Research Groups

The database group at UW–Madison was established in 1976 and is one of the top database groups in the world. Database research at UW–Madison focuses on all aspects of data management for modern analytics and data science. Our contributions span across diverse areas, including data platforms for large-scale analytics that run at the speed of the underlying hardware, cloud-native databases on heterogeneous hardware architectures, machine learning systems for end-to-end data integration and enrichment, and foundational work in data management theory. Our goal in the Database Group at UW–Madison is to help users go from data to actionable insights fast. To this end, we put emphasis on 1) building high-impact systems, such as Hustle (a microservices-based data platform for cloud architectures), Magellan (an end-to-end data integration platform), and HoloClean (a machine learning-based system for data enrichment); and 2) fundamental advances in emerging areas such as data pricing, uncertain data management, data enrichment, and emerging hardware devices. More about the database group here. 

The Visual Computing group takes a broad perspective on how computers are used to create and interact with things we see and do. Our research spans the areas of Computer Animation, Digital Content Creation, Multimedia, Visualization, Augmented/Virtual Reality, and Human-Robot Interaction. Some current projects include: Virtual Surgery—to simulate operations on the human body for training; High Performance Simulation—to create complex physical effects efficiently; Physical Human-Robot Interaction—to help robots better work together with people; Visual Data Science—to create interactive visualization tools to help people make sense of complex data sets; Visualization for Machine Learning—to create tools that facilitate the creation and use of data-centric systems; and AR/VR for Accessibility — to design AR/VR systems to empower people with disabilities.

The group conducts interdisciplinary research across a broad spectrum in HCI, including human-robot interaction (HRI), visualization, accessibility, augmented/virtual reality (AR/VR), and mobile interaction. Our mission is to explore human abilities and needs, build human-centered methods and principles for technology design, and develop intelligent and interactive systems to address challenges that people with diverse abilities face in their daily lives, whether it involves improving human interaction with robots, presentation of and interaction with data by leveraging visualization techniques, or the ability of people with disabilities to use AR/VR systems for day-to-day tasks.

We study the theory, algorithms, and applications of machine learning.  Themes include analysis of deep learning, computational learning theory, multi-armed bandits and reinforcement learning, robot learning, robust learning and adversarial machine learning, machine teaching, embedding methods and transfer learning for text data, non-convex optimization for matrix and tensor factorization, and fast randomized large scale learning methods.  Applications include bioinformatics, education, information extraction and data enrichment, adaptive agents, collaborative filtering, and interactive learning systems.

The networks group at Wisconsin works on a wide variety of areas including network verification, data center networking, big data systems, Internet robustness, measurements, mobile, wireless networking.  The Wisconsin Internet & Systems Research (WISR) group focuses on making networks and systems “future proof”, i.e., perform well and be robust, even as new, disruptive technologies arise, including both hardware and software. The Wisconsin Wireless and NetworkinG Systems (WiNGS) laboratory builds new, deployable wireless technologies such as high-bandwidth connectivity to moving vehicles, non-WIFI interference detection, etc. The Wisconsin Advanced Internet Laboratory (WAIL) conducts measurement based research to answer fundamental questions about the security, performance, topology and robustness of the Internet including the effects of climate change, detection of Internet level outages. The group also works on lightweight Network Time Protocol (NTP) for mobile and IoT devices and deployment models for edge computing.

Numerical analysis is the study of algorithms that use numerical approximation (as opposed to general symbolic manipulation) for mathematical analysis.  Researchers in our department specialize in approximation theory, splines, wavelets, Gabor systems, splines, and polynomial interpolation. Applications include data and signal analysis, including compression, denoising, deconvolution and more.

The Optimization group’s profile has continued to grow as our research becomes more and more crucial to many areas of science, engineering, and economics. Researchers in the group develop new methods, explore their mathematical properties, and apply them to a wide range of important real-world problems. Examples of ongoing projects include optimization algorithms for machine learning and data science, strategic optimization for improved water quality and enhanced natural resources, and design and operation of more reliable electrical power grids. Our tools and techniques benefit our domain science collaborators by enabling them to formulate and model their problems, solve these formulations using efficient algorithms, and extract relevant information from their data. Our group has been involved in projects of this nature in ecology, medicine, statistics, agriculture, chemistry, control engineering, and genetics. Two important online resources for researchers and users of Optimization—NEOS and Optimization Online—are  both hosted at Wisconsin.

The security and privacy group has a wide-range of interests, including cyber-physical systems (e.g., security of Internet-of-things, smart homes/buildings), adversarial machine learning, language-based security (e.g.,  machine code analysis and software “debloating”), differential privacy, software vulnerability assessment, and human aspects of security and privacy (e.g., user authentication, socio-technical aspects of security, automatic presentation of privacy information and controls).  Some of our recent work has focused on methods for detecting online attacks and fraud.

 We have identified vulnerabilities in modern self-driving cars, rolling-shutter cameras, quantitatively showed the perils of leaked passwords and how to protect user accounts from them,  and have improved the security of trigger-action-platforms (TAPs) such as If-This-Then-That by enabling them to compute on encrypted data. Please find details on the group’s website: https://madsp.cs.wisc.edu/.