Welcome to the homepage of the Information Fusion Lab at the College of Information and Computer Sciences, University of Massachusetts Amherst!
We are a team of researchers at UMass Amherst CICS working on ML for multimodal data, including methods that support a wide range of biomedical applications. Our research includes a wide variety of topics including deep learning for the fusion of multi-resolution time series, images and structured information, the incorporation of domain knowledge or saliency in imaging and integration of multiple views for MRI analysis. Most recently, we introduced new methods for normalizing flows and transfer of causal models. Please see our research page for a full list of projects and our GitHub page for code releases.
We have an excellent team of talented graduate students and undergrads. If you are a UMass student and are interested in joining the group, or are a prospective external collaborator, please see this page.
Here is some recent news about our team:
- PhD student Deep Chakraborty was awarded the Edward Riseman and Allen Hanson Scholarship
- PhD student Ke Xiao was awarded the Paul Utgoff Memorial Scholarship
- The paper ‘Normalizing Flows Across Dimensions‘, lead author Eddie Cunningham, was presented at the ICML 2020 INNF+ workshop
- The paper ‘Structure Mapping for Transferability of Causal Models‘, lead author Purva Pruthi, was presented at the ICML 2020 BIG workshop
About the College of Information and Computer Sciences:
CICS is internationally recognized for its research activities and has one of the highest ranked and most competitive graduate programs in the nation. With over 40 faculty affiliated with the Center for Data Science, the College is distinguished by its culture of collaboration and leadership in multidisciplinary research. The department is #11 in AI and #20 in Computer Science in the US, according to the US News graduate schools ranking system. According to csrankings, CICS is in the top 10 universities in the US on AI and #16 in the world.
Normalizing Flows Across Dimensions Workshop
International Conference on Machine Learning (ICML) Workshops 2020, ICML 2020 Inductive biases, invariances and generalization in RL (BIG) workshop, 2020.
Structure Mapping for Transferability of Causal Models Workshop
International Conference on Machine Learning (ICML) Workshops 2020, ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 2020.