Professor Gustavo Carneiro

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• Appointments

  Date	Position	Institution name
  2015	Associate Professor	University of Adelaide
  2014 - 2015	Humboldt Experienced Researcher	Technical University of Munich
  2011 - 2014	Senior Lecturer	University of Adelaide
  2011 - 2011	Marie Curie International Incoming Fellow	University of Lisbon
  2008 - 2010	Visiting Assistant Professor	University of Lisbon
  2006 - 2008	Senior Research Scientist	Siemens Corporate Research
  2004 - 2005	Postdoctoral Fellow	University of British Columbia
  2004 - 2004	Postdoctoral Fellow	University of California, San Diego

• Language Competencies

  Language	Competency
  English	Can read, write, speak, understand spoken and peer review
  French	Can read
  Portuguese	Can read, write, speak, understand spoken and peer review
  Spanish; Castilian	Can read and understand spoken

• Education

  Date	Institution name	Country	Title
  1999 - 2004	University of Toronto	Canada	PhD
  1997 - 1999	Instituto Militar de Engenharia	Brazil	MSc
  1992 - 1996	Universidade Federal do Rio de Janeiro	Brazil	Bachelor's degree

• Research Interests

  Biomedical Engineering Computer Vision Knowledge Representation and Machine Learning

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• Journals

  Year	Citation
  2019	Tran, T., Do, T. -T., Reid, I., & Carneiro, G. (2019). Bayesian Generative Active Deep Learning. Proceedings of Machine Learning Research, 97, 6295-6304.
  2019	Carneiro, G., Manuel, J., Tavares, R., Bradley, A., Papa, J., Nascimento, J., . . . Belagiannis, V. (2019). Editorial. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 7(3), 241. DOI
  2018	Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A. P., & Palmer, L. J. (2018). Producing radiologist-quality reports for interpretable artificial intelligence.. CoRR, abs/1806.00340.
  2018	Carneiro, G., Tavares, J., Bradley, A., Papa, J., Nascimento, J., Cardoso, J., . . . Belagiannis, V. (2018). 1st MICCAI workshop on deep learning in medical image analysis. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 6(3), 241-242. DOI
  2018	Liao, Z., Drummond, T., Reid, I., & Carneiro, G. (2018). Approximate Fisher information matrix to characterise the training of deep neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, OnlinePubl, 1-12. DOI
  2017	Dhungel, N., Carneiro, G., & Bradley, A. (2017). A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Medical Image Analysis, 37, 114-128. DOI Scopus48 Europe PMC10
  2017	Gale, W., Oakden-Rayner, L., Carneiro, G., Bradley, A. P., & Palmer, L. J. (2017). Detecting hip fractures with radiologist-level performance using deep neural networks.. CoRR, abs/1711.06504.
  2017	Carneiro, G., Nascimento, J., & Bradley, A. (2017). Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Transactions on Medical Imaging, 36(11), 2355-2365. DOI Scopus13 WoS8
  2017	Ngo, T., Lu, Z., & Carneiro, G. (2017). Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Medical Image Analysis, 35, 159-171. DOI Scopus75 WoS56 Europe PMC15
  2017	Ribeiro, D., Nascimento, J., Bernardino, A., & Carneiro, G. (2017). Improving the performance of pedestrian detectors using convolutional learning. Pattern Recognition, 61, 641-649. DOI Scopus17 WoS13
  2017	Lu, Z., Carneiro, G., Bradley, A., Ushizima, D., Nosrati, M., Bianchi, A., . . . Hamarneh, G. (2017). Evaluation of three algorithms for the segmentation of overlapping cervical cells. IEEE Journal of Biomedical and Health Informatics, 21(2), 441-450. DOI Scopus18 WoS11 Europe PMC2
  2017	Oakden-Rayner, L., Carneiro, G., Bessen, T., Nascimento, J., Bradley, A., & Palmer, L. (2017). Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework. Scientific Reports, 7(1), 13 pages. DOI Scopus26 WoS25 Europe PMC17
  2017	Liao, Z., & Carneiro, G. (2017). A deep convolutional neural network module that promotes competition of multiple-size filters. Pattern Recognition, 71, 94-105. DOI Scopus4 WoS4
  2017	Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2017). Automatic quantification of tumour hypoxia from multi-modal microscopy images using weakly-supervised learning methods. IEEE Transactions on Medical Imaging, 36(7), 1405-1417. DOI Scopus2 WoS2 Europe PMC1
  2017	Nascimento, J., & Carneiro, G. (2017). Deep learning on sparse manifolds for faster object segmentation. IEEE Transactions on Image Processing, 26(10), 4978-4990. DOI Scopus4 WoS3
  2015	Lu, Z., Carneiro, G., & Bradley, A. (2015). An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 24(4), 1261-1272. DOI Scopus67 WoS51 Europe PMC13
  2013	Carneiro, G., & Nascimento, J. (2013). Combining multiple dynamic models and deep learning architectures for tracking the left ventricle endocardium in ultrasound data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2592-2607. DOI Scopus49 WoS41 Europe PMC11
  2013	Carneiro, G. (2013). Artistic image analysis using graph-based learning approaches. IEEE Transactions on Image Processing, 22(8), 3168-3178. DOI Scopus4 WoS4
  2012	Carneiro, G., Nascimento, J., & Freitas, A. (2012). The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Transactions on Image Processing, 21(3), 968-982. DOI Scopus70 WoS55 Europe PMC16
  2009	Carneiro, G., & Vasconcelos, N. (2009). Minimum Bayes error features for visual recognition. Image and Vision Computing, 27(1-2), 131-140. DOI Scopus2 WoS2
  2009	Carneiro, G., & Jepson, A. (2009). The quantitative characterization of the distinctiveness and robustness of local image descriptors. Image and Vision Computing, 27(8), 1143-1156. DOI Scopus7 WoS6
  2009	Zalud, I., Good, S., Carneiro, G., Georgescu, B., Aoki, K., Green, L., . . . Okumura, R. (2009). Fetal biometry: a comparison between experienced sonographers and automated measurements. The Journal of Maternal - Fetal & Neonatal Medicine, 22(1), 43-50. DOI Scopus7 WoS8 Europe PMC3
  2009	Wels, M., Zheng, Y., Carneiro, G., Huber, M., Hornegger, J., & Comaniciu, D. (2009). Fast and robust 3-D MRI brain structure segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5762 LNCS(PART 2), 575-583. DOI Scopus13
  2008	Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., & Comaniciu, D. (2008). A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5241 LNCS(PART 1), 67-75. DOI Scopus51 WoS30 Europe PMC3
  2008	Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008). Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. IEEE Transactions on Medical Imaging, 27(9), 1342-1355. DOI Scopus110 WoS98 Europe PMC27
  2007	Carneiro, G., & Jepson, A. (2007). Flexible spatial configuration of local image features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12), 2089-2104. DOI Scopus49 WoS33 Europe PMC5
  2007	Carneiro, G., Chan, A., Moreno, P., & Vasconcelos, N. (2007). Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3), 394-410. DOI Scopus676 WoS413 Europe PMC33
  2007	Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2007). Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4792 LNCS(PART 2), 571-579. Scopus15 Europe PMC1
  2007	Carneiro, G., & Ricardo, M. (2007). QoS abstraction layer in 4G access networks. Telecommunication Systems, 35(1-2), 55-65. DOI Scopus2 WoS1
  1999	Aude, E., Carneiro, G., Serdeira, H., Silveira, J., Martins, M., & Lopes, E. (1999). CONTROLAB MUFA: a multi-level fusion architecture for intelligent navigation of a telerobot. Proceedings - IEEE International Conference on Robotics and Automation, 1, 465-472. Scopus12 WoS11
  —	Lu, Z., Carneiro, G., Dhungel, N., & Bradley, A. P. (n.d.). Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach.
  —	Snaauw, G., Gong, D., Maicas, G., Hengel, A. V. D., Niessen, W. J., Verjans, J., & Carneiro, G. (n.d.). End-to-End Diagnosis and Segmentation Learning from Cardiac Magnetic Resonance Imaging.
  —	Felix, R., Sasdelli, M., Reid, I., & Carneiro, G. (n.d.). Multi-modal Ensemble Classification for Generalized Zero Shot Learning.

• Books

  Year	Citation
  2016	Carneiro, G., Tavares, J., Bradley, A., Papa, J., Nascimento, J., Cardoso, J., . . . Lu, Z. (2016). Preface: DLMIA 2016 (Vol. 10008 LNCS).
  2016	Mateus, D., Peter, L., Carneiro, G., Loog, M., & Cornebise, J. (2016). Preface: LABELS 2016 (Vol. 10008 LNCS).

• Book Chapters

  Year	Citation
  2018	Carneiro, G., Tavares, J., Bradley, A., Papa, J., Belagiannis, V., Nascimento, J., . . . Conjeti, S. (2018). DLMIA 2018 Preface. In D. Stoyanov, Z. Taylor, G. Carneiro, & T. Syeda-Mahmood (Eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018 Granada, Spain, September 20, 2018 Proceedings (Vol. 11045 LNCS, pp. VII).
  2018	Carneiro, G., Tavares, J., Bradley, A., Papa, J., Belagiannis, V., Nascimento, J., . . . Conjeti, S. (2018). DLMIA 2018 Preface. In D. Stoyanov, Z. Taylor, G. Carneiro, & T. Syeda-Mahmood (Eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018 and 8th International Workshop, ML-CDS 2018 Held in Conjunction with MICCAI 2018 Granada, Spain, September 20, 2018 Proceedings (Vol. 11045 LNCS, pp. VII).
  2017	Carneiro, G., Zheng, Y., Xing, F., & Yang, L. (2017). Review of deep learning methods in mammography, cardiovascular, and microscopy image analysis. In L. Lu, Y. Zheng, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Image Computing: precision medicine, high performance and large-scale datasets (pp. 11-32). Switzerland: Springer. DOI Scopus7
  2017	Ngo, T., & Carneiro, G. (2017). Fully automated segmentation using distance regularised level set and deep-structured learning and inference. In L. Lu, Y. Zheng, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Image Computing: precision medicine, high performance and large-scale datasets (pp. 197-224). Switzerland: Springer. DOI Scopus1
  2017	Dhungel, N., Carneiro, G., & Bradley, A. (2017). Combining deep learning and structured prediction for segmenting masses in mammograms. In L. Lu, Y. Zheng, G. Carneiro, & L. Yang (Eds.), Deep Learning and Convolutional Neural Networks for Medical Image Computing: precision medicine, high performance and large-scale datasets (pp. 225-240). Switzerland: Springer. DOI Scopus3
  2017	Carneiro, G., Nascimento, J., & Bradley, A. (2017). Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions. In S. Zhou, H. Greenspan, & D. Shen (Eds.), Deep Learning for Medical Image Analysis (pp. 321-339). London: Elsevier. DOI Scopus5
  2016	Nascimento, J., Carneiro, G., & Freitas, A. (2016). Tracking and segmentation of the endocardium of the left ventricle in a 2D ultrasound using deep learning architectures and monte carlo sampling. In Biomedical Image Segmentation: Advances and Trends (pp. 387-406). DOI Scopus1

• Conference Papers

  Year	Citation
  2018	Cardoso, J., Marques, N., Dhungel, N., Carneiro, G., & Bradley, A. (2018). Mass segmentation in mammograms: a cross-sensor comparison of deep and tailored features. In Proceedings of the 24th IEEE International Conference on Image Processing (ICIP 2017) Vol. 2017 (pp. 1737-1741). Piscataway, N.J.: IEEE. DOI
  2018	Maicas Suso, G., Bradley, A., Nascimento, J., Reid, I., & Carneiro, G. (2018). Training medical image analysis systems like radiologists. In A. Frangi, J. Schnabel, C. Davatzikos, C. Alberola-Lopez, & G. Fichtinger (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2018: proceedings, part 1 Vol. 11070 LNCS (pp. 546-554). Granada: Springer. DOI
  2018	Felix, R., Vijay Kumar, B., Reid, I., & Carneiro, G. (2018). Multi-modal cycle-consistent generalized zero-shot learning. In Computer Vision - ECCV 2018: proceedings, part VI Vol. 11210 LNCS (pp. 21-37). Munich: Springer. DOI Scopus1
  2018	Pham, T., Vijay Kumar, B., Do, T., Carneiro, G., & Reid, I. (2018). Bayesian semantic instance segmentation in open set world. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision - ECCV 2018 15th European Conference: Proceedings, Part I Vol. 11214 LNCS (pp. 3-18). Munich: Springer. DOI
  2018	Pu, L. Z. C. T., Campbell, B., Burt, A. D., Carneiro, G., & Singh, R. (2018). COMPUTER-AIDED DIAGNOSIS FOR CHARACTERISING COLORECTAL LESIONS: INTERIM RESULTS OF A NEWLY DEVELOPED SOFTWARE. In GASTROINTESTINAL ENDOSCOPY Vol. 87 (pp. AB245). Washington, DC: MOSBY-ELSEVIER.
  2018	Pu, L. Z. C. T., Lu, K., Ovenden, A., Edwards, S., Wilson, B., Carneiro, G., . . . Singh, R. (2018). EFFECT OF TRAINING AND TIME OF THE DAY ON POLYP DETECTION RATES IN AUSTRALIA. In GASTROINTESTINAL ENDOSCOPY Vol. 87 (pp. AB161). Washington, DC: MOSBY-ELSEVIER.
  2018	Tran, T., Pham, T., Carneiro, G., Palmer, L., & Reid, I. (2018). A Bayesian data augmentation approach for learning deep models. In Advances in Neural Information Processing Systems 30 (NIPS 2017) Vol. 2017-December (pp. 1-10). Long Beach, CA: Neural Information Processing Systems Foundation. Scopus7
  2018	Camps, S., Houben, T., Edwards, C., Antico, M., Dunnhofer, M., Martens, E., . . . Fontanarosa, D. (2018). Quality Assessment of Transperineal Ultrasound Images of the Male Pelvic Region Using Deep Learning. In IEEE International Ultrasonics Symposium, IUS Vol. 2018-January. DOI
  2017	Williams, J., Carneiro, G., & Suter, D. (2017). Region of interest autoencoders with an application to pedestrian detection. In Y. Guo, H. Li, W. Cai, M. Murshed, Z. Wang, J. Gao, & D. Feng (Eds.), Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA 2017) Vol. 2017-December (pp. 580-587). Piscataway, NJ: IEEE. DOI
  2017	Johnston, A., Garg, R., Carneiro, G., Reid, I., & van den Hengel, A. (2017). Scaling CNNs for high resolution volumetric reconstruction from a single image. In Proceedings of the IEEE International Conference on Computer Vision Workshop (ICCVW 2017) Vol. 2018-January (pp. 930-939). Piscataway, NJ: IEEE. DOI Scopus2
  2017	Pu, L., Campbell, B., Carneiro, G., Burt, A., & Singh, R. (2017). Computer-aided diagnosis (CAD) for characterizing colorectal lesions: Initial results of newly developed software. In JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY Vol. 32 (pp. 34-35). WILEY.
  2017	Maicas, G., Carneiro, G., Bradley, A., Nascimento, J., & Reid, I. (2017). Deep reinforcement learning for active breast lesion detection from DCE-MRI. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Collins, & S. Duchesne (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2017: 20th International Conference. Proceedings, Part III Vol. 10435 LNCS (pp. 665-673). Quebec City, Canada: Springer. DOI Scopus8
  2017	Ribeiro, D., Carneiro, G., Nascimento, J., & Bernardino, A. (2017). Multi-channel convolutional neural network ensemble for pedestrian detection. In L. Alexandre, J. Sanchez, & J. Rodrigues (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10255 LNCS (pp. 122-130). Univ Algarve, Faro, PORTUGAL: SPRINGER INTERNATIONAL PUBLISHING AG. DOI
  2017	Carneiro, G., Oakden-Rayner, L., Bradley, A., Nascimento, J., & Palmer, L. (2017). Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography. In Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Vol. abs/1607.00267 (pp. 130-134). Online: IEEE. DOI Scopus4 WoS3
  2017	Dhungel, N., Carneiro, G., & Bradley, A. (2017). Fully automated classification of mammograms using deep residual neural networks. In Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 310-314). Online: IEEE. DOI Scopus16 WoS8
  2017	Maicas, G., Carneiro, G., & Bradley, A. (2017). Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior. In Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 305-309). Online: IEEE. DOI Scopus6 WoS2
  2017	Harwood, B., Kumar, V., Carneiro, G., Reid, I., & Drummond, T. (2017). Smart mining for deep metric learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017) Vol. 2017-October (pp. 2840-2848). Piscataway, NJ: IEEE. DOI Scopus15 WoS2
  2016	Dhungel, N., Carneiro, G., & Bradley, A. (2016). The automated learning of deep features for breast mass classification from mammograms. In Proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II - MICCAI 2016 Vol. 9901 (pp. 106-114). Athens, Greece: Springer. DOI Scopus29
  2016	Nascimento, J., & Carneiro, G. (2016). Multi-atlas segmentation using manifold learning with deep belief networks. In Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging Vol. 2016-June (pp. 867-871). Prague, Czech Republic: IEEE. DOI Scopus5 WoS2
  2016	Vijay Kumar, B., Carneiro, G., & Reid, I. (2016). Learning local image descriptors with deep siamese and triplet convolutional networks by minimizing global loss functions. In Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Vol. 2016-December (pp. 5385-5394). Las Vegas, NV: IEEE. DOI Scopus70 WoS28
  2016	Garg, R., Vijay Kumar, B., Carneiro, G., & Reid, I. (2016). Unsupervised CNN for single view depth estimation: geometry to the rescue. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Proceedings of the 14th European Conference on Computer Vision Vol. 9912 LNCS (pp. 740-756). Amsterdam, Netherlands: Springer International Publishing. DOI Scopus123 WoS69
  2016	Lee, H., Weerasinghe, A., Barnes, J., Oakden-Rayner, L., Gale, W., & Carneiro, G. (2016). CRISTAL: adapting workplace training to the real world context with an intelligent simulator for radiology trainees. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Proceedings of the 13th International Conference on Intelligent Tutoring Systems Vol. 9684 (pp. 430-435). Zagreb, Croaria: Springer. DOI Scopus1
  2016	Liao, Z., & Carneiro, G. (2016). On the importance of normalisation layers in deep learning with piecewise linear activation units. In Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (pp. 1-8). Lake Placid, NY: IEEE. DOI Scopus8
  2015	Dhungel, N., Carneiro, G., & Bradley, A. (2015). Deep structured learning for mass segmentation from mammograms. In Proceedings of the 2015 IEEE International Conference on Image Processing Vol. 2015-December (pp. 2950-2954). Quebec City, CANADA: IEEE. DOI Scopus15 WoS11
  2015	Chen, Q., & Carneiro, G. (2015). Artistic image analysis using the composition of human figures. In L. Agapito, M. M. Bronstein, & C. Rother (Eds.), Workshops Proceedings 13th European Conference on Computer Vision Vol. 1 (pp. 117-132). Switzerland: Springer International. DOI
  2015	Dhungel, N., Carneiro, G., & Bradley, A. (2015). Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms. In Proceedings of the 2015 IEEE 12th International Symposium on Biomedical Imaging Vol. 2015-July (pp. 760-763). New York, NY: IEEE. DOI Scopus7 WoS7
  2015	Dhungel, N., Carneiro, G., & Bradley, A. (2015). Deep learning and structured prediction for the segmentation of mass in mammograms. In N. Navab, J. Hornegger, W. Wells, & A. Frangi (Eds.), Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention - Part 1, MICCAI 2015, Vol. 9349 (pp. 605-612). Munich, GERMANY: Springer. DOI Scopus36 WoS22
  2015	Carneiro, G., Nascimento, J., & Bradley, A. (2015). Unregistered multiview mammogram analysis with pre-trained deep learning models. In N. Navab, J. Hornegger, W. Wells, & A. Frangi (Eds.), Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part III - MICCAI 2015 Vol. 9351 (pp. 652-660). Munich, GERMANY: Springer. DOI Scopus70 WoS43
  2015	Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2015). Flexible and latent structured output learning: Application to histology. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9352 (pp. 220-228). Switzerland: Springer International Publishing. DOI Scopus1
  2015	Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2015). Automatic detection of necrosis, normoxia and hypoxia in tumors from multimodal cytological images. In Proceedings of the 2015 IEEE International Conference on Image Processing Vol. 2015-December (pp. 2429-2433). Quebec City, CANADA: IEEE. DOI Scopus3 WoS2
  2015	Liao, Z., & Carneiro, G. (2015). The use of deep learning features in a hierarchical classifier learned with the minimization of a non-greedy loss function that delays gratification. In Proceedings of the 2015 IEEE International Conference on Image Processing Vol. 2015-December (pp. 4540-4544). Quebec City, CANADA: IEEE. DOI Scopus3 WoS3
  2015	Ngo, T., & Carneiro, G. (2015). Lung segmentation in chest radiographs using distance regularized level set and deep-structured learning and inference. In Proceedings - International Conference on Image Processing, ICIP Vol. 2015-December (pp. 2140-2143). Quebec City, CANADA: IEEE. DOI Scopus12 WoS9
  2015	Nascimento, J., & Carneiro, G. (2015). Towards reduction of the training and search running time complexities for non-rigid object segmentation. In Proceedings - International Conference on Image Processing, ICIP Vol. 2015-December (pp. 4713-4717). Quebec City, CANADA: IEEE. DOI
  2015	Johnston, A., Carneiro, G., Ding, R., & Velho, L. (2015). 3-D Modeling from Concept Sketches of Human Characters with Minimal User Interaction. In Proceedings of 2015 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). Adelaide, AUSTRALIA: IEEE. DOI Scopus1 WoS22
  2015	Dhungel, N., Carneiro, G., & Bradley, A. (2015). Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests. In Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (pp. 160-167). Adelaide, Australia: IEEE. DOI Scopus54 WoS31
  2015	Belagiannis, V., Rupprecht, C., Carneiro, G., & Navab, N. (2015). Robust optimization for deep regression. In Proceedings of the 2015 IEEE International Conference on Computer Vision Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 2830-2838). Santiago, CHILE: IEEE. DOI Scopus42 WoS21
  2015	Carneiro, G., Peng, T., Bayer, C., & Navab, N. (2015). Weakly-supervised structured output learning with flexible and latent graphs using high-order loss functions. In Proceedings of the 2015 IEEE International Conference on Computer Vision Vol. 2015 International Conference on Computer Vision, ICCV 2015 (pp. 648-656). Santiago, CHILE: IEEE. DOI Scopus3 WoS2
  2014	Nascimento, J., & Carneiro, G. (2014). Non-rigid segmentation using sparse low dimensional manifolds and deep belief networks. In Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 288-295). Columbus, OH: IEEE. DOI Scopus4 WoS4
  2014	Ngo, T., & Carneiro, G. (2014). Fully automated non-rigid segmentation with distance regularized level set evolution initialized and constrained by deep-structured inference. In Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3118-3125). Columbus, OH: IEEE. DOI Scopus26 WoS20
  2013	Ngo, T., & Carneiro, G. (2013). Left Ventricle Segmentation from Cardiac MRI Combining Level Set Methods with Deep Belief Networks.. In Proceedings of the IEEE 2013 20th International Conference on Image Processing (pp. 69-699). USA: IEEE. DOI Scopus27 WoS24
  2013	Nascimento, J., & Carneiro, G. (2013). Combining a bottom up and top down classifiers for the segmentation of the left ventricle from cardiac imagery. In Proceedings of the 2013 IEEE 20th International Conference on Image Processing, ICIP (pp. 743-746). USA: IEEE. DOI
  2013	Liu, W., Chin, T. J., Carneiro, G., & Suter, D. (2013). Point correspondence validation under unknown radial distortion. In Proceedings of the IEEE2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013 (pp. 1-8). USA: IEEE. DOI Scopus1
  2013	Carneiro, G., Liao, Z., & Chin, T. J. (2013). Closed-loop deep vision. In Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013 (pp. 1-8). USA: IEEE. DOI
  2013	Nascimento, J., & Carneiro, G. (2013). Top-down segmentation of non-rigid visual objects using derivative-based search on sparse manifolds. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1963-1970). United States: IEEE. DOI Scopus2 WoS1
  2013	Dell'Agnello, D., Carneiro, G., Chin, T., Castellano, G., & Fanelli, A. (2013). Fuzzy clustering based encoding for visual object classification. In Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013 (pp. 1439-1444). USA: IEEE. DOI Scopus2
  2013	Lu, Z., Carneiro, G., & Bradley, A. (2013). Automated nucleus and cytoplasm segmentation of overlapping cervical cells. In Proceedings of Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2013 Vol. 8149 LNCS (pp. 452-460). Germany: Springer. DOI Scopus50 WoS35 Europe PMC1
  2012	Nascimento, J., & Carneiro, G. (2012). On-line re-training and segmentation with reduction of the training set: application to the left ventricle detection in ultrasound imaging. In Proceedings of the 2012 19th IEEE International Conference on Image Processing, ICIP 2012 (pp. 2001-2004). USA: IEEE. DOI Scopus1
  2012	Tran, Q., Chin, T. J., Carneiro, G., Brown, M., & Suter, D. (2012). In defence of RANSAC for outlier rejection in deformable registration. In Proceedings of the12th European Conference on Computer Vision, ECCV 2012 Vol. 7575 LNCS (pp. 274-287). Germany: Springer-Verlag. DOI Scopus31 WoS21
  2012	Carneiro, G., da Silva, N., Del Bue, A., & Costeira, J. (2012). Artistic image classification: an analysis on the PRINTART database. In Proceedings of the 12th European Conference on Computer Vision, ECCV 2012 Vol. 7575 LNCS (pp. 143-157). Germany: Springer-Verlag. DOI Scopus14 WoS7
  2012	Carneiro, G., & Nascimento, J. (2012). The use of on-line co-training to reduce the training set size in pattern recognition methods: application to left ventricle segmentation in ultrasound. In Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 (pp. 948-955). USA: IEEE. DOI Scopus5 WoS4
  2011	Nascimento, J., & Carneiro, G. (2011). Reducing the training set using semi-supervised self-training algorithm for segmenting the left ventricle in ultrasound images. In Proceedings of 18th IEEE International Conference on Image Processing (ICIP 2011) (pp. 2021-2024). Belgium: IEEE. DOI
  2011	Carneiro, G., & Nascimento, J. (2011). Incremental on-line semi-supervised learning for segmenting the left ventricle of the heart from ultrasound data. In 2011 IEEE International Conference on Computer Vision (pp. 1700-1707). 345 E 47TH ST, NEW YORK, NY 10017 USA: IEEE. DOI Scopus5 WoS4
  2011	Carneiro, G. (2011). Graph-based methods for the automatic annotation and retrieval of art prints. In Proceedings of the 1st ACM International Conference on Multimedia Retrieval (pp. 1-8). New York, USA: ACM. DOI Scopus8
  2011	Carneiro, G., Nascimento, J., & Freitas, A. (2011). Semi-supervised self-training model for the segmentation of the left ventricle of the heart from ultrasound data. In Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011 (pp. 1295-1301). Chicago, IL: IEEE Computer Society. DOI
  2011	da Silva, N., Marques, M., Carneiro, G., & Costeira, J. (2011). Explaining scene composition using kinematic chains of humans: application to Portuguese tiles history. In Proceedings of SPIE 2011 Volume 7869 Computer Vision and Image Analysis of Art II Vol. 7869 (pp. 1-9). California, USA: SPIE. DOI Scopus3 WoS1
  2011	Cabral, R., Costeira, J., De La Torre, F., Bernardino, A., & Carneiro, G. (2011). Time and order estimation of paintings based on visual features and expert priors. In Proceedings of SPIE - The International Society for Optical Engineering. Computer Vision and Image Analysis of Art II Vol. 7869 (pp. 1-10). Online: SPIE. DOI Scopus4
  2011	Carneiro, G., & Costeira, J. (2011). The automatic annotation and retrieval of digital images of prints and tile panels using network link analysis algorithms. In Proceedings of SPIE - The International Society for Optical Engineering. Computer Vision and Image Analysis of Art II Vol. 7869 (pp. 1-12). Online: SPIE. DOI Scopus2
  2010	Ricardo, M., Carneiro, G., Fortuna, P., Abrantes, F., & Dias, J. (2010). WiMetroNet - A scalable wireless network for metropolitan transports. In 6th Advanced International Conference on Telecommunications, AICT 2010 (pp. 520-525). DOI Scopus6
  2010	Carneiro, G. (2010). The automatic design of feature spaces for local image descriptors using an ensemble of non-linear feature extractors. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010 (pp. 3509-3516). USA: IEEE Computer Society. DOI Scopus5 WoS3
  2010	Carneiro, G., & Nascimento, J. (2010). Multiple dynamic models for tracking the left ventricle of the heart from ultrasound data using particle filters and deep learning architectures. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010 (pp. 2815-2822). www: IEEE Computer Society. DOI Scopus26 WoS12
  2010	Nascimento, J., & Carneiro, G. (2010). Efficient search methods and deep belief networks with particle filtering for non-rigid tracking: application to lip tracking. In Proceedings of 17th IEEE International Conference on Image Processing (ICIP), 2010 (pp. 3817-3820). Online: IEEE Computer Society. DOI Scopus1
  2010	Carneiro, G., & Nascimento, J. (2010). The fusion of deep learning architectures and particle filtering applied to lip tracking. In Proceedings of 20th International Conference on Pattern Recognition (ICPR), 2010 (pp. 2065-2068). USA: IEEE Computer society. DOI Scopus5
  2010	Carneiro, G. (2010). A comparative study on the use of an ensemble of feature extractors for the automatic design of local image descriptors. In Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 2010 (pp. 3356-3359). Online: IEEE computer society. DOI
  2010	Carneiro, G., Nascimento, J., & Freitas, A. (2010). Robust left ventricle segmentation from ultrasound data using deep neural networks and efficient search methods. In Proceedings of the 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1085-1088). NJ, USA: IEEE. DOI Scopus22 WoS17
  2009	Wels, M., Zheng, Y., Carneiro, G., Huber, M., Hornegger, J., & Comaniciu, D. (2009). Fast and robust 3-D MRI brain structure segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009:12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II Vol. 5762 LNCS (pp. 575-583). Heidelberg: Springer-Verlag Berlin. DOI Scopus8 WoS9
  2008	Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., & Comaniciu, D. (2008). A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. In MICCAI'08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I Vol. 5241 LNCS (pp. 1-8). Heidelberg: Springer-Verlag Berlin. DOI Scopus24
  2008	Carneiro, G., Amat, F., Georgescu, B., Good, S., & Comaniciu, D. (2008). Semantic-based indexing of fetal anatomies from 3-D ultrasound data using global/semi-local context and sequential sampling. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008 (pp. 1-8). Online: IEEE Computer Society. DOI Scopus26
  2007	Zhou, S., Guo, F., Park, J., Carneiro, G., Jackson, J., Simopoulos, C., . . . Comaniciu, D. (2007). A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure. In Proceedings of ICCV 2007 (pp. 1-8). USA: IEEE. DOI Scopus9
  2006	Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2006). Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree. In Proceedings of MICCAI 2007 Part 2 Vol. 4792 LNCS (pp. 571-579). Heidelberg: Springer-Verlag Berlin. Scopus7 WoS7
  2006	Vasconcelos, M., Carneiro, G., & Vasconcelos, N. (2006). Weakly supervised top-down image segmentation. In Proceedings of CVPR (1) 2006 Vol. 1 (pp. 1001-1006). USA: IEEE Computer Society. DOI Scopus26
  2006	Carneiro, G., & Lowe, D. (2006). Sparse flexible models of local features. In Proceedings of the 9th European Conference on Computer Vision (ECCV 2006), as published in Lecture Notes in Computer Science Vol. 3953 LNCS (pp. 29-43). Berlin, Heidelberg: Springer-Verlag. DOI Scopus25 WoS12
  2005	Carneiro, G., & Vasconcelos, N. (2005). Formulating semantic image annotation as a supervised learning problem. In Proceedings of CVPR 2005 Vol. II (pp. 163-168). USA: IEEE. DOI Scopus114 WoS25
  2005	Carneiro, G., & Jepson, A. (2005). The distinctiveness, detectability, and robustness of local image features. In Proceedings of CVPR 2005 Vol. II (pp. 296-301). USA: IEEE. DOI Scopus13 WoS6
  2005	Carneiro, G., & Vasconcelos, N. (2005). A database centric view of semantic image annotation and retrieval. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 559-566). New York, USA: ACM. DOI Scopus45
  2005	Carneiro, G., & Vasconcelos, N. (2005). Minimum Bayes error features for visual recognition by sequential feature selection and extraction. In Proceedings of the Second Canadian Conference on Computer and Robot Vision (CRV 05) (pp. 253-260). USA: IEEE. DOI Scopus8 WoS2
  2004	Carneiro, G., & Jepson, A. (2004). Pruning local feature correspondences using shape context. In J. Kittler, M. Petrou, & M. Nixon (Eds.), Proceedings - International Conference on Pattern Recognition Vol. 3 (pp. 16-19). British Machine Vis Assoc, Cambridge, ENGLAND: IEEE COMPUTER SOC. DOI Scopus15 WoS1
  2004	Carneiro, G., & Jepson, A. (2004). Flexible spatial models for grouping local image features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2. Scopus25
  2003	Carneiro, G., & Jepson, A. (2003). Multi-scale phase-based local features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 1. Scopus83
  2002	Carneiro, G., & Jepson, A. (2002). Phase-based local features. In A. Heyden, G. Sparr, M. Nielsen, & P. Johansen (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 2350 (pp. 282-296). COPENHAGEN, DENMARK: SPRINGER-VERLAG BERLIN. Scopus52 WoS22
  2002	Vasconcelos, N., & Carneiro, G. (2002). What is the role of independence for visual recognition?. In A. Heyden, G. Sparr, M. Nielsen, & P. Johansen (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 2350 (pp. 297-311). COPENHAGEN, DENMARK: SPRINGER-VERLAG BERLIN. Scopus6 WoS1
  1999	Aude, E., Silveira, J., Lopes, E., Carneiro, G., Serdeira, H., & Martins, M. (1999). Integration of intelligent systems and sensor fusion within the CONTROLAB AGV. In D. Gage, & H. Choset (Eds.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 3838 (pp. 50-62). BOSTON, MA: SPIE-INT SOC OPTICAL ENGINEERING. DOI
  1999	Carneiro, G., Aude, E., Serdeira, H., Silveira, J., Martins, M., & Lopes, E. (1999). Internet request server architecture for telecommanding the CONTROLAB AGV through real time data and image. In Midwest Symposium on Circuits and Systems Vol. 2 (pp. 1074-1077). Scopus1

• Conference Items

  Year	Citation
  2017	Cheng, L. Z. T. P., Campbell, B., Carneiro, G., Burt, A. D., & Singh, R. (2017). Computer-aided diagnosis (CAD) for characterising colorectal lesions: Initial results of a newly developed software. Poster session presented at the meeting of JOURNAL OF GASTROENTEROLOGY AND HEPATOLOGY. WILEY.
  2017	Carneiro, G., Oakden-Rayner, L., Bradley, A. P., Nascimento, J. C., & Palmer, L. J. (2017). Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography.. Poster session presented at the meeting of ISBI. IEEE.
  2017	Carneiro, G., Tavares, J., Bradley, A., Papa, J., Nascimento, J., Cardoso, J., . . . Lu, Z. (2017). Preface DLMIA 2017. Poster session presented at the meeting of Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

• Patents

  Year	Citation
  2010	Carneiro, G., Good, S., Georgescu, B., Favaro, P., & Comaniciu, D. (2010). US 7,783,095 B2, System and Method for Fetal Biometric Measurements from Ultrasound Data and Fusion of same for Estimation of Fetal Gestational Age. USA.
  2008	Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008). US 2008/0240532 A1, System and method for detection of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. United States.

University of Adelaide

• Puzzle-Based Learning (Fall 2017)
• Computer Graphics (Fall 2017) 
• Topics in Computer Science (Fall 2017)
• Advanced Topics in Computer Science (Fall 2017)
• Puzzle-Based Learning (Fall 2016)
• Topics in Computer Science (Fall 2016)
• Object Oriented Programming (Spring 2015)
• Topics in Computer Science (Spring 2015)
• Computer Graphics (Fall 2015) - Videos of Best Projects
• Puzzle-Based Learning (Fall 2015)
• Topics in Computer Science (Fall 2015)
• Puzzle-Based Learning (Fall 2014)
• Software Engineering in Industry (Fall 2014)
• Topics in Computer Science (Fall 2014)
• Puzzle-Based Learning (Spring 2013)
• Software Engineering Group Project 1B (Spring 2013)
• Master of Software Engineering Project (Spring 2013)
• Computer Graphics (Fall 2013) - Videos of Best Projects
• Puzzle-Based Learning (Fall 2013)
• Puzzle-Based Learning (Spring 2012)
• Computer Vision (Fall 2012)
• Computer Graphics (Fall 2012) - Videos of Best Projects

​Instituto Superior Tecnico - University of Lisbon

• Signals and Systems (Fall 2009)
• Robotics (Spring 2009) 
• Modeling and Simulation (Spring 2009) 
• Signal Processing (Fall 2008) 
• Control (Fall 2008) 

University of Toronto

• CSC 324 - Principles of Programming Languages (Fall 2004)
• CSC 446, Computer Methods for Partial Differential Equations (TA) (Winter 2002). 
• CSC 418, Computer Graphics (TA) (1999-2003).
• CSC 458, Computer Networks (TA) (Winter 2000). 
• CSC 258, Computer Organization (TA) (Summer 2000).
• CSC 260, An Introduction to Scientific, Symbolic, and Graphical Computation (TA) (Winter 2003). 
• SCI 199, Computer and Images. (TA) (2000-2001)

Expand

• Current Higher Degree by Research Supervision (University of Adelaide)

  Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
  2019	Principal Supervisor	Investigating Cancer Genomic Evolution and Personalised Therapies with Machine Learning	Doctor of Philosophy	Doctorate	Part Time	Mr Po Liu
  2019	Co-Supervisor	Using Machine Learning to Predict Indicators of Glaucoma Progression	Doctor of Philosophy	Doctorate	Full Time	Mr Khan Pham
  2019	Principal Supervisor	Pedestrian detection assisted by semi-supervised learning and exploring the link between regularization and compression	Master of Philosophy	Master	Full Time	Mr Jerome Oskar Williams
  2019	Principal Supervisor	Medical Machine Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Yu Tian
  2018	Principal Supervisor	Computer Vision and Machine Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Cuong Cao Nguyen
  2018	Co-Supervisor	Deep Learning for Screening Mammography	Master of Clinical Science	Master	Part Time	Dr James John Joseph Condon
  2018	Principal Supervisor	Discovering and Localizing Biomarkers Associated with Chronic and Age-Related Diseases for Computed Tomographic Scans	Doctor of Philosophy	Doctorate	Full Time	Mr Renato Hermoza Aragones
  2018	Principal Supervisor	Radiomic-driven CAD system for Classification of Brain Tumor using CAN Trained by Multi-objective Reinforcement Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Hossein Askari Lyarjdameh
  2017	Co-Supervisor	Image Enhance Endoscopy in Sessile Serrated Lesions in the Colon	Doctor of Philosophy under a Jointly-awarded Degree Agreement with	Doctorate	Full Time	Dr Leonardo Zorron Cheng Tao Pu
  2017	Co-Supervisor	Feature learning in deep networks for robotic vision	Doctor of Philosophy	Doctorate	Full Time	Mr Huangying Zhan
  2016	Co-Supervisor	Radiomics and Deep learning methods to identify chronic disease in medical images	Doctor of Philosophy	Doctorate	Part Time	Mr Luke Oakden-Rayner
  2016	Principal Supervisor	Computer Vision and Machine Learning and Medical Image Analysis	Doctor of Philosophy	Doctorate	Full Time	Mr Toan Minh Tran
  2016	Principal Supervisor	Computer Vision and Machine Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Rafael Felix Alves
  2015	Principal Supervisor	Large Scale Geospatial Image Understanding and Visualisation	Doctor of Philosophy	Doctorate	Part Time	Adrian Robert Johnston

• Past Higher Degree by Research Supervision (University of Adelaide)

  Date	Role	Research Topic	Program	Degree Type	Student Load	Student Name
  2015 - 2018	Principal Supervisor	Pre-hoc and Post-hoc Diagnosis and Interpretation of Breast Magnetic Resonance Volumes	Doctor of Philosophy	Doctorate	Full Time	Mr Gabriel Maicas
  2013 - 2017	Principal Supervisor	Methods for Understanding and Improving Deep Learning Classification Models	Doctor of Philosophy	Doctorate	Full Time	Mr Zhibin Liao
  2013 - 2017	Co-Supervisor	Moving Least Squares Registration in Computer Vision: New Applications and Algorithms	Doctor of Philosophy	Doctorate	Full Time	Mr Xiang Liu
  2013 - 2016	Principal Supervisor	Automated Detection, Segmentation and Classification of Masses from Mammograms using Deep Learning	Doctor of Philosophy	Doctorate	Full Time	Mr Neeraj Dhungel
  2011 - 2016	Principal Supervisor	Medical Image Segmentation Combining Level Set Method and Deep Belief Networks	Doctor of Philosophy	Doctorate	Full Time	Mr Tuan Anh Ngo

• Position: Professor
• Phone: 83136164
• Email: gustavo.carneiro@adelaide.edu.au
• Fax: 8313 4366
• Campus: North Terrace
• Building: Ingkarni Wardli, floor 5
• Room: 5 42
• Org Unit: School of Computer Science