E-Mail:	jinbo bi AT siemens dot com
Mailing Address:	Computer Aided Diagnosis and Knowledge Solutions Siemens Medical Solutions, Inc. 51 Valley Stream Parkway Malvern, PA 19355
Ph.D Advisor:	Kristin P. Bennett
Research Interests:	mathematical programming, machine learning, data mining, medical image analysis, statistical computing, support vector machines, dimension reduction, healthcare information management, drug discovery

Working Experience

• Siemens Medical Solutions, Inc. USA, Malvern, PA, Sept 2003 until now
  Staff Scientist , develop algorithms that automatically identify and detect structures in human body that are possibly cancerous or that present abnormalities, based on medical image data from CT, MRI, Ultrasound and other clinical imaging modalities. Applications include heart motion abnormality detection for coronary artery disease, lung nodule detection and colon polyp detection for cancer prognosis, and detection of pulmonary embolism or diffuse parenchymal lung diseases, etc.
• The Pennsylvania State University, Great Valley, PA, August 2008 - now
  Adjunct Professor with The Engineering Division, teach courses, hold office hours, participate faculty meetings and seminars. Taught Soft Computing (SWENG 597C) in Fall 2008 (official course website is organized within Penn State ANGEL system).
• NEC Laboratories America, Inc., Princeton, NJ, May 2002 - December 2002
  Research Associate with Dr. Vladimir N. Vapnik, conduct studies and research on rigorous support vector machines (developed a C++ RSVM package), and related feature selection algorithms using mathematical programming techniques
• Rensselaer Polytechnic Institute, Troy, NY, May 2000 - Aug. 2003
  Research Assistant with Dr. Kristin P. Bennett at the Department of Mathematical Sciences, develop algorithms for machine learning, feature/variable selection, and support vector machines using mathematical programming, such as linear programming, quadratic programming, and integer programming. Apply the resulting algorithms to drug discovery, gene expression data analysis and portfolio selection analysis
• Rensselaer Polytechnic Institute, Troy, NY, Aug. 1999 - Apr. 2000
  Teaching Assistant at the Department of Mathematical Sciences, guide students' computer labs using Matlab and C for assignments hold office hours for Numerical computing and Probability theory and statistics
• Beijing Institute of Technology, Beijing, China, Aug. 1996 - Apr. 1999
  Research Assistant with Dr. Cang-Pu Wu at the Department of Automatic Control, conduct research on adaptive control optimization, neural networks, reinforcement learning and computer simulation of control systems

Selected Publications

• A Min-Max Framework of Cascaded Classifier with Multiple Instance Learning for Computer Aided Diagnosis,
  Dijia Wu, Jinbo Bi and Kim Boyer,
  Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'09), 2009.
• An Improved Multi-task Learning Approach with Applications in Medical Diagnosis,
  Jinbo Bi, Tao Xiong, Shipeng Yu, Murat Dundar, Bharat Rao,
  Proceedings of the 18th European Conference on Machine Learning (ECML'08), 2008.
• Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer,
  Vikas Raykar, Balaji Krishnapuram, Jinbo Bi, Murat Dundar, Bharat Rao,
  Proceedings of the 25th International Conference on Machine Learning (ICML'08), 2008.
• Reduction of Lymph Tissue False Positives in Pulmonary Embolism Detection,
  Bernard Ghanem, Jianming Liang, Jinbo Bi, Marcos Salganicoff and Arun Krishnan,
  Proceedings of SPIE Medical Imaging (SPIE'08), 2008.
• Local Characteristic Features for Computer Aided Detection of Pulmonary Embolism in CT Angiography,
  Jianming Liang and Jinbo Bi,
  Proceedings of Pulmonary Image Analysis at Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'08-PIA), 2008.
• Large Scale Diagnostic Code Classification for Medical Patient Records,
  Lucian Vlad Lita, Shipeng Yu, Stefan Niculescu and Jinbo Bi,
  Proceedings of the 3rd International Joint Conference on Natural Language Processing (IJCNLP'08), 2008.
• Automatic Medical Coding of Patient Records via Weighted Ridge Regression,
  Jianwu Xu, Shipeng Yu, Jinbo Bi, Lucian Vlad Lita, Stefan Niculescu and Bharat Rao,
  Proceedings of the 6th International Conference on Machine Learning and Applications (ICMLA'07), 2007.
• LungCAD: A Clinically Approved, Machine Learning System for Lung Cancer Detection,
  R Bharat Rao, Jinbo Bi, Glenn Fung, Marcos Salganicoff, Nancy Obuchowski and David Naidich,
  Proceedings of the 13th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD'07), 2007.
• A Mathematical Programming Formulation for Sparse Collaborative Computer Aided Diagnosis,
  Jinbo Bi and Tao Xiong,
  Proceedings of the 22nd International Conference on Artificial Intelligence (AAAI'07), 2007.
• Joint Optimization of Cascaded Classifiers for Computer Aided Detection,
  Murat Dundar and Jinbo Bi,
  Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'07), 2007.
• Multiple Instance Learning of Pulmonary Embolism Detection with Geodesic Distance along Vascular Structure,
  Jinbo Bi and Jianming Liang,
  Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'07), 2007.
  This paper focuses on the classification approaches used in Siemens research project of pulmonary embolism detection.
• Computer Aided Detection of Pulmonary Embolism with Tobogganing and Multiple Instance Classification in CT Pulmonary Angiography,
  Jianming Liang and Jinbo Bi,
  Proceedings of the 20th International Conference on Information Processing in Medical Imaging (IPMI'07), 2007.
  This paper focuses on the initial identification of suspicious regions for Siemens research project of pulmonary embolism detection.
• Robust Parametric Modeling Approach Based on Domain Knowledge for Computer Aided Detection of Vertebrae Column Metastases in MRI,
  Anna Jerebko, G.P. Schmidt, Xiang Zhou, Jinbo Bi, V. Anand, J. Liu, S. Schoenberg, I. Schmueching, B. Kiefer and A. Krishnan,
  Proceedings of the 20th International Conference on Information Processing in Medical Imaging (IPMI'07), 2007.
• Automated Heart Abnormality Detection Using Sparse Linear Classifiers,
  Maleeha Qazi, Glenn Fung, Sriram Krishnan, Jinbo Bi, Bharat Rao and Alan Katz,
  IEEE Engineering Magazine in Medicine and Biology, 26(2):56-63, March/April 2007.
  An early version appeared in Special session on "Application of Machine Learning in Medicine and Biology" of The 4th International Conference on Machine Learning and Applications (ICMLA), 2005.
• On the Medical Frontier: the 2006 KDD Cup Competition (the submitted version),
  Terran Lane, Bharat Rao, Jinbo Bi, Jianming Liang, Marcos Salganicoff,
  ACM Journal SIGKDD Explorations, 8(2):39-46, Dec 2006.
• Probabilistic Joint Feature Selection for Multi-task Learning ,
  Tao Xiong, Jinbo Bi, Bharat Rao and Vladimir Cherkassky
  Proceedings of SIAM International Conference on Data Mining (SDM'06), 2006.
• Learning Classifiers When the Training Data is not IID,
  Murat Dundar, Balaji Krishnapuram, Jinbo Bi and Bharat Rao,
  Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI'06), 2006.
• Efficient Model Selection for Regularized Linear Discriminant Analysis,
  Jieping Ye, Tao Xiong, Qi Li, Ravi Janardan, Jinbo Bi, Vladimir Cherkassky and Chandra Kambhamettu,
  Proceedings of the ACM Fifteenth Conference on Information and Knowledge Management (CIKM'06), 2006.
• Automatic View Recognition for Cardiac Ultrasound Images,
  Matthew E. Otey, Jinbo Bi, Sriram Krishnan, Bharat Rao, Jonathan Stoeckel, Alan Katz, Jing Han and Srinivasan Parthasarathy,
  Proceedings of the 1st International Workshop on Computer Vision for Intravascular and Intracardiac Imaging at Annual Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'06-CVII), 2006.
• Computer Aided Detection via Asymmetric Cascade of Sparse Hyperplane Classifiers,
  Jinbo Bi, Senthil Periaswamy, Kazunori Okada, Toshiro Kubota, Glenn Fung, Marcos Salganicoff and Bharat Rao,
  Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD'06), 2006.
• MILES: Multiple-Instance Learning via Embedded Instance Selection,
  Yixin Chen, Jinbo Bi, James Z. Wang,
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):1-17, 2006.
• Active Learning via Transductive Experimental Design,
  Kai Yu, Jinbo Bi, Volker Tresp,
  Proceedings of the 23rd International Conference on Machine Learning (ICML'06), 2006.
• Semi-supervised Mixture of Kernels via LPBoost Methods,
  Jinbo Bi, Glenn Fung, Murat Dundar, and Bharat Rao,
  Proceedings of the 15th IEEE International Conference on Data Mining (ICDM'05), 2005.
• A Sparse Support Vector Machine Approach to Region-based Image Categorization,
  Jinbo Bi, Yixin Chen and James Wang,
  Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005.
• Sparse Fisher Discriminant Analysis for Computer Aided Detection,
  Murat Dundar, Glenn Fung, Jinbo Bi, S. Sandilya and Bharat Rao,
  Proceedings of SIAM International Conference on Data Mining (SDM'05), 2005.
• Clustering by Maximizing Sum-of-squared Separation Distance,
  Yixin Chen and Jinbo Bi,
  Proceedings of SIAM Data Mining Workshop on Clustering High Dimensional Data and its Applications, 2005.
• Support Vector Classification with Input Data Uncertainty,
  Jinbo Bi and Tong Zhang,
  Advances in Neural Information Processing Systems (NIPS'04), vol. 17, pp 161-168, 2004.
• Column-Generation Boosting Methods for Mixture of Kernels,
  Jinbo Bi, Tong Zhang and Kristin Bennett,
  Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'04), pp. 521-526, 2004.
• A Fast Iterative Algorithm for Fisher Discriminant using Heterogeneous Kernels,
  Glenn Fung, Murat Dundar, Jinbo Bi and Bharat Rao,
  Proceedings of the 21st International Conference on Machine Learning (ICML'04), 2004.
• Regression Error Characteristic Curves,
  Jinbo Bi and Kristin Bennett,
  Proceedings of the 20th International Conference on Machine Learning (ICML'03), 2003.
• Multi-Objective Programming in SVMs,
  Jinbo Bi,
  Proceedings of the 20th International Conference on Machine Learning (ICML'03), 2003.
• Learning with Rigorous Support Vector Machines,
  Jinbo Bi and Vladimir Vapnik,
  Proceedings of the 16th Annual Conference on Learning Theory (COLT'03), 2003.
• A Geometric Approach to Support Vector Regression,
  Jinbo Bi and Kristin Bennett,
  Neurocomputing, 55(1-2):79-108, 2003.
• Dimensionality Reduction via Sparse Support Vector Machines,
  Jinbo Bi, Kristin Bennett, Mark Embrechts, Curt Breneman and Minghu Song,
  Journal of Machine Learning Research, 3:1229-1243, 2003.
• Prediction of Protein Retention Times in Anion-exchange Chromatography Systems Using Support Vector Machines,
  M. Song, C. Breneman, Jinbo Bi, N. Sukumar, K. Bennett, S. Cramer and N. Tugcu,
  Journal of Chemical Information and Computer Science. 42(6):1347-1357, 2003.
• Descriptor Generation, Selection and Model Building in Quantitative Structure-Property Analysis,
  C. Breneman, K. Bennett, M. Embrechts, S. Cramer, M. Song and Jinbo Bi,
  A book chapter in Experimental Design for Combinatorial and High Throughput Materials Development, J Crawse Ed. 2002.
• Duality, Geometry, and Support Vector Regression,
  Jinbo Bi and Kristin Bennett,
  Advances in Neural Information Processing Systems (NIPS'01), 2001.

Professional Services

• Reviewer for International Journals
  Journal of Machine Learning Research, Machine Learning Journal, Journal of Artificial Intelligence Research, Data Mining and Knowledge Discovery Journal, Optimization Methods and Software, European Journal of Operations Research, Neurocomputing, Neural Computation, IEEE Trans. on Neural Networks, IEEE Trans. on Pattern Analysis and Machine Intelligence, IEEE Trans. on Systems, Man and Cybernetics (B), Pattern Analysis and Applications Journal, Pattern Recognition Letters
• Reviewer or Program Committee Member for International Conferences
  Neural Information Processing Systems (NIPS), ACM International Conference on Data Mining and Knowledge Discovery (SIGKDD), International Conference on Machine Learning (ICML), SIAM International Conference on Data Mining (SDM), International Conference on Machine Learning and Applications (ICMLA)
• Editorial Review Board Member
  Advances in Chemoinformatics and Computational Methods (ACCM) Book Series
• Reviewer for Grants Proposal
  Louisiana Board of Regents' Research Competitiveness EPSCoR-style Grants Program 2004
• Session Chair and Contest Organizer
  Organize a session at INFORMS 2007 annual meeting on Machine Learning Approaches to Medical Diagnosis and Health Care
  Co-organize KDD CUP 2006 contest on Pulmonary Embolism detection from CT images

Presentations and Abstract/Clinical Publications

Conferences

• An Adaptive, Knowledge-Driven Medical Image Search Engine for Interactive Diffuse Parenchymal Lung Disease Quantification ,
  Yimo Tao, Xiang Zhou, Jinbo Bi, Anna Jerebko, Matthias Wolf, Marcos Salganicoff and Arun Krishnan, To appear in the Proceedings of SPIE medical imaging with an oral presentation, 2009.
• Assessment of Computer-aided Nodule Detection (CAD) Algorithm on Pathology Proved CT Data Sets,
  Sangmin Park, Tae-jung Kim, Vikas Raykar, Vikram Anald, Anna Jerebko, Maneesh Dewan, Jinbo Bi and Marcos Salganicoff, Abstract at Radiologist Society of North America (RSNA) with an oral presentation, Nov. 2008.
• Computer-aided Detection of Pulmonary Nodules on CT: Evaluation of a New Prototype for Detection of Ground-glass and Part-Solid Nodules,
  Myrna Godoy, Tae Jung Kim, Jane Ko, Charles Florin, Anna Jerebko, David Naidich, Sangmin Park, Ioannis Vlahos, Jinbo Bi and Marcos Salganicoff, Abstract at Radiologist Society of North America (RSNA) with an oral presentation, Nov. 2008.
• Training a CAD Classifier with Correlated Data,
  Murat Dundar, Balaji Krishnapuram, Matthias Wolf, Sarang Lakare, Locu Bogoni, Jinbo Bi and Bharat Rao, Proceedings of SPIE Medical Imaging with an oral presentation, 2008.
• Reducing a Biomarkers List via Mathematical Programming: Application to Gene Signatures to Detect Time-dependent Hypoxia in Cancer,
  Glenn Fung, Renaud Seigneuric, Sriram Krishnan, Bharat Rao, Brad Wouters, Philippe Lambin, Jinbo Bi presented at Special Session on Machine Learning Approaches to Medical Diagnosis and Health Care at INFORMS 2007 Annual Meeting, 2007.
• Assessment of Computer-aided Lung Nodules Detection Algorithm on CT Data Sets Acquired Under Imaging Database Resources Initiative,
  Anna Jerebko, Jinbo Bi, Matthias Wolf, Senthil Periaswamy, Jianming Liang and Sanming Park, Abstract at Radiological Society of North America (RSNA) with an oral presentation, Nov. 2007.
• Computer-aided Detection of Skeletal Metastases in MRI STIR Imaging of the Spine,
  A. Jerebko, G.P. Schmidt, X. Zhou, Jinbo Bi, V. Anand, J Liu, S. Schoenberg, I. Schmuecking, B. Kiefer and A. Krishnan, an oral presentation at International Society for Magnetic Resonance in Medicine, 2007.
• Joint Optimization of Cascaded Classifiers for Computer Aided Detection,
  Oral presentation given by Jinbo Bi at IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'07), June 2007.
• CAD Performance Analysis for Pulmonary Nodule Detection on Thin-slice MDCT Scans,
  M. Wolf, A. Krishnan, M. Salganicoff, Jinbo Bi, M. Dundar, G. Fung, J. Stoeckel, S. Periaswamy, H. Shen, P. Herzog and D. Naidich, CARS 2005 Computer Assisted Radiology and Surgery , June 2005. (a related paper is published in its proceedings)
• Clinical Evaluation of a Novel Automatic Real-time Myocardial Tracking and Wall Motion Scoring Algorithm for Echocardiography,
  A. Katz, S. Krishnan, X. Zhou, B. Georgescu, D. Comaniciu, Jinbo Bi, G. Fung, and J. Liang, American College of Cardiology, March 2005.
• In Silico Screening for the hERG Potassium Channel Affinity,
  M. Song, Jinbo Bi, C. M. Breneman, K. P. Bennett, 228th American Chemical Society meeting, Philadelphia, August 2004.
• Developing in silico Carcinogenicity Classification model based on the kernel combination approach,
  M. Song, Jinbo Bi, K. P. Bennett, C. M. Breneman, ADMET-1 Conference, San Diego, Febuary 2004.
• Prediction of Protein Retention Times in Anion-exchange Chromatography System using Support Vector Regression,
  M. Song, C. M. Breneman, N. Sukumar, K. P. Bennett, Jinbo Bi, S. Cramer and N. Tugcu, 224th American Chemical Society meeting, Boston, August 2002.
• QSPR Model Generation and Validation for Virtual High Throughput Screening,
  M. Song, C. M. Breneman, N. Sukumar, Jinbo Bi, K. P. Bennett, S. Cramer, Gordon Conference on Combinatorial & High Throughput Materials Science, Kimball Union Academy, 2002.
• Caco-2 permeability modeling: Feature selection via sparse support vector machines,
  C. M. Breneman, K. P. Bennett, Jinbo Bi, Mark J. Embrechts, M. Song, 223rd American Chemical Society meeting , Orlando, April, 2002
• Displacer Efficacy prediction by Electronic Shape/Property-Encode descriptors,
  M. Song, Jinbo Bi, A. Ladiwala, W. Deng, N. Sukumar, M. Sundling, S. Cramer, C. M. Breneman, Albany Biotechnology Conference, September 2001.
• Duality, Geometry, and Support Vector Regression,
  Jinbo Bi and K. P. Bennett, Advances in Neural Information Processing Systems (NIPS-2001). Vancouver. Dec. 2001
• Dimensionality Reduction and Model Visualization via Sparse Support Vector Machines ,
  K. P. Bennett and Jinbo Bi, NIPS 2001 Workshop on Feature Selection. Vancouver. Dec. 2001
• SVM, GAFEAT-NN, GA-PLS Modeling of Caco-2 Permeability using Electron Density-based Descriptors,
  M. Song, Jinbo Bi, M. Ozdemir, N. Sukumar, M. J. Embrechts, Kristin Bennett, Curt M. Breneman, 222nd American Chemical Society meeting, Chicago, August 2001.
• Support Vector Machines for Pharmaceutical Data Analysis,
  Jinbo Bi, The 18th International Conference on Machine Learning (ICML-2001). Williams College. June 2001

Seminars and invited talks

• Princeton University, Machine-Learning Group guided by Dr. Robert Schapire, Oct. 2004 -- Uncertainty Problems in Classification with Ultrasound Images
• Canadian Imperial Bank of Commerce, Customer Behaviour Analytics Group, Apr. 2003 -- Support Vector Machines - Regression, Classification, and Some Applications
• NEC Research Institute, Inc. August 2002 -- Rigorous Support Vector Machine and Feature Selection
• Seminars in Mathematics Department at RPI
• A brief introduction to learning theory and learning algorithms March 2003
• Krylov subspace methods: A technique for partial least squares regression? November 2001

Informative Links (Obsolete, the last update was in 2003)