Zhu, X., Ayday, E., & Vitenberg, R.(2019).A privacy-preserving framework for outsourcing location-based services to the cloud.IEEE Transactions on Dependable and Secure Computing.
Von Thenen, N., Ayday, E., & Cicek, A Ercument, E.(2018).Re-identification of individuals in genomic data-sharing beacons via allele inference.Bioinformatics,35(3),365--371.
Guo, Q., Deng, W., Bebek, O., Cavusoglu, M. C., Mastrangelo, C. C., & Young, D. C.(2018).Personal Inertial Navigation System Assisted by MEMS Ground Reaction Sensor Array and Interface ASIC for GPS-Denied Environment.IEEE Journal of Solid-State Circuits.
Ayday, E., Jiang, X., & Malin, B.(2018).GenoPri'16: International Workshop on Genome Privacy and Security.IEEE/ACM Transactions on Computational Biology and Bioinformatics.
Sar\iy\ild\iz, Mert B\"ulent, Cinbi\cs, Ramazan G\"okberk, & Ayday, E.(2018).Key Protected Classification for GAN Attack Resilient Collaborative Learning..
G\"ursoy, Gamze, Harmanci, A., Tang, H., Ayday, E., & Brenner, S.(2018).When Biology Gets Personal: Hidden Challenges of Privacy and Ethics in Biological Big Data.World Scientific.
Verma, A., French, R. H., & Carter, J. W.(2017).Physics Informed Network Models: Data Science Approach to Metals Design.Integrating Materials and Manufacturing Innovation/Springer,6(4),279–287.
Chankong, V.(2017).A System Dynamics Model for Predicting Supply and Demand of Medical Education Talents in China.EURASIA Jounal of Mathecal Science and Technology Education,13
Liu, C., Wang, J., Li, H., Fietkiewicz, C., & Loparo, K. A.(2017).Modeling and Analysis of Beta Oscillations in the Basal Ganglia.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,PP(99).
Alilou, M., Beig, N., Orooji, M., Rajiah, P., Velcheti, V., Rakshit, S., Reddy, S., Yang, M., Jacono, F., Gilkeson, R., Linden, P., & Madabhushi, A.(2017).An integrated segmentation and shape based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT..Medical physics.
Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A.(2017).A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers..Cytometry. Part A : the journal of the International Society for Analytical Cytology.
Rusu, M., Purysko, A., Verma, S., Kiechle, J., Gollamudi, J., Ghose, S., Herrmann, K., Gulani, V., Paspulati, R., Ponsky, L., Böhm, M., Haynes, A., Moses, D., Shnier, R., Delprado, W., Thompson, J., Stricker, P., & Madabhushi, A.(2017).Computational imaging reveals shape differences between normal and malignant prostates on MRI..Scientific reports,7, 41261.
Viswanath, S. E., Tiwari, P. E., Lee, G. E., & Madabhushi, A. E.(2017).Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases..BMC medical imaging,17(1),2.
Kim, J., Bennett, N., Devita, M., Chahar, S., Viswanath, S. E., Lee, H. E., Jung, H. E., Shao, P. E., Childers, E. E., Liu, C. E., Kulesa, A. E., Garcia, B. E., Becker, M. E., Hwang, W. E., Madabhushi, A. E., Verzi, M. E., & Moghe, P. E.(2017).Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells..Scientific reports,7, 39406.
Maxwell, S., Chance, M., & Koyutürk, M.(2017).Linearity of network proximity measures: implications for set-based queries and significance testing.Bioinformatics,33(9),1354--1361.
Hu, Y., Gunapati, V., Zhao, P., Gordon, D., Wheeler, N., Hossain, M., Peshek, T., Bruckman, L., Zhang, G., & French, R. H.(2017).A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites.IEEE Journal of Photovoltaics,7(1),230–236.
Morrison, P., Pandita, R., Xiao, X., Chillarege, R., & Williams, L.(2017).Are vulnerabilities discovered and resolved like other defects?.Empirical Software Engineering.
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..Journal of magnetic resonance imaging : JMRI.
Liao, W., Du, W., Salinas, S., & Li, P.(2016).Efficient Privacy-preserving Outsourcing of Large-scale Convex Separable Programming for Smart Cities.the 14th IEEE International Conference on Smart City (SmartCity’16).
Tiwari, P., Prasanna, P., Wolansky, L., Pinho, M., Cohen, M., Nayate, A., Gupta, A., Singh, G., Hatanpaa, K., Sloan, A., Rogers, L., & Madabhushi, A.(2016).Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study..AJNR. American journal of neuroradiology,37(12),2231-2236.
Prasanna, P., Tiwari, P., & Madabhushi, A.(2016).Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor..Scientific reports,6, 37241.
Shiradkar, R., Podder, T., Algohary, A., Viswanath, S. E., Ellison, C. E., & Madabhushi, A. E.(2016).Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI..Radiation oncology (London, England),11(1),148.
Prasanna, P., Patel, J., Partovi, S., Madabhushi, A., & Tiwari, P.(2016).Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings..European radiology.
Liao, W., Salinas, S., Li, Z., Li, P., & Pan, M.(2016).Energy-Source-Aware Cost Optimization for Green Cellular Networks with Strong Stability. IEEE Transactions on Emerging Topics in Computing,4(4),541-555.
De Leon, A., Lee, G., Shih, N., Elliott, R., Feldman, M., & Madabhushi, A.(2016).Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images..Journal of medical imaging (Bellingham, Wash.),3(4),047502.
Madabhushi, A., & Lee, G.(2016).Image analysis and machine learning in digital pathology: Challenges and opportunities..Medical image analysis,33, 170-5.
Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A.(2016).Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images..Scientific reports,6, 32706.
Janowczyk, A., & Madabhushi, A.(2016).Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases..Journal of pathology informatics,7, 29.
Penzias, G., Janowczyk, A., Singanamalli, A., Rusu, M., Shih, N., Feldman, M., Stricker, P., Delprado, W., Tiwari, S., Böhm, M., Haynes, A., Ponsky, L., Viswanath, S. E., & Madabhushi, A. E.(2016).AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments..Scientific reports,6, 29906.
Bhargava, R., & Madabhushi, A.(2016).Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology..Annual review of biomedical engineering,18, 387-412.
Navas, P., Yu, R., López-Querol, S., & Li, B.(2016).Dynamic consolidation problems in saturated soils solved through u–w formulation in a LME meshfree framework.Computers and Geotechnics [0266352X],79, 55-72.
Salinas, S., Luo, C., Liao, W., & Li, P.(2016).Efficient Secure Outsourcing of Large-scale Quadratic Programs.ACM Asia Conference on Computer and Communications Security - ASIA CCS.
Xu, J., Luo, R., Wang, P., Gilmore, H., & Madabhushi, A.(2016).A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images..Neurocomputing,191, 214-223.
Janowczyk, A., Basavanhally, A., & Madabhushi, A.(2016).Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology..Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.
Toth, R., Sperling, D., & Madabhushi, A.(2016).Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings..PloS one,11(4),e0150016.
Antunes, J., Viswanath, S. E., Rusu, M. E., Valls, L. E., Hoimes, C. E., Avril, N. E., & Madabhushi, A. E.(2016).Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study..Translational oncology,9(2),155-62.
Madabhushi, A.(2016).Brief exposure to preoperative bevacizumab reveals a TGF-β signature predictive of response in HER-2 negative breast cancers.International Journal of Cancer,138(3),10.
Varadan, V., Kamalakaran, S., Gilmore, H., Banerjee, N., Janevski, A., Miskimen, K., Williams, V., Basavanhalli, A., Madabhushi, A., Lezon-Geyda, K., Bossuyt, V., Lannin, D., Abu-Khalaf, M., Sikov, W., Dimitrova, N., & Harris, L.(2016).Brief-exposure to preoperative bevacizumab reveals a TGF-β signature predictive of response in HER2-negative breast cancers..International journal of cancer,138(3),747-57.
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.
Xue, Z., Luo, X., Wang, G., Gilmore, H., & Madabhushi, A.(2016).A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial and Stromal Regions in Histopathological Images.Neurocomputing [09252312],191, 214-223.
Karabalin, R., Rimm, D., Ganesan, S., & Madabhushi, A.(2016).Abstract P5-07-12: Local nuclear architecture features from H&E images predict early versus distant recurrence in lymph node negative, ER+ breast cancers.Cancer Research [00085472],76(4 Supplement),P5-07-12-P5-07-12.
Niazi, M., Yao, K., Zynger, D., Clinton, S., Chen, J., Koyutürk, M., LaFramboise, T., & Gurcan, M.(2016).Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer.IEEE Transactions on Information Technology in Biomedicine [10897771].
Romo-Bucheli, D., Janowczyk, A., Gilmore, H., Romero, E., & Madabhushi, A.(2016).Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images.Scientific Reports [20452322],6
Tiwari, P., Prasanna, P., Wolansky, L., Pinho, M., Cohen, M., Nayate, A., Gupta, A., Singh, G., Hatanpaa, K., Sloan, A., Rogers, L., & Madabhushi, A.(2016).Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.American Journal of Neuroradiology [01956108],37(12),2231-2236.
Madabhushi, A., Ginsburg, S., & Lee, G.(2016).Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology.IEEE Transactions on Medical Imaging,35(1),76 - 88.
Madabhushi, A., & Lee, G.(2016).Image analysis and machine learning in digital pathology: Challenges and opportunities.Medical Image Analysis [13618415],33, 170-175.
Singanamalli, A., Rusu, M., Sparks, R., Shih, N., Ziober, A., Wang, Y., Tomaszewski, J., Rosen, M., Feldman, M., & Madabhushi, A.(2016).Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer..Journal of magnetic resonance imaging : JMRI,43(1),149-58.
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].
Toth, R., Sperling, D., & Madabhushi, A.(2016).Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings.PLoS ONE [19326203],11(4).
Janowczyk, A., Basavanhally, A., & Madabhushi, A.(2016).Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.Computerized Medical Imaging and Graphics [08956111].
Fada, J., Wheeler, N., Zabiyaka, D., Goel, N., Peshek, T., & French, R. H.(2016).Democratizing an electroluminescence imaging apparatus and analytics project for widespread data acquisition in photovoltaic materials.Review of Scientific Instruments [00346748],87(8).
Cheng, W., Guo, Z., Zhang, X., & Wang, Z.(2016).CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering.ACM Transactions on Knowledge Discovery from Data [15564681],10(4),27-Jan.
Wu, D., Terpenny, J., Zhang, L., Gao, R. X., & Kurfess, T. X.(2016).Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems.ASME International Manufacturing Science and Engineering Conference.
Sahoo, S., Wei, A., Valdez, J., Wang, L., Zonjy, B., Tatsuoka, C., Loparo, K. A., & Lhatoo, S. A.(2016).NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig.Frontiers in Neuroinformatics [16625196],10
Savel, D., LaFramboise, T., Grama, A., & Koyutürk, M.(2016).Pluribus - Exploring the Limits of Error Correction Using a Suffix Tree.Computational Biology and Bioinformatics, IEEE-ACM Transactions on [15455963].
Penzias, G., Janowczyk, A., Singanamalli, A., Rusu, M., Shih, N., Feldman, M., Stricker, P., Delprado, W., Tiwari, S., Böhm, M., Haynes, A., Ponsky, L., Viswanath, S. E., & Madabhushi, A. E.(2016).AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments.Scientific Reports [20452322],6
Janowczyk, A., & Madabhushi, A.(2016).Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.Journal of Pathology Informatics [21533539],7(1).
Madabhushi, A.(2016).Stacked Sparce Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology images.IEEE Transactions on Medical Imaging,35(1),119 - 30.
Ginsburg, S., Lee, G., Karabalin, R., & Madabhushi, A.(2016).Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology..IEEE transactions on medical imaging,35(1),76-88.
Gawlik, A., Lee, G., Whitney, J., Epstein, J., Veltri, R., & Madabhushi, A.(2016).MP02-17 COMPUTER EXTRACTED NUCLEAR FEATURES FROM FEULGEN AND H&E IMAGES PREDICT BIOCHEMICAL RECURRENCE IN PROSTATE CANCER PATIENTS FOLLOWING RADICAL PROSTATECTOMY.The Journal of Urology [00225347],195(4),e16-e17.
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.
Al-Qudah, Z., Johnson, E., Rabinovich, M., & Spatcheck, O.(2016).Internet with Transient Destination-Controlled Addressing.ACM/IEEE Trans. on Networking.
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].
Cohn, H., Lu, C., Paspulati, R., Katz, J., Madabhushi, A., Stein, S., Cominelli, F., Viswanath, S. E., & Dave, M. E.(2016).Tu1966 A Machine-Learning Based Risk Score to Predict Response to Therapy in Crohn's Disease via Baseline MRE.Gastroenterology [00165085],150(4).
Yang, W., Xiao, X., Li, D., Li, H., Liu, X., Wang, H., Guo, Y., & Xie, T.(2016).Security Analytics for Mobile Apps: Achievements and Challenges.Journal of Cyber Security ,1(2),1-14.
Wu, Y., Zhu, X., Liu, L., Fan, W., Jin, R., & Zhang, X.(2016).Mining Dual Networks: Models, Algorithms, and Applications.ACM Transactions on Knowledge Discovery from Data [15564681],10(4),Jan-37.
Fan, Z., Gao, R. X., Wang, P. X., & Kazmer, D. X.(2016).Multi-sensor data fusion for improved measurement accuracy in injection molding.IEEE International Instrumentation and Measurement Technology Conference.
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.
Ayati, M., & Koyutürk, M.(2016).PoCos: Population Covering Locus Sets for Risk Assessment in Complex Diseases.PLoS computational biology,12(11),e1005195.
Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A.(2016).A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization [21681163].
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
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.
Litjens, G., Elliott, R., Shih, N., Feldman, M., Kobus, T., De Hulsbergen-van Kaa, C., Barentsz, J., Huisman, H., & Madabhushi, A.(2016).Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging..Radiology,278(1),135-45.
Salinas, S., Chen, X., Ji, J., & Li, P.(2016).A Tutorial on Secure Outsourcing of Large-scale Computations for Big Data.IEEE Access [21693536],4, 1406-1416.
Antunes, J., Viswanath, S. E., Rusu, M. E., Valls, L. E., Hoimes, C. E., Avril, N. E., & Madabhushi, A. E.(2016).Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study.Translational Oncology [19365233],9(2),155-162.
Fietkiewicz, C., Shafer, G., Platt, E., & Wilson, C.(2016).Variability in respiratory rhythm generation: In vitro and in silico models.Communications in Nonlinear Science and Numerical Simulation [10075704],32, 158-168.
Wu, D., Wang, P., Zhang, X., Yan, R., & Gao, R. X.(2016).A correlation-based approach to trustworthy sensing for cyber-physical systems.IEEE International Instrumentation and Measurement Technology Conference.
Mohammadi-Abdar, H., Ridgel, A., Discenzo, F., & Loparo, K. A.(2016).Design and development of a smart exercise bike for motor rehabilitation in individuals with Parkinson’s disease.IEEE/ASME Transactions on Mechatronics,21(3),1650–1658.
Ruffalo, M., Koyutürk, M., & Sharan, R.(2015).Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.PLoS Computational Biology.
Fu, M. J., Knutson, J. J., & Chae, J. J.(2015).Stroke Rehabilitation using Virtual Environments.Physical Medicine And Rehabilitation Clinics of North America,26(4),747-757.
Kangas, M., Glisic, S., Fang, Y., & Li, P.(2015).Resource Harvesting in Cognitive Wireless Computing Networks With Mobile Clouds and Virtualized Distributed Data Centers: Performance Limits.IEEE Transactions on Cognitive Communications and Networking.
Al-Khaleel, O., Al-Qudah, Z., Al-Khaleel, M., Bani-Hani, R., Papachristou, C. A., & Wolff, F. A.(2015).Efficient Hardware Implementations of Binary-to-BCD Conversion Schemes for Decimal Multiplication.JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS,24(2),-.
Jayapandian, C., Wei, A., Ramesh, P., Zonjy, B., Lhatoo, S., Loparo, K. A., Zhang, G. A., & Sahoo, S. A.(2015).A scalable neuroinformatics data flow for electrophysiological signals using MapReduce.Frontiers in neuroinformatics,9
Hayes, M., & Li, J.(2015).An integrative framework for the identification of double minute chromosomes using next generation sequencing data.BMC GENETICS,16, -.
Gao, R. X., Wang, Y. X., Teti, R. X., Dornfeld, D. X., Kumara, S. X., Mori, M. X., & Helu, M. X.(2015).Cloud-enabled prognosis for manufacturing.,64(2),749-772.
Ellis, D., Carter, J. W., & Ferry, M. W.(2015).A Statistical Study of the Effects of Processing Upon the Creep Properties of GRCop-84.MATERIALS SCIENCE AND ENGINEERING A,640, 1-15.
Pinto, M., Clochesy, J., Hickman, R., & Buchner, M.(2013).Avatar-based depression self-management technology: Promising approach to improve depressive symptoms among young adults..Appl. Nurs,26(1), 45-8.