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Workshop / Seminar

MME Seminar: Bayesian Decision Making and Learning in Complex Uncertain Systems

Join us via Zoom

About the event

Dr. Seyede Fatemeh Ghoreishi, Postdoctoral Research Fellow, Maryland Robotics Center, University of Maryland

Design and decision making are pervasive in scientific and industrial endeavors: scientists design experiments to gain insights into physical and social phenomena, engineers design machines to execute tasks more efficiently, and pharmaceutical researchers design new drugs to fight disease. All these problems are fraught with choices, choices that are often complex and high-dimensional, with constraints and uncertainties that make them difficult for individuals to reason about. Despite several advances made in design and decision making in recent years, lack of reliability and lack of scalability have prevented their applications to a wide range of practical problems. This talk will focus on large-scale and reliable design and decision-making from the machine learning and Bayesian statistical perspective.


Seyede Fatemeh Ghoreishi is a postdoctoral research fellow in the Maryland Robotics Center at the University of Maryland. She received her M.Sc. and Ph.D. degrees both in Mechanical Engineering from Texas A&M University in 2016 and 2019 respectively. She holds a minor in Applied Statistics from the department of Statistics at Texas A&M University. She also received a M.Sc. degree in Biomedical Engineering from Iran University of Science and Technology in 2014 and a B.Sc. degree in Mechanical Engineering from the University of Tehran in 2012. She was selected as Rising Stars in Computational and Data Sciences at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin in 2019 and the Rising Stars in Mechanical Engineering at University of California, Berkeley in 2020.