Gang Luo

  Biographical Sketch
Gang is a professor in the Department of Biomedical Informatics and Medical Education of the School of Medicine at the University of Washington. He received a Bachelor's degree in Computer Science from Shanghai Jiaotong University, P.R.China, in 1998, and a Ph.D. degree in Computer Science with a minor in Mathematics at the University of Wisconsin-Madison in 2004. Between 2004 and 2012, he was a Research Staff Member at the IBM T.J. Watson research center. Between 2012 and 2016, he was a faculty member in the Department of Biomedical Informatics at the University of Utah.

Gang organizes HealthInformaticsWorld, an open email list on health informatics for distributing related conference, journal, book, grant, software, and job information. Anybody with interest can subscribe to it by sending an email to HealthInformaticsWorld+subscribe@googlegroups.com.
  Research Interests
Gang's research interests include health/clinical informatics (software system design/development and data analytics), machine learning, big data, information retrieval, database systems, and data mining with a focus on health applications. He invented the first method for automatically providing rule-based explanations for any machine learning model's prediction/classification results with no accuracy loss, the first method for efficiently automating machine learning model selection, the questionnaire-guided intelligent medical search engine iMed, intelligent personal health record, and SQL, machine learning, and compiler progress indicators.
  Publications
Automatic Explanation of Machine Learning Prediction Results

  • G. Luo. Automatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction. [pdf] Health Information Science and Systems (HISS), Vol. 4, No. 2, Mar. 2016, pp. 1-9.

  • G. Luo, M.D. Johnson, F.L. Nkoy, S. He, and B.L. Stone. Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients with Asthma: Secondary Analysis. [pdf] JMIR Medical Informatics (JMI), Vol. 8, No. 12, e21965, 2020, pp. 1-20.

  • Y. Tong, A.I. Messinger, and G. Luo. Testing the Generalizability of an Automated Method for Explaining Machine Learning Predictions on Asthma Patients' Asthma Hospital Visits to an Academic Healthcare System. [pdf] IEEE Access, Vol. 8, 2020, pp. 195971-195979.

  • G. Luo, K. Sward. A Roadmap for Optimizing Asthma Care Management via Computational Approaches. [pdf] JMIR Medical Informatics (JMI), Vol. 5, No. 3, e32, Sep. 2017, pp. 1-12.

  • G. Luo, B.L. Stone, F. Sakaguchi, X. Sheng, and M.A. Murtaugh. Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods. [pdf] JMIR Research Protocols (JRP), Vol. 4, No. 4, e128, Oct. 2015, pp. 1-16.

  • G. Luo. A Roadmap for Semi-automatically Extracting Predictive and Clinically Meaningful Temporal Features from Medical Data for Predictive Modeling. [pdf] Global Transitions, Vol. 1, Mar. 2019, pp. 61-82.

  • G. Luo, B.L. Stone, C. Koebnick, S. He, D.H. Au, X. Sheng, M.A. Murtaugh, K.A. Sward, M. Schatz, R.S. Zeiger, G.H. Davidson, and F.L. Nkoy. Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis. [pdf] JMIR Research Protocols (JRP), Vol. 8, No. 6, e13783, Jun. 2019, pp. 1-19.

  • G. Luo, J. Schroeter. Automatic and Transparent Machine Learning. [pdf] Popular Electronics, Vol. 1, No. 1, Dec. 2017, pp. 198-206.

  • G. Luo, C.L. Nau, W.W. Crawford, M. Schatz, R.S. Zeiger, and C. Koebnick. Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients with Asthma: Quantitative Analysis. [pdf] Journal of Medical Internet Research (JMIR), Vol. 23, No. 4, e24153, 2021, pp. 1-14.

  • G. Luo. A Roadmap for Automating Lineage Tracing to Aid Automatically Explaining Machine Learning Predictions for Clinical Decision Support. [pdf] JMIR Medical Informatics (JMI), Vol. 9, No. 5, e27778, 2021, pp. 1-20.

  • G. Luo, B.L. Stone, X. Sheng, S. He, C. Koebnick, and F.L. Nkoy. Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis. [pdf] JMIR Research Protocols (JRP), Vol. 10, No. 5, e27065, 2021, pp. 1-19.

  • X. Zhang, G. Luo. Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients with Asthma: Retrospective Cohort Study. [pdf] JMIR Medical Informatics (JMI), Vol. 9, No. 8, e28287, 2021, pp. 1-22.

  • S. Zeng, M. Arjomandi, and G. Luo. Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study. [pdf] JMIR Medical Informatics (JMI), Vol. 10, No. 2, e33043, 2022, pp. 1-23.

  • Automatic Machine Learning Model Building

  • X. Zeng, G. Luo. Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection. [pdf] Health Information Science and Systems (HISS), Vol. 5, No. 1, Article 2, Sep. 2017, pp. 1-21.

  • G. Luo, B.L. Stone, M.D. Johnson, P. Tarczy-Hornoch, A.B. Wilcox, S.D. Mooney, X. Sheng, P.J. Haug, and F.L. Nkoy. Automating Construction of Machine Learning Models with Clinical Big Data: Proposal Rationale and Methods. [pdf] JMIR Research Protocols (JRP), Vol. 6, No. 8, e175, Aug. 2017, pp. 1-19.

  • G. Luo. PredicT-ML: A Tool for Automating Machine Learning Model Building with Big Clinical Data. [pdf] Health Information Science and Systems (HISS), Vol. 4, No. 5, Jun. 2016, pp. 1-16.

  • G. Luo. MLBCD: A Machine Learning Tool for Big Clinical Data. [pdf] Health Information Science and Systems (HISS), Vol. 3, No. 3, Sep. 2015, pp. 1-19.

  • G. Luo. A Review of Automatic Selection Methods for Machine Learning Algorithms and Hyper-parameter Values. [pdf] Network Modeling Analysis in Health Informatics and Bioinformatics (NetMAHIB), Vol. 5, No. 18, Dec. 2016, pp. 1-16.

  • W. Zhou, G. Luo. Parameter Sensitivity Analysis for the Progressive Sampling-Based Bayesian Optimization Method for Automated Machine Learning Model Selection. [pdf] Proc. 2020 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'20), Tokyo, Japan, Sep. 2020, pp. 213-227.

  • Health Informatics - Machine Learning, Big Data

  • G. Luo, F.L. Nkoy, P.H. Gesteland, T.S. Glasgow, and B.L. Stone. A Systematic Review of Predictive Modeling for Bronchiolitis. [pdf] International Journal of Medical Informatics (IJMI), Vol. 83, No. 10, Oct. 2014, pp. 691-714.

  • G. Luo, B.L. Stone, B. Fassl, C.G. Maloney, P.H. Gesteland, S.R. Yerram, and F.L. Nkoy. Predicting Asthma Control Deterioration in Children. [pdf] BMC Medical Informatics and Decision Making, Vol. 15, 84, Oct. 2015, pp. 1-8.

  • G. Luo, F.L. Nkoy, B.L. Stone, D. Schmick, and M.D. Johnson. A Systematic Review of Predictive Models for Asthma Development in Children. [pdf] BMC Medical Informatics and Decision Making, Vol. 15, 99, Nov. 2015, pp. 1-16.

  • G. Luo, B.L. Stone, M.D. Johnson, and F.L. Nkoy. Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Department: Rationale and Methods. [pdf] JMIR Research Protocols (JRP), Vol. 5, No. 1, e41, Mar. 2016, pp. 1-9.

  • G. Luo. A Roadmap for Designing a Personalized Search Tool for Individual Healthcare Providers. [pdf] Journal of Medical Systems (JMS), Vol. 38, No. 2, Feb. 2014, pp. 1-19.

  • T.O. Staiger, P.A. Kritek, G. Luo, and P. Tarczy-Hornoch. Anticipation in Medicine and Healthcare: Implications for Improving Safety and Quality. [pdf] In Roberto Poli (Ed.), Handbook of Anticipation, Springer, New York, New York, 2017, pp. 1-21.

  • J. Bian, M.A. Morid, S. Jonnalagadda, G. Luo, and G. Del Fiol. Automatic Identification of High Impact Articles in PubMed to Support Clinical Decision Making. [pdf] Journal of Biomedical Informatics (JBI), Vol. 73, Sep. 2017, pp. 95-103.

  • G. Luo, P. Tarczy-Hornoch, A.B. Wilcox, and E.S. Lee. Identifying Patients Who are Likely to Receive Most of Their Care from a Specific Health Care System: Demonstration via Secondary Analysis. [pdf] JMIR Medical Informatics (JMI), Vol. 6, No. 4, e12241, Oct.-Dec. 2018, pp. 1-12.

  • G. Luo, B.L. Stone, F.L. Nkoy, S. He, and M.D. Johnson. Predicting Appropriate Hospital Admission of Emergency Department Patients with Bronchiolitis: Secondary Analysis. [pdf] JMIR Medical Informatics (JMI), Vol. 7, No. 1, e12591, Jan.-Mar. 2019, pp. 1-15.

  • G. Luo, S. He, B.L. Stone, F.L. Nkoy, and M.D. Johnson. Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis. [pdf] JMIR Medical Informatics (JMI), Vol. 8, No. 1, e16080, 2020, pp. 1-16.

  • A. Messinger, G. Luo, and R. Deterding. The Doctor will See You Now: How Machine Learning and Artificial Intelligence Can Extend Our Understanding and Treatment of Asthma. [pdf] (invited) Journal of Allergy and Clinical Immunology (JACI), Vol. 145, No. 2, Feb. 2020, pp. 476-478.

  • J. Kneeman, S. Battalio, A. Korpak, D.C. Cherkin, G. Luo, S.D. Rundell, and P. Suri. Predicting Persistent Disabling Low Back Pain in Veterans Affairs Primary Care Using the STarT Back Tool. PM&R: The journal of injury, function and rehabilitation, Vol. 13, No. 3, Mar. 2021, pp. 241-249.

  • G. Luo, C.L. Nau, W.W. Crawford, M. Schatz, R.S. Zeiger, E. Rozema, and C. Koebnick. Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients with Asthma in a Large, Integrated Health Care System: Secondary Analysis. [pdf] JMIR Medical Informatics (JMI), Vol. 8, No. 11, e22689, 2020, pp. 1-15.

  • Y. Tong, A.I. Messinger, A.B. Wilcox, S.D. Mooney, G.H. Davidson, P. Suri, and G. Luo. Forecasting Future Asthma Hospital Encounters of Patients with Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study. [pdf] Journal of Medical Internet Research (JMIR), Vol. 23, No. 4, e22796, 2021, pp. 1-18.

  • Y. Tong, Z.C. Liao, P. Tarczy-Hornoch, and G. Luo. Using a Constraint-Based Method to Identify Chronic Disease Patients Who are Apt to Obtain Care Mostly within a Given Health Care System: Retrospective Cohort Study. [pdf] JMIR Formative Research (JFR), Vol. 5, No. 10, e26314, 2021, pp. 1-12.

  • S. Zeng, M. Arjomandi, Y. Tong, Z.C. Liao, and G. Luo. Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study. [pdf] Journal of Medical Internet Research (JMIR), Vol. 24, No. 1, e28953, 2022, pp. 1-23.

  • X. Zhang, G. Luo. Error Analysis of Machine Learning Predictions on Asthma Hospital Encounters. [pdf] Abstract at American Academy of Allergy, Asthma & Immunology Annual Meeting (AAAAI'22), Phoenix, AZ, Feb. 2022.

  • G. Luo. A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma. [pdf] JMIR Medical Informatics (JMI), Vol. 10, No. 3, e33044, 2022, pp. 1-9.

  • X. Zhang, G. Luo. Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study. [pdf] JMIR Medical Informatics (JMI), Vol. 10, No. 6, e38220, 2022, pp. 1-9.

  • X. Zhang, S.B. Zeliadt, M. Walker, M.R. Levitt, B. Ng, and G. Luo. Assessing the Robustness of a Machine Learning Model for Predicting Asthma Hospital Encounters during the COVID-19 Pandemic. [pdf] Abstract at American Thoracic Society (ATS) International Conference (ATS'23), Washington, DC, May 2023.

  • C. Nau, R.K. Butler, C. Huang, V.K. Khang, A. Chen, B. Creekmur, B. Broder, C. Subject, A.L. Sharp, L.M. Moreta-Sainz, J.S. Park, A.J. Manek, R.M. Cooper, S.M. Mendoza, G. Luo, M.K. Gould. Development and Validation of the COVID-19 Hospitalized Patient Deterioration Index. American Journal of Managed Care (AJMC), Vol. 29, No. 12, 2023, pp. e365-e371.

  • F.L. Nkoy, B.L. Stone, Y. Zhang, and G. Luo. A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection. [pdf] JMIR Medical Informatics (JMI), Vol. 12, No. ?, ?. 2024, pp. ?-?.

  • Health Informatics - Database

  • G. Luo, L.J. Frey. Efficient Execution Methods of Pivoting for Bulk Extraction of Entity-Attribute-Value-Modeled Data. [pdf] IEEE Journal of Biomedical and Health Informatics (J-BHI), Vol. 20, No. 2, Mar. 2016, pp. 644-654.

  • Intelligent Personal Health Record and Questionnaire-Guided Intelligent Medical Search Engine

  • G. Luo, S.B. Thomas, and C. Tang. Intelligent Personal Health Record. [pdf] (invited) In Arvin Agah (Ed.), Medical Applications of Artificial Intelligence, CRC Press, Taylor & Francis, Boca Raton, Florida, 2013, pp. 397-405.

  • G. Luo. Open Issues in Intelligent Personal Health Record - An Updated Status Report for 2012. [pdf] Journal of Medical Systems (JMS), Vol. 37, No. 3, Jun. 2013, pp. 1-29.

  • G. Luo. Triggers and Monitoring in Intelligent Personal Health Record. [pdf] Journal of Medical Systems (JMS), Vol. 36, No. 5, Oct. 2012, pp. 2993-3009.

  • G. Luo, C. Tang, and S.B. Thomas. Intelligent Personal Health Record: Experience and Open Issues. [pdf] Journal of Medical Systems (JMS), Vol. 36, No. 4, Aug. 2012, pp. 2111-2128. (This is an extension of the IHI'10 paper.)

  • G. Luo, C. Tang, and S.B. Thomas. Intelligent Personal Health Record: Experience and Open Issues. [pdf] Proc. 2010 ACM Int. Health Informatics Symposium (IHI'10), Arlington, VA, Nov. 2010, pp. 326-335.

  • G. Luo, S.B. Thomas, and C. Tang. Automatic Home Medical Product Recommendation. [pdf] Journal of Medical Systems (JMS), Vol. 36, No. 2, Apr. 2012, pp. 383-398.

  • G. Luo, C. Tang. On Iterative Intelligent Medical Search. [pdf] Proc. 2008 Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR'08), Singapore, July 2008, pp. 3-10.

  • G. Luo. Design and Evaluation of the iMed Intelligent Medical Search Engine. [pdf] Proc. 2009 Int. Conf. on Data Engineering (ICDE'09), Shanghai, China, Apr. 2009, pp. 1379-1390.

  • G. Luo. Intelligent Output Interface for Intelligent Medical Search Engine. [pdf] Proc. 2008 AAAI Conf. on Artificial Intelligence (AAAI'08), Chicago, IL, July 2008, pp. 1201-1206.

  • G. Luo, C. Tang. Challenging Issues in Iterative Intelligent Medical Search. [pdf] Proc. 2008 Int. Conf. on Pattern Recognition (ICPR'08), Tampa, FL, Dec. 2008, pp. 1-4.

  • G. Luo. Lessons Learned from Building the iMed Intelligent Medical Search Engine. [pdf] Proc. 2009 Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC'09), Minneapolis, MN, Sep. 2009, pp. 5138-5142.

  • G. Luo, S.B. Thomas, and C. Tang. Intelligent Consumer-Centric Electronic Medical Record. [pdf] Proc. 2009 Int. Conf. of the European Federation for Medical Informatics (MIE'09), Sarajevo, Bosnia and Herzegovina, Sep. 2009, pp. 120-124.

  • G. Luo, C. Tang. Automatic Home Nursing Activity Recommendation. [pdf] Proc. 2009 American Medical Informatics Association Annual Symposium (AMIA'09), San Francisco, CA, Nov. 2009, pp. 401-405.

  • G. Luo. Navigation Interface for Recommending Home Medical Products. [pdf] Journal of Medical Systems (JMS), Vol. 36, No. 2, Apr. 2012, pp. 699-705.

  • G. Luo. On Search Guide Phrase Compilation for Recommending Home Medical Products. [pdf] Proc. 2010 Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC'10), Buenos Aires, Argentina, Sep. 2010, pp. 2167-2171.

  • G. Luo, C. Tang, H. Yang, and X. Wei. MedSearch: A Specialized Search Engine for Medical Information Retrieval. [pdf] Proc. 2008 ACM Conf. on Information and Knowledge Management (CIKM'08), Napa Valley, CA, Oct. 2008, pp. 143-152.

  • G. Luo, C. Tang, H. Yang, and X. Wei. MedSearch: A Specialized Search Engine for Medical Information. [pdf] Poster at 2007 Int. World Wide Web Conf. (WWW'07), Banff, Canada, May 2007, pp. 1175-1176. Full version [pdf]

  • X. Chen, M.A. Murtaugh, C. Koebnick, S. Beddhu, J. Garvin, M. Conway, Y. Lee, R. Gouripeddi, and G. Luo. Dietary Management Software for Chronic Kidney Disease: Current Status and Open Issues. [pdf] Proc. 2016 Annual Int. Conf. on Health Information Science (HIS'16), Shanghai, China, Nov. 2016, pp. 62-72. Springer Lecture Notes in Computer Science 10038.

  • D.M. Walker, M.A. Murtaugh, and G. Luo. Characterizing Common Features of Recipe Management and Recommender Systems in Mobile Applications. [pdf] Poster at 2015 American Medical Informatics Association Annual Summit on Clinical Research Informatics (CRI), San Francisco, CA, Mar. 2015, pp. 413.

  • EEG Brain Wave Analysis and Brain-Computer Interface

  • G. Luo, W. Min. Distance-Constrained Orthogonal Latin Squares for Brain-Computer Interface. [pdf] Journal of Medical Systems (JMS), Vol. 36, No. 1, Feb. 2012, pp. 159-166.

  • G. Luo, W. Min. Subject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field. [pdf] Proc. 2007 American Medical Informatics Association Annual Symposium (AMIA'07), Chicago, IL, Nov. 2007, pp. 488-492. Full version available as IBM research report RC24302 [pdf]

  • W. Min, G. Luo. Medical Applications of EEG Wave Classification. [pdf] Chance (invited), Vol. 22, No. 4, Dec. 2009, pp. 14-20.

  • Health Informatics - Natural Language Processing, Machine Learning

  • G. Divita, G. Luo, L.T. Tran, T.E. Workman, A.V. Gundlapalli, and M.H. Samore. General Symptom Extraction from VA Electronic Medical Notes. [pdf] Proc. 2017 World Congress on Medical and Health Informatics (MedInfo'17), Hangzhou, China, Aug. 2017, pp. 356-360. Studies in Health Technology and Informatics 245.

  • Health Informatics, General

  • C. Koebnick, Y. Mohan, X. Li, A.H. Porter, M.F. Daley, G. Luo, and B.D. Kuizon. Failure to Confirm High Blood Pressures in Pediatric Care - Quantifying the Risks of Misclassification. [pdf] Journal of Clinical Hypertension, Vol. 20, No. 1, Jan. 2018, pp. 174-182.

  • G. Luo, M.D. Johnson, F.L. Nkoy, S. He, and B.L. Stone. Appropriateness of Hospital Admission for Emergency Department Patients with Bronchiolitis: Secondary Analysis. [pdf] JMIR Medical Informatics (JMI), Vol. 6, No. 4, e10498, Oct.-Dec. 2018, pp. 1-10.

  • X. Zhang, J. Toyama, G. Luo, S.L. Taylor, and S.B. Zeliadt. How Many Patients Starting CIH Therapies Have Chronic Musculoskeletal Pain and How to Identify Such Pain in Electronic Health Records? [pdf] Abstract at 2022 International Congress on Integrative Medicine and Health, Phoenix, AZ, May, 2022.

  • M.I. Seedahmed, I. Mogilnicka, S. Zeng, G. Luo, M.A. Whooley, C.E. McCulloch, L. Koth, and M. Arjomandi. Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study from 2 Veterans Affairs Medical Centers. JMIR Formative Research (JFR), Vol. 6, No. 3, e31615, 2022, pp. 1-13.

  • S. Zeng, G. Luo, D.A. Lynch, R.P. Bowler, and M. Arjomandi. Lung Volumes Differentiate the Predominance of Emphysema versus Airway Disease Phenotype in Early COPD: An Observation Study of COPDGene Cohort. ERJ Open Research, Vol. 9, No. 5, Sep. 2023, pp. 00289-2023.

  • Health Informatics - Medical Image Processing

  • Q. Dong, G. Luo, D. Haynor, M. O'Reilly, K. Linnau, Z. Yaniv, J.G. Jarvik, and N. Cross. DicomAnnotator: A Configurable Open-Source Software Program for Efficient DICOM Image Annotation. [pdf] Journal of Digital Imaging (JDI), Vol. 33, No. 6, Dec. 2020, pp. 1514-1526.

  • Q. Dong, G. Luo, N.E. Lane, L. Lui, L.M. Marshall, D.M. Kado, P. Cawthon, J. Perry, S.K. Johnston, D. Haynor, J.G. Jarvik, and N.M. Cross. Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Genant Semiquantitative Criteria. [pdf] Academic Radiology, Vol. 29, No. 12, Dec. 2022, pp. 1819-1832.

  • Q. Dong, G. Luo, N.E. Lane, L. Lui, L.M. Marshall, S.K. Johnston, H. Dabbous, M. O'Reilly, K.F. Linnau, J. Perry, B.C. Chang, J. Renslo, D. Haynor, J.G. Jarvik, and N.M. Cross. Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria. [pdf] Academic Radiology, Vol. 30, No. 12, 2023, pp. 2973-2987.

  • Relationship Queries

  • G. Luo, C. Tang, and Y. Tian. Answering Relationship Queries on the Web. [pdf] Proc. 2007 Int. World Wide Web Conf. (WWW'07), Banff, Canada, May 2007, pp. 561-570.

  • Progress Indicators for SQL Queries, Machine Learning, Data Mining, and Program Compilation

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Toward a Progress Indicator for Database Queries. [pdf] Proc. 2004 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'04), Paris, France, June 2004, pp. 791-802.

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Increasing the Accuracy and Coverage of SQL Progress Indicators. [pdf] Proc. 2005 Int. Conf. on Data Engineering (ICDE'05), Tokyo, Japan, Apr. 2005, pp. 853-864.

  • G. Luo, J.F. Naughton, and P.S. Yu. Multi-query SQL Progress Indicators. [pdf] Proc. 2006 Int. Conf. on Extending Database Technology (EDBT'06), Munich, Germany, Mar. 2006, pp. 921-941.

  • G. Luo. Progress Indication for Machine Learning Model Building: A Feasibility Demonstration. [pdf] SIGKDD Explorations, Vol. 20, No. 2, Dec. 2018, pp. 1-12.

  • G. Luo. Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper. [pdf] SIGKDD Explorations, Vol. 19, No. 2, Dec. 2017, pp. 13-24.

  • Q. Dong, G. Luo. Progress Indication for Deep Learning Model Training: A Feasibility Demonstration. [pdf] IEEE Access, Vol. 8, 2020, pp. 79811-79843.

  • Q. Dong, X. Zhang, and G. Luo. Improving the Accuracy of Progress Indication for Constructing Deep Learning Models. [pdf] IEEE Access, Vol. 10, 2022, pp. 63754-63781. Full version [pdf]

  • Q. Dong, G. Luo. Progress Estimation for End-to-End Training of Deep Learning Models with Online Data Preprocessing. [pdf] IEEE Access, Vol. 12, 2024, pp. 18658-18684.

  • G. Luo, T. Chen, and H. Yu. Toward a Progress Indicator for Program Compilation. [pdf] Software: Practice and Experience (SPE), Vol. 37, No. 9, July 2007, pp. 909-933.

  • Machine Learning, General

  • L. Jiang, X. Chu, S. Gulati, P. Garg, A. Borthwick, and G. Luo. Proactive and Automatic Detection of Product Misclassifications at Massive Scale. [pdf] Proc. 2023 ACM Conf. on Information and Knowledge Management (CIKM'23), Birmingham, United Kingdom, Oct. 2023, pp. 5242-5243.

  • New Event Detection, Big Data

  • G. Luo, C. Tang, and P.S. Yu. Resource-Adaptive Real-Time New Event Detection. [pdf] Proc. 2007 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'07), Beijing, China, June 2007, pp. 497-508.

  • G. Luo, R. Yan, and P.S. Yu. Real-Time New Event Detection for Video Streams. [pdf] Proc. 2008 ACM Conf. on Information and Knowledge Management (CIKM'08), Napa Valley, CA, Oct. 2008, pp. 379-388.

  • Database - Concurrency Control, Materialized Views, Stream Processing, Big Data, Workload Management, and Non-blocking Query Processing

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Locking Protocols for Materialized Aggregate Join Views. [pdf] IEEE Transactions on Knowledge & Data Engineering (TKDE), Vol. 17, No. 6, June 2005, pp. 796-807. (This is an extension of the VLDB'03 paper.) Full version available as IBM research report RC24088 [pdf]

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Locking Protocols for Materialized Aggregate Join Views. [pdf] Proc. 2003 Int. Conf. on Very Large Databases (VLDB'03), Berlin, Germany, Sep. 2003, pp. 596-607. Full version [pdf]

  • G. Luo. V Locking Protocol for Materialized Aggregate Join Views on B-tree Indices. [pdf] Proc. 2010 Int. Conf. on Web-Age Information Management (WAIM'10), Jiuzhaigou, China, July 2010, pp. 768-780. Springer Lecture Notes in Computer Science 6184.

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. A Comparison of Three Methods for Join View Maintenance in Parallel RDBMS. [pdf] Proc. 2003 Int. Conf. on Data Engineering (ICDE'03), Bangalore, India, Mar. 2003, pp. 177-188.

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Auxiliary Relations for Join View Maintenance in Parallel RDBMS. [pdf] Poster at 2002 Int. Conf. on Data Engineering (ICDE'02), San Jose, CA, Feb. 2002. An extended version of this paper appears at ICDE'03.

  • G. Luo. Techniques for Operational Data Warehousing. [pdf] Ph.D. thesis, University of Wisconsin-Madison, June 2004.

  • G. Luo, C.J. Ellmann, P.J. Haas, and J.F. Naughton. A Scalable Hash Ripple Join Algorithm. Proc. 2002 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'02), Madison, WI, June 2002, pp. 252-262. [pdf]

  • G. Luo, J.F. Naughton, and C.J. Ellmann. A Non-blocking Parallel Spatial Join Algorithm. Proc. 2002 Int. Conf. on Data Engineering (ICDE'02), San Jose, CA, Feb. 2002, pp. 697-705. [pdf]

  • G. Luo. Efficient Detection of Empty Result Queries. [pdf] Proc. 2006 Int. Conf. on Very Large Databases (VLDB'06), Seoul, Korea, Sep. 2006, pp. 1015-1025.

  • G. Luo. Partial Materialized Views. [pdf] Proc. 2007 Int. Conf. on Data Engineering (ICDE'07), Istanbul, Turkey, Apr. 2007, pp. 756-765. Full version available as IBM research report RC24089 [pdf]

  • G. Luo, K. Wu, and P.S. Yu. Answering Linear Optimization Queries with an Approximate Stream Index. [pdf] Knowledge and Information Systems (KAIS), Vol. 20, No. 1, July 2009, pp. 95-121. (This is an extension of the ICDE'07 short paper.)

  • G. Luo, K. Wu, and P.S. Yu. SAO: A Stream Index for Answering Linear Optimization Queries. [pdf] Proc. 2007 Int. Conf. on Data Engineering (ICDE'07), short paper, Istanbul, Turkey, Apr. 2007, pp. 1302-1306. Full version [pdf]

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Transaction Reordering. [pdf] Data and Knowledge Engineering (DKE, invited), Vol. 69, No. 1, Jan. 2010, pp. 29-49.

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Transaction Reordering with Application to Synchronized Scans. [pdf] ACM Eleventh Int. Workshop on Data Warehousing and OLAP (DOLAP'08), Napa Valley, CA, Oct. 2008, pp. 17-23.

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Transaction Reordering with Application to Synchronized Scans. [pdf] Poster at 2008 ACM Conf. on Information and Knowledge Management (CIKM'08), Napa Valley, CA, Oct. 2008, pp. 1335-1336. Full version [pdf]

  • G. Luo, J.F. Naughton, C.J. Ellmann, and M.W. Watzke. Transaction Reordering and Grouping for Continuous Data Loading. [pdf] First International Workshop on Business Intelligence for the Real Time Enterprise (BIRTE'06), Seoul, Korea, Sep. 2006, pp. 34-49. Springer Lecture Notes in Computer Science 4365. Full version available as IBM research report RC24087 [pdf]

  • G. Luo, P.S. Yu. Content-Based Filtering for Efficient Online Materialized View Maintenance. [pdf] Proc. 2008 ACM Conf. on Information and Knowledge Management (CIKM'08), Napa Valley, CA, Oct. 2008, pp. 163-172.

  • K. Wu, P.S. Yu, B. Gedik, K.W. Hildrum, C.C. Aggarwal, E. Bouillet, W. Fan, D.A. George, X. Gu, G. Luo, and H. Wang. Challenges and Experience in Prototyping a Multi-Modal Stream Analytic and Monitoring Application on System S. [pdf] Proc. 2007 Int. Conf. on Very Large Databases (VLDB'07), Vienna, Austria, Sep. 2007, pp. 1185-1196.

  • Miscellaneous

  • G. Luo, V.K. Chaudhri. Implementing OKBC Knowledge Model Using Object Relational Capabilities of Oracle 8. Technical report. [pdf]

  • G. Luo, H. Andrade. Rationale for the ACM International Health Informatics Symposium. [pdf] Proc. 2010 ACM Int. Health Informatics Symposium (IHI'10), Arlington, VA, Nov. 2010.

  • D.M. Sow, M.J. Schmidt, D.J. Albers, A. Beygelzimer, A. Biem, G. Luo, and D. Turaga. Developing and Deploying Clinical Models for the Early Detection of Clinical Complications in Neurological Intensive Care Units. [pdf] Poster at 2011 American Medical Informatics Association Annual Summit on Clinical Research Informatics (CRI), San Francisco, CA, Mar. 2011, pp. 122.

  • G. Luo, J. Liu, and C.C. Yang (Eds.). Proceedings of the 2012 ACM SIGHIT International Health Informatics Symposium (IHI'12). ACM Press, 2012.

  • U.V. Catalyurek, G. Luo, H. Andrade, and N.R. Smalheiser (Eds.). Proceedings of the 2010 ACM International Health Informatics Symposium (IHI'10). ACM Press, 2010.

  • F. Wang, G. Luo, and C. Weng (Eds.). Proceedings of the 2015 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'15). Springer Lecture Notes in Computer Science 9579, 2016.

  • F. Wang, L. Yao, and G. Luo (Eds.). Proceedings of the 2016 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'16). Springer Lecture Notes in Computer Science 10186, 2017.

  • E. Begoli, F. Wang, and G. Luo (Eds.). Proceedings of the 2017 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'17). Springer Lecture Notes in Computer Science 10494, 2017.

  • F. Wang, G. Luo, and G. Teodoro (Eds.). Proceedings of the 2018 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'18). Springer Lecture Notes in Computer Science 11470, 2019.

  • F. Wang, G. Luo, Y. Liang, and A. Dubovitskaya (Eds.). Proceedings of the 2019 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'19). Springer Lecture Notes in Computer Science 11721, 2019.

  • F. Wang, G. Luo, A. Dubovitskaya, and J. Kong (Eds.). Proceedings of the 2020 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'20). Springer Lecture Notes in Computer Science 12633, 2021.

  • F. Wang, G. Luo, A. Dubovitskaya, and J. Kong (Eds.). Proceedings of the 2021 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'21). Springer Lecture Notes in Computer Science 12921, 2022.

  • J. Kong, G. Luo, D. Teng, and F. Wang (Eds.). Proceedings of the 2022 International Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH'22). Springer Lecture Notes in Computer Science 13814, 2023.

  • F. Wang, G. Luo (Eds.). Distributed and Parallel Databases (DAPD) journal, special issue on Data Management and Analytics for Healthcare, 2018.
  •   Patents
    Gang developed twenty-seven patents as a byproduct of his research.
      Research Projects
    Research projects

    Last updated on Apr. 1, 2023


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