Sajadi, A., Roslaniec, L., Klos, M., Biczel, P., & Loparo, K. A.(2016).An emulator for fixed pitch wind turbine studies.Renewable Energy [09601481],87, 391-402.
Romo-Bucheli, D., Janowczyk, A., Romero, E., Gilmore, H., & Madabhushi, A.(2016).Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images.SPIE Medical Imaging [Conference],9791
Sun, S., & Podgurski, A. A.(2016).Properties of Effective Metrics for Coverage-Based Statistical Fault Localization.IEEE International Conference on Software Testing, Verification and Validation (ICST).
Bhamidipati, H., Saab, D. G., & Abraham, J. G.(2016).Single Trojan injection model generation and detection.Latin-American Test Symposium (LATS) [Conference].
Eckford, A., Loparo, K. A., & Thomas, P. A.(2016).Finite-state channel models for signal transduction in neural systems.IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Madabhushi, A.(2016).Stacked Sparce Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology images.IEEE Transactions on Medical Imaging,35(1),119 - 30.
Romo-Bucheli, D., Janowczyk, A., Romero, E., Gilmore, H., & Madabhushi, A.(2016).Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images.,9791
Zhuo, G., Jiang, Q., Guo, L., Li, Z., & Li, P.(2016).Privacy-preserving Verifiable Set Operation in Big Data for Cloud-assisted Mobile Crowdsourcing.IEEE Internet of Things Journal [23274662].
Vandendriessche, B., Abas, M., Dick, T., Loparo, K. A., & Jacono, F. A.(2016).Mapping multiscalar variability dynamics in 1/f physiologic phase space for tracking the state of sepsis patients.The FASEB Journal,30(1 Supplement),1003–1.
Bhargava, R., & Madabhushi, A.(2016).Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology.Annual Review of Biomedical Engineering [15239829],18(1),387-412.
Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Stangl, J., & Madabhushi, A.(2016).Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images..IEEE transactions on medical imaging,35(1),119-30.
Rojas, J., Newman, W. S., Li, Z. S., & Chen, Q. S.(2016).A Steering Wheel Manipulation Scheme by an Anthropomorphic Humanoid Robot in a Constrained Vehicle Environment.IEEE.
Zong, B., Xiao, X., Li, Z., Wu, Z., Qian, Z., Yan, X., Singh, A., & Jiang, G.(2016).Behavior Query Discovery in System-Generated Temporal Graphs.International Conference on Very Large Data Bases (VLDB).
Mohammadi-Abdar, H., Ridgel, A., Discenzo, F., Phillips, R., Walter, B., & Loparo, K. A.(2016).Test and Validation of a Smart Exercise Bike for Motor Rehabilitation in Individuals with Parkinson's Disease.IEEE Transactions on Neural Systems and Rehabilitation Engineering [15344320],24(11),1254-1264.
Li, L., Rusu, M., Viswanath, S. E., Penzias, G. E., Pahwa, S. E., Gollamudi, J. E., & Madabhushi, A. E.(2016).Multi-modality registration via multi-scale textural and spectral embedding representations.SPIE Medical Imaging [Conference],9784
Coskun, M., Grama, A., & Koyutürk, M.(2016).Efficient Processing of Network Proximity Queries via Chebyshev Acceleration.ACM SIGKDD international conference on Knowledge discovery and data mining - KDD.
Xiao, X., Lou, J., Lu, S., Shepherd, D., Peng, X., & Wang, Q.(2016).Roundtable: Research Opportunities and Challenges for Large-Scale Software Systems.Journal of Computer Science and Technology,31(5),851–860.
Ayati, M., & Koyutürk, M.(2016).PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases.PLoS computational biology,12(11),e1005195.
Avllazagaj, E., Ayday, E., & Cicek, A Ercument, E.(2016).Privacy-Related Consequences of Turkish Citizen Database Leak.arXiv preprint arXiv:1605.05847.
Ginsburg, S., Algohary, A., Pahwa, S., Gulani, V., Ponsky, L., Aronen, H., Boström, P., Böhm, M., Haynes, A., Brenner, P., Delprado, W., Thompson, J., Pulbrock, M., Taimen, P., Villani, R., Stricker, P., Rastinehad, A., Jambor, I., & Madabhushi, A.(2016).Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study: Radiomic Features for Prostate Cancer Detection on MRI.Journal of Magnetic Resonance Imaging [10531807].
