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About

Maryam Sharifi is a robotics researcher whose work bridges adaptive control, multi-agent coordination, and safety-critical navigation. Her research focuses on enabling robots to interact safely with uncertain environments and human tissues, with key contributions in force estimation, finite-time consensus, and predictive control under constraints. In her 2015 paper on adaptive estimation of robot-environment force during soft tissue interaction (7 citations), she developed a position control strategy that estimates interaction forces without direct sensing, critical for surgical and rehabilitation robotics. Her 2020 work on finite-time consensus of nonlinear multi-agent systems (3 citations) tackled the challenge of unknown communication delays and partial signal access, providing delay-independent stability criteria for swarms. More recently, her 2024 paper on robust prescribed-time predictive control (2 citations) introduced a novel NMPC framework for mobile robot navigation that guarantees arrival within a user-specified time while respecting state and input constraints. Sharifi’s 2022 paper on enhancing data-driven reachability analysis with temporal logic side information (6 citations) addresses the conservatism of data-driven methods by incorporating logical specifications, improving safety guarantees for robots operating from noisy historical data. Her work is increasingly relevant as autonomous systems must operate reliably in human-centered, time-critical environments.