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Saeed Hassanpour

Associate Professor at Biomedical Data Science, Dartmouth College4 Followers

Recognized as a leader in the field of biomedical data science, Saeed Hassanpour serves as an Associate Professor at Dartmouth College, where he also holds adjunct positions in Computer Science and Epidemiology. His academic journey began with a B.S. from Sharif University of Technology, followed by an M.Math from the University of Waterloo, culminating in a Ph.D. from Stanford University. His postdoctoral training at Stanford University School of Medicine further honed his expertise in computational methods for analyzing complex biomedical data. Dr. Hassanpour's research is at the forefront of developing innovative data analysis techniques, with a particular focus on genomic data and electronic health records. His work aims to bridge the gap between data science and healthcare, providing insights that can lead to improved patient outcomes and more efficient healthcare systems. His contributions to the field have been recognized through numerous publications and collaborations with leading researchers and institutions. Before embarking on his academic career at Dartmouth, Dr. Hassanpour gained valuable industry experience as a Research Engineer at Microsoft. This role allowed him to apply his computational skills to real-world problems, further enriching his research and teaching endeavors. His interdisciplinary approach, combining elements of computer science, epidemiology, and biomedical data science, positions him as a pivotal figure in advancing the understanding and application of data-driven solutions in healthcare. At Dartmouth, Dr. Hassanpour is not only dedicated to his research but also to mentoring the next generation of scientists and researchers. He is actively involved in guiding students through the complexities of biomedical data science, fostering an environment of innovation and critical thinking. His commitment to education and research continues to inspire those around him, making significant strides in the ever-evolving landscape of data science in medicine.

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