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Sara Mohammad Taheri
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Sara Mohammad Taheri is a PhD student at Northeastern University’s Khoury College of Computer Sciences, advised by Professor Olga Vitek. She is a member of the computational biology and statistical methods for studies of biomolecular systems group. A native of Tehran, Iran, Sara earned both her bachelor’s degree and master’s degree in mathematics from Sharif University of Technology.
Sara’s research area is causal inference in computational biology that includes causal discovery from biomolecular data, estimation of causal effect, and counterfactual inference. Currently, she is working on the estimation of causal effects in complex models that include hidden components known as latent variable models. Sara’s research can help personalize treatment for each patient which leads to the betterment of individuals’ lives. For example, counterfactual inference enables more precise predictions regarding who would be likely to survive without receiving a treatment, who would be likely to die even if they receive the treatment, and who would likely survive only if they received treatment.
Sara Mohammad Taheri is a PhD student at Northeastern University’s Khoury College of Computer Sciences, advised by Professor Olga Vitek. She is a member of the computational biology and statistical methods for studies of biomolecular systems group. A native of Tehran, Iran, Sara earned both her bachelor’s degree and master’s degree in mathematics from Sharif University of Technology.
Sara’s research area is causal inference in computational biology that includes causal discovery from biomolecular data, estimation of causal effect, and counterfactual inference. Currently, she is working on the estimation of causal effects in complex models that include hidden components known as latent variable models. Sara’s research can help personalize treatment for each patient which leads to the betterment of individuals’ lives. For example, counterfactual inference enables more precise predictions regarding who would be likely to survive without receiving a treatment, who would be likely to die even if they receive the treatment, and who would likely survive only if they received treatment.