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Location:

Gray Lab, 214

Phone (Primary):

(410) 706-6510

Fax:

(410) 328-2618

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Education and Training

Biosketch

Dr. Zhang obtained his PhD in Industrial and Systems Engineering with a concentration in applying Operations Research/Numerical Methods to Radiation Oncology. Among his contributions during his thesis work was the development of an optimization framework for radiotherapy treatment planning. After the completion of his graduate work, he spent 3 years in the department of Radiation Oncology as a post-doctoral research fellowship. As a post-doctoral fellow, he gained more thorough knowledge base of biomedical/clinical research. One of his research work during this time with mathematical modeling is to show that patient demographic and behavioral characteristics as well as HPV biomarkers are not an accurate substitute for clinical testing of tumor HPV status. Even at the early stage of his research career as a junior research faculty, Dr. Zhang has been academically productive, publishing 17 total peer-reviewed journal papers including 7 first-author papers and more than 100 abstracts and conference proceedings. In 2008, one of his abstract contributions was selected as a finalist for the Young Investigator Symposium at the AAPM annual meeting. One of his papers was selected as a Pierskalla Prize Finalist at the INFORMS Annual Meeting in 2009. His most recent paper titled "Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications?" was highlighted as Editors' Choice for the Medical Physics Scitation and medphys.org websites in September 2016. He was one of the two junior researchers received AAPM Research Seed Funding Initiative Awards to support his research in radiotherapy in 2011. He was invited to contribute book chapters to “Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques", "Healthcare Data Analytics" and "Healthcare Applications in Operations Research". His work using machine learning techniques to predict pathologic response of locally advanced esophageal cancer with PET/CT features received Best In Physics Award and John S. Laughlin Science Council Research Symposium Award from AAPM. His recent work of estimating benefit of dose reduction to highly ventilated lung regions for stage III non-small cell lung cancer patients received "Basic/Translation Science Abstract Award" from ASTRO in 2017.

Research/Clinical Keywords

Applying Operations Research and data analytics to cancer research; Medical Informatics and Bioinformatics; Healthcare IT.

Highlighted Publications

Zhang, HH, Shi, L., Meyer, RR, Nazareth, D., D’Souza, WD. Solving beam angle selection and dose optimization simultaneously via High-Throughput Computing, INFORMS Journal on Computing, Vol. 21(3), pp. 427-444, 2009.

Zhang, HH, D’Souza, WD, Shi, L., Meyer, RR. Modeling plan-related clinical complications using machine learning tools in a multi-plan IMRT framework.  International Journal of Radiation Oncology, Biology, Physics. Vol. 74, pp. 1617-1626, 2009.

Zhang, HH, Meyer, RR, Shi, L., D’Souza, WD. Minimum knowledge base for predicting organ-at-risk dose-volume levels and plan-related complications in IMRT planning. Physics in Medicine and Biology. Vol. 55, pp. 1935-1947, 2010.

Zhang, H., Tan, S., Chen, W., Kligerman, S., Kim, G., D’Souza W., Suntharalingam, M., Lu, W. Modeling pathologic response of esophageal cancer to chemo-radiotherapy using spatial-temporal FDG-PET features, clinical parameters and demographics. International Journal of Radiation Oncology, Biology, Physics. Vol. 88, pp. 195-203, 2014.

Lan, F., Jeudy, J., Senan, S., van Sornsen de Koste, JR, D’Souza, W., Tseng, H., Zhou, J., Zhang, HH. Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications? Medical Physics. 43:5093, 2016.

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