The American Family Insurance Data Science Institute announced the results of the third round of the American Family Funding Initiative, a research competition for data science projects.
American Family Insurance has partnered with the University of Wisconsin–Madison through the Institute to offer mini-grants of $75,000-to-$150,000 for data science research at UW–Madison. Since its inception in the spring of 2020, this initiative has awarded nearly three million dollars to 21 teams of UW–Madison faculty and collaborators.
The call for proposals for a fourth round of awards will be announced early in 2022.
The goal of the American Family Funding Initiative is to stimulate and support highly innovative research. The successful projects, reviewed by faculty and staff from across campus, were evaluated based on their potential contributions to the field of data science, practical use and the novelty of their approaches.
AFIDSI brings people together to launch new research in data science and apply findings to solve problems. In collaboration with researchers across campus and beyond, AFIDSI focuses on the fundamentals of data science research and on translating that research into practice.
More information about this initiative and data science at UW-Madison is available at datascience.wisc.edu.
Projects funded in the third round of the American Family Funding Initiative include:
Dynamic Workflow Optimization and Planning for Insurance Applications (PI: Laura Albert)
Machine learning tools that recognize patterns or predict claims have the potential to improve service and reduce costs in the insurance industry. This project will use an optimization modeling framework to prescribe innovative, dynamic workflow routing decisions, balance the workload across claims agents, improve client satisfaction, and control costs.
Laura Albert (laura@engr.wisc.edu) is a professor of Industrial and Systems Engineering and a Harvey D. Spangler Faculty Scholar at UW-Madison. Her research interests are in the field of operations research, with a particular focus on discrete optimization with application to homeland security and emergency response problems.
Reducing Bias in Human-AI Conversation (PI: Kaiping Chen; Co-PI: Sharon Li)
AI models that power intelligent assistants like Google Home and other chatbots may produce responses biased towards dominant groups, while marginalizing the needs of underrepresented populations. This project seeks to mitigate inequality in AI decision-making through reducing unfairness in the algorithms that power chatbot responses.
Kaiping Chen (kchen67@wisc.edu) is an assistant professor of Computational Communication in the Department of Life Sciences Communication and faculty affiliate of the UW-Madison Robert and Jean Holtz Center for Science and Technology Studies, the Center of East Asian Studies, and the African Studies Program. Her research examines how deliberative designs can improve the quality of public discourse on controversial and emerging technologies.
Facilitating Wildfire Insurance Business with Big Data and Machine Learning (PI: Min Chen; Co-PI: Volker Radeloff)
Recent wildfires across the western US have caused enormous environmental hazards and economic losses. This project will prototype a machine learning framework, modeled on fires in California, that will improve prediction of wildfire probability and severity at daily, weekly, and monthly scales.
Min Chen (mchen392@wisc.edu) is an assistant professor in the Department of Forest and Wildlife Ecology and an affiliate with the Nelson Institute Center for Climatic Research, UW-Madison. His research focuses on investigating terrestrial ecosystem carbon, water, and energy dynamics and their interactions with the climate system.
Safe and Reliable Machine Learning through Out-of-Distribution Detection (PI: Sharon Li; Co-PI: Jerry Zhu)
While machine learning models commonly assume that training and test data distributions must be identical, these models may encounter (and fail to safely handle) anomalous data that differs from the training distribution. This project will tackle this fundamental problem in machine learning, with the goals of automating detection and mitigating unexpected out-of-distribution (OOD) data.
Sharon Yixuan Li (sharonli@cs.wisc.edu) is an assistant professor in the Department of Computer Sciences at UW-Madison. Her broad research interests are in deep learning and machine learning. The goal of her research is to enable transformative algorithms and practices towards reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data streams.
Developing Novel Mixed Reality Tools for Consumer Insurance Documentation (PI: Kevin Ponto; Co-PI: Ross Tredinnick)
Documentation and assessment of personal property following accidents or disasters is a major component of the insurance claims process. This project will take advantage of recent technological advances to create mixed reality 3D models and develop an application that allows for a more thorough inspection of damages.
Kevin Ponto (kbponto@wisc.edu) is an associate professor at the Wisconsin Institute for Discovery and the Design Studies Department in the School of Human Ecology. His research aims to develop techniques to better the experience of virtual reality through new devices, interfaces, and techniques.
Lightweight Self-Attention for Detection and Image Classification (PI: Vikas Singh; Co-PI: Zhanpeng Zeng)
Machine learning and computer vision methods that drive applications such as voice recognition make use of models known as “transformers” that can require weeks or months to train. The overarching goal of this project is to enable efficient training of such models for potential use in natural language processing and object recognition for images.
Vikas Singh (vsingh@biostat.wisc.edu) is a Vilas Distinguished Achievement Professor in the Department of Biostatistics and Medical Informatics at UW-Madison. His group works on algorithm development for image analysis, computer vision, and machine learning problems motivated from applications in biomedical sciences, engineering, and industrial applications.
Data-Aware Model Recycling (PI: Shivaram Venkataraman; Co-PI: Dimitris Papailiopoulos)
Data scientists spend considerable time training and fine-tuning machine learning models used in applications ranging from risk assessment to recommendation engines. This project will develop tools that can automate and accelerate these processes by intelligently reusing past computations.
Shivaram Venkataraman (shivaram@cs.wisc.edu) is an assistant professor in the Department of Computer Sciences at UW-Madison. His research interests are in designing systems and algorithms for large scale data analysis and machine learning.
Query Design for Crowdsourced Clustering: Efficiency vs. Noise Trade-off (PI: Ramya Korlakai Vinayak)
While crowdsourcing is a popular way to collect labeled training data for supervised machine learning, non-expert crowdworkers often provide noisy answers. This project aims to increase understanding of how the ability of humans to learn and retain new concepts affects the quality and cost of crowdsourced data.
Ramya Korlakai Vinayak (ramya@ece.wisc.edu) is an assistant professor in the Department of Electrical and Computer Engineering at UW-Madison. Her research interests span the areas of machine learning, statistical inference, and crowdsourcing. Her work focuses on addressing theoretical and practical challenges that arise when learning from societal data.
Fast Machine Learning with Rich Human-Machine Interactions (PI: Jerry Zhu)
Enormous sets of labeled data are required to train a good machine learning model, and even methods such as active learning that speed up training require a significant investment in human annotators. The goal of this project is to design a set of novel interactive training methods that are theoretically guaranteed to out-perform active learning.
Jerry Zhu (jerryzhu@cs.wisc.edu) is a professor in the Department of Computer Sciences at UW-Madison. His research interest is in machine learning, particularly machine teaching and adversarial sequential decision making.