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    Last Name

    Hua Fang PhD

    TitleAssociate Professor
    InstitutionUniversity of Massachusetts Medical School
    DepartmentQuantitative Health Sciences
    AddressUniversity of Massachusetts Medical School
    55 Lake Avenue North
    Worcester MA 01655
      Other Positions
      InstitutionUMMS - Graduate School of Biomedical Sciences
      DepartmentClinical Population Health Research

      InstitutionUMMS - Graduate School of Biomedical Sciences
      DepartmentMasters in Clinical Investigation



        Dr. Fang is Associate Professor in Division of Biostatistics and Health Services Research, Department of Quantitative Health Science since 2014. Before joining UMass Medical School in 2010, she had served as Research Assistant Professor/ Biostatistician in University of Nebraska-Lincoln for four years. She graduated from Ohio University in 2006 with her Ph.D. core in Statistics. 

        She won a paper award at the 2006 Joint Research Conference on Statistics in quality industry and technology. She won Layman Awards for missing data modeling and growth trajectory pattern recognition via UNL research council competition in 2008.

        She is a recipient of the 2012 UMass CTSA Pilot Project award for modeling heterogeneity of treatment effects (HTE) in longitudinal RCT and observational studies, including 3 RCTs and 2 observational studies for comparative effectiveness research.

        She is the PI of NIH/NIDA R01 DISC project (2013-2017): Develop behavioral trajectory pattern recognition methods and tools for large-scale unstructured data from longitudinal RCT behavioral intervention studies for smoking cessation, generally, substance use, such as internet-delivered RCT interventions, and small-scaled culturally-tailored cognitive interventions. Her methods are used to capture behavioral (e.g, engagment/response) changes, identify and validate patterns, inform which  components or measures are working for which patients/users at what time and to what degree, therefore to clarify the efficacy of a trial and the effectiveness of a treatment/exposure. This approach is expanding to various application areas.

        Dr. Fang has been a statistical consultant in health, medicine, economics, and bio-engineering areas for years. She also participated in large-scale multi-disciplinary projects at both state and federal levels. She is PI/Co-I/Statistician on several extramural grants: NIH, VA or PCORI.  

        Dr. Fang's research interests include computational statistics, behavioral trajectory pattern recogntion, research design, statistical modeling and analyses in clinical and translational research. She is interested in developing novel methods and applying emerging robust techniques to enable or improve the health studies that can have potential impact on the treatment or prevention of human diseases. Her research applications are in data science, substance use, infectious diseases, immunology, nutritional epidemiology, behavioral medicine, and E-/M-health.

        Computational Statistics and Data Science (CSDS) lab --- PI: Julia Hua Fang


        See more selected Publications on E-scholarship

        selected publications
        List All   |   Timeline
        1. Kim SS, Sitthisongkram S, Bernstein K, Fang H, Choi WS, Ziedonis D. A randomized controlled trial of a videoconferencing smoking cessation intervention for Korean American women: preliminary findings. Int J Womens Health. 2016; 8:453-62.
          View in: PubMed
        2. Carreiro S, Wittbold K, Indic P, Fang H, Zhang J, Boyer EW. Wearable Biosensors to Detect Physiologic Change During Opioid Use. J Med Toxicol. 2016 Sep; 12(3):255-62.
          View in: PubMed
        3. Zhang Z, Fang H, Wang H. Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth. J Med Syst. 2016 Jun; 40(6):146.
          View in: PubMed
        4. Carreiro S, Fang H, Zhang J, Wittbold K, Weng S, Mullins R, Smelson D, Boyer EW. iMStrong: Deployment of a Biosensor System to Detect Cocaine Use. J Med Syst. 2015 Dec; 39(12):186.
          View in: PubMed
        5. Fang H, Zhang Z, Wang CJ, Daneshmand M, Wang C, Wang H. A survey of big data research. IEEE Netw. 2015 Sep-Oct; 29(5):6-9.
          View in: PubMed
        6. Kim SS, Kim SH, Fang H, Kwon S, Shelley D, Ziedonis D. A Culturally Adapted Smoking Cessation Intervention for Korean Americans: A Mediating Effect of Perceived Family Norm Toward Quitting. J Immigr Minor Health. 2015 Aug; 17(4):1120-9.
          View in: PubMed
        7. Zhang Z, Wang H, Wang C, Fang H. Cluster-based Epidemic Control Through Smartphone-based Body Area Networks. IEEE Trans Parallel Distrib Syst. 2015 Feb 9; 26(3):681-690.
          View in: PubMed
        8. Kim SS, Fang H, McKee SA, Ziedonis D. Prospective Evaluation of Factors Predicting Nicotine Withdrawal Symptoms Among Korean Americans. J Smok Cessat. 2014; 2014.
          View in: PubMed
        9. Fang H, Dukic V, Pickett KE, Wakschlag L, Espy KA. Detecting graded exposure effects: a report on an East Boston pregnancy cohort. Nicotine Tob Res. 2012 Sep; 14(9):1115-20.
          View in: PubMed
        10. Fang H, Johnson C, Stopp C, Espy KA. A new look at quantifying tobacco exposure during pregnancy using fuzzy clustering. Neurotoxicol Teratol. 2011 Jan-Feb; 33(1):155-65.
          View in: PubMed
        11. Fang H, Johnson C, Chevalier N, Stopp C, Wiebe S, Wakschlag LS, Espy KA. Using propensity score modeling to minimize the influence of confounding risks related to prenatal tobacco exposure. Nicotine Tob Res. 2010 Dec; 12(12):1211-9.
          View in: PubMed
        12. Fang H, Rizzo ML, Wang H, Espy KA, Wang Z. A new nonlinear classifier with a penalized signed fuzzy measure using effective genetic algorithm. Pattern Recognit. 2010; 43(4):1393-1401.
          View in: PubMed
        13. Fang H, Espy KA, Rizzo ML, Stopp C, Wiebe SA, Stroup WW. Pattern Recognition of Longitudinal Trial Data with Nonignorable Missingness: An Empirical Case Study. Int J Inf Technol Decis Mak. 2009 Sep 1; 8(3):491-513.
          View in: PubMed
        14. Espy KA, Fang H, Charak D, Minich N, Taylor HG. Growth mixture modeling of academic achievement in children of varying birth weight risk. Neuropsychology. 2009 Jul; 23(4):460-74.
          View in: PubMed
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