Ferit Akova
Ferit Akova
Senior Data Scientist, Health Data Link, Inc.
Verified email at
Cited by
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Discovering the unknown: detection of emerging pathogens using a label‐free light‐scattering system
B Rajwa, MM Dundar, F Akova, A Bettasso, V Patsekin, E Dan Hirleman, ...
Cytometry Part A 77 (12), 1103-1112, 2010
A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects
M Dundar, F Akova, HZ Yerebakan, B Rajwa
BMC bioinformatics 15 (1), 1-15, 2014
Bayesian nonexhaustive learning for online discovery and modeling of emerging classes
M Dundar, F Akova, A Qi, B Rajwa
arXiv preprint arXiv:1206.4600, 2012
A machine‐learning approach to detecting unknown bacterial serovars
F Akova, M Dundar, VJ Davisson, ED Hirleman, AK Bhunia, JP Robinson, ...
Statistical Analysis and Data Mining: The ASA Data Science Journal 3 (5 …, 2010
Self-adjusting models for semi-supervised learning in partially observed settings
F Akova, M Dundar, Y Qi, B Rajwa
2012 IEEE 12th International Conference on Data Mining, 21-30, 2012
Region competition via local watershed operators
H Tek, F Akova, A Ayvaci
2005 IEEE Computer Society Conference on Computer Vision and Pattern …, 2005
Region competition via local watershed operators
H Tek, F Akova, A Ayvaci
US Patent 7,394,933, 2008
Generalizations with probability distributions for data anonymization
ME Nergiz, S Cetintas, F Akova
Digital microbiology: detection and classification of unknown bacterial pathogens using a label-free laser light scatter-sensing system
B Rajwa, MM Dundar, F Akova, V Patsekin, E Bae, Y Tang, JE Dietz, ...
Sensing Technologies for Global Health, Military Medicine, Disaster Response …, 2011
Non-exhaustive learning for bacteria detection
F Akova, D Hirleman, AK Bhunia, B Rajwa, MM Dundar
2009 International Conference on Network-Based Information Systems, 206-211, 2009
A nonparametric Bayesian perspective for machine learning in partially-observed settings
F Akova
Purdue University, 2013
A Nonparametric Bayesian Perspective for Machine Learning in Partially-Observed Settings
F Akova
Purdue University, 2013
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