Upcoming Mason Science Series Event

Dr. Cody W. Edwards, Dean, College of Science 
Dr. Ferah Munshi, Associate Professor, Department of Physics and Astronomy
Dr. Anamaria Berea, Associate Professor, Department of Computational & Data Sciences

Join us for a conversation on how the future of space exploration depends on collaboration across disciplines. Dr. Ferah Munshi and Dr. Anamaria Berea will discuss how astronomers, engineers, data scientists, policymakers, entrepreneurs, educators, and students can work together to tackle the biggest challenges and opportunities in space, and how Mason is helping to shape this new collaborative model.

Dr. Ferah Munshi
Associate Professor, Department of Physics and Astronomy

Ferah Munshi is an astrophysicist and dark matter hunter exploring some of the universe’s most elusive mysteries. As a Simons Emmy Noether Fellow and recipient of the COS Dean’s Early Career Excellence Award (2025) at George Mason University, her work bridges theory, computation, and observation to understand the invisible (dark) matter shaping galaxies and cosmic structure. Passionate about asking questions at the edge of the known, Munshi challenges how we define discovery when the universe refuses to reveal its secrets easily. Beyond research, she is committed to expanding who gets to participate in science and how bold ideas are cultivated in academia. Through her work, she invites us to rethink what it means to search for something we cannot see—but know must exist.

Dr. Anamaria Berea
Associate Professor, Department of Computational & Data Sciences
 

Dr. Berea holds a Ph.D. in economics (2010) and computational social science (2012) and her current research is focused on the emergence of communication in biological and social networks, by applying theories and methods from economics, complex systems, and information theory to understand fundamental patterns of communication in both living and non-living systems. She is an interdisciplinary scientist and uses a wide range of computational methods (agent-based modeling, network analysis, predictive analytics, natural language processing techniques, Bayesian Networks, semantic analyses and machine learning) as well as complex science and economic modeling to study heterogeneous, noisy, complex systems phenomena.