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Fowler School of Engineering

» Research Opportunities

At Fowler School of Engineering, we want our students to engage in the deeply impactful experiences that accompany research projects and programming. That’s why we foster a research community that serves both undergraduate and graduate-level students, providing early access to student and faculty-led endeavors.

Here to Uncover, Understand and Solve

Fowler Engineering faculty conduct research on a wide range of topics, including cybersecurity, behavioral health, assistive technologies, artificial intelligence/machine learning, optical computing, the internet of things, green computing, and the mathematical foundations of computer science.

We encourage our students to connect directly with faculty and discuss their role in faculty-led research.

Dr. Emad Arasteh’s research is focused on modeling embedded computer systems and bridging the gap between hardware and software design. These efforts enable building future intelligent computer systems that are efficient, safe, and reliable.

Students may:

  • Design system-level model of system-on-chips (SoCs).
  • Develop simulation models for parallel computers.
  • Explore new hardware architectures and programming models.

Dr. LouAnne Boyd's research is focused on the design, development, and evaluation of innovative assistive and accessible technologies. Students working on projects in her group apply theories and techniques from human-computer interaction to support diversity and inclusion. 

Students may: 

  • Conduct user studies using an iterative design process to discover inclusive user requirements.
  • Develop new interaction designs for novel platforms to improve accessibility.
  • Evaluate the impact of prototypes for diverse user groups.

Dr. Franceli L. Cibrian's research is focused on the design, development, and evaluation of ubiquitous interactive technology to support child development and vulnerable population.  

Students may: 

  • Conduct user studies to design emerging technology and applications that support healthcare and education. 
  • Develop novel technology (e.g., voice assistants, wearables, and deformable displays) to support child development and individuals with disabilities. 
  • Evaluate the impact of novel devices using qualitative and quantitative methodologies and automatic data analysis. 

Dr. Nasim Mohammadi Estakhri's research is focused on the design of optical and photonic meta-structures that involve enhanced and function-based light-matter interaction phenomena.

Students may:  

  • Implement machine learning algorithms to design advanced optical and photonic components and optical computational platforms.
  • Develop 3D printing-based structures for next-generation antennas and work on nanophotonic structures for radiative cooling.
  • Design planar structured materials (i.e., metasurfaces) for on-demand wave-shaping functionalities and develop and design new biosensors.

Dr. Maryam Etezad’s research is on leveraging software-defined radio (SDR) technology with an emphasis on portable FMCW radar to detect inattentive human behavior.  

Students may: 

  • Design and build SDR-based passive radar using provided transceivers and receivers.
  • Develop software for remote sensing systems.
  • Study modern software-defined radio radar technology and its trends. 

Dr. Christopher Girard investigates the interface between the nervous system and implanted electronics, with a particular focus on efficiently simulating their complex interactions. More broadly, he seeks to apply modern tools and techniques that accelerate the prototyping of electrical, mechanical, and software projects.

Students may:

  • Develop tools for efficient simulation of neural interfaces and their surroundings.
  • Investigate the benefits and tradeoffs of automating complex tasks.
  • Combine circuits, mechanics, and code to prototype embedded systems.

Dr. Mark Harrison's research is focused on the development of new photonic and plasmonic devices for use in optical computing and sensing applications. These projects will allow students to learn about optics and photonics while applying computer architecture and digital logic principles to novel systems.

Students may:

  • Develop new integrated optical device designs via high-powered simulations.
  • Characterize fabricated circuit behavior with benchtop equipment, including lasers and fiber optics.
  • Conduct experiments to explore device behavior for either sensing or computing applications.

Dr. Alexander Kurz works on the mathematical foundations of computer science in general and programming languages and software engineering in particular.

Students may:

  • Learn logic and category theory.
  • Study multi-agent systems and software verification, including Bitcoin and blockchain.
  • Discover probabilistic programming languages and model checking.

Dr. Erik Linstead is the principal investigator of the MLAT Lab at Chapman University. His research interests span all areas of artificial intelligence and machine learning, with applications to software engineering, remote sensing, and developmental disorders. Most recently, his work has focused on autism spectrum disorder and deep learning. 

Students may:

  • Build machine learning models with multiple programming languages.
  • Use GPU-enabled computing to develop computer vision algorithms, and architect software solutions to quickly find and retrieve patterns in big data.
  • They will co-author papers that make novel contributions to the scientific literature.

Dr. Andrew Lyon's research is focused on the development of new materials for regenerative medicine applications. These efforts then couple with collaborative research on how those new materials can be applied to wound healing and tissue regeneration.

Students may:

  • Perform new polymer synthesis.
  • Develop approaches for high-resolution microscopy of nanomaterials.
  • Develop image processing algorithms to extract quantitative data from those microscopies.

Dr. Chelsea Parlett-Pelleriti's research involves applying statical methods (particularly Bayesian methods) and machine learning methods to behavioral data. She has worked specifically on improving inferences for a specific meta-memory task, as well as in research about the autism spectrum, and other behavioral applications.

Students may:

  • Apply methods such as Bayesian parameter estimation, Item Response Theory, or Unsupervised Machine Learning to problems in psychology, law, ecology, or other behavioral fields. 
  • Take complex methods and communicate clearly about them in order to bridge the gap between applied statisticians and practitioners in other fields.
  • Research for a specific memory task and autism spectrum.

Dr. Thomas Piechota’s research is focused on the investigation of climate impacts on regional water resources and society using high-resolution climate data/information, forecasting of water supply under changing climate conditions, drought, and flood impacts in urban stormwater in urban environments, use of remote sensing data and geographic information systems for improved evaluation of hydrology and water systems.

Students may:

  • Use large data sets to evaluate the impacts of climate on water and society
  • Use statistical analysis, programming, and enhanced graphics
  • Co-author papers that make novel contributions

Dr. Trudi Qi’s research is at the intersection of computer graphics, human-computer interaction, virtual reality (VR), and artificial intelligence (AI). She is particularly interested in studying multidisciplinary problems with practical impact by connecting visual computing technologies with real-world human-centered endeavors. Potential application areas of her research include healthcare education and training, assistive technology, collaborative VR, and computational creativity.

Students may:

  • Build human-interactive 3D modeling and animation tools for content creation in VR/AR and develop VR/AR simulation techniques for medical education and training.
  • Build ML models for 3D computer graphics problems such as shape classification.
  • Develop interactive VR motion data visualization and statistical analysis tools and utilize AI/ML technologies to assist with human learning and training in VR/AR.

Dr. Elizabeth Stevens’ research sits at the intersection of machine learning and behavior analysis. In particular, she has explored unsupervised techniques for modeling subtypes of autism spectrum disorder (ASD) and how they respond to treatment intensity and duration. 

Students may:

  • Explore how learning objectives map to certain identified subgroups.
  • Research clustering within challenging behaviors.
  • The overall goal of this research is to effectively impact treatment and tailor therapy plans to best fit the individual in hopes of obtaining the highest rates of success.

Dr. Nicole Wagner aims to provide students with opportunities to perform hands-on research, build a project portfolio, and use tools in the Fowler School of Engineering Maker Space. Students will learn how to formulate hypotheses, design experiments, perform data analysis, and communicate technical results.
Student may:

  • Create new 3D-printed structures.
  • Develop new composite materials for application in 3D printing.
  • Integrating different technologies to develop novel processes and parts.

Dr. Yuxin Wen's research is focused on data science and big data analytics in complex systems with applications in manufacturing, aerospace, healthcare, and traffic, etc.

Students may:

  • Develop advanced machine learning tools for real-time equipment health monitoring, diagnostics, and prognostics that enable decision-making.
  • Conduct change-point, anomaly detection for quality and reliability improvement in the manufacturing process.
  • Build robust models on severity assessment and progression prediction for diseases.

Dr. Peiyi Zhao's research is focused on the development of integrated circuits to achieve low power, low energy consumption in order to reduce the energy consumption of data centers in big data era, allowing students to learn about clocking integrated circuits to be used in digital processors.  

Students may:

  • Design digital integrated circuits and Design logic circuits/architecture using CADS tools.
  • Measure the power and speed of integrated circuits.
  • Investigate/examine different low-power/energy techniques.

Changing Care Through Data