About Me

Greetings!

My name is Madalina (Ina) Fiterau, I am an Assistant Professor at the College of Information and Computer Sciences at UMass Amherst, having started there in the Fall of 2018. See my UMass profile page.

Previously, I was a Postdoctoral Scholar in the Computer Science Department at Stanford University and part of the Mobilize Center, working with Professors Chris Ré and Scott Delp.

I have obtained my PhD in Machine Learning from CMU in the Fall of 2015. My PhD thesis advisor is Dr. Artur Dubrawski, head of the Auton Lab. I obtained my master's degree in Machine Learning from CMU in 2012.

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NOTE: This webpage will be kept updated with publications from my PhD and Postdoc for a while, but will ultimately be replaced with a faculty webpage, which is currently under construction.
In the meantime, please read about my past and ongoing projects below.

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I aim to build intelligent systems that aid decision-making by extracting compact models and salient representations from multimodal, heterogeneous data.
Topics: dimensionality reduction, interpretability, transfer learning, hybrid models, vision, pattern detection, time series analysis.
I am especially interested in the use of machine learning systems for medical applications and have co-organized several NIPS workshops on this topic: MLCDA NIPS 2013, ML4CHG NIPS 2014, ML for Health 2016, ML for Health 2017.

I started off as a software engineer, having received my BEng from Politehnica Timisoara, Romania in 2009. Designing and coding software systems is something I enjoy, which is one of the reasons I've twice interned at Google, working with Jonathan Scott (summer of 2011) and Parisa Haghani (summer of 2013). I have also completed a research internship at Microsoft Research Cambridge, working with Peter Kontschieder and Samuel Rota-Buló.

Curriculum Vitae

Full resume. Compact resume.

Awards

• Invited participant: Rising Stars Workshop 2016, LATTICE Symposium 2017


• Star Research Award at the Society of Critical Care Medicine Conference (SCCM) 2016.


• Marr Prize for Best Paper at the International Conference of Computer Vision (ICCV) 2015.


• GE Foundation Scholar Leader Award 2007.

Speaking Engagements*

*invited presentations, outside of contributed talks at conferences

• Invited panelist at the Learning with Limited Data Workshop at NIPS, 9th of December 2017.
  Panel topic: Limited Labeled Data in Medical Imaging.


• Oxford, September 26th 2017. Host: Mihaela van der Schaar.
  Topic: hybrid machine learning models for heterogeneous, multimodal data.


• University of Southern California, May 26th 2017. Hosts: Dave Kale, Greg ver Steeg, Aram Galstyan
  Topic: learning representations from biomedical time series in the presence of structured information.


• University of Washington, May 22nd 2017. Host: Noah Smith.
  Topic: learning representations from biomedical time series in the presence of structured information.

Active Projects (Stanford)

• Learning cross-modal representations from biomedical signals of various types.
  [representation learning; deep learning; time series; images]


• Application: Activity recognition from triaxial accelerometer data.
  [ensemble learning; time series]


• Application: Predicting surgical outcomes for children with cerebral palsy from clinical indicators and gait analysis.
  [classification; decision support; gait kinematics]


• Application: Predicting the incidence of running injuries based on walking and running patterns.
  [classification; pattern detection; gait kinematics]


• Application: Pathology detection in heart MRIs from the UK Biobank.
  [deep learning; image processing; weak supervision]

Past Projects (CMU)

• Visualization for Informative Projection Retrieval (VIPR). Explores the idea of building small ensembles of low-dimensional components which are applicable to significant subsets of data, such that any given sample can be handled using one of these sub-models or a sparse mixture of them. The methods I'm developing optimize the assignment of samples to sub-models while reducing the dimensionality of the solvers in the ensemble. We introduced a regression-based algorithm which identifies informative projections by optimizing over a matrix of point-wise loss estimators, learning compact models for classification, clustering, and regression. The framework also works in an active learning setting.
  [ensemble methods; structured sparsity; feature selection; projection retrieval; query-specific models]


• Application: Artifact adjudication for health status alerts. This is a collaborative project with a team from the University of Pittsburgh: Dr. Gilless Clermont, Dr. Marilyn Hravnak, Dr. Michael Pinsky. We work with a cardio-respiratory monitoring system designed to process multiple vital signs indicative of the health status of ICU patients. The system issues an alert whenever some form of instability requires attention, but in practice, a substantial fraction of these alerts are artifacts. A subset of the data has been manually labeled and the aim is to use that subset to determine which of of the unlabeled samples are worth the experts' attention.
  [active learning; dimensionality reduction; ensemble methods]


• Application: Detection and adjudication of radiation levels from vehicle scans. One of the many projects under way at the Auton Lab, this presents a multiclass discrimination problem where expert feedback is available. The models need to be interpretable. Another interesting aspect of this dataset is that the training and test samples come from different distributions.
  [multilabel classification; interpretable models; transductive learning]


• Vital Sign Monitoring System that predicts clinical alarms and detects anomalous patterns in patient vital signs. This project uses the waveform and treatment data in the Mimic II dataset.
  [dynamic Bayesian Networks; variational methods]


• Explanation-Oriented Partitioning - the task of finding sets of high-accuracy regions in low-dimensional subspaces. The framework we developed uses any given hypothesis class and enables visualization of the classification process.
  [ensemble methods; query-specific models]


• Finding explanations in datasets by exploring areas in low-dimensional projections of the feature space. I've implemented some heuristic approaches as well as LPs which explicitly minimize entropy-based objective functions.
  [feature selection; information theory; linear programming]

Teaching Assistantships

• Spring 2014 - 10-801 Advanced Optimization and Randomized Methods, taught by Alex Smola and Suvrit Sra.


• Spring 2013 - 10-701 Introduction to Machine Learning, taught by Alex Smola and Barnabas Poczos.


• Fall 2011 - 15-826 Multimedia Databases and Data Mining, taught by Christos Faloutsos.

Publications

Below are all my publications by year. View publication list by year and category.

2017

• Ke Xiao, Heliodoro Tejeda, Madalina Fiterau, Jason Fries, James Priest and Christopher Ré, Automated Classification of Aortic Valve Morphology from Phase-Contrast Cardiac MRI Using an Augmented CNN, Medical Imaging Workshop at the Association of Neural Information Processing Systems Conference (MED-NIPS) 2017.


• Vincent Chen, Paroma Varma, Madalina Fiterau, Seung-Pyo Lee and James Priest and Christopher Ré, Generating Training Labels for Cardiac Phase-Contrast MRI Images, Medical Imaging Workshop at the Association of Neural Information Processing Systems Conference (MED-NIPS) 2017.


• Ferdinand Legros, Madalina Fiterau, Jennifer Hicks, Michael Schwartz and Scott Delp, Interpretable Hamstring Surgery Outcome Prediction with Linear Continuous Bayesian Networks.Interpretable Machine Learning Symposium at the Association of Neural Information Processing Systems Conference (IML-NIPS) 2017. Poster.


• Madalina Fiterau, Suvrat Bhooshan, Jason Fries, Charles Bournhonesque, Jennifer Hicks, Eni Halilaj, Christopher Ré and Scott Delp. ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information. 3rd Conference on Machine Learning for Healthcare, MLHC 2017. Spotlight.
  This research was also presented as a poster at the Women in Statistics and Data Science Conference (WSDS) and Women in Machine Learning Workshop (WiML) 2017. Abstract.


• Ferdinand Legros, Madalina Fiterau, Jennifer Hicks, Michael Schwartz and Scott Delp. Predicting the Outcome of Hamstring Surgery in Patients with Cerebral Palsy via Bayesian Networks. Big Data in Biomedicine, May 2017, Stanford.

2016

• Madalina Fiterau, Jason Fries, Eni Halilaj, Nopphon Siranart, Suvrat Bhooshan, Christopher Re, Similarity-based LSTMs for Time Series Representation Learning in the Presence of Structured Covariates, NIPS 2016 Recurrent Neural Networks Symposium. Poster.


• Madalina Fiterau, Learning representations from time series data through contextualized LSTMs, Women in Machine Learning Workshop, December 2016. Oral Presentation.


• Manisha Desai, Madalina Fiterau, Jennifer Hicks, Thomas Robinson, Accelerometer Wear and Non-wear Classification using an Ensemble of Unsupervised Predictors, Joint Statistical Meeting, August 2016. Poster.


• Donghan Wang, Madalina Fiterau and Artur Dubrawski, VIPR: An Interactive Tool for Meaningful Visualization of High-Dimensional Data, Demonstration at the International Joint Conference in Artificial Intelligence IJCAI 2016. Poster.


• Peter Kontschieder, Madalina Fiterau, Antonio Criminisi and Samuel Rota-Bulo. Deep Neural Decision Forests, Invited paper at the Sister Conference Best Paper Track at the International Joint Conference in Artificial Intelligence IJCAI 2016. Slides.


• Fiterau M, Wang J, Dubrawski A, Clermont G, Hravnak M, Pinsky MR. Using expert review to calibrate semi-automated adjudication of vital sign alerts in step-down units. Society of Critical Care Medicine Annual Congress 2016. Abstract and slides.
  Star Research Award.


• Lujie Chen, Artur Dubrawski, Donghan Wang, Madalina Fiterau, Mathieu Guillame-Bert, Eliezer Bose, Ata M. Kaynar, Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data. Critical care medicine 2016.

2015

• Peter Kontschieder, Madalina Fiterau, Antonio Criminisi and Samuel Rota-Bulo. Deep Neural Decision Forests, International Conference in Computer Vision, ICCV 2015. Spotlight and poster.
  Marr Prize for Best Paper.


• Madalina Fiterau, Artur Dubrawski, Karen Chen, Donghan Wang, Gilles Clermont, Marilyn Hravnak and Michael R. Pinsky, Detecting Artifacts in Clinical Alerts from Vital Signs, NIPS 2015 Workshop on Machine Learning in Healthcare. Poster.


• Madalina Fiterau, Artur Dubrawski, Donghan Wang. An Interactive System for the Extraction of Meaningful Visualizations from High-Dimensional data, Demonstration at the Neural Information Processing Systems Conference NIPS 2015. Demo website.


• Madalina Fiterau and Artur Dubrawski, Theoretical Guarantees for the Construction of Informative Projection Ensembles using k-NN Classifiers, Women in Machine Learning Workshop, Montreal, Quebec, Canada, December 2015. Poster.


• Madalina Fiterau and Artur Dubrawski, Discovering Compact and Informative Structures through Data Partitioning. PhD Thesis. CMU, Machine Learning Department, Fall 2015. Committee: Geoff Gordon, Andreas Krause and Alex Smola. Full thesis.


• Fiterau M, Dubrawski A, Wang D, Chen L, Guillame-Bert M, Hravnak M, Clermont G, Bose E, Holder A, Murat Kaynar A, Wallace D, Pinsky MR. Semi-automated Adjudication of Vital Sign Alerts in Stepdown Units. Annual Congress of the European Society of Intensive Care Medicine, ESICM 2015.


• Madalina Fiterau and Artur Dubrawski. Active learning for Informative Projection Recovery. In the Conference of the Association for the Advancement of Artificial Intelligence, volume 29, AAAI 2015. Spotlight and poster.


• Nick Gisolfi, Madalina Fiterau and Artur Dubrawski. Finding Meaningful Gaps to Guide Data Acquisition for a Radiation Adjudication System . In the Conference of the Association for the Advancement of Artificial Intelligence, volume 29, AAAI 2015. Spotlight and poster.


• Matt Barnes, Nick Gisolfi, Madalina Fiterau and Artur Dubrawski. Leveraging Common Structure to Improve Prediction across Related Datasets. In the Conference of the Association for the Advancement of Artificial Intelligence, volume 29, AAAI 2015. Spotlight and poster.


• Fiterau M, Dubrawski A, Chen L, Hravnak M, Bose E, Gilles C, Pinsky MR. Archetyping artifacts in monitored noninvasive vital signs data. Society of Critical Care Medicine Annual Congress 2015. Oral Presentation.


• Wang D, Fiterau M, Dubrawski A, Hravnak M, Clermont G and Pinsky MR. Interpretable active learning in support of clinical data annotation. Society of Critical Care Medicine Annual Congress 2015. Poster.

2014

• Madalina Fiterau and Artur Dubrawski. Reducing Annotation Effort through Projection Retrieval in an Active Learning Setting. Women in Machine Learning Workshop, Montreal, Quebec, Canada, December 2014. Poster.


• Madalina Fiterau and Artur Dubrawski. Discovering Compact and Informative Structures through Data Partitioning. Thesis Proposal. CMU, ML Department, Fall 2014.


• Fiterau M, Dubrawski A, Chen L, Hravnak M, Clermont G, Bose E, Guillame-Bert M, Pinsky MR. Artifact adjudication for vital sign step-down unit data can be improved using Active Learning with low-dimensional models. Annual Congress of the European Society of Intensive Care Medicine 2014. Electronic poster.


• Wang D, Chen L, Fiterau M, Dubrawski A, Hravnak M, Bose E, Wallace D, Kaynar M, Clermont G, Pinsky MR. Multi-tier ground truth elicitation framework with application to artifact classification for predicting patient instability. Annual Congress of the European Society of Intensive Care Medicine 2014. Electronic poster.


• Hravnak M, Chen L, Dubrawski A, Clermont G, Bose E, Fiterau M, Guillame-Bert M, Pinsky MR. Supervised Machine learning can classify artifact in multi-signal vital sign monitoring data from Step-Down Unit (SDU) Patients. Annual Congress of the European Society of Intensive Care Medicine 2014.


• Hravnak M, Chen L, Fiterau M, Dubrawski A, Clermont G, Guillame-Bert M, Bose E, Pinsky MR. Active machine learning to increase annotation efficiency in classifying vital sign events as artifact or real alerts in continuous noninvasive monitoring. American Journal of Respiratory and Critical Care Medicine 189; 2014:A3627. Oral Presentation.


• Nicholas Gisolfi, Madalina Fiterau and Artur Dubrawski. Finding Gaps in Data to Guide Development of a Radiation Threat Adjudication System. 2014 Symposium on Radiation Measurements and Applications. Poster.

2013

• Madalina Fiterau and Artur Dubrawski. Informative projection recovery for classification, clustering and regression. In International Conference on Machine Learning and Applications, volume 12, ICMLA 2013. Oral Presentation.


• Fiterau M, Dubrawski A, Chen L, Hravnak M, Clermont G, Pinsky MR. Automatic identification of artifacts in monitoring critically ill patients. Annual Congress of the European Society of Intensive Care Medicine 2014; 39 Suppl 2: S470. Electronic Poster.


• Madalina Fiterau and Artur Dubrawski. Detecting Artifacts in Clinical Data through Projection Retrieval. ICML Workshop on the Role of Machine Learning in Transforming Healthcare: Recent progress, Challenges and Opportunities, June 2013, Atlanta. Spotlight and poster.


• Madalina Fiterau and Artur Dubrawski. An Application of Divergence Estimation to Projection Retrieval for Semi-supervised Classification and Clustering, ICML Workshop on Divergences and Divergence Learning. June 2013, Atlanta. Oral Presentation.

2012

• Madalina Fiterau and Artur Dubrawski. Projection Retrieval for Classification. In Advances in Neural Information Processing Systems 25, pages 3032–3040, NIPS 2012. Poster.


• Madalina Fiterau and Leila Wehbe. Feature-Task Bi-clustering in Multitask Regression. Women in Machine Learning Workshop, Lake Tahoe, Nevada, December 2012. Poster.


• Madalina Fiterau and Artur Dubrawski. Trade-offs in Explanatory Model Learning. Data Analysis Project, CMU, Machine Learning Department, Spring 2012.

2011

• Madalina Fiterau and Artur Dubrawski. Explaining Datasets through High-Accuracy Regions. Women in Machine Learning Workshop, Granada, Spain, December 2011. Oral presentation.


• Madalina Fiterau, Artur Dubrawski, Can Ye, Real-time Adaptive Monitoring of Vital Signs for Clinical Alarm Preemption, international Society for Disease Surveillance Annual Conference 2011. Poster.


• Madalina Fiterau, Andrew Sheng, Venkat Senapati, Nagasrikanth Kallakuri, Robert Walzer Real-time Algorithmic Detection of a Landing Site using Sensors aboard a Lunar Lander, Technical Report, Advanced Mobile Robot Development, CMU, Spring 2011.

2010

• Madalina Fiterau and Andrew Sheng. Pinpoint Landing through Landscape Matching and Terrain Evaluation, Technical Report, Mobile Robot Development, CMU, Fall 2010.

2009

• Madalina Fiterau, Olga Ormond, Gabriel-Miro Muntean, Performance of Handover for Multiple Users in Heterogeneous Wireless Networks, IEEE Conference on Local Computer Networks 2009. Poster.


• Madalina Fiterau, Dr. Gabriel-Miro Muntean, Prof. Ioan Jurca, Handover Algorithm Design and Simulation, Diploma Project, Bachelors in Computer Engineering, Politehnica University of Timisoara, July 2009.