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Our Current Team & Tasks

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Task: Explainable AI (XAI) based Breast cancer diagnostic and prognostic decision support system for Pathologists and Oncologists

This task focuses on designing and developing an Explainable AI (XAI)-driven precision diagnostic and prognostic decision support system. Tailored for oncologists and pathologists, this system will leverage histopathology image data to enhance the accuracy and explainability of breast cancer diagnostics and prognosis.

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Task: Real-time Video to Sign Language Video Synthesis

This task focuses on developing a holistic spoken-language to sign-language (SLG) framework. This framework will translate normal spoken-language videos into sign language videos, ensuring a visually consistent and inclusive experience for the hard-of-hearing community.

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Task: AI-Driven Smart Material-Actuated (SMA) Soft-Robotic Gripper with Tactile Sensor Array for Precise Gripping Control

This task focuses on the design and development of an AI-controlled Smart Material-based Actuator (SMA) robotic gripper. It incorporates tactile array feedback for precise control, aiming for high accuracy in classification and regression tasks.

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Task: AI for Project Resource Integration and Management for Efficient Governance (AI-PRIME)

This task focuses on developing an AI-powered system for managing and integrating project resources, enabling precise, context-aware matching via similarity searches and seamless information access through advanced conversational AI and machine learning techniques.

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Task: Advancing Digital Metrology through AI

This task focuses on designing and developing metrology-aligned models for evaluating and quantifying uncertainty in AI/ML-based measurement models. Specifically for thermocouple and AMR sensors, this work aims to ensure traceability, reproducibility, and compliance with international metrological standards.

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Task: Foundational methods for Learning-from-Demonstrations validated for Autonomous Navigation

This task focuses on developing and validating a foundational, domain-agnostic Learning-from-Demonstration (LfD) framework for autonomous navigation. This framework aims to improve task decomposition, reward learning, and adaptation, all while requiring minimal expert demonstrations.

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Task: Spatio-Geometric Foundational Models

This task focuses on the development of spatio-geometric foundational models that can be applied to a wide range of downstream applications.

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