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 (ddmorgan@wisc.edu, bafflerbach@wisc.edu) 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.
Skunkworks Projects
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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, john.yin@wisc.edu | 4 | Possibly (summer 2021) | Possibly | Ongoing | https://www.youtube.com/watch?v=2zL2E50oRr8 | Active-Full | 09/01/2025 |
| 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, jhu238@wisc.edu | 1 | No | Yes | Ongoing | Active-Full | 22/08/2024 | ||
| 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, john.yin@wisc.edu | 4 | possibly summer 2021 | yes | Ongoing | https://wid.wisc.edu/featured-science/origins-of-life-and-john-yin/ | Active-Full | 04/01/2025 | |
| 28 | Generative Machine Learning | Assessing generative models for predicting materials structure and properties | This project is a remote and in-person project and is open to students from outside of UW-Madison. One of the fundamental goals of materials science is to be able to understand and predict a potential material's properties without having to first synthesize it. Machine learning models can potentially give us this information by training on large datasets of properties and building up an understanding of the aspects of each material that impacting properties. One of these aspects that has been hard to handle is the structure of the material. Intuitively we might know for example that a liquid, solid, and gas of the same elements might have dramatically different properties. One reason for that is that the atoms are arranged into drastically different structures. The same is true for materials we describe as single crystals, poly-crsytals or amorphous to name a few. All of these descriptions exist to tell scientists some information about the underlying structure of atoms. By using this information as the input along with deep learning diffusion models, we can ask the model to first encode the property and structural information, and then decode that information again to give new prediction about materials with those targetted properties. That's the ultimate goal of the project, to use generative models to predict novel new materials with targeted properties of interest! | Sept. 1, 2024 | 10 | Ideally experience working with Python deep learning packages (pytorch or tensorflow). Familiarity with running code from the command line will be useful as we'll be using remote computing resources based in Linux. | Benjamin | Afflerbach | Materials Science and Engineering | bafflerbach@wisc.edu | Benjamin Afflerbach, bafflerbach@wisc.edu, Dane Morgan (dmorgan@wisc.edu), MS&E | 20 | No | Yes (UW-Madison) | 9/6 | will be building off cdvae code here initially: https://github.com/txie-93/cdvae | Active-Full | 06/01/2025 | |
| 31 | The River Food Pantry | Optimizing Rate of Return from Donation Campaigns using ML Modeling and Feature Analysis | Machine Learning models provide predictive power as well as the opportunity to explore and understand the underlying data used in their creation. This collaborative project with The River Food Pantry seeks to provide insights into factors that may impact donation campaigns with the goal of providing key takeaways for optimizing future campaigns. The River is South Central Wisconsin’s busiest food pantry. Services include free groceries and freshly prepared meals for pickup or delivery, online grocery orders, mobile meals, and after-hours food lockers. Since 2006, The River has grown to serve over 2,500 people every week in pursuit of its vision: a fully nourished community. As a non-profit organization The River relies on generous donations from individuals and community partners and through participation in this project you will get first-hand experiences not only with the technical aspects of training and assessing ML models, but will also get to participate and work together with The River to how best to learn from and utilize the results we obtain. | Sept. 1, 2024 | 10 | Experience with Python, Familiarity with tree based regression models, common ML model training techniques (Featurization, Cross Validation). | Benjamin | Afflerbach | MS&E | bafflerbach@wisc.edu | 512-934-4497 | Benjamin Afflerbach, MSE, bafflerbach@wisc.edu | 10 | No | Yes | 9/6/2024 | https://www.riverfoodpantry.org/ | Active-Full | 06/01/2025 |
| 32 | Translanguage LLMs | Developing Translanguaging LLMs for Multilingual Classrooms | Recognizing and engaging with multilingual students’ ideas expressed in non-dominant ways is important to support their learning and identity development. Translanguaging, which occurs when speakers make fluid and dynamic movement across different languages by using more than one language at a time, such as with Spanglish, is one of the ways in which multilingual learners communicate, make meaning, and connect with others. Yet, teachers (often monolingual but also bilingual) feel unprepared to support students in bringing their full linguistic repertoire to classrooms. Our study will investigate how effective the use of multilingual large language models (MLLMs) is in supporting translanguaging in K12 classrooms. | Ongoing | 10 | - Comfortable with programming in python, especially reading, writing and manipulating CSV files, familiarity with data processing and analysis libraries - (Preferred) Experience with machine learning or data science through coursework or past projects. | Shamya | Karumbaiah | Educational Psychology | shamya.karumbaiah@wisc.edu | Shamya Karumbaiah, EdPysch, shamya.karumbaiah@wisc.edu | 4 | No | Yes | Ongoing | Active-Full | 22/08/2024 | ||
| Status |