Unser Team
Ein missionsorientiertes Team von Entwicklern und Forschern der Massey University, Neuseeland
Sarah Liu
KI, Produkt & Integration
Information Science student at Massey University and the original initiator of the HiLily project. Involved throughout the entire workflow—from user requirement discovery, outreach and partner communication, to product design and hands-on engineering. Focuses on Microsoft AI stack integration, long-term memory pipeline, and end-to-end MVP integration.
Alex Luo
Hardware, Prompt Engineering, Demo & Nutzerforschung
Master's student in Information Science at Massey University with strong C/C++ and Python foundation. Owns the hardware workstream (ESP32-based voice device), contributes to prompt engineering, supports user needs research through requirement discovery and collection, and leads product demos to showcase the end-to-end MVP.
Yu Tang
System Design, Data & Pitch
Data Science student at the University of Auckland with a strong interest in AI-driven systems and social impact. Focuses on system design, product structure, and data-driven solution development. Leads pitch strategy and project presentations, ensuring the problem framing, technical approach, and value proposition are communicated clearly and effectively to judges and stakeholders.
Jayden Chan
Pitch Presenter
Master of Information Science student at Massey University. Served as the pitch presenter during Phase 1 of the Imagine Cup competition, helping communicate the problem framing, technical approach, and value proposition to judges.
Akademischer Berater
Dr. Tong Liu
Lecturer, School of Mathematical and Computational Sciences
Massey University
Specializes in Artificial Intelligence, Machine Learning, and Generative AI. Provides academic and technical guidance on AI methodology, model evaluation, and research rigor for the project.
AI Coach
Jay Liu
AI Architect and Audio Algorithm Specialist
Volunteer AI Coach for HiLily, guiding AI system architecture design including AI pipeline development, conversational AI with long-term memory systems, and AI-based acoustic fall detection. Provides technical guidance on hardware audio algorithm design for edge voice processing.
Unser Design begann mit unseren Nutzern
15
interviewte Senioren
(Alter 68-85)
12
befragte Familienangehörige
als Pflegende
3
konsultierte Seniorenpflege-
organisationen