About

Narges Razavian is an Assistant Research Professor at New York University, where she works in the Medical School on Machine Learning for Healthcare. With over 10 years of relevant experience, Narges has a strong background in research and data science. Narges started her academic journey at Sharif University of Technology, where she earned a Bachelor of Science in Software Engineering and Computer Engineering Department. She then went on to complete her Master of Science in Information Technology at ECE Department, University of Tehran, followed by two Master of Science degrees in Computer Science at Carnegie Mellon University. Finally, she earned her PhD in Computer Science from Carnegie Mellon University, where she focused on Probabilistic Graphical Models and applications. After completing her PhD, Narges worked as a Postdoctoral Associate at Courant Institute, New York University, where she gained experience in temporal predictive modeling for clinical informatics. Her work involved using deep learning and more traditional data-science based baselines. Narges' current role at New York University involves using machine learning to improve healthcare outcomes. Her work focuses on developing predictive models for clinical decision-making, using deep learning techniques and other data science approaches. Narges has a strong technical skillset, with expertise in research, ML, and deep learning. She is a highly skilled research scientist with a passion for using data to solve complex problems. Her work has been published in numerous academic journals, and she is a sought-after speaker at conferences and events. Overall, Narges Razavian is a talented researcher and data scientist with a proven track record of success. Her work in machine learning for healthcare has the potential to make a significant impact on patient outcomes, and she is a valuable asset to the New York University community.