Jinbo Bi

	Jinbo Bi   Frederick H Leonhardt Professor of Computer Science Associate Head Department of Computer Science & Engineering School of Engineering University of Connecticut, Storrs, CT 06269   Also Affiliated with Department of Community Medicine and Health Care University of Connecticut Health Center, Farmington, CT 06030 Department of Mathematics Department of Statistics College of Liberal Arts and Sciences Department of Biomedical Engineering School of Engineering
Education	Ph.D., Rensselear Polytechnic Institute M.Sc., Beijing Institute of Technology
Contact Information:	371 Fairfield Way, Unit 4155 Storrs, CT 06269-4155 Office: ITEB 233 Tel: 860-486-1458 Fax: 860-486-4817 (CSE Dept) Email: jinbo dot bi AT uconn dot edu
Research Interests:	Artificial Intelligence, Machine learning, Data mining, Pattern recognition, Optimization, Computer vision, Bioinformatics, Medical informatics, Drug discovery, Google Scholar Page

Laboratory

• Laboratory of Machine Learning & Health Informatics
• Laboratory of Artificial Intelligence & Drug Discovery

Openings

• There are multiple postdoctoral fellowship positions in our laboratory

Research Projects

     National Science Foundation

• PI – RI: Small: Multi-View Latent Class Discovery and Prediction with a Streamlined Analytics Platform (8/1/2017 – 7/31/2021)
• PI – AF:Medium: A High Performance Computing Foundation to Whole-Genome Prediction (7/1/2015 – 6/30/2021), REU supplement was awarded in 2020
• Co-PI – BIGDATA:F:DKA:DKM: Novel Out-of-core and Parallel Algorithms for Processing Biological Big Data (9/1/2014 – 8/31/2018)
• PI – ABI Innovation: An Integrative Approach to Identifying Highly Heritable Components of Complex Phenotypes (7/1/2014 – 6/30/2020), REU supplement was awarded in 2016
• Co-PI – SCH:EXP:LifeRhythm: A Framework for Automatic and Pervasive Depression Screening Using Smartphones (8/1/2014 – 7/31/2017)
• PI – III:Small: Is Imprecise Supervision Useful? Leveraging Ambiguous, Incomplete or Conflicting Data Annotations (9/1/2013 – 8/31/2016)

    National Institutes of Health

• Co-I for the UConn Site – U19AI171421: Development of outpatient antiviral cocktails against SARS-CoV-2 and other potential pandemic RNA viruses (Biological Core: Programmable antivirals: Targeting viral RNA secondary structures with LNAs and small molecules) (5/16/2022 – 4/30/2025)
• Contact PI – R01-DA051922: Multi-level Statistical Classification of Substance Use Disorders (9/30/2020 – 6/30/2024)
• MPI – R01-MH119678: Personalized Depression Treatment Support by Mobile Sensor Analysis (7/18/2019 – 6/30/2023)
• PI – K02-DA043063: Classifying Addictions Using Machine Learning Analysis of Multidimensional Data (2/1/2017 – 1/31/2022)
• PI – R01-DA037349: Quantitative Methods to Subtype Drug Dependence and Detect Novel Genetic Variants (2/1/2015 – 12/31/2018)

    Department of Veterans Affairs

• Subcontract PI – I21 RX001731: Brain-Computer Interface (BCI) Enabled Memory Training for Schizophrenia (9/1/2015 – 8/31/2017)

    Industrial Partnerships

• PI – Travelers Insurance Inc.: Claim Data Analysis using Natural Language Processing (3/01/2023 – 7/31/2025, second phase)
• PI – Travelers Insurance Inc.: Change and Storm-Damage Detection from Aerial Images (8/23/2021 – 2/22/2023, first phase)
• Co-PI – Pfizer Pharmaceuticals Inc.: Exploring the Impact of Microbiome Diversity on Toxicological Outcomes in Preclinical Species (1/01/2021 – 12/31/2022)

Teaching

• Spring 2023, Machine Learning (Reinforcement Learning)
• Spring 2022, Machine Learning (Reinforcement Learning)
• Spring 2019, Machine Learning
• Spring 2018, Machine Learning
• Spring 2017, Machine Learning
• Spring 2016, Artificial Intelligence
• Spring 2015, Artificial Intelligence
• Fall 2014, Machine Learning
• Fall 2014, Introduction to Discrete Systems
• Spring 2014, Introduction to Discrete Systems
• Fall 2013, Machine Learning
• Fall 2013, Numerical Methods in Scientific Computation
• Spring 2013, Machine Learning in Biomedical Informatics
• Fall 2012, Introduction to Discrete Systems
• Fall 2012, CSE Special Topic: Computational Biomedical Informatics
• Spring 2012, Introduction to Discrete Systems
• Fall 2011, Numerical Methods in Scientific Computation
• Spring 2011, Introduction to Discrete Systems
• Fall 2010, CSE Special Topic: Computational Medical Informatics

Prior Working Experience

• Department of Defense Bioanalysis Institutes, (joint appointment with Partners Healthcare System), Boston, MA, November 2009 – June 2010
  Research Scientist II, develop and investigate informatics-based solutions to improve the medical protection and care of US military personel. Analyze and quantify physiology databases for computerized diagnosis of health conditions such as hemorrhage, traumatic brain injury, chest injury etc. Deploy the developed informatics tools also to the civilian health care settings, such as MedFlight. Establish partnerships with industry and academia, and participate in emerging technology development in medical informatics.
• Siemens Medical Solutions, Inc. USA, Malvern, PA, September 2003 – October 2009
  Staff Scientist, develop algorithms that automatically identify and detect structures in human body that are possibly cancerous or that present abnormalities, based on medical images from CT, MRI, Ultrasound and other clinical imaging modalities. Applications include heart motion abnormality detection for coronary artery disease, lung nodule detection (Syngo Lung CAD – FDA approved product) and colon polyp detection for cancer prognosis, and detection of pulmonary embolism or diffuse parenchymal lung diseases. Investigate cutting-edge methods for medical applications where free text or natural language data are present in heterogeneous forms of clinical records. Supervise summer interns and associates to work on research projects and software implementations.
• The Pennsylvania State University, Great Valley, PA, August 2008 – October 2009
  Adjunct Professor with The Engineering Division, Taught Soft Computing (SWENG 597C) in Fall 2008 (official course website was 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, Auguest 1999 – August 2003
  Research Assistant and Teaching Assistant at the Department of Mathematical Sciences. Developed optimization algorithms for machine learning. Apply the resulting algorithms to drug discovery and gene expression data analysis. Assisted in teaching Numerical computing and Probability theory and statistics.

Selected Peer-Reviewed Publications

I have stopped updating the list of publications below, and use the Google Scholar website directly. Please check Google Scholar for more publications.

• Explaining Graph Neural Networks with Mixed-Integer Programming,
  Blake Gaines, Chunjiang Zhu, Jinbo Bi
  Under review.
• Identifying Shared Neural Markers Across Positive and Negative Valence for Depression and Anxiety,
  Tan Zhu, Guangfei Li, Yu Chen, Chiang-Shan R. Li, Jinbo Bi
  In Preparation.
• Effective Proximal Methods for Non-convex Non-smooth Regularized Learning,
  Guannan Liang, Qianqian Tong, Jiahao Ding, Miao Pan, Jinbo Bi
  Proceedings of IEEE International Conference on Data Mining (ICDM), 2020
• Automatic Depression Prediction Using Internet Traffic Characteristics on Smartphones,
  Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, Bing Wang
  Smart Health, 18:e100137, 2020
• Towards Plausible Differentially Private ADMM Based Distributed Machine Learning,
  Jiahao Ding, Jingyi Wang, Guannan Liang, Jinbo Bi, Miao Pan
  Proceedings of 29th ACM International Conference on Information and Knowledge Management (CIKM), 2020
• Hybrid-DCA: A Double Asynchronous Approach for Stochastic Dual Coordinate Ascent,
  Soumitra Pal, Tingyang Xu, Tianbao Yang, Sanguthevar Rajasekaran, Jinbo Bi
  Journal of Parallel and Distributed Computing, 143:47-66, 2020
• Predicting Depressive Symptoms Using Smartphone Data,
  Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, Bing Wang
  Smart Health 15:e100093, 2020
• Effect of Three-Dimensional Porosity Gradients of Biomimetic Coatings on Their Bonding Strength and Cell Behavior,
  Le Yu, Tomas Silva Santisteban, Qinqing Liu, Changmin Hu, Jinbo Bi, and Mei Wei
  Journal of Biomedical Materials Research, e37046, 2020
• Predicting Outcomes of Chemical Reactions: A Seq2Seq Approach with Multi-view Attention and Edge Embedding,
  Xia Xiao, Chao Shang, Jinbo Bi, Sanguthevar Rajasekaran
  Proceedings of International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2020
• An Effective Hard Thresholding Method Based on Stochastic Variance Reduction for Nonconvex Sparse Learning.
  Guannan Liang, Qianqian Tong, Chun Jiang Zhu, Jinbo Bi
  Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), pp.1585-1592, 2020
• Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics,
  Jayesh Kamath, Jinbo Bi, Alexander Russell, Bing Wang
  Journal of Psychiatry and Brain Science 5:e200010, 2020
• Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges,
  Guang Chen, Zhiqiang Shen, Akshay Iyer, Umar Farooq Ghumman, Shan Tang, Jinbo Bi, Wei Chen, Ying Li
  Polymers 12 (1), 163, 2020
• Convolutional Neural Network for Automated Mass Segmentation in Mammography,
  Dina Abdelhafiz, Jinbo Bi, Reda Ammar, Clifford Yang, and Sheida Nabavi
  BMC Bioinformatics, 21(S)-192, 2020
• Improved Dynamic Graph Learning through Fault-Tolerant Sparsification,
  Chunjiang Zhu, Sabine Storandt, Kam-Yiu Lam, Song Han, and Jinbo Bi
  Proceedings of the 36th International Conference on Machine Learning (ICML), pp.7624-7633, 2019.
• A Genome-wide Association Study of Cocaine Use Disorder Accounting for Phenotypic Heterogeneity and Gene-Environment Interaction,
  Jiangwen Sun, Henry R Kranzler, Joel Gelernter, and Jinbo Bi
  Journal of Psychiatry and Neuroscience, 45(1):34-44, 2019.
• Multi-view Cluster Analysis with Incomplete Data to Understand Treatment Effects,
  Guoqing Chao, Jiangwen Sun, Jin Lu, An-Li Wang, Daniel D. Langleben, Chiang-Shan (Ray) Li, and Jinbo Bi
  Information Science, vol 494, pp. 278-293, https://doi.org/10.1016/j.ins.2019.04.039, 2019.
• Communication-Optimal Distributed Dynamic Graph Clustering,
  Chunjiang Zhu, Tan Zhu, Kam-Yiu Lam, Song Han, and Jinbo Bi
  Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019.
• End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion,
  Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou
  Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019.
• On the VC-dimension of Unique Round-trip Shortest Path Systems,
  Chunjiang Zhu, Kam-Yiu Lam, Joseph Kee Yin Ng, Jinbo Bi
  Information Processing Letters 145, 1-5, 2019
• Accelerating Large-Scale Molecular Similarity Search through Exploiting High Performance Computing,
  Chun Jiang Zhu, Tan Zhu, Haining Li, Jinbo Bi, Minghu Song
  Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.330-333, 2019
• Residual Deep Learning System for Mass Segmentation and Classification in Mammography,
  Dina Abdelhafiz, Sheida Nabavi, Reda Ammar, Clifford Yang, Jinbo Bi
  Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp.475-484, 2019
• Alcohol Expectancy and Cerebral Responses to Cue-Elicited Craving in Adult Nondependent Drinkers,
  Simon Zhornitsky, Sheng Zhang, Jaime S. Ide, Herta H Chao, Wuyi Wang, Thang M. Le, Robert F. Leeman, Jinbo Bi, John H. Krystal, and Chiang-shan R. Li
  Biological Psychiatry: Cogntive Neuroscience and Neuroimaging, S2451-9022(18)30328-8, https://doi.org/10.1016/j.bpsc.2018.11.012, 2018.
• Top-down Indoor Localization with Wi-Fi Fingerprints using Deep Q-network,
  Fei Dou, Jin Lu, Zigeng Wang, Xia Xiao, Jinbo Bi, Chun-Hsi Huang
  Proceedings of the 15th IEEE International Conference on Mobile Ad-hoc and Sensor Systems, 2018.
• Large-scale Automatic Depression Screening Using Meta-data from Wi-Fi Infrastructure,
  Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang
  Proceedings of the ACM on Interactive Mobile, Wearable and Ubiquitous Technologies (IMWUT), a premier journal for research relevant to the post-PC era, 2(4):195, pp.195:1-195:27, 2018.
• Fusion Location Data for Depression Prediction,
  Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang
  IEEE Transactions on Big Data, DOI: 10.1109/TBDATA.2018.2872569, 2018.
• Inferring Phenotypes from Substance Use via Collaborative Matrix Completion,
  Jin Lu, Jiangwen Sun, Xinyu Wang, Henry R. Kranzler, Joel Gelernter, and Jinbo Bi
  BMC Systems Biology, 12(S6):104, DOI: 10.1186/s12918-018-0623-5, 2018.
• Reforming Generative Autoencoders via Goodness-of-Fit Hypothesis Testing,
  Aaron Palmer, Dipak K. Dey, and Jinbo Bi
  Proceedings of the Uncertainty in Artificial Intelligence (UAI), 2018.
• Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning,
  Jin Lu, Chao Shang, Chaoqun Yue, Reynaldo Morillo, Shweta Ware, Jayesh Kamath, Athanasios Bamis, Alexander Russell, Bing Wang, and Jinbo Bi
  Proceedings of the ACM on Interactive Mobile, Wearable and Ubiquitous Technologies (IMWUT), a premier journal for research relevant to the post-PC era, 2(1):21, pp.21:1-21:21, 2018.
• Latent Sparse Modeling of Longitudinal Multi-dimensional Data,
  Ko-Shin Chen, Tingyang Xu, and Jinbo Bi
  Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI), pp. 2135-2142, 2018.
• Collaborative Phenotype Inference from Comorbid Substance Use Disorders and Genotypes,
  Jin Lu, Jiangwen Sun, Xinyu Wang, Henry R. Kranzler, Joel Gelernter, and Jinbo Bi
  Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 392-397, 2017.
• VIGAN: Missing View Imputation with Generative Adversaial Networks,
  Chao Shang, Aaron Palmer, Jiangwen Sun, Ko-Shin Chen, Jin Lu, and Jinbo Bi
  Proceedings of IEEE International Conference on Big Data (BigData), 2017.
• Identifying and Quantifying Nonlinear Structured Relationships in Complex Manufactural Systems,
  Tingyang Xu, Tan Yan, Dongjin Song, Wei Cheng, Haifeng Chen, Guofei Jiang, and Jinbo Bi
  Proceedings of IEEE International Conference on Big Data (BigData), 2017.
• Fusing Location Data for Depression Prediction,
  Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Anthanasios Bamis, and Bing Wang
  Proceedings of the 14th International Conference on Ubiquitous Intelligence and Computing (UIC), 2017.
• Classification of Neurological Gait Disorders Using Multi-task Feature Learning,
  Ioannis Papavasileiou, Wenlong Zhang, Xin Wang, Jinbo Bi, Li Zhang, Song Han
  Proceedings of the 2nd IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2017.
• A Sparse Inductive Model for Matrix Completion with Side Information,
  Jin Lu, Guannan Liang, Jiangwen Sun, and Jinbo Bi
  Advances in Neural Information Processing Systems 29, pp. 4071 – 4079, 2016.
  A supplementary file with proofs can be found here. A more efficient proof will be published in a journal version.
• Behavior vs. Introspection: Refining Prediction of Clinical Depression via Smartphone Sensing Data,
  Asma Ahmad Farhan, Chaoqun Yue, Reynaldo Morillo, Shweta Ware, Jin Lu, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang
  Proceedings of IEEE Wireless Health Conference, 2016.
• A Cross-species Bi-clustering Approach to Identifying Conserved Co-regulated Genes,
  Jiangwen Sun, Zhongliang Jiang, Xiuchun Tian, and Jinbo Bi
  Bioinformatics, 32(12):137-146, PMID: 27307610, PMCID: PMC4908362; DOI: 10.1093/bioinformatics/btw278, 2016.
• Bi-convex Optimization to Learn Classifiers from Multiple Biomedical Annotations,
  Xin Wang and Jinbo Bi
  IEEE/ACM Transactions on Computaitonal Biology and Bioinformatics, PMID: 27295686 DOI: 10.1109/TCBB.2016.2576457, pp. 1-13, 2016.
• Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression,
  Asma Ahmad Farhan, Jin Lu, Jinbo Bi, Alexander Russell, Bing Wang, and Athanasios Bamis
  Proceedings of IEEE International Conference on Connected Health: Application, Systems and Engineering Technologies (CHASE), pp.264–273, 2016.
• Machine Learning Identification of EEG Features Predicting Working Memory Performance in Schizophrenia and Healthy Adults,
  Jason Johannesen, Jinbo Bi, Ruhua Jiang, Joshua Kenney and Chi-Ming Chen
  BMC Neuropsychiatric Electrophysiology, 2:3 DOI: 10.1186/s40810-016-0017-0, 2016.
• Quantifying Feed Efficiency of Dairy Cattle for Genome-wide Association Analysis,
  Tingyang Xu, Jiangwen Sun, Erin E Connor, and Jinbo Bi
  Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.131-134, 2015.
• An Effective Method to Identify Heritable Components from Multivariate Phenotypes,
  Jiangwen Sun, Henry R. Kranzler, and Jinbo Bi
  PLoS ONE 10(12):e0144418. doi:10.1371/journal.pone.0144418, 2015. The related software package is in github.
• Multiplicative Multi-Task Feature Learning,
  Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, and Minghu Song
  Accepted by the Journal of Machine Learning Research, 2015.
• Refining Multivariate Disease Phenotypes for High Chip Heritability,
  Jiangwen Sun, Henry R. Kranzler, and Jinbo Bi
  BMC Medical Genomics, 8(Suppl 3):S3, DOI:10.1186/1755-8794-8-S3-S3, pp. 1-14, 2015. The related software package is here.Announcement: A lot of typeset/edit errors were created in the production process of BMC Medical Genomics that affect the scientific content of this paper. Hence, we suggest readers to ignore the online version from the journal website.
• Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction,
  Tingyang Xu, Jiangwen Sun, and Jinbo Bi
  Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 1345-1354, 2015.
• Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization,
  Jiangwen Sun, Jin Lu, Tingyang Xu, and Jinbo Bi
  Proceedings of the 32nd International Conference on Machine Learning (ICML), pp. 757-766, 2015.
• Spatio-temporal Modeling of EEG Data for Understanding Working Memory,
  Jinbo Bi, Tingyang Xu, Chi-Ming Chen, and Jason Johannesen
  Peer reviewed and archived by ICML Workshop on Statistics, Machine Learning and Neuroscience, 2015.
• Learning Classifiers from Dual Annotation Ambiguity via a Min-Max Framework,
  Jinbo Bi and Xin Wang
  Neurocomputing, 151(2):891-904, 2015.
• Detecting Tympanostomy Tubes from Otoscopic Images via Offline and Online Training,
  Xin Wang, Tulio A Valdez, and Jinbo Bi
  Computers in Biology and Medicine, 61:107-118, 2015.
• On Multiplicative Multi-Task Feature Learning,
  Xin Wang, Jinbo Bi, Shipeng Yu, and Jiangwen Sun
  Advances in Neural Information Processing Systems (NIPS), pp. 2411-2419, 2014.
• Identifying Heritable Composite Traits from Multivariate Phenotypes with Genome-wide SNPs,
  Jiangwen Sun, Jinbo Bi and Henry R. Kranzler
  Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 340-344, 2014.
• A Sparse Integrative Cluster Analysis for Understanding Soybean Phenotypes,
  Jinbo Bi, Jiangwen Sun, Tingyang Xu, Jin Lu, Yansong Ma, and Lijuan Qiu
  Workshop Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp.1-7, 2014. Won the best paper award out of 200 workshop papers in 17 workshops.
• A Viewpoint of Security for Digital Health Care: What’s There? What Works? What’s Needed?,
  Steve Demurjian, Alberto De la Rosa Algarin, Jinbo Bi, Soloman Berhe, Thomas Agresta, Xiaoyan Wang, Michael Blechner
  International Journal of Privacy and Health Information Management, 2(1):1-21, 2014.
• Transcriptional Profiles of Bovine In Vivo Pre-implantation Development,
  Zongliang Jiang, Jiangwen Sun, Hong Dong, Oscar Luo, Xinbao Zheng, Craig Obergfell, Yong Tang, Jinbo Bi, Rachel O’Neill, Yijun Ruan, Jingbo Chen, and Xiuchun (Cindy) Tian
  BMC Genomics, 15:756-770, 2014. (Identified as a “highly accessed” article by the journal.)
• Translating Effective Paper-based Disease Management into Electronic Medical Systems,
  Tingyang Xu, Michelle M Cloutier, and Jinbo Bi
  Proceedings of the 2nd IEEE International Conference on Health Informatics (ICHI), pp. 101-108, 2014.
• Multi-view Singular Value Decomposition for Disease Subtyping and Genetic Associations,
  Jiangwen Sun, Jinbo Bi, and Henry R. Kranzler
  BMC Genetics, 15(73):1-12, 2014. (Identified as a “highly accessed” article by the journal.) The related software package is here.
• Multi-view Biclustering for Genotype-Phenotype Association Studies of Complex Diseases,
  Jiangwen Sun, Jinbo Bi and Henry R. Kranzler
  Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 316-321, 2013.
• Quadratic Optimization to Identify Highly Heritable Quantitative Traits from Complex Phenotypic Features,
  Jiangwen Sun, Jinbo Bi and Henry R. Kranzler
  Proceedings of ACM Special Interest Group on Knowledge Discovery from Data Mining (SIGKDD), pp. 811-819, 2013.
• Comparing the Utility of Homogeneous Subtypes of Cocaine Use and Related Behaviors with DSM-IV Cocaine Dependence as Traits for Association Analysis,
  Jinbo Bi, Joel Gelernter, Jiangwen Sun and Henry R. Kranzler
  American Journal of Medical Genetics (Part B) Neuropsychiatric Genetics (AJMG), 165B(2):148-156, 2014.
• Dopamine D1 Receptor Gene Variation Modulates Opioid Dependence Risk by Affecting Transition to Addiction,
  Feng Zhu, Chunxia Yan, Yi-chong Wen, Jiayin Wang, Jinbo Bi, Ya-ling Zhao, Yang Zhao, Lai Wei, Yu-cheng Guo, Jing Wang, Yan Zhao, Chengge Gao, Wei Jia and Shengbin Li
  PLoS ONE, 8(8):e70805-1-11, 2013.
• Multiview Comodeling to Improve Genetic Association of Complex Disease Phenotypes,
  Jiangwen Sun, Jinbo Bi and Henry R. Kranzler
  IEEE Journal of Biomedical and Health Informatics, 18(2):548-554, 2014.
• A Machine Learning Approach to College Drinking Prediction and Risk Factor Identification,
  Jinbo Bi, Jiangwen Sun, Yu Wu, Howard Tennen, Stephen Armeli
  ACM Transactions on Intelligent Systems and Technology, 4(4):72:1-24, 2013.
• Efficient Techniques for Genotype-Phenotype Correlational Analysis,
  Subrata Saha, Sanguthevar Rajasekaran, Jinbo Bi and Sudipta Pathak
  BMC Medical Informatics and Decision Making, 13(1):41-59, 2013.
• A Multi-Objective Program for Quantitative Subtyping of Clinically Relevant Phenotypes,
  Jiangwen Sun, Jinbo Bi and Henry R. Kranzler
  Proceedings of IEEE International Conference on Bioinformatics and Biomedicine (BIBM2012), pp.256-261, 2012.
• Improved Methods to Identify Stable, Highly Heritable Subtypes of Opioid Use and Related Behaviors,
  Jiangwen Sun, Jinbo Bi, Grace Chan, David Oslin, Lindsay Farrer, Joel Gelernter, Henry R. Kranzler
  Addictive Behaviors, 37(10):1138-1144, 2012.
• 1-Norm Support Vector Machine for College Drinking Risk Factor Identification,
  Michael Zuba, Joseph Gilbert, Yu Wu, Jinbo Bi, Howard Tennen and Stephen Armeli
  Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 651-660, Jan. 2012.
• An Intelligent Web-based Decision Support Tool for Enhancing Asthma Guideline Adherence,
  Jinbo Bi and Arun Abraham
  Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 51-59, Jan. 2012.
• Robust Large Scale Prone-Supine Polyp Matching Using Local Features: A Metric Learning Approach,
  Meizhu Liu, Le Lu, Jinbo Bi, Vikas Raykar, Matthias Wolf and Marcos Salganicoff
  Proceedings of the 14th International Conference on Medical Image Computing and Computer Assisted Intervention, 2011.
• AdaBoost on Low-Rank PSD Matrices for Metric Learning,
  Jinbo Bi, Dijia Wu, Le Lu, Meizhu Liu, Yimo Tao and Matthias Wolf
  Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR2011), 2011.
• Effective 3D Object Detection and Regression Using Probabilistic Segmentation Features in CT Images,
  Le Lu, Jinbo Bi, Matthias Wolf and Marcos Salganicoff
  Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR2011), 2011.
• Matrix-Variate and Higher-Order Probabilistic Projections,
  Shipeng Yu, Jinbo Bi and Jieping Ye,
  Data Mining and Knowledge Discovery, 22(3):372-392, 2011.
• Correcting Misalignment of Automatic 3D Detection by Classification: Ileo-cecal Valve False Positive Reduction in CT Colonography,
  Le Lu, Matthias Wolf, Jinbo Bi, and Marcos Salganicoff,
  Proceedings of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention. Joint with Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging, 2010.
• Stratified Learning of Local Anatomical Context for Lung Nodules in CT Images,
  Dijia Wu, Le Lu, Jinbo Bi, Yoshihisa Shinagawa, Kim Boyer, Arun Krishnan, Marcos Salganicoff,
  Proceedings of the 13th IEEE International Conference on Computer Vision and Pattern Recognition (CVPR2010), 2010.
• Hierarchical Learning for Tubular Structure Parsing in Medical Imaging: A Study on Coronary Arteries Using 3D CT Angiography,
  Le Lu, Jinbo Bi, Shipeng Yu, Zhigang Peng, Arun Krishnan and Xiang Zhou,
  Proceedings of the IEEE International Conference on Computer Vision (ICCV’09), 2009.
• A Two-Level Approach Towards Semantic Colon Segmentation: Removing Extra-colonic Findings,
  Le Lu, Matthais Wolf, Jianming Liang, Murat Dundar, Jinbo Bi and Marcos Salganicoff,
  Proceedings of Annual Conference on Medical Imaging Computing and Computer Assisted Intervention (MICCAI’09), 2009.
• 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.
• 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.
• Stratified Regularity Measures with Jensen-Shannon Divergence,
  Kazunori Okada, Senthil Periaswamy and Jinbo Bi,
  Proceedings of the IEEE International Computer Vision and Pattern Recognition Workshops, 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,
  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
  Pattern Recognition Letters
  Journal of Biomedical Informatics
  Science in China, Series A: Mathematics
• Program Committee Member/Reviewer for International Conferences 
  Neural Information Processing Systems (NIPS) (served in multiple years)
  International Conference on Machine Learning (ICML) (served in multiple years)
  ACM International Conference on Data Mining and Knowledge Discovery (SIGKDD) (served in multiple years)
  ACM International Health Informatics Symposium (SIGHINF) (2010)
  ACM SIGMM International Conference on Multimedia Information Retrieval (MIR) (2010)
  SIAM International Conference on Data Mining (SDM) (2006)
  International Conference on Machine Learning and Applications (ICMLA) (2006)
• Session Chair and Contest Organizer 
  Organize and initiate ACM International Health Informatics Symposium, 2010
  Organize ACM SIGMM International Conference on Multimedia Information Retrieval, 2010
  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
• Editorial Review Board Member 
  Advances in Chemoinformatics and Computational Methods (ACCM) Book Series
• Panelist for Funding Agencies 

  National Science Foundation

  – Smart and Connected Health (SCH)

  – Information and Intelligent Systems (IIS)

  – Advances in Biological Informatics (ABI)

  – Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA)

  National Institutes of Health

  – Biodata Management and Analysis (BDMA)

  – Behavioral Genetics and Epidemiology Study Section (BGES)

  – Big Data to Knowledge (BD2K)

  – Biomedical Computing and Health Informatics (BCHI)

  Louisiana Board of Regents’ Research Competitiveness EPSCoR-style Grants Program 2004

Patents

• Multi-level Contextual Learning of Data, Dijia Wu, Le Lu, Jinbo Bi, Yoshihisa Shinagawa, Marcos Salganicoff, Patent No. 8,724,866, filed on December 8, 2010, and granted on May 13, 2014.
• Computer Aided Detection of Pulmonary Embolism with Local Characteristic Features in CT Angiography, Jianming Liang, Jinbo Bi, Patent No. 8,244,012, filed on March 5, 2009, and granted on August 14, 2012.
• Reduction of Lymph Tissue False Positives in Pulmonary Embolism Detection, Bernard Ghanem, Jianming Liang, Jinbo Bi, Patent No. 8,126,229, filed on July 30, 2008, and granted on February 28, 2012.
• Sparse Collaborative Computer Aided Diagnosis, Jinbo Bi, Patent No. 8,064,662, filed on July 12, 2007, and granted on November 22, 2011.
• System and Method for Computer Aided Detection of Pulmonary Embolism in Tobogganing in CT Angiography, Jianming Liang, Jinbo Bi, Patent No. 8,036,440, filed on January 30, 2008, and granted on October 11, 2011.
• System and Method for Joint Optimization of Cascaded Classifiers for Computer Aided Detection, Jinbo Bi, Dundar Murat, Patent No. 7,962,428, filed on November 29, 2007, and granted on June 14, 2011.
• Method of Multiple Instance Learning and Classification with Correlations in Object Detection, Jinbo Bi, Jianming Liang, Patent No. 7,822,252, filed on November 26, 2007, and granted on Oct 26, 2010.
• System and Method for Computer Aided Detection via Asymmetric Cascade of Sparse Linear Classifiers, Jinbo Bi, P. Senthil, O. Kazunori, K. Toshiro, F. Glenn, S. Marcos, R. B. Rao, Patent No. 7,756,313, filed on November 3, 2006, and granted on July 13, 2010.
• Hierarchical Modeling in Medical Abnormality Detection, K. Sriram, Jinbo Bi, R. B. Rao, Patent No. 7,653,227, filed on February 8, 2005, and granted on January 26, 2010.
• Support Vector Classification with Bounded Uncertainty Input Data, Jinbo Bi, Patent No. 7,480,639, filed on June 1, 2005, and granted on January 20, 2009.
• System and Method for a Sparse Kernel Expansion for a Bayes Classifier, Murat Dundar, Glenn Fung, Jinbo Bi and Bharat Rao. Patent No. US 7,386,165 B2, filed on Feb. 2nd, 2005, and granted on June 10, 2008.
• 13 Other Patent Applications Filed

Abstracts and 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,
  Proceedings of SPIE medical imaging with an oral presentation, pages 7260-7263, 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.
• 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.
• 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,
  The 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,
  The 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,
  The 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,
  The 222nd American Chemical Society meeting, Chicago, August 2001.
• Support Vector Machines for Pharmaceutical Data Analysis,
  Jinbo Bi,
  A student poster at The 18th International Conference on Machine Learning (ICML-2001). Williams College. June 2001

Seminars and Invited Talks

• Chinese Academy of Science, Mathematics Institute, July 2010
  an invited talk on Solving Ambiguity in Medical Data Classification via Mathematical Programming and Modeling
• The 20th International Symposium on Mathematical Programming, Chicago, IL, August 2009
  an invited talk on MetricBoost: AdaBoosting Positive Semi-definite Matrices for Metric Learning
• NEC Laboratories of America, Princeton, NJ, April 2009
  an invited talk on Statistical Data Mining Meets Challenges in Computerized Medical Diagnosis
• Northeastern University, ECE Department, Boston, MA, March 2009
  Siminar on Statistical Classification Meets Challenges in Computerized Medical Diagnosis
• The Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting, Seattle WA, November 2007
  Reducing a Biomarkers list via Mathematical Programming: Applications to Gene Signatures to Detect Time-dependent Hypoxia in Cancer
• Princeton University, Machine-Learning Group guided by Dr. Robert Schapire, Oct. 2004
  an invited talk on 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
  talk on 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