An extra big congrats to Siyuan, Vishal, Jolene, Haidong, and Natalie on winning the ACM BCB pest paper award for our work on Transparent single-cell set classification with kernel mean embeddings!!
Congrats to Avinash and Gedas on our ACM MM 2022 Paper: Learning to Retrieve Videos by Asking Questions!
Congrats to Siyuan, Vishal, Jolene, Haidong, and Natalie on our BCB 2022 Paper: Transparent single-cell set classification with kernel mean embeddings!
Congrats to Vishal, Jolene, Siyuan, and Natalie on our BCB 2022 Paper: Distribution-based sketching of single-cell samples!
Congrats to Chris, Patrick, and Mike on our ICLR 2022 Paper: Practical Integration via Separable Bijective Networks!
A big thanks for NSF for supporting our work on Machine Detectives with a $500K NSF Small Award!
Congrats to Ryan on our NeurIPS 2021 paper: Arbitrary Conditional Distributions with Energy!
Welcome to the LUPA Lab, a research group devoted to machine learning and artificial intelligence. We are looking to see what makes data tick, and understanding data at an aggregate, holistic level. The LUPA Lab is using techniques ranging from modern deep learning architectures to nonparametric statistics to make strides in areas like: high-dimensional density estimation and modeling; sequential modeling and RNNs; and learning over complex or structured data. Take a glance below for a word-cloud of our interests, or see our projects page for what we are working on.
We are exploring collective approaches (that exploit collections like sets and distributions) to bridge the gap between machine and human learning by providing further context than myopic point estimation approaches. We prefer simple estimators that make few assumptions; that is, flexible and powerful methods that are able to generalize and extrapolate. Further, we are developing techniques for analyzing massive datasets, both in terms of instances and covariates.
This work will help us solve problems like predicting whether a Twitter topic will go viral, or predicting the risk of disease given a person's functional brain data, or predicting the future distribution of dark matter particles. Application areas include: health and medicine; science; business; earth and climate; and computer vision.
Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, graphical models, generative models, kernel methods, scalability, complex datasets, optimization, density estimation.