We investigate and develop machine learning methods to extract knowledge from complex systems. Our methods fall in the space of deep learning, nonparametric methods, and statistical graph modeling. Our applied work studies, web, social, and geospatial systems.
Please see our projects page to get an idea of what we are up to right now!
|Faculty||Graduate Students||Undergrad Students||Alumni||Logan Rickert||Nathan Rude (MS, 2016)|
Samir Yelne (MS, 2016)
Kyle Brown (PhD)
|Nripesh Trivedi (BS, 2015)|
Matt Maurice (MS)
Latest News (see the archive for past updates)
11/15/17: Relating Input Concepts to Convolutional Neural Network Decisions accepted at NIPS Workshop on Explainable Machine Learning. This work develops a novel algorithm to find distributed representations of input concepts in a convolutional neural network. It then studies how types of representations could affect the decisions the deep learning system makes. Well done Ning!
11/3/17: EmojiNet, our machine readable sense inventory for Emoji co-developed by Lakshika, has been selected for publication as a Kaggle dataset. Check it out here. If you play around with it let us know what you find!
10/10/17:Realistic Traffic Generation for Web Robots accepted at IEEE Intl. Conference on Machine Learning and Applications 2017. This work presents a generative process for producing synthetic sequences of web robot traffic. A stochastic process moderates temporal properties while a Bayesian model assigns behavioral attributes. The synthetic traffic impacts the performance of web server caches in similar ways as real robot traffic. Well done Kyle!
WaCS is very thankful to the following organizations for their support: