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,
Volume 90, Pages 22-27, 2017.
Murat Dundar, Qiang Kou, Baichuan Zhang, Yicheng He, Bartek Rajwa, “Simplicity of Kmeans versus Deepness of Deep Learning: A Case of Unsupervised Feature Learning with Limited Data,” In Proceedings of IEEE International Conference on Machine Learning Applications, Miami, FL, USA, December 11-13, 2015.
Halid Z. Yerebakan, Bartek Rajwa, Murat Dundar, "The
Infinite Mixture of Infinite Gaussian Mixtures,"
Murat Dundar, Ferit Akova, Halid Z. Yerebakan, Bartek
Rajwa, "A Non-parametric Bayesian Model for Joint Cell
Clustering and Cluster Matching: Identification of
Anomalous Sample Phenotypes with Random Effects," BMC
Bioinformatics 15 (1), 314, 2014. Online
Murat Dundar, Halid Z. Yerebakan, Bartek Rajwa,
"Batch Discovery of Recurring Rare Classes toward
Identifying Anomalous Samples," In Proceedings of the 20th Annual SIGKDD
International Conference on Knowledge Discovery and Data
Mining (SIGKDD'14), New York, USA, Aug 24-27 2014.
Murat Dundar, Ferit Akova, Yuan Qi, Bartek Rajwa, “Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes,” In John Langford and Joelle Pineau (Eds.),
Proceedings of the 29th International Conference on Machine Learning (ICML'12), Edinburgh, Scotland, June 26-July 1, 2012 (pp. 113-120). Omnipress, 2012.
Murat Dundar, Sunil Badve, Gokhan Bilgin, Vikas Raykar, Olcay Sertel, Metin N. Gurcan, “Computerized Classification of Intraductal Breast Lesions using Histopathological Images”,
IEEE Transactions on Biomedical Engineering, 58(7):1977-1984, 2011.
Murat Dundar, Matthias Wolf, Sarang Lakare, Marcos Salganicoff, Vikas Raykar “Polyhedral Classifiers for Target Detection: A Case Study: Colorectal Cancer”, In
Proceedings of the 25th International Conference on Machine Learning (ICML 2008), pp.288-295, Helsinki, July 2008.
Murat Dundar, Glenn Fung, Balaji Krishnapuram, Bharat Rao, “Multiple Instance Learning Algorithms for Computer Aided Diagnosis”,
IEEE Transactions on Biomedical Engineering, Volume 55, No. 3, pp 1005-1015, March 2008.
Murat Dundar, Jinbo Bi, “Joint optimization of cascaded classifiers for computer aided detection”, In
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ‘07), Minneapolis, Minnesota, USA, 18-23 June 2007. IEEE Computer Society 2007. (Oral paper, acceptance rate: 5%)
Murat Dundar, Balaji Krishnapuram, Jinbo Bi, Bharat Rao, “Learning from Non-IID Data”, In
Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India, January 6-12, 2007.
Glenn Fung, Murat Dundar, Balaji Krishnapuram, Bharat Rao, “Multiple Instance Algorithms for Computer Aided Diagnosis”,
Advances in Neural Information Processing Systems 19 (NIPS 2006), Vancouver, CA, 2006.
Murat Dundar, Glenn Fung, Jinbo Bi, Sandilya Sathyakama, Bharat Rao, “Sparse Fisher Discriminant Analysis for Computer Aided Detection”, In
Proceedings of the SIAM International Conference of Data Mining (SDM ’05), Newport Beach, CA, USA, 2005.
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
PhD Thesis Defense (5/17)
Congratulations to Halid Yerebakan for successfully defending his PhD Thesis entitled "Hierarchical
Non-Parametric Bayesian Mixture Models and Applications
on Big Data".
Computer trained to predict which AML patients will go into remission, which will relapse
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.