Informatics Skunkworks members are working on a range of research projects which are summarized in the table below. Prospective participants are encouraged to reach out to the listed team leads to inquire about participation in their respective projects.
In addition to projects hosted by mentors at UW, there are a number of projects hosted by collaborators at Boise State University (BSU) and students are able to participate remotely if they have strong interest in a project at another institution. Please see the Boise projects page here.
More detailed information such as project requirements can be found in the full project spreadsheet which feeds into the project table below. For project mentors interested in adding or editing existing projects please reach out to Dane Morgan or Ben Afflerbach (email@example.com, firstname.lastname@example.org) via email or the Skunkworks Slack Workspace to receive the editable link to the project spreadsheet.
Skunkworks Successful Project Highlights
For select research projects that have concluded we have created a brief Project Highlight which summarizes the goals, structure, and outcomes of the project.
|Index||Short Name||Title||Project Description||Application deadline||Requirements: hrs/wk||Requirements:skills/courses/other||Contact: First Name||Contact: Last Name||Contact: Department||Contact: Email||Contact: Phone||Team leads (Names, Departments, Emails)||Number of Positions (max)||Funds available?||Credits available||Response date||Relevant links||Status||Date last updated|
|1||Brain imaging||Removing motion effects in brain MRI||Unlike a camera, which takes an image with exposure rates of about 1/100th of a second and therefore “freezes” motion, magnetic resonance imaging (MRI) of the human brain takes several minutes and is therefore sensitive to motion, even subtle motions less than a fraction of a millimeter. We have developed a successful method to correct for motion, but it is computationally slow and does not correct for rapid motions like those that could occur from a crying baby. This project will develop deep learning motion correction methods that are computationally faster (near real time) and offer improved motion correction for challenging cases with rapid motions. Methods to image with higher spatial resolution also introduce similar artifacts to motion, so potential extensions and applications of this work are/would be cortical assessments with higher image spatial resolution.||Ongoing||10||Python, basic machine learning||Steven||Kecskemeti||Medical Physicsemail@example.com||Steven Kecskemeti, Medical Physics, firstname.lastname@example.org||4||No||Yes||Ongoing||Active-Full||01/06/2022|
|2||Domain Error v2||Exploring Error and Model Domains in Materials Machine Learning||I am interested in exploring the accuracy of different error estimation methods and how they might be used to identify data as within or outside the domain of a model. The goal of this work is to develop automated approaches to obtaining reliable predictions, with uncertainty estimates, of engineering properties. We will use standard machine learning models in scikit-learn. We will focus on Bayesian approaches (e.g., Guassian Process Regression), ensembles approaches (e.g., bootstrap methods), outlier identification methods (e.g. Isolation Forests), and some domain specific approaches (e.g., using composition in materials systems). The results would be valuable to many engineers as more machine learned models are used to predict values. I expect this project to take 1-2 years and engage two teams of students of about 4 each, although the same students may not all stay the whole time. The project will provide opportunities for learning skills in machine learning and statistics, python programming, and teamwork, and we expect the work to yield one or more scientific publications and open source code packages. This is a "regression" type project.||Ongoing||10||None||Dane||Morgan||Materials Science and Engineering||Dane Morgan||Dane Morgan, MS&E, email@example.com||3||No||Yes||Ongoing||Active-Full||27/08/2022|
|3||Foundry Models||Cloud Based Prediction Tools for Materials Properties||I am interested in developing machine learning models for materials properties and sharing them as cloud resources through our recently developed Foundry infrastructure. The goal of this work is to make useful models available and showcase the infrastructure as a key resource for the materials community. We will use standard machine learning models typically from scikit-learn, tensorflow, and pytorch and typically apply gradient boosted trees or deep learning neural networks. The resulting models will provide values data for materials scientists and demonstrate a new way to share models and data for this community. An example might be developing integrated models and data for properties of small organic molecules. I expect this project to take ~2 years and engage multipleteams of students of about 2-4 each, although the same students may not all stay the whole time. The project will provide opportunities for learning skills in machine learning, modest python programming, and teamwork, and we expect the work to yield one or more scientific publications and online models and databases.||Ongoing||10||None||Dane||Morgan||Materials Science and Engineering||Dane Morgan||608-234-2906||Dane Morgan, MS&E, firstname.lastname@example.org||12||No||Yes||Ongoing||Active-Recruiting||26/01/2023|
|4||NLP Data Extraction||Materials Databases Generated by Natural Language Processing||I am interested in developing natural language processing (NLP) models to extract materials properties from published papers. The goal of this work is to generate useful databases for the materials community, as well as provide tools for others to generate such databases. We will use existing NLP frameworks, such as BERT or SciBERT with some retraining, to try and analyze the contents of scientific papers and extract sentences containing data. The resulting databases will provide values data for materials scientists and the approaches potentially transform how data is collected in the materials community. I expect this project to take 2-3 years and engage one team of students of about 4, although the same students may not all stay the whole time. The project will provide opportunities for learning skills in machine learning, python programming, and teamwork. I expect the work to yield one or more scientific publications and online models and databases. Meetings for this project are held online via MS Teams. This is a "regression" type project.||Inactive||10||Python, basics of string/text processing||Maciej||Polak||Materials Science and Engineeringemail@example.com||Maciej Polak, MS&E, firstname.lastname@example.org||8||No||Yes||Ongoing||Completed||25/01/2023|
|5||Transformer for Chemistry||Transformers for Chemistry||BERT is the most popular NLP models used in the deep learning community now since it offers the unique ability to process sequential data. Flashpoint is an important material property and it has been studied by our group using a Graph-based neural network and a comprehensive SMILES string of compounds with corresponding flashpoint values has been collected too. Recently, people find that we can apply BERT to study the SMILES strings(https://arxiv.org/pdf/2007.16012.pdf), so an interesting question is that could we use BERT to investigate the SMILES string of flashpoint of compounds? This is a "deep learning" type project.||10||None||Ben||Afflerbach||Materials Science and Engineering||Benjamin Afflerbach||512-934-4497||Benjamin Afflerbach, MS&E, email@example.com||4||No||Yes||Ongoing||https://arxiv.org/pdf/2007.16012.pdf||Completed||29/08/2022|
|6||NLP Scientific Data Extraction||A wealth of valuable research data is locked within the millions of research articles published every year. Reading and extracting pertinent information from those articles has become an unmanageable task for scientists. Moreover, these data are loosely structured, encoded in manuscripts of various formats, embedded in different content types, and are, in general, not machine accessible. Thus, studies that automatically leverage this valuable information are not tractable or even possible. Current approaches employ humans to manually extract data, define extraction rules, or annotate training corpora for machine learning approaches through tedious, time-consuming, error-prone and sometimes expensive processes. In the specific case of scientific information extraction, the need for pointed expertise increases costs and decreases the generalization of extraction methods. Our work so far has explored ways to identify sentences from artciles from which experts could identify the information to be extracted. We have two publications showing how we are able to drastically reduce the text to be reviewed by experts (6%) and achieve 100% recall. We are now looking deeper into identifying the information within these sentences using word vectors. This is a machine learning, natural language processing project, at the intersection of data science and materials science, which involves working python programming.||Ongoing||10||Python||Roselyne||Tchoua||Materials firstname.lastname@example.org||Roselyne Tchoua, email@example.com||2||No||Ongoing||https://via.library.depaul.edu/cdm_etd/25/||Inactive||25/01/2023|
|7||MAST-ML Coding||Development of the Materials Simulation Toolkit for Machine Learning||The Materials Simulation Toolkit for Machine Learning (MAST-ML) is an automated, open-source toolkit designed to acclerate data-driven materials research. We are interested in augmenting our current MAST-ML software with new analysis routines central to understanding the performance of machine learning models as they pertain to materials research. Some examples include a robust model error estimation framework, establishing domain of valid model performance, and materials-centric statistical tests, such as training and validating models on certain data subsets as defined, for example, by materials within some composition threshold. This project will provide opportunities for learning skills in machine learning, data analysis, python programming, and materials science.||Inactive||10||Python||Ryan||Jacobs||Materials Science and Engineering||Ryan Jacobs||Ryan Jacobs, MS&E, firstname.lastname@example.org||4||No||Yes||Ongoing||https://github.com/uw-cmg/MAST-ML||Inactive||25/01/2023|
|11||BLADDER||A New Paradigm for Systems Physiology Modeling: Biomechanistic Learning Augmentation with Deep Differential Equation Representations (BLADDER)||A major unsolved challenge that cuts across many domains of computational science and engineering is to bridge the gap between data-driven machine-learning approaches and insight-rich mechanistic modeling by differential equations. ML scales with the data, but offers little or no mechanistic insight; in contrast, ODE models can incorporate physical, chemical, or biological mechanisms and yield insights into how systems work, but they don’t scale well with ‘big data.’ In the domain of human health, Stimulating Peripheral Activity to Relieve Conditions (SPARC) is a program of the National Institutes of Health (NIH), is seeking to bridge this gap. Our multi-investigator two-year project (Fall20 to Fall22) is bringing together computer scientists, mathematicians, biologists, and engineers to create a hybrid computational modeling approach, guided by data from laboratory experiments, linking ODE models using neural networks. Priority in the selection of student co-workers will be for those with experience or interest in ODE-modeling of physical, chemical, or biological systems and a strong foundation in computational science or engineering. More details: https://projectreporter.nih.gov/project_info_description.cfm?aid=10206953||ongoing||10||Python||John||Yin||Chemical and Biological Engineering, Wisconsin Institute for Discovery (WID)||John Yin||608-316-4323||John Yin, CBE/WID, email@example.com||4||Possibly (summer 2021)||Possibly||Ongoing||https://www.youtube.com/watch?v=2zL2E50oRr8||Active-Recruiting||25/01/2023|
|13||CNN-based Conductivity Prediction||Predicting the ionic conductivity of polymer-ceramic composite solid electrolytes using 3D Convolutional Neural Network||Currently used Li-ion Batteries usually consist of flammable liquid electrolytes. Nonflammable solid-state electrolytes have been proposed to replace the liquid electrolytes for making an all-solid-state battery that is safe, durable, and particularly attractive for powering large devices such as electric vehicles and drones. Although significant progress has been made through almost five decades of development, hallmarked by the discovery of various solid electrolytes with excellent Li-ion conductivity which allows for fast charging, it is still challenging to achieve a fast Li-ion transport across electrolyte-electrode interface. To overcome this barrier, a polymer-ceramic composite electrolyte has been proposed, which has the potential of enabling a fast Li-ion transport both in the electrolyte and across the electrode-electrolyte interface. The purpose of this project is to train a 3D convolutional neural network (CNN) model for achieving a fast and accurate prediction of the effective Li-ion conductivity of the composite polymer-ceramic solid electrolytes. It is a regression type task, and we will have the datasets of both the input “microstructure” of the composite and the target Li-ion conductivity “three numbers” ready. Extensive experiences in CNN regression type task are required. We already have a preliminary 3D CNN model for you to start with. Students with good writing skills are preferred. The student is expected to work in a relatively high degree of independence, complete the majority of CNN model training, testing, and data representation, and have a weekly discussion with the faculty advisor and a third-year PhD student. The student needs to be enrolled in independent study for credits. We will cover the expense needed to run CNN on Euler at WACC https://wacc.wisc.edu/.||ongoing||10||Relatively experienced in CNN regression; Good skills in writing science||Jiamian||Hu||Materials Science and Engineering||Jiamian Hu||Jiamian Hu, MS&E, firstname.lastname@example.org||1||No||Yes||Ongoing||Active-Full||25/01/2023|
|14||COoL||Chemical Origins of Life (COoL): a reaction network approach||The chemistry which led to the origin of life on the early Earth remains poorly understood. One of the major challenges is the combinatorial explosion that occurs when unspecific chemistries are used to synthesize sequence-specific polymers, like proteins. We aim to investigate the kinetics of amino acids forming peptides in prebiotic systems, a process containing hundreds of potential reactions and thus requires advanced network reconstruction tools. Similar tasks have been addressed by modelling tools; however, these tools have not yet been adapted to the specific constraints of origins of life systems. The goal of this project is to adapt the Rule Input Network Generator (RING) to study prebiotic peptide reactions. Students will be co-mentored by Prof. John Yin (UW Madison, Chemical and Biological Engineering) and Prof. Srinivas Rangarajan (Lehigh University, Chemical and Biomolecular Engineering). Priority will be given to students with an interest in computational network modeling and a background in chemical engineering or computational science.||ongoing||10||domain specific English-like reaction language||John||Yin||Chemical and Biological Engineering, Wisconsin Institute for Discovery (WID)||John Yin||John Yin, CBE/WID, email@example.com||4||possibly summer 2021||yes||Ongoing||https://wid.wisc.edu/featured-science/origins-of-life-and-john-yin/||Active-Full||25/01/2023|
|15||Machine learning driving kinetic Monte Carlo||Coupling machine learning and kinetic Monte Carlo||The objective of this project is to develop a python framework that enables machine learning driven atomic kinetic Monte Carlo (AKMC) simulations in concentrated alloys. In AKMC simulations, transition rates for KMC events are computed based on transition barriers and prefactors, which are dependent on the local atomic environment – atomic arrangement. Here, we adopt the smooth overlap atomic position (SOAP) descriptor, as computed by the python code Dscribe, for describing the local atomic environment. A machine learning model has been trained in the scikit-learn python package for predicting the transition barrier. The AKMC algorithm has also been implemented in the SPPARKS code, which is c++. We are looking for a motivated student for coupling the python codes, DScribe and scikit-learn, with SPPARKS. The student is also expected to explore and optimize the ML model for improved accuracy in predicting the transition barrier. The student will work with a postdoc and a graduate student on the same project and be provided with the opportunities to learn programming, machine learning, and materials modeling. We expect this be a paid part-time position for six months. Other potential outcomes include one or more scientific publications and an open-source framework to be shared with the community.||ongoing||10||programming; python; c++;||Yongfeng||Zhang||Engineering Physics||YONGFENG ZHANG||608-890(3779)||Yongfeng Zhang, firstname.lastname@example.org||1||yes||Yes||ongoing||zhang.ep.wisc.edu||Inactive||27/07/2022|
|18||Liquid crystal analysis||Data-driven analysis of liquid crystal optical responses||Liquid crystal (LC) thin films are valuable materials for real-time sensing applications because molecular binding events cause LC films to exhibit rich, time-dependent changes in optical properties (optical responses) that provide information on the type and concentration of molecules that trigger the response (analytes). Optical responses lead to spatially varying colors and intensities that can be visualized as videos. The goal of this work is to build a data-driven computational toolkit for the analysis of LC optical responses. We will incorporate methods for image manipulation to facilitate data set curation, compare the ability of different machine learning methods (convolutional neural networks, recurrent neural networks, etc.) to relate optical responses to analyte properties, and implement methods to understand what features of the videos are most important for analyte identification. Our initial data set consists of hundreds of videos of optical responses triggered by the binding of environmental contaminants. We expect this project to involve collaboration between a team of undergraduate students for 1-2 years. Students will learn skills in machine learning, statistics, and Python programming, and will benefit from working in a collaborative environment involving five different principal investigators and their groups. The resulting toolkit will be valuable both for the analysis of environmental contaminants and more broadly to the analysis of LC-based sensor materials. We expect this work to be released as an open-source code package and a potential publication.||Ongoing||10||Experience with deep learning using Python (PyTorch or Tensorflow) preferred||Reid||Van Lehn||Chemical and Biological Engineering||REID VAN LEHN||608-263-9487||Reid Van Lehn, email@example.com and Victor Zavala, firstname.lastname@example.org||4||No||Yes||Ongoing||Active-Full||23/09/2022|
|19||ML tools for AM||Machine learning tool for identifying defect formation mechanisms from in-situ high-speed X-ray imaging data||Defect formation in metal additive manufacturing (AM, also known as 3D printing) severely hinders their applications because defects can cause failure of parts. Keyhole-induced porosity is one of the most common and detrimental defects in metal additive manufacturing. However, the formation mechanism is not fully understood. Here, the real-time keyhole porosity generation process during additive manufacturing has been experimentally recorded by high-speed high-resolution X-ray imaging. The first objective of this work is to develop an automated approach using machine vision to identify the keyhole (a depression cavity in liquid metal induced by intensive vaporization of materials during laser heating) and keyhole-induced pores out of thousands of X-ray images. The algorithm is expected to achieve fast and accurate marking/labelling of keyhole profile and pore profile in given X-ray images. After all the images were processed, the second objective is to use machine learning to explore the correlations between keyhole fluctuations and pore formation. This project will provide opportunities for learning skills in machine vision, machine learning, and X-ray imaging of additive manufacturing processes.||Ongoing||10||Python||Qilin||Guo||Mechanical Engineering||Qilin GUO||608-234-2906||Lianyi Chen, email@example.com||8||Yes||Yes||Ongoing||Active-Recruiting||25/01/2023|
|20||Glass Mechanical Properties||Machine Learning predictions of mechanical properties of metallic glasses||Metallic Glasses are a class of metals that can potentially have very interesting and useful properties for a range of applications. However, they have a large drawback in that discovery and tuning of new glassy compositions is time consuming and difficult experimentally. Therefore, we will train and assess models for predicting mechanical properties of glasses from simple compositional features, allowing experimentalists greater insight into the materials before sythesis. This can help guide and accelerate research by limiting searches to the most promsising materials based on our model's predictions.||Ongoing||10||machine learning||Benjamin||Afflerbach||Materials Science and Engineering||Benjamin Afflerbach||512-934-4497||Benjamin Afflerbach, MS&E, firstname.lastname@example.org||6||Yes||Yes||Ongoing||Completed||29/08/2022|
|21||Computational Design||Undergraduate Research in Compuational Design||Computational Design and Manufacturing Lab is interested in advancing computer methods for design and manufacturing applications. Current research activities in CDM Lab focus on machine learning and topology optimization for design and additive manufacturing applications. Example projects for undergraduates include the training of deep neural network for optimal design of heat sinks and heat exchangers, and the training of convolutional neural network for computer-aided design (CAD) modeling such as constructing NURBS surfaces.||Ongoing||10||Students with interest in mathematics and computer programing (Python and/or C++) are encouraged to apply.||Xiaoping||Qian||Mechanical Engineering||Xiaoping Qian||608-890-1925||Xiaoping Qian, Mech E, email@example.com||No||Yes||Ongoing||Active-Full||25/01/2023|
|22||Kidney projects-Cancer||Deep Learning to identify aggressive tumor features in Renal cell carcinoma (kidney cancer)||As the volume of computed tomography (CT) performed for a variety of indications continues to increase, the incidence of renal cell carcinoma (RCC) has also continued to rise. Spatial heterogeneity is a common feature of RCC, with multiple studies demonstrating variability within tumors with respect to pathologic features, genomics, and RNA/protein expression. This heterogeneity gives rise to a spectrum of biologic and clinical behavior, with an increasingly less aggressive management approach in more indolent disease and a move towards nephron sparing approaches in cases where intervention is warranted. Pathologic markers of tumor aggressiveness such as higher nuclear grade or presence of sarcomatoid features may only be present in a small portion of the tumor but may profoundly impact treatment decisions and prognosis. These small areas can be challenging to identify on biopsy, and although radiomic features provide more global assessment and have shown some promise in capturing and characterizing tumor heterogeneity, some aggressive tumor features have remained elusive at imaging. The purpose of this project is to apply deep learning to previously segmented renal cell carcinomas on CT images with known pathologic features to help identify an imaging signature for aggressive features (high nuclear grade, sarcomatoid features). We have manually segmented approximately 124 large renal cell carcinomas (> 7cm) into volumetric regions of interest (ROIs) which represents the nuclear grade cohort. We have segmented a second data set of 45 RCCs with sarcomatoid features with 49 size matched non-sarcomatoid RCCs to serve as controls with extracted radiomics features on which we can perform similar analysis. The team would create a deep learning model to evaluate the CT images in this patient cohort with known kidney cancers. The model may require coding to incorporate the mask (image segmentations) with the CT image stacks showing the kidney tumor, or could also be run without the annotations on the date.||Ongoing||10||Machine learning, ?CNNs||Meghan||Lubner||Radiology, UWSMPHfirstname.lastname@example.org||608-263-9028||Meghan Lubner (radiology), Andrew Wentland (radiology), E. Jason Abel (urology), Dane Morgan (Material Sciences)||0||Possibly||Possibly||Ongoing||Active-Recruiting||27/08/2022|
|23||Kidney projects-benign masses||Machine and Deep Learning to predict clinical outcomes in angiomyolipomas||Angiomyolipomas are histologically benign tumors that occur in the kidneys, either sporadically or associated with syndromes such as tuberous sclerosis. They consist of fat, muscle, and abnormal vessels. Because of these abnormal vessels, these lesions can be at risk for spontaneous and at times, life threatening hemorrhage. Based on small clinical series, a size threshold of 4 cm has been associated with hemorrhage and need for treatment. We have collected a series of patients with angiomyolipomas imaged over time with at least one baseline computed tomography study (many pts in the cohorts have both imaging and clinical follow up). We have segmented the tumors and extracted a large number of radiomics features. This extracted data would be amenable to machine learning analysis to try to identify radiomic features that may predict bleeding risk or need for intervention. This could be performed with XGBoost models etc. In addition, this segmented image data could be used as input to a deep learning model that could be used to predict similar outcomes. In theory, a model similar to the project above could be re-trained and applied to this data.||Ongoing||10||MAchine learning, ?CNNs||Meghan||Lubner||Radiology, UWSMPHemail@example.com||608-263-9028||Meghan Lubner (radiology), Andrew Wentland (radiology), E. Jason Abel (urology), Dane Morgan (Material Sciences)||0||Possibly||Possibly||Ongoing||Active-Full||25/01/2023|
|24||TRASHBOT||Computer vision for automated trash pickup with drones||I am interested in using a drone to autonomously collect and dispose of trash. The drone will be programmed with python to search a space based on a set of GPS coordinates. The drone will be outfitted with a camera used to scan the ground, and trash will be identifed by running the deep learning object detection model YOLO in real time via an edge computing device. The drone will need to be outfitted with a device for collecting the trash and bringing the trash to a designated area for disposal. This project will provide opportunities for developing skills in computer vision, deep learning, robotics, and integration of software with edge computing devices.||Ongoing||10||Python, deep learning (YOLO model), integration with edge computing devices (NVIDIA Jetson), robotics||Ryan||Jacobs||Materials Science and Engineering||Ryan Jacobs||Ryan Jacobs, MS&E, firstname.lastname@example.org||No||Yes||Ongoing||Active-Full||23/08/2022|
|25||Automated Feature Selection||Best practices for selecting and validating feature sets for accurate machine learning models||Building accurate and transferable machine learning models depends critically on the feature set used to numerically represent the data of interest. Here, we are interested in developing new practical methods of feature selection, with an emphasis on evaluating our methods on a number of experimental and computed materials property datasets. This work will leverage and build on the MAST-ML package, which was developed in our group to streamline and automate construction of machine learning models. This work will result not only in building practical machine learning and data science experience, but potentially result in a generally useful method for constructing robust feature sets for production of accurate models of numerous materials properties.||Ongoing||10||Python, MAST-ML familiarity||Benjamin||Afflerbach||Materials Science and Engineering||Benjamin Afflerbach||512-934-4497||Benjamin Afflerbach, MS&E, email@example.com||5||No||Yes||Ongoing||Active-Recruiting||29/08/2022|
|26||Hirsch Index||Factors Controlling the Hirsch Index||The Hirsch Index (HI) is the maximum number of papers n a researchers has with over n citations. It is perhaps the most widely used single number to assess the impact of scientific researchers. It has some known issues that makes its implications difficult to assess, e.g., strong bias to have higher values for people working in large fields where there are many papers. However, there are likely many other issues that are less clear. One possible issue is that HI does not distinguish work which a researcher led intellectually from work they just supported or in which they were guided by others. In this project we seek to determine the effect of such factors on HI. We will collect data on researchers and attempt to quantify impact on HI work done under guidance of their PhD/postdoc advisors or coauthored works not led by them, ideally as a function of key variables like time, research HI, advisor's HIs, etc. This project will likely involve a lot of data extraction, text processing, and correlative analysis. It may involve some Natural Language Processing and/or machine learning but it is likely that this will not play a role in the early stages of the work. Meetings for this project are held online via MS Teams.||Ongoing||10||Python||Maciej||Polak||Materials Science and Engineeringfirstname.lastname@example.org||Maciej Polak, Izabela Szlufarska, Dane Morgan (MS&E)||6||No||Yes||Ongoing||https://en.wikipedia.org/wiki/H-index||Active-Recruiting||29/08/2022|
|27||Article abstracts||Assessing the impact of scientific papers based on their abstracts||Each journal article begins with an abstract, a short (usually below 200 words) paraph, in which the main objective as well as the key results of the paper are summarized. A good abstract contains all the relevant information about the content of the paper and its relevance in the field. In this project we will explore if with we could assess the potential impact of the whole paper based solely on its abstract, with the use of Natural Language Processing (NLP) models. The project will involve working with journal publishers and journal database APIs, fine-tuning and applying various NLP models and processing text. Meetings for this project are held online via MS Teams.||Ongoing||10||Python, basics of string/text processing||Maciej||Polak||Materials Science and Engineeringemail@example.com||Maciej Polak (firstname.lastname@example.org), Dane Morgan (email@example.com), MS&E||6||No||Yes||Ongoing||Active-Full||07/09/2022|
|28||Image/Audio analysis||Working with company in China to develop new image/audio analysis tools||This project is a collaboration with Shenzhen Jiangyun Intelligence, Ltd, founded in 2019 by Dr. James Sun, a UW-Madison College of Engineering alum. Jiangyun focuses on AI solutions that empower machines to drive innovation and design, such as creative images for content illustrations. The present projects will focus on using and evolving state-of-the-art deep learning image/audio analysis tools to help analyze image/audio data for various applications (more details to be provided for those who join the projects). Students participating will be asked to sign a Non-Disclosure Agreement (NDA). Participants will learn advanced deep learning tools and how to work and evolve projects in an exciting and agile startup company environment.||Ongoing||8||Python||Dane||Morgan||Materials Science and Engineeringfirstname.lastname@example.org||608-234-2906||Dane Morgan (email@example.com), Chris (CTO of Shenzhen Jiangyun)||8||No||Yes||Ongoing||Active-Recruiting||02/12/2022|