Undergraduate Research Assistantship (URA) Opportunities

Dr. Stone’s research focuses on human performance engineering, with research questions centered on biomechanical, biomedical, and cognitive engineering applications. This area of research is broad and encompasses many domain areas, including but not limited to the medical, robotic, exoskeleton, sports, emergency services, artificial intelligence AI, virtual reality VR/ augmented reality AR and gaming fields. This year’s focus is on developing sustainable micro framing systems, exoskeleton design and testing, and intelligent game systems for skill enhancement using AI and AR technologies. The URA will contribute to developing physical systems and software to support efforts in human skill development. The URA will gain experience in prototype design, testing, and core programming to help these prototypes.

As AI systems become more prevalent, they are helping people make important decisions, like medical diagnoses, factory optimization, and financial planning. But how do users know when to trust these systems, especially when they’re built on probabilities and can make mistakes? Our research explores if, when, and how an AI system should tell users how sure it is. Should it convey uncertainty through words, numbers, or visuals? How does this affect trust and decision-making? A graduate student supported by Professor Stephen Gilbert, is conducting interviews and developing a game-like research testbed to study how people interpret uncertainty in AI systems. The URA may assist with either data wrangling (cleaning and analyzing interview transcripts) or testbed coding (building and testing the software). This student needs to have strong language, communication, and collaboration skills. Data analysis and programming (Unity) skills are preferred but not required.

If you have ever felt nauseous or had a headache from using virtual reality (VR), then you have experienced cybersickness. Cybersickness is common among VR users and stands as a barrier to widespread adoption of VR technology. One major hurdle in cybersickness research is the large variations in how different people experience cybersickness symptoms – from none at all to so extreme that they can’t use VR for more than a minute. What if there were a way to categorize VR users into groups based on how they might react to cybersickness stimuli?
This summer, professors Stephen Gilbert and Michael Dorneich supported IE graduate student Stephen Fieffer and several undergraduates in designing a virtual environment to use as a testbed for categorizing individual susceptibility to specific triggers. In this year’s URA project within Dr. Gilbert’s research group, the URA will help Stephen Fieffer finalize the testbed using the Unity development environment, create human subject research (HSR) procedures, conduct HSR participant sessions for data collection, and analyze results. The student on this project needs to have good people skills and readiness to iterate rapidly in a fast-paced environment. Unity, Python, and simple data analysis skills are preferred but can be developed along the way if needed.

Dr. Dorneich’s research interests focus on creating joint human-machine systems that enable people to be effective in the complex and often stressful environments found in aviation, military, robotic, and space applications. Adaptive systems are becoming more necessary as intelligent assistants are spreading into every aspect of work, education, and home life. Recent work includes investigating display compellingness in driver displays, and developing new training protocol to help student pilots learn not only “what decisions to make” under adverse weather conditions but also “how to make” those decisions. We are looking for undergraduates who can assist with developing training materials and collecting data from human participants.

Dr. Liu’s research focuses on human factors and human-system integration, with research questions centered on driving safety, trust in autonomous vehicles, and in-vehicle interface design. Specifically, he works on designing human-in-the-loop learning algorithms that leverage implicit and hidden human feedback to achieve transparent and responsive interaction without being intrusive. In addition, he explores how humans interact with robots and developing algorithms and frameworks for trust-aware and adaptive robotic systems. The URA will contribute by designing and developing experimental scenarios in the driving simulator, assisting with human subject data collection, and analyzing data. In addition, the URA will gain experience in research communication and presentation skills.

Dr. Wei’s research focuses on statistical learning for spatio-temporal processes, with emphasis on environmental and energy systems such as wildfire spread prediction, power grid resilience, and satellite data analysis. His work integrates physical models with advanced data-driven methods, including Partial Differential Equation-based statistical learning and spatio-temporal point process modeling. The URA will assist with data collection, cleaning, and management, and will play an active role in developing, testing, and applying statistical models. In addition, the URA will gain valuable skills in data science and analytics, including statistical modeling, exploratory data analysis, predictive modeling, visualization, and effective communication of results for scientific and practical applications.

Dr. Jose’s research applies Operations Research methods such as optimization, data analytics, machine learning, and game theory to various problems in disaster relief, fire management, and information collection in both military and civilian settings. Specifically, she works with interdisciplinary (social science + engineering) teams to understand the risk perceptions and fears of decision makers and the public to make better decisions. She has also used Game Theory to model public-private partnerships in prescribed fires. The URA will contribute by collecting data related to prescribed fires and wildfires and assisting in designing and testing optimization models for prescribed fire decisions. If the URA wishes, they can contribute to other applications mentioned above or new ones identified through discussion with Dr. Jose. The URA will also gain experience in research communication, presenting/pitching ideas clearly, presentation, and data analytics.

Dr. Li’s research explores statistics, data analytics, and machine learning, with exciting applications in engineering. One area is nondestructive evaluation (NDE), a technology that detects hidden defects in materials without causing damage—critical for safety in industries like aerospace, automotive, and energy. A key challenge in NDE is estimating the probability of detection (POD), which measures how well inspections find defects. Traditional POD studies often require large, costly datasets, but Dr. Li’s team is developing advanced statistical methods to make accurate predictions using smaller samples. As part of this project, the team is building user-friendly software packages to share these tools with engineers and researchers worldwide. Dr. Li is looking for two undergraduate research assistants (URAs) with interests in data analysis and statistics, plus some experience in R programming. This is a great opportunity to strengthen your coding and research skills, work alongside a Ph.D. student, and contribute to impactful real-world applications.

Dr. Hamilton’s research centers on developing new hybrid manufacturing processes through machine and process design and transitioning burgeoning automation techniques into real-world applications. These include the development of a large-scale laser-wire and polymer 3D printing robot and automated robotic grinding using machine vision. His work leverages sensors, real-time signal processing, and process-informed decision making to improve manufacturing material quality, geometric accuracy, and process reliability. The URA will contribute to automation development by operating industrial robots, developing software for improved data handling, and troubleshooting operation of the robotic cell. The URAs will gain technical skills in data handling and software development and communication via group presentations and manuscript writing.

Dr. Li’s research areas include new alloy design and manufacturing guided by multiscale modeling and simulations, and ultrasonic 3D printing. Ultrasonic 3D printing is a solid-state additive manufacturing (AM) technique. Unlike arc, laser or electron-based 3D printing processes in which the feedstock, e.g., powder or wire is melted by the heat source, no melting of solid and solidification of liquid is involved in ultrasonic 3D printing. Instead, high frequency, ultrasonic wave is applied to the feed stock which is a thin sheet of metal. The normal pressure and lateral motion cause localized plastic deformation at the interface between the metal sheets, which produces metallurgical bonding with minimal heat. This unique feature allows additive manufacturing of components with properties that cannot be achieved by other AM techniques. The URA will explore the potential of our new ultrasonic 3D printer and build components from advanced materials.

Dr. Liao’s research centers on advanced manufacturing process innovation towards applications in biomedical systems, energy storage, structural engineering, and soft robotics. Current areas of focus include surface engineering of structural metals for enhanced durability, advanced metal 3D printing, and nanomanufacturing of functional nanomaterials. We are seeking undergraduate research assistants to join our team and contribute to ongoing projects involving advanced surface remanufacturing and repair of metallic materials, as well as laser 3D printing of high performance metallic components.

Cameron MacKenzie’s research focuses on decision and risk analysis within the operations research and analytics area. His three main application areas are: (i) homeland security and emergency management, (ii) engineering design and manufacturing, and (iii) supply chain risk management. He frequently uses simulation and optimization tools to analyze and solve applied mathematical models of complex problems. He is currently leading a project to develop a multiplayer online computer game to help train emergency management officials in responding to a severe weather event. The project for the URA will involve examining challenges that the property and casualty (P&C) insurance industry faces as severe weather events are becoming more frequent. The research will likely focus on learning about catastrophe models and exploring different pricing strategies for the P&C insurance industry.