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1. K. Le, T. Butterfield, K.E. Kelly, Citizen scientist engagement in air quality monitoring, Proc. ASEE Annual Meeting, June 23 – 27, Salt Lake City, UT.

   Le, K.; Butterfield, T.; Kelly, K.E. (July 2018, Review & directory - American Society for Engineering Education)

   Citizen scientist efforts, wherein members of the public who are not professional scientists participate in active research, have been shown to effectively engage the public in STEM fields and result in valuable data, essential to answering pressing research questions. However, most citizen scientist efforts have been centered in colleges of science, and a limited number have crossed into research areas important to chemical engineering fields. In this work we report on the results of a project to recruit high school and middle school students across Utah’s Salt Lake Valley as citizen scientists and potential engineering students who work in partnership with chemical engineering researchers in an effort to create a distributed online network of air quality sensors. Middle and high school students were trained by undergraduate mentors to monitor and maintain their own outdoor air quality sensor with the help of teaching materials that were co-developed with Breathe Utah, a local community group concerned with air quality. With the help of these tailored teaching modules, students learned about the science behind air quality research and the difficulties common to physical measurements to better prepare them to analyze their data. Once trained, students are expected to become semi-independent researchers in charge of monitoring and maintaining their piece of a larger air quality map. We describe in this work the hurdles inherent in citizen science engagement within a chemical engineering research program and the means to address them. We describe successful means of engaging classrooms, training citizen scientists, obtaining faculty buy-in within the confines of state curricular demands, and addressing school administration concerns. With this model, we have directly engaged over 1,000 high school and over 3,000 middle school students. The project has resulted in a growing network of citizen-maintained sensors that contributes to a real-time air quality map. Student scientists may also use the sensors to participate in active research or conduct science fair projects. Student response to this citizen scientist project, where it may be measured, has been enthusiastic and almost wholly positive. 

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2. Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation

   Heckel, Reinhard; Soltanolkotabi, Mahdi (January 2020, International Conference on Machine Learning)

   Un-trained convolutional neural networks have emerged as highly successful tools for image recovery and restoration. They are capable of solving standard inverse problems such as denois- ing and compressive sensing with excellent results by simply fitting a neural network model to measurements from a single image or signal without the need for any additional training data. For some applications, this critically requires additional regularization in the form of early stopping the optimization. For signal recovery from a few measurements, however, un-trained convolutional networks have an intriguing self-regularizing property: Even though the network can perfectly fit any image, the network recovers a natural image from few measurements when trained with gradient descent until convergence. In this paper, we provide numerical evidence for this property and study it theoretically. We show that—without any further regularization—an un-trained convolutional neural network can approximately reconstruct signals and images that are sufficiently structured, from a near minimal number of random measurements 

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3. Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation

   Heckel, Reinhard; Soltanolkotabi, Mahdi (July 2020, International Conference on Machine Learning)

   Un-trained convolutional neural networks have emerged as highly successful tools for image recovery and restoration. They are capable of solving standard inverse problems such as denoising and compressive sensing with excellent results by simply fitting a neural network model to measurements from a single image or signal without the need for any additional training data. For some applications, this critically requires additional regularization in the form of early stopping the optimization. For signal recovery from a few measurements, however, un-trained convolutional networks have an intriguing self-regularizing property: Even though the network can perfectly fit any image, the network recovers a natural image from few measurements when trained with gradient descent until convergence. In this paper, we provide numerical evidence for this property and study it theoretically. We show that---without any further regularization---an un-trained convolutional neural network can approximately reconstruct signals and images that are sufficiently structured, from a near minimal number of random measurements. 

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4. Applying virtual histology to reflectance confocal microscopy to distinguish malignant from benign cutaneous squamous neoplasms

   Kokikian, N; Wahhab, R; Li, J; Martin, S; Beynet, D; Ozcan, A; Scumpia, P O (May 2024, Society for Investigative Dermatology (SID) Annual Meeting)

   Reflectance confocal microscopy (RCM) is a noninvasive optical imaging technique that uses a laser to capture cellular-level resolution images based on differing refractive indices of tissue elements. RCM image interpretation is challenging and requires training to interpret and correlate the grayscale output images that lack nuclear features with tissue pathology. Here, we utilize a deep learning-based framework that uses a convolutional neural network to transform grayscale images into virtually-stained hematoxylin and eosin (H&E)-like images enabling the visualization of various skin layers. To train the deep-learning framework, a series of a minimum of 7 time-lapsed, successive “stacks” of RCM images of excised tissue, spaced 1.52μm apart to a depth of 60.96μm were obtained using the Vivascope 1500. The tissue samples were stained with a 50% acetic acid solution to enhance cell nuclei. These images served as the “ground truth” to train a deep convolutional neural network with a conditional generative adversarial network (GAN)-based machine learning algorithm to digitally convert the images into GAN-based H&E-stained digital images. The machine learning algorithm was initially trained and subsequently retrained with new samples, specifically focusing on squamous neoplasms. The trained algorithm was applied to skin lesions that had a clinical differential diagnosis of squamous neoplasms including squamous cell carcinoma, actinic keratosis, seborrheic keratosis, and basal cell carcinoma. Through continuous training and refinement, the algorithm was able to produce high-resolution, histological quality images of different squamous neoplasms. This algorithm may be used in the future to facilitate earlier diagnosis of cutaneous neoplasms and enable greater uptake of noninvasive imaging technology within the medical community. 

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5. Implementing the Elements of Course-Based Undergraduate Research Experiences (CUREs) in a First-Year Undergraduate Chemistry Laboratory with Bioremediation Relevance

   https://doi.org/10.1021/acs.jchemed.2c00360  

   Silsby, Carson; McCormack, Roslyn; Roll, Mark F.; Moberly, James G.; Waynant, Kristopher V. (July 2022, Journal of Chemical Education)

   General chemistry laboratories are a core requirement for nearly all STEM (Science, Technology, Engineering, and Mathematics) majors and have the greatest breadth in disciplines and audience of any STEM course at a university. A bioremediation Course-based Undergraduate Research Experience (CURE) for first-year undergraduate students was developed to capture and engage student interest for this diverse group. In this multiweek laboratory exercise, students joined an NSF-funded research project designed to enhance the bioremediation of chlorinated aliphatic hydrocarbons. Students explored various biocompatible polymer blends and cross-linkers for encapsulation to create protection for bioremediation microbes. In this “guided research” model, students constructed the measurement apparatus, made hydrogel blends, and then monitored the diffusion of acid via pH measurements using a custom instrument. Herein, we describe how CURE elements were implemented within the bioremediation research experience culminating in student teams presenting posters at our university’s undergraduate research symposium. An open-laboratory format facilitated an active research group experience and recitation “group meeting” provided flexibility and needed time for reflection and discussion. Student survey data and course evaluations indicated that students saw value in this genuine research experience and enjoyed the freedom and time to practice and hone skills as both a scientist and teammate in a laboratory setting. 

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