TECHNICAL PUBLICATIONS (journals, books and book chapters)

 

  1. Glenn Fung,Romer Rosales, Bharat Rao. Knowledge-Driven Medicine: A Machine Learning Approach . Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. To be published in April 2011 (http://www.crcpress.com/product/isbn/9781439838877)
  2. Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer Dy, Bharat Rao. Using Local Dependencies within Batches to Improve Large Margin Classifiers. Journal of Machine Learning Research 10 (2009) 183-206.

 

  1. Glenn Fung, Learning Sparse Similarity Functions for Heart Wall Motion Abnormality Detection. Invited chapter for the book: Recent Advances in Biomedical Signal Processing. To appear

 

  1. Murat Dundar, Glenn Fung, Kernelís Fisherís discriminate for remote sensing. Invited chapter for the book: Kernel Methods for remote sensing data analysis. Nov 2009, Wiley.
  2. O.L. Mangasarian, E.W. Wild and Glenn Fung. Privacy-preserving classification of vertically partitioned data via random kernels. Sept. 07. ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 2 ,Issue 3, October 2008.
  3. O. L. Mangasarian, E. W. Wild and Glenn Fung. Proximal Knowledge-Based Classification. Statistical Analysis and Data Mining, Volume 1, Issue 4, March 2009.
  4. Volkan Vural, Glenn Fung, Jennifer Dy, Bharat Rao. Semi-supervised Classifiers using A-priori Metric Information. Jan. 2006, Optimization Methods and Software Journal, Special Issue in Machine Learning, Volume 23, Issue 4, August 2008.
  5. Glenn Fung, Murat Dundar, Balaji Krishnapuram, Bharat Rao , Multiple Instance Learning algorithms for Computer Aided Detection. IEEE Transactions in biomedical engineering 2007.
  6. Glenn Fung, Maleeha Qazi, Sriram Krishnan, Jinbo Bi, Bharat Rao, Alan Katz. Automated Heart Wall Motion Abnormality Detection using Sparse Linear Classifiers. IEEE Engineering on Medicine and Biology Magazine, special issue in machine learning in life sciences. 2007.
  7. G. Fung, S. Sandilya and B. Rao. Rule Extraction from Linear Support Vector Machines via Mathematical Programming. Invited chapter in Rule Extraction from Support Vector Machines, Springer Series: Studies in Computational Intelligence, Vol. 80. 2008
  8. Glenn Fung, Jonathan Stoeckel: SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information. Knowledge andInformation Systems. 11(2): 243-258 (2007)
  9. G. Fung and O. L. Mangasarian. Breast Tumor Susceptibility to Chemotherapy via Support Vector Machines. Special Issue on Support Vector Machines, Journal of Computational Management Science , Vol 3, 2006, 103-112 .
  10. G. Fung, O. L. Mangasarian. A Feature Selection Newton Method for Support Vector Machine Classification. Computational Optimization and Applications, Volume 28, Issue 2, July 2004, Pages 185 - 202.
  11. G. Fung, O. L. Mangasarian. Finite Newton Method for Lagrangian SVM Classification. Special Issue on Support Vector Machines, Neurocomputing, Volume 55, Issues 1-2, Sept. 2003,39-55.
  12. G. Fung, O. L. Mangasarian. Multicategory Proximal Support Vector Classifiers. Machine Learning Journal. Volume 59. Numbers 1-2, pages 77-97. May 2005.
  13. G. Fung, O. L. Mangasarian and A. Smola. Minimal Kernel Classifiers. Journal of Machine Learning research. Vol 3(Nov): 303-321, 2002.
  14. G. Fung and O. L. Mangasarian. Semi-Supervised Support Vector Machines for Unlabeled Data Classification. Optimization Methods and Software 15, 2001, 29-44.
  15. D. Cores and G. Fung. A fast 3D Ray Tracer using Nonlinear Optimization techniques. Journal of Applied Geophysics, Volume 45, Issue 4, 2000, 273-284.

 

TECHNICAL PUBLICATIONS (Conferences)

  1. Mark Schmidt, Glenn Fung and Romer Rosales. Optimization Methods for L1 Regularization. Jan. 10. University of British Columbia technical report. TR-2009-19, submiktted.

 

  1. Mahdokht Masaeli, Glenn. Fung and Jennifer Dy, Transformation Based Feature Selection. Jan 10, ICML 2010. Accepted.

 

  1. Yan Yan, Glenn Fung, Jennifer Dy, Romer Rosales. Semi-supervised Learning by Modeling Multiple-Annotator Expertise. March 10, submitted.

 

  1. Yan Yan, Glenn Fung, Jennifer Dy, Romer Rosales. Medical Coding Classification: Leveraging Inter-Code Relationships. Jan 10, KDD 2010. accepted.

 

  1. Yan Yan, Glenn Fung, Jennifer Dy, Romer Rosales. Modeling annotator expertise: Learning when everybody knows a bit of something. Nov 09, AISTATS 2010, accepted.

 

  1. Mahdokht Masaeli, Yan Yan, Ying Cui, Glenn. Fung and Jennifer Dy, Convex Principal Feature Selection. Oct. 09.SIAM Data Mining conference 2010 (SDM 2010), accepted.

 

  1. B. Rao, G. Fung,  B. Krishnapuram,  J. Bi,  M. Dundar, V. Raykar, S. Yu,  S. Krishnan, X. Zhou, A. Krishnan, M. Salganicoff, L. Bogoni, M. Wolf, A. Jerebko, J. Stoeckel. Mining Medical Images
    KDD 2009. Best data mining practice award.

 

  1. Romer Rosales, Glenn Fung and Wei Tong. Automatic discrimination of mislabeled training points for large margin classifiers. Accepted in the Snowbird Learning Workshop, Hilton Clearwater, April 13-16, 2009

 

  1. Volkan Vural, Glenn Fung, Jennifer Dy and Romer Rosales. Learning Multi-Class Classifiers and Their Underlying Shared Class Structure. Jan 09, accepted in IJCAI 2009

 

  1. K. Jayasurya, G. Fung, S. Yu, C. Dehing-Oberije, D. De Ruysscher, A. Dekker, P. Lambin. Survival Prediction in Lung Cancer Treated with Radiotherapy - Bayesian Networks vs. Support Vector Machines in Handling Missing Data. International conference in machine learning and its applications. ICMLA 2009.

 

  1. Shipeng Yu, Glenn Fung,Romer Rosales, Sriram Krishnan, R. Rao Bharat, Cary Dehing-Oberije,Philippe Lambin, Privacy-Preserving Cox Regression for Survival Analysis. KDD 2008.

 

  1. Glenn Fung, Harald Steck, Shipeng Yu, Phan Giang. Improving medical predictive models via Likelihood Gamble Pricing. ICML 2008 workshop in Machine learning in health sciences.

 

  1. Shipeng Yu, Glenn Fung,Romer Rosales, Sriram Krishnan, R. Rao Bharat, Cary Dehing-Oberije,Philippe Lambin, Privacy-Preserving Predictive Models for Lung Cancer Survival Analysis. March 08. Privacy-preserving workshop at the SIAM data mining conference 2008.
  2. Glenn Fung, Romer Rosales and Bharat Rao. On the Dangers of Cross-Validation. An Experimental Evaluation. SIAM data mining conference SDM08. Accepted.
  3. Glenn Fung, Shipeng Yu, Cary Dehing-Oberije, Dirk De Ruysscher, Philippe Lambin, Sriram Krishnan, R. Rao Bharat. Privacy-preserving Predictive Models for Lung Cancer Survival Analysis. Jan. 08. Privacy-preserving workshop at the SIAM data mining conference SDM08. Accepted.
  4. Mark Schmidt, Romer Rosales, Glenn Fung and Kevin Murphy. Structure Learning in Random Fields for Heart Motion Abnormality Detection. CVPR 2008. Accepted
  5. Glenn Fung, R. Seignauric, S. Krishnan, B. Rao, B. Wouters and P. Lambin. Reducing a biomarkers list via mathematical programming: Applications to gene signatures to detect time-dependent hypoxia in Cancer. International conference on machine learning applications. ICMLA 2007.
  6. Glenn Fung and Hui Chen. Learning sparse surface similarity functions for automatic heart wall motion abnormality detection. Innovative Applications of Artificial Intelligence (IAAI) 2008. Accepted
  7. R Bharat Rao, Jinbo Bi, Glenn Fung, Marcos Salganicoff, Nancy Obuchowsky and David Naidich. LungCAD: A clinically approved, machine learning system for lung cancer detection. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2007.
  8. Mark Schmidt, Glenn Fung and Romer Rosales. Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches. Proceedings of the European conference in machine learning. ECML 2007.
  9. Glenn Fung, Romer Rosales. Feature Selection and Kernel Design via Linear Programming. Proceedings of the International joint Conference in Artificial intelligence, IJCAI 2007.
  10. Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer Dy, Bharat Rao. Batch-wise Classification with Applications to Computer Aided Diagnosis. Proceedings of the European conference in machine learning. ECML 2006.
  11. Romer Rosales, Glenn Fung. Learning Sparse Metrics via Linear Programming. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006.
  12. Maleeha Qazi, Glenn Fung, Sriram Krishnan, Romer Rosales, Harald Steck, Bharat Rao, Don Poldermans. Automated Heart Wall Motion Abnormality Detection from Ultrasound Images Using Segmental Knowledge. Distinguished Paper Award at the International joint Conference in Artificial intelligence, IJCAI 2007.
  13. Jinbo Bi, Senthyl Periaswamy, Kasunori Okada, Toshiro Kubota, Glenn Fung, Marcos Salganicoff, Bharat Rao. Computer Aided Detection via Asymmetric Cascade of Sparse Hyperplane Classifiers. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006.
  14. Glenn Fung, Murat Dundar, Balaji Krishnapuram, Bharat Rao . Multiple Instance Algorithms for Computer Aided Diagnosis . Advances in Neural Information Processing Systems 15, NIPS 2006.
  15. Glenn Fung, Romer Rosales, Balaji Krishnapuram, Learning Rankings via Convex Hull Separations, Advances in Neural Information Processing Systems , NIPS 2005.
  16. Glenn Fung, Maleeha Qazi, Sriram Krishnan, Jinbo Bi, Bharat Rao, Alan Katz. Sparse classifiers for Automated Heart Wall Motion Abnormality Detection . Proceedings of the IEEE international conference on machine learning and its applications. ICMLA 2005.
  17. J. Stoeckel and G. Fung. A Mathematical Programming Approach for Automatic Classification of SPECT Images of Alzheimer's Disease. Proceedings of the IEEE international conference in data mining (ICDM) 2005.
  18. M. Dundar, G. Fung, J. Bi, S. Sandilya and B. Rao. Sparse Fisher Discriminant Analysis for Computer Aided Detection. Proceedings of the SIAM Data Mining Conference 2005.
  19. G. Fung, S. Sandilya and B. Rao. Rule Extraction for Hyperplane Classifiers. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2005.
  20. J. Bi , M. Dundar, G. Fung and B. Rao. Semi-supervised Mixture of Kernels via Generalized LP Boost. Proceedings of the IEEE international conference in data mining (ICDM) 2005.
  21. G. Fung, M. Dundar, J. Bi and B. Rao. A Fast Iterative Algorithm For Fisher Discriminant Using Heterogeneous Kernels. Proceedings of the twenty-first international conference on machine learning, ICML 2004, 313-320.
  22. Glenn Fung. The Disputed Federalist Papers: SVM and Feature Selection via Concave Minimization. Proceedings of the 2003 Conference of Diversity in Computing. 2003, 42-46.
  23. G. Fung, O. L. Mangasarian and J. Shavlik. Knowledge-based Nonlinear Kernel Classifiers. Learning Theory and Kernel Machines. COLT 2003 proceedings, 102-113.
  24. G. Fung, O. L. Mangasarian and J. Shavlik. Knowledge-Based SVM Classifiers. Advances in Neural Information Processing Systems 15, NIPS 2002, 521-528.
  25. G. Fung and O. L. Mangasarian. Incremental Support Vector Machine Classification. Proceedings of the Second SIAM International Conference on Data Mining. April 11-13, 2002, 247-260.
  26. G. Fung and O. L. Mangasarian. Proximal Support Vector Classifiers. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 26-29, 2001, 77-86
  27. G. Fung and O. L. Mangasarian. Data Selection for SVM Classifiers. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 20-23, 2000, 64-70.

 

CLINICAL PUBLICATIONS, ABSTRACTSAND POSTERS

 

  1. K. Jayasurya, G. Fung, S. Yu, C. Dehing-Oberije, D. De Ruysscher, A. Dekker, P. Comparison of Bayesian Network and Support Vector Machine models for 2 year survival prediction in lung cancer patients treated with radiotherapy. Accepted in Medical Physics. To appear.

 

  1. S. Yu, C. Dehing-Oberije, D. De Ruysscher, K. van Beek3 Y. Lievens, J. Van Meerbeeck, W. De Neve, G. Fung, B. Rao, P. Lambin.. Development, External Validation and further Improvement of a Prediction Model for Survival of Non-Small Cell Lung Cancer Patients treated with (Chemo) Radiotherapy. ASTRO 2008.

 

  1. C. Dehing-Oberije, S. Yu, D. De Ruysscher, S. Meersschout, K. Van Beek, Y. Lievens, J. Van Meerbeeck, W. De Neve, G. Fung, B. Rao, S. Krishnan, P. Lambin. Development and external validation of a prediction model for survival of non-small cell lung cancer patients treated with (chemo) radiotherapy: identification of a subgroup with a good prognosis. ESTRO 2008.

 

  1. S. Yu, C. Dehing-Oberije, D. De Ruysscher, G. Fung, S. Krishnan, R. B. Rao, P. Lambin.Blood biomarkers combined with clinical factors yield a better prediction model for 2-year survival of non-small cell lung cancer patients treated with (chemo) radiotherapy. ESTRO 2008.

 

  1. C. Dehing-Oberije, S. Yu, D. De Ruysscher,S. Meersschout, K. Van Beek, Y.Lievens, J. Van Meerbeeck, W. De Neve, G. Fung, B. Rao, S. Krishnan, H. van der Weide, P. Lambin. Development and external validation of a prognostic model for 2-year survival of non-small cell lung cancer patients treated with (chemo) radiotherapy Feb. 08.Int. Journal of Radiation Oncology. To appear.
  2. M. Starmans, G. Fung, H. Steck, B. Wouters, P. Lambin. Validation is the key inmicroarray signature research. Poster in the Keystone Symposia on Biomarker Discovery, Validation and Applications. Feb 08.
  3. P. Bamberger, I. Leichter, N. Merlet, E. Ratner, G. Fung, A. Lederman. Optimizing the CAD Process for Detecting Mammographic Lesions by a New Generation Algorithm Based on Linear Classifiers and a Gradient Based Method. Jan 08. IWDM 2008 Proceedings.
  4. Leichter, A. Lederman, E. Ratner, N. Merlet, G. Fung, B. Krishnapuram, P. Bamberger. Does a mammography CAD algorithm with varying filtering levels of detection marks, used to reduce the false mark rate, adversely affect the detection of small masses?. Jan 08. IWDM 2008 Proceedings.
  5. C. Dehing, D. De Ruysscher, H. van der Weide, G. Fung, S. Krishnan, R.B. Rao, and Ph. Lambin. The limitations of dosimetric parameters for the prediction of radiation-inducedlung toxicity: an approach based on machine learning techniques. Annual meeting of the American society for therapeutic radiology and oncology (ASTRO) 2007.
  6. R. Seigneuric, M. Startmans, G. Fung, B. Krishnapuram, D. Nuyten, A. Van Erk, M. Magagnin, K. Rouschop, S. Krishnan, B. Rao, C. Evelo, A. Begg, B. Wouters and P. Lambin.Impact of supervised gene signatures of early hypoxia on patient survival. Radiotherapy and Oncology journal, 83, (2007) 374-382.
  7. Greenberg, W. N. Rom, E. M. Tang, D. Naidich, H. Steck, G. Fung, B. Rao and M. Salganicoff.Tumor Associated antigens assays (TAA) in managment of ground glass nodules (GGN) indeterminate in CT. European Congress of Radiology, ECR 2007, Vienna, Austria, March 9-13, 2007.
  8. Glenn Fung, Balaji Krishnapuram, Sriram Krishnan, and Bharat Rao. Addressing image variability while learning classifiers for detecting clusters of micro-calcifications, proceedings of the 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006.
  9. Pascal Cathier and Glenn Fung. Population classification using global brain shape features inferred from massive T1-MRI registration .Poster at the Twelfth Annual Meeting of the Organization for Human Brain Mapping, Florence, Italy, June 11-15, 2006.
  10. J. Hung, M. Jagen, A. Greenberg, E. Tan, D. Naidich, H. Steck, G. Fung, M. Salganikoff, R. B. Rao, B Phalan, E. Eylers and W. Rom , Autoantibody Reactivity to Tumor Associated Antigens as a Biomarker for Early Lung Cancer, poster at the International Conference of the American Thoracic Society, 2006.
  11. S. Katz, S. Krishnan, X. Zhou, B. Georgescu, M. Gera, D. Comaniciu, J. Bi, G. Fung, J. Liang, B. Rao, R. Grimson, N. Reichek. Clinical Evaluation of a Novel Automatic Real-Time Myocardial Tracking and Wall Motion Scoring Algorithm for Echocardiography Introduction. American College of Cardiology Annual Scientific Session, Orlando, Florida, March 2005.
  12. M. Wolf, J. Stoeckel, M. Salganicoff, G. Fung, M. Dundar, S. Periaswamy, J. Bi and H. Shen. CAD Performance Analysis for Pulmonary Nodule Detection on Thin-slice MDCT Scans. CARS 2005 Computer Assisted Radiology and Surgery, 19th International Congress and Exhibition June 22 - 25, 2005 Berlin, Germany.

74.  L. Bogoni, A. Jerebko, P. Cathier, S. Periaswamy, M. Dundar, J. Liang, G. Fung, B. Rao, A. Megibow and M. Macari. Automatic Polyp Detection:CAD System Performance. ESGAR 2004: European Society of Gastrointestinal and Abdominal Radiology.

  1. M. Dundar, G. Fung, L. Bogoni, M. Macari, A. Megibow, B. Rao. A Methodology for Training and Validating a CAD System and Potential Pitfalls. CARS 2004 - Computer Assisted Radiology and Surgery, Proceedings of the 18th International Congress and Exhibition,Chicago, USA, June 23 - 26, 2004.
  2. P. Cathier, S. Periaswamya, A. Jerebko, M. Dundar, J.Liang, G.Fung, J.Stoeckel, T. Venkat, R.Amara, A.Krishnan, B.Rao, A.Gupta, E.Vega, S.Laks, A.Megibow, M.Macari and L.Bogoni. CAD for Polyp Detection: an Invaluable Tool to Meet the Increasing Need for Colon-Cancer Screening. CARS 2004 - Computer Assisted Radiology and Surgery. Proceedings of the 18th International Congress and Exhibition, Chicago, USA, June 23 - 26, 2004.