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Hua Fang PhD

TitleAdjunct Associate Professor
InstitutionUniversity of Massachusetts Medical School
DepartmentPopulation and Quantitative Health Sciences
AddressUniversity of Massachusetts Medical School
368 Plantation Street, AS6-1074
Worcester MA 01605
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    Other Positions
    InstitutionUMMS - School of Medicine
    DepartmentPopulation and Quantitative Health Sciences
    DivisionBiostatistics And Health Services Research

    InstitutionUMMS - Graduate School of Biomedical Sciences
    DepartmentClinical Population Health Research

    InstitutionUMMS - Graduate School of Biomedical Sciences
    DepartmentMasters in Clinical Investigation


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    Sichuan International Studies University, Chongqing, , ChinaBABusiness English
    Ohio University, Athens, OH, United StatesMAFinancial Economics
    Ohio University, Athens, OH, United StatesPHDStatistics

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    Biography


    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 (2012-2014) 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 (2013-2018): 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. 


    She is also the PI of a NSF/CNS research project (2017-2019): Explore new statistical modeling approaches to characterize the 60GHz WBAN in mHealth applications.


    Dr. Fang has been a statistical consultant in health, medicine, economics, and bio-engineering areas over a decade. She also participated in large-scale multi-disciplinary projects at both state and federal levels. She has sustained continous funding as PI/Co-I/Statistician from over 20 extramural grants from  NSF, NIH, VA or PCORI in the past ten years.  


    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




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    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Faculty can login to make corrections and additions.
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    1. Gurugubelli VS, Li Z, Wang H, Fang H. eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data. Int Conf Comput Netw Commun. 2018 Mar; 2018:912-916. PMID: 30906794.
      View in: PubMed
    2. Kim SS, Fang H, Bernstein K, Zhang Z, DiFranza J, Ziedonis D, Allison J. Acculturation, Depression, and Smoking Cessation: a trajectory pattern recognition approach. Tob Induc Dis. 2017; 15:33. PMID: 28747857.
      View in: PubMed
    3. Fang H. MIFuzzy Clustering for Incomplete Longitudinal Data in Smart Health. Smart Health (Amst). 2017 Jun; 1-2:50-65. PMID: 28993813.
      View in: PubMed
    4. Wang J, Fang H, Carreiro S, Wang H, Boyer E. A New Mining Method to Detect Real Time Substance Use Events from Wearable Biosensor Data Stream. Int Conf Comput Netw Commun. 2017 Jan; 2017:465-470. PMID: 28993811.
      View in: PubMed
    5. Fang H, Zhang Z. An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data. IEEE Trans Big Data. 2018 Jun; 4(2):289-298. PMID: 29888298.
      View in: PubMed
    6. 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. PMID: 27660494.
      View in: PubMed
    7. Zhang Z, Fang H. Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data. IEEE Int Conf Connect Health Appl Syst Eng Technol. 2016 06; 2016:219-228. PMID: 29034067.
      View in: PubMed
    8. 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. PMID: 27334894.
      View in: PubMed
    9. Zhang Z, Fang H, Wang H. A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data. IEEE Access. 2016; 4:2272-2280. PMID: 27482473.
      View in: PubMed
    10. 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. PMID: 27126063.
      View in: PubMed
    11. Wang CJ, Fang H, Wang H. ESammon: A Computationaly Enhanced Sammon Mapping based on Data Density. Int Conf Comput Netw Commun. 2016 Feb; 2016. PMID: 27668263.
      View in: PubMed
    12. 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. PMID: 26490144.
      View in: PubMed
    13. Zhang Z, Wang H, Wang C, Fang H. Modeling Epidemics Spreading on Social Contact Networks. IEEE Trans Emerg Top Comput. 2015 Sep; 3(3):410-419. PMID: 27722037.
      View in: PubMed
    14. 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. PMID: 26504265.
      View in: PubMed
    15. 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. PMID: 24878686.
      View in: PubMed
    16. 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. PMID: 25741173.
      View in: PubMed
    17. Kim SS, Fang H, McKee SA, Ziedonis D. Prospective Evaluation of Factors Predicting Nicotine Withdrawal Symptoms Among Korean Americans. J Smok Cessat. 2014; 2014. PMID: 26413165.
      View in: PubMed
    18. Zhang Z, Wang H, Wang C, Fang H. Interference Mitigation for Cyber-Physical Wireless Body Area Network System Using Social Networks. IEEE Trans Emerg Top Comput. 2013 Jun; 1(1):121-132. PMID: 25436180.
      View in: PubMed
    19. Moormann AM, Sumba PO, Chelimo K, Fang H, Tisch DJ, Dent AE, John CC, Long CA, Vulule J, Kazura JW. Humoral and cellular immunity to Plasmodium falciparum merozoite surface protein 1 and protection from infection with blood-stage parasites. J Infect Dis. 2013 Jul; 208(1):149-58. PMID: 23539744.
      View in: PubMed
    20. 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. PMID: 22266824.
      View in: PubMed
    21. 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. PMID: 21256430.
      View in: PubMed
    22. 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. PMID: 21030468.
      View in: PubMed
    23. 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. PMID: 20300543.
      View in: PubMed
    24. 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. PMID: 20336179.
      View in: PubMed
    25. 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. PMID: 19586210.
      View in: PubMed
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