Madalina Fiterau

Assistant Professor

Research Interests

Dr. Fiterau aims to build hybrid systems that learn expressive representations of multimodal, heterogeneous data for predictive models designed to interact with human users. Her 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, her team has introduced new methods for normalizing flows and transfer of causal models.

Dr. Fiterau is especially interested in the use of machine learning systems for medical applications and has co-organized several NIPS workshops on this topic: MLCDA NIPS 2013, ML4CHG NIPS 2014, ML for Health 2016, ML for Health 2017.

Current Research

Dr. Fiterau's current research focuses on modeling disease trajectories and forecasting clinical outcomes by integrating multi-resolution, irregularly-sampled time series and images. Dr. Fiterau is leading the IALS-funded project 4Thought aims to address one of the biggest needs in Alzheimer’s disease research: the ability to get an early diagnosis for this disease. Currently the disease progresses for many years prior to a diagnosis making it much more difficult to find therapeutic treatments for a significantly advanced disease. 4Thought encompasses the development of novel diagnostic technology based on brain structural MRIs, cognitive test scores and biomarkers. Dr. Fiterau's team is developing cutting-edge techniques for Alzheimer’s Disease forecasting, using hybrid deep learning methodology that leverages complex, multimodal data and domain knowledge. The project is also geared towards establishing a startup venture, with efforts supported by the UMass Institute for Applied Sciences.

Academic Background

Dr. Fiterau has completed a PhD in Machine Learning at Carnegie Mellon University in Fall 2015, where she was a member of the Auton Lab. Between Fall 2015 and Fall 2018, she was a Postdoctoral Fellow in the Mobilize Center at Stanford University. She has joined the College of Information and Computer Sciences at UMass in September 2018.

Publications

Brett Beaulieu-Jones; Samuel G Finlayson; Corey Chivers; Irene Chen; Matthew McDermott; Jaz Kandola; Adrian V Dalca; Andrew Beam; Madalina Fiterau; Tristan Naumann, Trends and Focus of Machine Learning Applications for Health Research, JAMA network open, 2 (10), pp. e1914051–e1914051, 2019.

Yi Ren Fung; Ziqiang Guan; Ritesh Kumar; Yeahuay Joie Wu; Madalina Fiterau, Alzheimer's Disease Brain MRI Classification: Challenges and Insights, IJCAI ARIAL Workshop, 2019.

Surya Teja Devarakonda; Yeahuay Joie Wu; Yi Ren Fung; Madalina Fiterau, FLARe: Forecasting by Learning Anticipated Representations, Proceedings of the Machine Learning for Healthcare Conference, MLHC 2019, 9-10 August 2019, Ann Arbor, Michigan, USA, pp. 53–65, Proceedings of Machine Learning Research, 2019.

Jason A Fries; Paroma Varma; Vincent S Chen; Ke Xiao; Heliodoro Tejeda; Priyanka Saha; Jared Dunnmon; Henry Chubb; Shiraz Maskatia; Madalina Fiterau; Scott Delp; Euan Ashley; Christopher Ré; James R Priest, Weakly Supervised Classification of Aortic Valve Malformations using Unlabeled Cardiac MRI Sequences, Nature communications, 10 (1), pp. 1–10, 2019.

Rheeya Uppaal; Bryon Kucharski; Bhanu Pratap; Iman Deznabi; Madalina Fiterau, Multi-resolution Attention with Signal Splitting for Multivariate Time Series Classification Workshop, International Conference on Machine Learning (ICML) 2019, ICML Time Series Workshop, 2019.

Abhinav Shaw; Natcha Simsiri; Iman Deznaby; Madalina Fiterau; Tauhidur Rahman, Personalized Student Stress Prediction with Deep Multitask Network, International Conference on Machine Learning (ICML) Workshops 2019, ICML Adaptive and Multitask Learning Workshop, 2019.

Bhanu Pratap Singh; Iman Deznabi; Bharath Narasimhan; Bryon Kucharski; Rheeya Uppaal; Akhila Josyula; Madalina Fiterau, Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT), 2019 https://arxiv.org/abs/1905.00125

Ziqiang Guan; Ritesh Kumar; Yi Ren Fung; Yeahuay Wu; Madalina Fiterau, A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Networks Technical Report 2019. http://arxiv.org/abs/1904.07950

Madalina Fiterau

Institute for Applied Life Sciences