As a Ph.D. student in Quantitative Marketing at the Johnson Business School of Cornell University, I am passionate about applying machine learning techniques to analyze unstructured data, such as text and images, and uncover insights that can inform marketing decisions and strategies. I am currently working as a graduate research assistant at Cornell, where I am involved in a research project on facial expression recognition using computer vision methods, and exploring its potential applications in the field of marketing.
Prior to joining Cornell, I completed a Master of Science in Mathematics from the University of British Columbia, where I focused on the utilization of deep learning algorithms for predicting waiting times in service and healthcare systems. I demonstrated the value of textual data in enhancing prediction accuracy published my findings in a peer-reviewed journal, and received the third-best research paper award at the POMS International Conference 2022. I also hold a Bachelor of Science in Industrial Engineering from Tehran Polytechnic, where I developed an innovative task allocation method for service systems, using a combination of tree-based ensemble classifiers. My core competencies include statistical data analysis, computer vision, machine learning, and text mining.
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Business Administration/ManagementPhDCornell University2028-08-01 United States of America (USA)