Human-Machine Perception Lab

Overview

The Human–Machine Perception Laboratory at UNR, under Dr. Tavakkoli’s direction, pioneers the integration of AI-driven medical imaging, wearable diagnostics, and digital twin technologies to enhance personalized healthcare, natural and environmental sciences, engineering, and biomedical fields. We have been investigating various computational frameworks for efficient and reliable integration of human and machine perception in interdisciplinary fields. Through a blend of immersive XR platforms, cutting‑edge machine learning, and real‑world sensing networks, the lab continues to push the boundaries of how machines perceive, interpret, and interact with complex biological and environmental systems.

Projects

Neuro-Oculo-Motor Screening

An extended‑reality (XR) ophthalmic diagnostic suite that harnesses real‑time eye‑tracking within XR headsets (e.g., Pico 4) to perform visual field, color vision, and RAPD assessments—exemplifying the lab’s commitment to immersive, data-rich wearable diagnostics.

Medical Diagnostics

In collaboration with leading medical institutions, the lab develops deep‑learning architectures to automate the detection of various pathologies and their progression contributing to advancements in remote, AI‑based medical screening.

Medical Digital Twins

We explores digital twin models that mirror human visual physiology, enabling the simulation of ophthalmic deficits and aiding clinicians in managing disease progression.

Environmental Digital Twins

We extend its visual‑AI expertise to fire science and remote sensing, leveraging computer vision and UAV‑IoT frameworks to improve wildfire detection and environmental monitoring—areas increasingly critical amid rising wildfire risks.