my BS degree from Bogazici University, Istanbul, Turkey,
in 1997 and MS and PhD degrees from Purdue University,
West Lafayette, IN, USA, in 1999 and 2003 respectively,
all in Electrical Engineering. Between 2003 and 2008 I
was with the CAD and Knowledge Solutions group of
Siemens Health. At Siemens Health, I was involved in the
development of a broad spectrum of computer aided
diagnosis/detection applications including FDA-approved
Lung and Colon CAD products. I have joined IUPUI
Computer and Information Science Department as a
tenure-track assistant professor in 2008, where I became
an associate professor in 2014.
My area of expertise is in machine learning with a special focus on self-adjusting models and inference, where
we aim to replace the traditional brute-force approach of fitting a fixed model onto the data with more flexible models that can account for the non-stationary nature of real-world machine learning problems.
We achieve this by placing suitably chosen non-parametric Bayesian priors over class distributions to model not only observed classes but unobserved ones as well in an effort to perform joint classification and clustering. Scalable online and offline stochastic inference for non-parametric Bayesian models that can potentially enable self-adjusting machine learning has been at the center of
my most recent research efforts. My research is
mainly driven by real-world problems in computer aided
diagnosis/detection, hyper-spectral data analysis and
remote sensing, flow cytometry data
retrieval, and topic modeling.
For more information on my research
please visit my
Google Scholar research
Halid Z. Yerebakan and Murat Dundar, “Partially Collapsed Parallel Gibbs Sampler for Dirichlet Process Mixture Models,” Pattern Recognition Letters. To appear subject to minor revisions.
Xun Wu, Jean Sanders, Murat Dundar, and Ömer Oralkan, “Multi-wavelength photoacoustic imaging for monitoring lesion formation during high-intensity focused ultrasound therapy,” in Proc. SPIE 10064, Photons Plus Ultrasound: Imaging and Sensing 2017, Feb. 2017.
Baichuan Zhang, Murat Dundar, Muhammed Hasan, “Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams,” in Proceedings of ACM CIKM, Indianapolis, US, Oct 2016.
Murat Dundar, Bethany Ehlmann, “Rare Jarosite Detection in CRISM Imagery by Non-Parametric Bayesian Clustering,” in Proceedings of IEEE WHISPERS'16, Los Angeles, US, Aug 2016.
Bartek Rajwa, Paul Wallace, Elizabeth Griffiths, Murat Dundar, “Automated Assessment of Disease Progression in Acute Myeloid Leukemia by Probabilistic Analysis of Flow Cytometry Data,” IEEE Transactions on Biomedical Engineering. Hard copy to appear, electronic copy published in July 2016.
Selman Delil, Rahmi N. Celik, Sayin San, Murat Dundar, “Understanding health service delivery using spatio-temporal patient mobility data: a clustering approach to assess mobility patterns across eighty-one provinces in Turkey,” BMC Health Services Research. To appear subject to minor revisions.
I am actively recruiting graduate
students who have keen interest in machine learning and
data mining and their applications to problems in life
sciences. An ideal candidate should have a BS in CS/ECE/Math/Stats
and have a strong background in linear algebra,
calculus, probability and random variables. Extensive
experience with Matlab/C programming is also required.
doctoral/master programs in computer sciences awards Purdue University
Machine Learning Predicts Leukemia Remission with 100% Accuracy
Highlighting our work on doubly non-parametric Bayesian
clustering and its application to AML remission/relapse
Training computers to differentiate between people with the same name
Highlighting our work on name disambiguation
Undergraduate Research Award (3/15)
Congratulations to Nhan Do for receiving IUPUI Undergraduate Research Program (RISE) Award.
New Software (5/14)
Group clustering with random effects implemented in
C++ for clustering and cluster matching across a batch of samples.
PhD Thesis Defense (6/13)
Congratulations to Ferit Akova for successfully defending his PhD Thesis entitled "A Non-parametric Bayesian Perspective for Machine Learning in Partially-observed Settings".
CAREER Grant Awarded (3/13)
CAREER Grant “CAREER: Self-adjusting Models as a New Direction in Machine Learning” was awarded by NSF.
NIH Grant Awarded (7/12)
Grant with Bartek Rajwa “Automated spectral data
transformations and analysis pipeline for
high-throughput flow cytometry” was awarded by NIH/NIBIB.