Data Science Research Funded by American Family Insurance

Welcome to the first American Family Funding Initiative Networking Meeting, hosted by the American Family Insurance Data Science Institute. The purpose of this meeting is to learn about UW-Madison data science research being funded by American Family, share ideas, strengthen existing relationships, and build new partnerships.

With this in mind, we’ve provided research summaries and contact information for UW-Madison PIs and their participating research team members, so you can continue conversations after the meeting. Click on the names below to expand the information about each person. You can also peruse Funding Initiative awards by project title and year/round funded.

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Saurabh Agarwal

Research Assistant
Department of Computer Sciences
Shivaram Venkataraman’s team

Laura Albert, PI

Dynamic Workflow Optimization and Planning for Insurance Applications
Research Assistant: Eric Stratman

Portrait of 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 ( 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.

Joe Austerweill, PI

Question Asking with Differing Knowledge and Goals
Research Assistant: Kesong Cao

Photo of Joe AusterweillPeople spend a significant proportion of their time asking each other questions to gather information. Entire professions, such as academia and customer service, are dedicated to asking and answering questions. Despite tremendous progress in machine learning, automated methods that answer a person’s questions are still inferior to answers from people.

Why are people better at answering questions? One reason is that question-askers leave out information that those answering the questions can fill in from their rich knowledge of language and the world. A recent machine learning method addresses this issue by asking multiple, reformulated versions of a human question, providing multiple answers, and learning to select the answer that is most likely to satisfy a person. However, this is done purely from data and does not incorporate psycholinguistic research demonstrating that people prefer simpler answers that are tailored to their personal goals and knowledge.

This project investigates whether incorporating psycholinguistic factors can improve automated question-answering methods. If so, then researchers can test novel, potential psycholinguistic factors and learn more about the underlying mechanisms that enable people to answer questions.

Joe Austerweil ( is an assistant professor in the Department of Psychology. He uses computational models and behavioral experiments to understand how people reason and make decisions.

Suman Banerjee, PI

Multi-Modal Analytics for Unbiased Estimation of Driving Behavior

Suman Banerjee Understanding driving behavior is central to efficient, safe transportation and associated insurance mechanisms. This project seeks to create an unbiased system for evaluating driving behavior that will use multi-modal signals, especially from audio-visual sensors, to learn contextual information about why certain behaviors happen.

Suman Banerjee ( is the David J. DeWitt Professor in the Department of Computer Sciences at UW-Madison. His primary research interests include networking and distributed systems, specifically mobile and wireless networking systems, with many applications in smart transportation, smart healthcare, and in secure and sustainable systems.

Aubrey Barnard

Department of Biostatistics and Medical Analytics
Irene Ong’s team

Jirayu Burapacheep

Undergraduate Student
Department of Computer Sciences
Kaiping Chen’s team

Kesong Cao

Research Assistant
Department of Psychology
Joe Austerweill’s team

Tzu-Tao Chang

Teaching Assistant
Department of Computer Sciences
Shivaram Venkataraman’s team

Kaiping Chen, PI

Reducing Bias in Human-AI Conversation
Co-PI: Sharon Li; Research Assistants: Anqi Shao and Jirayu Burapacheep

Portrait of Kaiping ChenAI 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 ( 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.

Min Chen, PI

Facilitating Wildfire Insurance Business with Big Data and Machine Learning
Co-PI: Voelker Radeloff; Postdoctoral Research Associate: Fa Li

Portrait of Min ChenRecent 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 ( is an assistant professor in the Department of Forest and Wildlife Ecology and an affiliate with 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.

Amy Cochran, PI

Quasi-Experimental Designs for Learning Systems
Co-PIs: Gabriel Zayas-Caban and Brian Patterson; Ph.D. student: Valerie Odeh-Couvertier

A growing number of systems, including hospitals and insurance companies, aim to derive knowledge from internal data to improve their day-to-day operations. This project will develop a causal inference framework for estimating the effects of interventions on these systems and provide algorithms to guide their use of risk prediction models in their operations, with the ultimate goal of improving services and reducing costs.

Amy Cochran ( is an assistant professor at UW-Madison with a joint appointment in the Departments of Mathematics and Population Health Sciences. She works in the areas of computational psychiatry, digital mental health, and causal inference.

Mark Craven

Department of Biostatistics and Medical Informatics
Colin Dewey’s team

Colin Dewey, PI

Machine Learning Approaches for Metadata Standardization
Co-PI: Mark Craven; Scientist: Yuriy Sverchkov; Research Assistant: Tapanmitra Ravi

Photo of Colin DeweyResearchers and businesses are increasingly using large data sets, compiled from many sources, for training machine learning systems and performing statistical analyses. A major bottleneck arises from the fact that compiled data sets often contain unstandardized, unstructured metadata that describe each record. Manual standardization of metadata is labor intensive and often requires substantial expertise in the field of study.

To mitigate this issue, this project will develop machine learning approaches for automating the task of metadata standardization in large, heterogeneous data sets. The researchers will use state-of-the-art natural language processing models and develop active learning algorithms, which facilitate identification of records that would most benefit from human expert input. They will demonstrate the performance of these methods on the Sequence Read Archive—a vast repository of public biological sequence data.

Colin Dewey ( is a professor in the Department of Biostatistics and Medical Informatics at the School of Medicine and Public Health. His research includes algorithms and statistical models for genomics, with a focus on large-scale sequence analysis.

Jon Eckhardt, PI

Using Data to Foster Entrepreneurship and Innovation in the Madison Ecosystem
Research Assistant: Bekhzod Khoshimov

Entrepreneurship is an important path for upward mobility and wealth creation. Student entrepreneurship matters, in part, because student startups are not necessarily modest endeavors. In 1979, recent UW-Madison graduate Judy Faulkner founded the electronic medical records company Epic, which today employs over 10,000 people. Research indicates that student-entrepreneurship at UW-Madison is surprisingly prevalent.
Despite the impact of student entrepreneurship, little is known about what drives entrepreneurial intentions and activity amongst students, such as an interest in starting a company. Further, female students are less than half as likely as male students to self-report entrepreneurial intentions or actions.

The goal of this project is to support the work of the Academic Entrepreneurship Study Team at UW-Madison. This team is using data analysis techniques to enhance the impact and management of entrepreneurship programs at UW-Madison and other U.S. universities. Insights from this research will support the creation of evidence-based interventions to increase the prevalence and effectiveness of student entrepreneurship.

Jon Eckhardt ( is an associate professor in the UW-Madison School of Business, as well as a Discovery Fellow at the Wisconsin Institute for Discovery. His research is focused on university driven entrepreneurship, venture finance, innovation, and applications of data science to business.

Kassem Fawaz, PI

Fairness Guarantees for Learners Without Explicit Access to Demographics

The most advanced sample complexity bounds on fair machine learning are called multicalibration convergence bounds. These bounds specify the number of samples required to achieve performance parity across many population demographics. This project will yield additional perspective on both algorithmic fairness and multicalibration error convergence bounds. Further, it will enable machine learning practitioners to easily understand the convergence behavior of multicalibration error for a myriad of classifier architectures.

Kassem Fawaz ( is an assistant professor in the Department of Electrical and Computer Engineering at UW-Madison. His research interests primarily include security and privacy for users interacting with their devices, with emphasis on voice interfaces, privacy policies, malware detection, data privacy, and wireless security and privacy.

Michael Ferris, PI

Adaptive Operations Research and Data Modeling for Insurance Applications
Research Assistant: Cheng-Wei Lu

Photo of Michael FerrisUncertainty abounds in decision problems and optimization is a key tool used to mitigate its effects, utilizing the power of data science. This project will deploy a new approach that separates strategic decision making from operational modeling, in the context of a claim adjustment problem in the insurance industry. In this setting, random accidents occur across a large service area, requiring agents to deploy to the site to assess, document and determine appropriate courses of action. Our approach differentiates normal workload from crisis situations. It will inform an operational model that schedules resources over time to service both routine, normal workloads in a cost-effective manner, and enable the company to react efficiently to crisis situations. The model can be applied to problems as diverse as disaster recovery, chemical spill mitigation and electricity planning for extreme weather events.

Michael Ferris ( is a professor of Computer Sciences in the School of Computer, Data, and Informational Sciences, as well as the director of the Data Sciences Hub at the Wisconsin Institute for Discovery. His research focuses on the usage and applications of optimization.

Song Gao, PI

A Deep Learning Approach to User Location Privacy Protection
Co-PI: Jerry Zhu

Picture of Song GaoUser location information is a key component of both research and business intelligence. With the increasing availability of mobile devices and popularity of mobile apps, users in social network platforms actively share rich information about their locations on the Earth, the places they go and the activities they engage in. Those location-based profiles provide an invaluable source of information. However, mobility data is among the most sensitive data being collected by mobile apps, and users increasingly raise privacy concerns. The proposed research aims to develop a deep learning architecture that will protect users’ location privacy while keeping the capability for location-based business recommendations. The algorithms developed through this research may be applied in usage-based insurance (UBI) and other location intelligence domains.

Song Gao ( is an associate professor of Geographic Information Science (GIS). He conducts research in GeoAI, geospatial data science, and location intelligence.

Josiah Hanna, PI

Counterfactual Evaluation of Sequential Decision Policies

One way to evaluate AI-based decision-making policies and autonomous systems before they are deployed is to take data from a previously used policy and answer the counterfactual question, “What would have happened if the new policy had been making decisions instead of the older policy?” This project will introduce novel methods for counterfactual policy evaluation in sequential decision-making, where even small changes in how decisions are made can lead to drastically different outcomes over time.

Josiah Hanna ( is an assistant professor in the UW-Madison Computer Sciences Department. His research develops and applies reinforcement learning algorithms that learn with small amounts of data. His long-term research goal is developing AI systems that can quickly learn new capabilities from experience.

Ryan Kassab

Research Assistant
Department of Biostatistics and Medical Informatics
Irene Ong’s team

Bekhzod Khoshimov

Research Assistant
Department of Management and Human Resources
Jon Eckhardt’s team

Ramya Korlakai Vinayak, PI

Auto-labeling Foundations
Co-PI: Fred Sala

While crowdsourcing is a popular way to collect labeled training data for machine learning, it is expensive and time-consuming to hand-label each data point. Systems that automatically label data points while actively learning a model perform well in practice, but there is no theoretical understanding of what performance guarantees can be expected from these systems, or whether the resulting biased datasets can even be trusted. This project aims to close this gap by developing theoretical foundations for characterizing the performance of auto-labeling systems.

Query Design for Crowdsourced Clustering: Efficiency vs. Noise Trade-off

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 ( 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.

Kangwook Lee, PI

GAN-mixup: A New Approach to Improve Generalization in Machine Learning
Co-PI: Dimitris Papailiopoulos

Photo of Kangwook LeeThe recent successes of machine learning hinge on the ability of predictive models to generalize, or adapt well to previously unseen data. Data augmentation, the process of injecting artificial data points into a training set, is widely employed for improving generalization. One of the most prominent data augmentation algorithms is mixup, which helps achieve state-of-the-art generalization performance across several benchmark tasks.

While mixup algorithms are useful for improving generalization for a wide class
of tasks, they have a few critical limitations. Mixup sometimes degrades generalization, restricting the applicability of these tasks. Moreover, current mixup algorithms do not have any theoretical performance guarantees. To address these challenges, the researchers will develop a computationally efficient mixup algorithm based on a generative adversarial network (GAN). They will also develop a theoretical framework for analyzing the performance of various mixup algorithms. This research will provide a new approach to improve generalization, with provable performance guarantees.

Kangwook Lee ( is an assistant professor in the Departments of Electrical and Computer Engineering and Computer Sciences. His research is focused on ML algorithms and systems, with a goal of using data efficiently.

Fa Li

Postdoctoral Research Associate
Department of Forest and Wildlife Ecology
Min Chen’s team

Sharon Li, PI

Safe and Reliable Machine Learning through Out-of-Distribution Detection
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 ( 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.

Jeff Linderoth, PI

Integer Programming for Mixture Matrix Completion
Co-PIs: Jim Luedtke and Daniel Pimentel-Alarcon; Research Assistant: Akhilesh Soni

Photo of Jeff LinderothMatrix completion, or filling in the unknown entities in a matrix, is one of the most fundamental problems in data science. Matrix completion is used in applications such as recommender systems that predict the rating a user would give to an item, such as a movie or product, and then make recommendations to the user. This project will develop algorithms for solving a mixture matrix completion problem (MMCP), which has important applications not only in recommender systems, but also in computer vision systems for processing and analyzing visual images, data inference, and outlier detection.

Key to this research will be the development and application of advanced algorithmic techniques from integer programming, a powerful mathematical tool for solving optimization problems involving discrete choices. The work will pave the way towards the application of integer programming for a broad class of large-scale data science problems.

Jeff Linderoth ( is a professor of Industrial and Systems Engineering. He has worked in mixed-integer linear and nonlinear programming, focusing on large-scale optimization problems.

Cheng-Wei Lu

Research Assistant
Wisconsin Institute for Discovery
Michael Ferris’s team

Emaad Manzoor, PI

Expanding Knowledge Graphs with Humans in the Loop
Co-PI: Jordan Tong

Knowledge graphs encode human expertise in a structured manner to enhance the performance of recommender, forecasting, and other machine learning systems. As new concepts emerge in a domain, knowledge graphs need to be expanded to include them. However, expanding knowledge graphs manually is infeasible at scale. In this work, we propose methods to automatically expand knowledge graphs that are designed from the ground up to operate with humans in the loop.

Emaad Manzoor ( is an assistant professor in the Department of Operations and Information Management at the Wisconsin School of Business. His research interests include randomized and quasi-experimental design, causal inference with text, knowledge graphs, and persuasion through text.

Michael Morgan, PI

Developing a State-of-the-Science Regional Weather Forecasting System
Co-PI: Brett Hoover

This project will develop an ensemble weather prediction system for American Family Insurance that will provide high-resolution weather forecasting run entirely in cloud computing infrastructure. This project will improve the accuracy of forecasting hazardous weather by producing many realizations of the same forecast from slightly varying initial conditions.

The probabilistic forecasts will provide advanced warning of not only hazards including hail, wind gusts, and hurricane impacts in targeted regions, but also the uncertainty associated with the predictability of these hazards. This novel research will provide a state-of-the-science technique in regional weather modeling.

Michael Morgan ( is a professor in the Department of Atmospheric and Oceanic Sciences. His research interests focus on the analysis and prediction of tropical weather systems, as well as interpreting forecast sensitivity fields. He is currently serving as U.S. Assistant Secretary of Commerce for Environmental Observation and Prediction.

Subhojyoti Mukherjee

Research Assistant
Department of Electrical and Computer Engineering
Rob Nowak’s team

Robert Nowak, PI

Optimizing Question and Answer Systems via User Feedback
Research Assistant: Subhojyoti Mukherjee

Question-and-Answer (Q&A) systems are online software systems that aim to answer questions asked by users. Such systems are increasingly common throughout business, industry and healthcare. This project aims to develop new theory and methods for optimizing Q&A systems based on user feedback.

This project will begin with text embeddings that map words, sentences and whole documents into numerical representations that find similarities and connections in language. The research will draw on recent advances in the field of multi-armed bandit problems—a modeling approach that balances the choice of acquiring new knowledge with the competing choice of relying only on existing knowledge—to explore new approaches for Q&A systems. The research team will develop scalable algorithms for these systems with attention to search optimization and computation time, as human users of Q&A systems will not tolerate large delays in receiving answers to questions.

Robert Nowak ( is the Nosbusch Professor in the Department of Electrical and Computer Engineering and a Discovery Fellow at the Wisconsin Institute for Discovery. His research focused on the mathematical foundations of machine learning and artificial intelligence.

David Noyce, PI

Improving Traffic Safety Outcomes Through Data Science

While advances during the last 40 years in vehicle design, traffic engineering and driver behavior have led to significant improvements in transportation safety, recent trends have shown a leveling—and in some cases an increase—in the number of traffic crash fatalities. Emerging data provide new opportunities for incentives and technologies that move the trend towards zero fatalities once again. However, there are vital research questions about which technologies hold the most promise and how these different solutions work together to help drivers make informed, safe decisions.

The vision for this research is to translate advances in automotive technology and data science into tools that will improve driver safety and bolster the safety performance of emerging technologies, such as advanced driver assistance systems and automated vehicles. The researchers will conduct collaborative data science research, including machine learning and other approaches, to develop algorithms focused on incentivizing positive driver behavior. Researchers will also quantify the safety performance of emerging technologies, filling information gaps for automated vehicle developers, insurance companies, policy makers and the public.

David Noyce ( is a professor and associate dean in the College of Engineering. His work surrounds traffic operations and communications, with focuses on safety and efficiency.

Valerie Odeh-Couvertier

Ph.D. Student
Department of Industrial and Systems Engineering
Amy Cochran’s team

Irene Ong, PI

Learning Causal Relationships from Data
Co-PI: Aubrey Barnard; Research Assistant: Ryan Kassab

Photo of Irene OngHumans naturally develop an understanding of cause and effect by exploring the world. But causality is not nearly so easy for machines to learn. As a result, causal understanding is often missing from artificially intelligent systems, as you may have noticed when your digital assistant goes awry. To help improve the causal reasoning abilities of such systems, this research project develops an algorithm for learning causal relationships from data, one that is more efficient, accurate and robust than similar algorithms. These characteristics make causal learning more usable and likely to be incorporated into systems like your digital assistant in the future.

For the time being, the causal learning algorithm will be applied to discovering the environmental factors that prevent or cause asthma, and to identify relationships in electronic health data that will help prevent severe drug reactions and improve patient care by tailoring it to each individual patient.

Irene Ong ( is an assistant professor of both Obstetrics and Gynecology and Biostatistics and Medical Informatics in the School of Medicine and Public Health and a faculty at the Center for Human Genomics and Precision Medicine. Her research interests include data science, machine learning, and probabilistic methods, with a focus on clinical and biological/molecular data.

Daniel Pimentel Alarcon, PI

Optimal Features for Heterogeneous Matrix Completion
Co-PIs: Jeff Linderoth and Jim Luedtke

Matrix completion, or filling in the unknown entities in a matrix, is one of the most fundamental problems in data science, and existing models are incompatible with some types of data. This project will develop a new model and algorithms specifically tailored to complete matrices with heterogeneous data, with important applications in recommender systems, computer vision systems for processing and analyzing visual images, data inference, and outlier detection.

Daniel Pimentel-Alarcon ( is an assistant professor in the Department of Biostatistics and Medical Informatics in the UW-Madison School of Medicine and Public Health. His research focuses on robust machine learning methods to identify patterns in big and messy data. He specifically examines robust machine learning of mixtures, and linear and nonlinear structures.

Kevin Ponto, PI

Developing Novel Mixed Reality Tools for Consumer Insurance Documentation
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.

3D Capture and Scanning Technology for Insurance Documentation

Insurance claims adjusters constantly face the challenge of inspecting and assessing a scene to understand potential risk, or what took place after an event. They typically do this using tools such as digital photography. Recent advances in 3D capture technologies have created new ways to digitize the world around us. The overall goal of this project is to design and implement a system that utilizes 3D scanning and capture technology for automated documentation of scenes. This has the potential to reduce disputes between insurance companies and their clients, saving money and time for both parties.

Kevin Ponto portraitAs the utilization of 3D capture technology in this area is quite novel, and upcoming technological changes may create new directions of inquiry, the project will focus on research and design of an automated inventory system. This work will provide foundational knowledge for how 3D capture technologies may benefit the insurance industry.

Kevin Ponto ( 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.

Tapanmitra Ravi

Research Assistant
Department of Biostatistics and Medical Informatics
Colin Dewey’s team

Anqi Shao

Research Assistant
Department of Life Sciences Communication
Kaiping Chen’s team

Vikas Singh, PI

Doing More with Linear Transformers
Research Assistant: Zhanpeng Zeng

Machine learning and computer vision methods that drive applications such as object detection, image recognition, language understanding, and voice recognition make use of models known as “transformers” that can require weeks or months to train. Deployment of such parameter-heavy models also involves access to specialized hardware resources. This project will significantly extend the capabilities of current models based on algorithmic and implementation improvements. We will focus on ultra-long temporal/spatio-temporal sequences, coming from a broad variety of applications, and study the key challenges that need to be overcome to allow efficient training and deployment of transformer models in these settings.

Lightweight Natural Language and Vision Algorithms for Data Analysis

Natural language processing is a form of artificial intelligence that helps computers read and understand human language. Efficient and accurate natural language processing models are central to various applications but have a significant computational footprint. The overarching goal of this project is to accelerate the time it takes to train and test these models by developing alternative solutions that are based on much faster image processing primitives.

Vikas Singh ( is a Vilas Distinguished Achievement Professor in the Department of Biostatistics and Medical Informatics at the UW-Madison School of Medicine and Public Health. His group works on algorithm development for image analysis, computer vision, and machine learning problems motivated from applications in biomedical sciences, engineering, and industry.

Akhilesh Soni

Research Assistant
Department of Industrial and Systems Engineering
Jeff Linderoth’s team

Eric Stratman

Research Assistant
Department of Industrial and Systems Engineering
Laura Albert’s Team

Yuriy Sverchkov

Department of Biostatistics and Medical Informatics
Colin Dewey’s Team

Jordan Tong

Associate Professor
Department of Operations and Information Management
Emaad Manzoor’s Team

Ross Tredinnick

Wisconsin Institute for Discovery
Kevin Ponto’s Team

Shivaram Venkataraman, PI

Data-Aware Model Recycling
Co-PI: Dimitris Papailiopoulos; Research Assistants: Saurabh Agarwal and Tsu-Tao Chang

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.

Model Recycling: Accelerating Machine Learning by Re-using Past Computations
Co-PI: Dimitris Papailiopoulos

Data scientists train machine learning models that are used in a wide range of domains, from drug discovery to recommendation engines. Training a machine learning model, and fine-tuning the parameters that control how well a model performs, take significant time and resources. The process of incremental fine-tuning is often manual and involves retraining models from scratch. This project will automate and accelerate this process of fine-tuning by reusing and sharing past computations from prior training jobs, using a technique called model recycling. The researchers will develop a software framework that can help data scientists accelerate model fine-tuning, and a proposed intelligent predictor that can automatically save prior computation results, based on their importance.

Shivaram Venkataraman ( 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.

Gabriel Zayas-Caban

Assistant Professor
Department of Industrial and Systems Engineering
Amy Cochran’s team

Zhanpeng Zeng

Research Assistant
Department of Biostatistcs and Medical Informatics
Vikas Singh’s team

Jerry Zhu, PI

Fast Machine Learning with Rich Human-Machine Interactions
Research Assistant: Ara Vartanian

Portrait of Jerry ZhuEnormous 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 ( 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.