Current Position

• 2020 - Present | Instructor in Radiation Oncology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.

Postdoctoral Fellowship

• 2018-2020 | Research Fellow, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.

Education

• 2017 | Ph.D.,  Department of Industrial & Systems Engineering, University of Washington, Seattle, WA.
• 2012 | M.Sc., Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
• 2009 | B.Sc., Department of Industrial Engineering, Isfahan University of Technology, Isfahan, Iran.

 

Research Interests (see here for more details)

• Optimal stopping of radiation therapy (OSRT).
• Stochastic and Bayesian analytics for dynamic assessment of patients' response to radiation treatment (RT), using Partially-observable Markov Decision Making Processes (POMDP) methods. 
• Developing interpretable machine learning models for predicting RT response, using Bayesian Networks and Random Forest.
• Dynamic robust optimization methods for addressing radiobiological uncertainties in RT treatment planning.
• Developing predictive biomarker panels for radiotherapy response using multi-modality imaging and blood-biomarkers.

Research Statement

Treatment personalization according to individual patient's biological characteristics remain the ultimate goal in cancer care. Three pillars of treatment personalization are (i) Discovery of predictive biomarkers of treatment response, (ii) Developing accurate predictive models for biomarker-based response prediction, and (iii) Devising dynamic and robust optimization methods for treatment adaptation. My research interest lies in the intersection of these areas.

I am interested in using advanced (big) data analytics on medical imaging, genomic, and proteomics data for discovery of novel predictive biomarkers of response to radiation therapy. I use state-of-the-art machine learning tools, with an additional focus on "interpretable machine learning",  to derive predictive models of RT response by synthesizing patient-specific information from clinical, pathological, and biomarker data. Furthermore, I heavily rely on advanced optimization methods, with a focus on robustness and adaptability, to adapt the treatment plans according to biomarker information and model's predictions. 

Select Publications

• See publications on Harvard Catalyst
• See publications on Google Scholar