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About

Helena R. Torres is a rising leader in the intersection of medical robotics and dermatological laser therapy, with a focus on developing autonomous systems for non-invasive treatments. Her research centers on three key areas: robotic-guided laser surgery, task-based design optimization for medical robots, and AI-driven 3D reconstruction for oncological interventions. Torres’s most cited work, "Characterization of the Workspace and Limits of Operation of Laser Treatments for Vascular Lesions of the Lower Limbs" (2022, 5 citations), addresses the growing demand for infection-free, non-invasive treatments for an aging population by quantifying the operational constraints of robotic laser systems. Her subsequent study, "Design optimization of medical robotic systems based on task performance metrics" (2023, 4 citations), introduces a novel framework that eliminates designer bias in end-effector tool development, a significant advancement for medical robotics. Most notably, Torres pioneered a robust 3D breast reconstruction method using monocular images and artificial intelligence (2023, 3 citations), directly targeting the challenges of ultrasound-guided biopsies—a procedure notoriously hindered by poor image quality and probe complexity. This work promises to enhance precision in robotic-guided oncological interventions. With a growing citation footprint, Torres is establishing herself as a key innovator in automating delicate medical procedures.