About Me


My name is Madalina (Ina) Fiterau, I have obtained my PhD in Machine Learning from CMU in the Fall of 2015. Currently, I am a Postdoctoral Scholar in the Computer Science Department at Stanford University and part of the Mobilize Center. My postdoctoral advisors are Professors Chris Ré and Scott Delp.

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.

My main interests are query-specific models for decision support systems, learning with structured sparsity, dimensionality reduction in an active learning setting and anomalous pattern detection. My current research 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.

Application-wise, I am working on analysing vital sign data obtained by monitoring patients admitted to the Intensive Care Unit. I am generally interested in the use of machine learning systems for medical applications. This is the focus of the MLCDA NIPS workshop, which I co-organized in 2013 with Jenna Wiens and Finale Doshi-Velez, and the ML4CHG NIPS workshop, which I co-organized in 2014 with Julia Vogt and Gunnar Rätsch.

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 why 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.

Active Projects

  • Regression for Informative Projection Retrieval (RIPR) - 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 is currently being adapted to work in an active learning setting.
    [ensemble methods; 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. 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 of nuclear threats from vehicles scanned at the US border. 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]

Past Projects

  • 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


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









Madalina Fiterau-Brostean
PhD Student in Machine Learning
Carnegie Mellon University
5000 Forbes Ave.
6105 Gates-Hillman Complex
Pittsburgh, PA 15213
office phone: 412-268-3370

To obtain my e-mail address, run the following script in the python interpreter:
''.join([x for s in map(lambda(x): x[0:x.find('ad') if x.find('ad')>=0 else len(x)],list(['madalina','fiterau'])) + zip(list(['@','.','.']),list(['cs','cmu','edu'])) for x in s])

In case you don't have python handy (seriously?), my e-mail address is username@domain where my username is on the URL of the page (after ~) and the domain is cs.cmu.edu.
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14th of July 2016
VIPR demo presented at IJCAI 2016.
View website | camera ready | poster.

12th of July 2016
Deep Neural Decision Forests presented at IJCAI 2016.
View camera ready | slides.

22nd of February 2016
Expert study for RIPR presented at SCCM 2016.
View abstract | slides.

14th of December 2015
Deep Neural Decision Forests wins Marr prize for Best Paper at ICCV 2015.
View camera ready | spotlight | poster.


27th of January 2015
The ActiveRIPR framework was presented at AAAI 2015.
view camera ready | poster.

18th of January 2015
The research on archetyping artifacts from alerts in vital sign monitoring data was presented at SCCM.
view slides.

12th of December 2014
The 2nd Workshop on Machine Learning for Clinical Data Analysis, Healthcare and Genomics took place at NIPS.
Many thanks to our speakers, panelists, poster presenters, and PC members!

27th of October 2014
Completed thesis proposal, Discovering Compact and Informative Structures through Data Partitioning.
Many thanks to my committee members and collaborators!
view slides.

10th of December 2013
The Machine Learning for Clinical Data Analysis Workshop took place at NIPS.
Many thanks to our speakers, panelists, poster presenters and PC members!

4th of December 2013
The RIPR algorithm presented at ICMLA 2013.
view camera ready and slides.

30th of September 2013
The paper Automatic Identification of Alarm Artifacts in Monitoring Critically Ill Patients presented at ESICM-LIVES.
view electronic poster.

20th of June 2013
RIPR models for artifact adjudication shown at the ICML-HEALTH Workshop.
view camera ready | poster.

3rd of December 2012
The RECIP algorithm was shown at NIPS 2012.
view camera ready | poster.

3rd of December 2012
'Feature-Task Co-clustering Regression' was presented at WiML 2012.
view abstract | poster.

21st of March 2012
Data Analysis Project. Committee: Prof. Artur Dubrawski, Prof. Geoff Gordon, Prof. Jeff Schneider
view slides pptx|pdf.

12th of December 2011
the algorithm `Explanation-Oriented Partitioning' was presented at the WiML workshop in Granada.
view slides.

7th of December 2011
the paper `Real-time Adaptive Monitoring of Vital Signs for Clinical Alarm Preemption' presented at the iSDS Annual Conference in Atlanta.
view poster.

29th of November 2011
Aiming for the Moon, an outline of the Lander visual registering project was presented during the ML Journal Club.