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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

@article{beaulieu2019trends,

title = {Trends and Focus of Machine Learning Applications for Health Research},

author = {Brett Beaulieu-Jones and Samuel G Finlayson and Corey Chivers and Irene Chen and Matthew McDermott and Jaz Kandola and Adrian V Dalca and Andrew Beam and Madalina Fiterau and Tristan Naumann},

url = {https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2753523},

year  = {2019},

date = {2019-10-25},

journal = {JAMA network open},

volume = {2},

number = {10},

pages = {e1914051--e1914051},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@workshop{DBLP:journals/corr/abs-1906-04231,

title = {Alzheimer's Disease Brain MRI Classification: Challenges and Insights},

author = {Yi Ren Fung and Ziqiang Guan and Ritesh Kumar and Yeahuay Joie Wu and Madalina Fiterau},

url = {http://arxiv.org/abs/1906.04231},

year  = {2019},

date = {2019-08-17},

booktitle = {IJCAI ARIAL Workshop},

keywords = {},

pubstate = {published},

tppubtype = {workshop}

}

@inproceedings{DBLP:conf/mlhc/DevarakondaWFF19,

title = {FLARe: Forecasting by Learning Anticipated Representations},

author = {Surya Teja Devarakonda and Yeahuay Joie Wu and Yi Ren Fung and Madalina Fiterau},

url = {http://proceedings.mlr.press/v106/devarakonda19a.html},

year  = {2019},

date = {2019-08-09},

booktitle = {Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA},

volume = {106},

pages = {53--65},

publisher = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@article{fries2019weakly,

title = {Weakly Supervised Classification of Aortic Valve Malformations using Unlabeled Cardiac MRI Sequences},

author = {Jason A Fries and Paroma Varma and Vincent S Chen and Ke Xiao and Heliodoro Tejeda and Priyanka Saha and Jared Dunnmon and Henry Chubb and Shiraz Maskatia and Madalina Fiterau and Scott Delp and Euan Ashley and Christopher Ré and James R Priest},

url = {https://www.nature.com/articles/s41467-019-11012-3},

doi = {https://doi.org/10.1038/s41467-019-11012-3},

year  = {2019},

date = {2019-07-15},

journal = {Nature communications},

volume = {10},

number = {1},

pages = {1--10},

publisher = {Nature Publishing Group},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@workshop{DBLP:conf/cvpr/GhoseDBCFR19,

title = {Pedestrian Detection in Thermal Images Using Saliency Maps},

author = {Debasmita Ghose and Shasvat Mukeshkumar Desai and Sneha Bhattacharya and Deep Chakraborty and Madalina Fiterau and Tauhidur Rahman},

url = {http://openaccess.thecvf.com/content_CVPRW_2019/html/PBVS/Ghose_Pedestrian_Detection_in_Thermal_Images_Using_Saliency_Maps_CVPRW_2019_paper.html},

year  = {2019},

date = {2019-06-16},

booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019},

pages = {988--997},

publisher = {Computer Vision Foundation},

keywords = {},

pubstate = {published},

tppubtype = {workshop}

}