs.

The Shankar Lab is collaborating with Canadian industry to fabricate and test optoelectronic devices based on new organic semiconductors (OSCs) which have applications in displays (e.g. OLEDs), solar energy conversion (OPV) and flexible/wearable electronics. The relative permittivity of a material is a complex function of frequency with both real and imaginary components at each frequency. If the relative permittivities of thin OSC films are quantified over the entire ultraviolet, visible and near-infrared spectral range, the optical and electronic properties of the OSC are determined to a large extent. The hands-on component of this project involves mastering an instrument known as an ellipsometer which uses polarized light to collect permittivity data. The data analysis component of this project is math-intensive. Prof. Shankar will pay the nanoFAB user fees for the DRA intern's use of the ellipsometer and other instrumentation specific to this project.

Surface plasmons (usually just 'plasmons') are quantum quasiparticles. They represent the collective and coherent oscillations of quadrillions of conduction band electrons in metals or metal-like solids. These oscillations propagate at metal-dielectric interfaces. In metallic or quasi-metallic nanoparticles, plasmons are confined to a tiny volume and the squeezing effect of confinement results in a large enhancement of the local electromagnetic field at the surface of the nanoparticle. Such localized surface plasmon resonances (LSPR) can be excited in nanoparticles made of Ag, Au, Cu and Al (and their alloys) by visible light. Plasmonic nanosponges are porous metal nanoparticles with more complex underlying physics. This project seeks to design, fabricate and characterize plasmonic nanosponges. Optoelectronic devices exploiting the plasmonic nanosponges are also planned

Nanoporous metal oxides have a broad range of applications ranging from biosensors to energy storage & conversion devices such as batteries, supercapacitors and solar cells. Most of the aforementioned device applications involve interfaces between dissimilar materials e.g. metal oxide and polymer OR metal oxide and noble metal OR metal oxide and chalcogenides. Such dissimilar materials typically fail to achieve intimate contact, a problem that is exacerbated in nanopores due to challenges with pore infiltration. Interface-modifying self-assembled monolayers (SAMs) offer a solution by acting as a buffer layer that interacts strongly with both components of a heterojunction. This project will coat nanoporous metal oxides with SAMs and measure the resulting wetting and imbibition dynamics when infiltrated with polymers and metals. This project mainly concerns microfluidics, which is a sub-discipline of mechanical engineering, but interested students from any major are welcome to apply.

This project focuses on developing a low-cost prosthetic robot hand using a clutch-motor-based actuation mechanism. The goal is to reduce the number of motors while still enabling multiple finger motions and functional grasping. Students will contribute to the mechanical design, prototyping, actuation mechanism, and basic control of the prosthetic hand. The project is well suited for students interested in robotics, mechanism design, mechatronics, and assistive devices.

This project focuses on developing a wearable haptic device for teleoperation using a novel actuation mechanism. The goal is to provide compact, lightweight, and effective tactile feedback to the user while interacting with a remote robotic system. Students will work on actuator integration, mechanical design, prototyping, and basic control, with applications in robotic teleoperation and human-robot interaction.

This project focuses on the application of computer vision and machine learning to evaluate mask fit and protective performance. The student will work closely with a postdoctoral researcher to analyze RGB and depth images, extract quantitative features related to mask–face fit, and develop predictive models under different wearing conditions and body postures. The project provides hands-on research experience in computer vision, machine learning, image processing, and occupational health, with opportunities to contribute to the development of data-driven methods for assessing respiratory protection. A student in Computer Engineering is preferred.