Bartek Rajwa

Bartek Rajwa

Address:
Bartek Rajwa, Ph.D.
Research Assistant Professor
Bindley Bioscience Center
1203 W. State Street
West Lafayette, IN
tel.: (765) 496-1153
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
web: web.ics.purdue.edu/~brajwa

Bartek Rajwa is a Research Associate Professor in the Bindley Bioscience Center at Purdue University (since November 2010) where he is conducting studies on high-throughput cytometry, high-content imaging, biological image analysis, biological pattern recognition, and applications of statistical machine learning in cell biology, cancer research, and bioengineering.

Research agenda

In the advent of the $1000 genome-sequencing era, personalized medicine becomes a realistic objective. Consequently, one of the most important programmatic goals in biology is the integration of emerging “–omics” fields in order to define the biological functions of the genes encoded in each sequenced genome. This will lead to reconstruction and understanding of the complex networks of functional interactions that define cellular systems. This cutting-edge area of research is referred to in the recent literature as phenomics. Dr. Rajwa’s expertise is in this emerging research field, integrating a variety of linked technologies from flow cytometry through biological imaging to proteomics and functional genomics. The unifying theme for phenomics is an integrated study of the functional role of cellular systems via quantitative mathematical description of their phenotypes. The research in which Dr. Rajwa is involved employs applied mathematics, computer science, and machine learning to decipher and interpret complex phenotypic patterns observed via quantitative cell analysis. Dr. Rajwa has been conducting studies on high-throughput cytometry, high-content imaging, biological image analysis, biological pattern recognition, and applications of statistical machine learning in cell biology, cancer research, and bioengineering. The underlying, long-term theme of his research is the use of innovative data-science tools (and specifically machine-learning methods) to reduce complex phenotypic information into essential statistical models and gain insight into biological processes characterized by incomplete and noisy data.

Recent selected publications

  • Zhang, C., Huang K-C, Rajwa, B., Li J., Yang S., Lin H., Liao, C-s., et al. (2017): Stimulated Raman Scattering Flow Cytometry for Label-Free Single-Particle Analysis. Optica 4(1):103–9. doi:10.1364/OPTICA.4.000103
  • Rajwa B. (2016): Effect-size measures as descriptors of assay quality in high-content screening. A brief review of some available methodologies. ASSAY and Drug Development Technologies 15(1):15-29. doi:10.1089/adt.2016.740
  • Rajwa B., Wallace P.K., Griffiths E.A., and M. Dundar. (2016): Automated Assessment of Disease Progression in Acute Myeloid Leukemia by Probabilistic Analysis of Flow Cytometry Data. IEEE Transactions on Biomedical Engineering 64(5):1089-1098 doi:10.1109/TBME.2016.2590950
  • Azad A., Rajwa B., Pothen A. (2016): Immunophenotype Discovery, Hierarchical Organization, and Template-based Classification of Flow Cytometry Samples. Frontiers Oncology 6:188. doi:10.3389/fonc.2016.00188
  • Azad A, Rajwa B, and Pothen A. (2016): flowVS: Channel-Specific Variance Stabilization in Flow Cytometry. BMC Bioinformatics 17: 291. doi:10.1186/s12859-016-1083-9
  • Dundar, M.; Akova, F.; Yerebakan, H. Z.; Rajwa, B. (2014): A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects. BMC Bioinformatics 15. http://dx.doi.org/10.1186/1471-2105-15-314
  • Ahmed, W. M.; Bayraktar, B.; Bhunia, A. K.; Hirleman, E. D.; Robinson, J. P.; Rajwa, B. (2013): Classification of Bacterial Contamination Using Image Processing and Distributed Computing. IEEE Journal of Biomedical and Health Informatics 17, 1, 232-239. http://dx.doi.org/10.1109/TITB.2012.2222654
  • Novo, D.; Gregori, G.; Rajwa, B. (2013): Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices. Cytometry Part A 83A, 5, 508-520. http://dx.doi.org/10.1002/cyto.a.22272
  • Akova, F.; Dundar, M.; Qi, Y.; Rajwa, B.; Zaki, M.; Siebes, A.; Yu, J.; Goethals, B.; Webb, G.; Wu, X. (2012): Self-adjusting Models for Semi-supervised Learning in Partially observed Settings. 12th IEEE International Conference on Data Mining (ICDM 2012), 21-30. http://dx.doi.org/10.1109/ICDM.2012.60
  • Bernas, T.; Starosolski, R.; Robinson, J. P.; Rajwa, B. (2012): Application of detector precision characteristics and histogram packing for compression of biological fluorescence micrographs. Computer Methods and Programs in Biomedicine 108, 2, 511-523. http://dx.doi.org/10.1016/j.cmpb.2011.03.012
  • Gregori, G.; Patsekin, V.; Rajwa, B.; Jones, J.; Ragheb, K.; Holdman, C.; Robinson, J. P. (2012): Hyperspectral cytometry at the single-cell level using a 32-channel photodetector. Cytometry Part A 81A, 1, 35-44. http://dx.doi.org/10.1002/cyto.a.21120
  • Rajwa, B.; Dundar, M. M.; Akova, F.; Bettasso, A.; Patsekin, V.; Hirleman, E. D.; Bhunia, A. K.; Robinson, J. P. (2010): Discovering the Unknown: Detection of Emerging Pathogens Using a Label-Free Light-Scattering System. Cytometry Part A 77A, 12, 1103-1112. http://dx.doi.org/10.1002/cyto.a.20978
  • Akova, F.; Hirleman, D.; Bhunia, A. K.; Rajwa, B.; Dundar, M. M.; IEEE (2009): Non-exhaustive Learning for Bacteria Detection. 2009 International Conference on Network-Based Information Systems, 206-211. http://dx.doi.org/10.1109/NBiS.2009.79
  • Dundar, M. M.; Hirleman, E. D.; Bhunia, A. K.; Robinson, J. P.; Rajwa, B. (2009): Learning with a Non-exhaustive Training Dataset A Case Study: Detection of Bacteria Cultures using Optical-Scattering Technology. KDD-09: 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 279-287, http://dl.acm.org/citation.cfm?id=1557055
  • Rajwa, B.; Venkatapathi, M.; Ragheb, K.; Banada, P. P.; Hirleman, E. D.; Lary, T.; Robinson, J. P. (2008): Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier. Cytometry Part A 73A, 4, 369-379. http://dx.doi.org/10.1002/cyto.a.20515