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.

Recent Publications

@proceedings{,

title = {Augmenting Randomized Controlled Trials with Foundation Models as Synthetic Units},

author = {Shiv Shankar and Madalina Fiterau},

doi = {10.1145/3765612.3767256},

isbn = {9798400722004},

year  = {2025},

date = {2025-07-02},

urldate = {2025-07-02},

booktitle = {Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics},

number = {32},

publisher = {ACM},

keywords = {},

pubstate = {published},

tppubtype = {proceedings}

}

@conference{nokey,

title = {Experimentation under Treatment Dependent Network Interference},

author = {Shiv Shankar and Ritwik Sinha and Madalina Fiterau},

editor = {Silvia Chiappa},

url = {https://proceedings.mlr.press/v286/shankar25a.html},

year  = {2025},

date = {2025-02-12},

urldate = {2025-02-12},

booktitle = {41st Conference on Uncertainty in Artificial Intelligence},

volume = {286},

pages = {3787-3808},

publisher = {PMLR},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@article{nokey,

title = {Dynamic Clustering via Branched Deep Learning Enhances Personalization of Stress Prediction from Mobile Sensor Data},

author = {Iman Deznabi, Yunfei Lou, Abhinav Shaw, Natcha Simsiri, Tauhidur Rahman, Madalina Fiterau},

url = {https://www.nature.com/articles/s41598-024-56674-2},

doi = {10.1038/s41598-024-56674-2},

year  = {2024},

date = {2024-03-19},

urldate = {2024-03-19},

journal = {Nature Scientific Reports},

volume = {14},

number = {6631},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@conference{nokey,

title = {Adaptive instrument design for indirect experiments},

author = {Chandak, Yash and Shankar, Shiv and Syrgkanis, Vasilis and Brunskill, Emma},

url = {https://people.cs.umass.edu/~sshankar/publications/iclr24a.pdf},

year  = {2024},

date = {2024-01-18},

booktitle = {International Conference on Learning Representations},

volume = {2024},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@workshop{Deznabi2024,

title = {Zero-shot micro-climate prediction with deep learning},

author = {Iman Deznabi, Peeyush Kumar, Madalina Fiterau},

url = {https://arxiv.org/abs/2401.02665},

year  = {2024},

date = {2024-01-05},

urldate = {2024-01-05},

journal = {Tackling Climate Change with Machine Learning workshop NeurIPS (2023)},

abstract = {Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be accessible for a new, previously unmonitored location. In response to these challenges, we propose a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations.},

keywords = {},

pubstate = {published},

tppubtype = {workshop}

}