Ben Kizaric
Subspace Clustering for interpretable, semi-supervised machine learning tasks; Image Compression.
Tag: machine learning
Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications. Researchers at WID are developing new methodology and theory for extracting useful information from data which may be noisy or high-dimensional, contain missing elements, or come from a variety of different sensors or streaming input.
Machine learning at WID plays a key role in problems as diverse as understanding the immune system, finding the funniest cartoon captions, and developing driverless cars.
Machine learning is an important component of WID’s Data Science Hub.
Xudong Tang
Neural network models on soil micobiome data to predict characteristics of potato growth.
Sushrut Karmalkar
Designing efficient estimators which are robust to aggressive noise models
Jasper Lee
Designing practical, data-efficient algorithms with rigorous guarantees for statistical applications
Jinsu Gim
Injection molding, Polymer processing, Process monitoring and optimization
Ahmet Alacaoglu
Continuous optimization for machine learning, reinforcement learning and other data science problems
Christopher Endemann
Data Science Facilitator
Facilitating connections and training researchers in data science.
UW researchers join three national artificial intelligence institutes
Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE) led by Robert Nowak, UW–Madison professor of electrical and computer engineering and researchers at Ohio State.
Tolulope Perrin-Stowe
Relationship between genomes and their environments to predict organisms' fitness correlations.
Jeremy Johnson
I am interested in the cross-section of algebraic geometry and data science.
Jifan Zhang
Efficient and robust active learning that reduces data labelling costs at scale
Chaobing Song
Large scale and robust optimization in machine learning, min-max problems with game theory
Julian Katz-Samuels
Developing adaptive learning algorithms that are data-efficient and scalable
