Gireeja Ranade

Gireeja Ranade is an Assistant Teaching Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She previously served as a Researcher at Microsoft Research AI in the Adaptive Systems and Interaction Group. Her educational background includes a PhD in Electrical Engineering and Computer Science from UC Berkeley and an undergraduate degree from MIT.

Research Focus

Prof. Ranade's research spans control theory, information theory, and machine learning, with applications in wireless communication, algorithmic fairness, and misinformation analysis. Her work addresses fundamental challenges in system stabilization under uncertainty, real-time control optimization, and equitable resource allocation. She maintains strong collaborations across disciplines, resulting in publications at premier venues like IEEE Transactions on Automatic Control, PNAS, and The Web Conference.

Her recent publications demonstrate a consistent focus on robustness in control systems, fairness in algorithmic decision-making, and analysis of information propagation in online ecosystems. The work frequently combines theoretical rigor with practical implementations in robotics, networking, and social systems.

Awards and Recognition

• 2017 UC Berkeley Electrical Engineering Award for Outstanding Teaching
• 2020 UC Berkeley Award for Extraordinary Teaching in Extraordinary Times

Academic Leadership

Prof. Ranade leads a dynamic research group including PhD candidates, master's students, and undergraduates. She has advised over 25 students on projects ranging from neural network controllers to fairness metrics in resource allocation. She founded the CalMentors program, which connects UC Berkeley students with K-12 learners for tutoring support during the COVID-19 pandemic.

Educational Innovation

She co-designed and teaches UC Berkeley's introductory EECS 16A/B sequence, integrating linear algebra with applications in machine learning and circuit design. She has also developed courses on optimization (EECS127/227A) and data science (Data 102), with publicly available lecture videos demonstrating her teaching methodology.