Research Objective
This research investigates GenAI-driven Ambient Intelligence and Smart Home Monitoring systems to bridge the semantic gap between clinical needs and technical implementation in Parkinson's disease care. The principal investigator is a Technical Project Manager with over 10 years of software delivery experience and 10+ years of hands-on family caregiving for a Parkinson's disease patient, holding an M.S. in Information Technology, with admission to Georgia Tech's OMSCS program. This unique combination of longitudinal caregiving experience and engineering expertise positions the researcher to contribute to a Smart Environments or Assistive Technology research group by leveraging real-world longitudinal data from Japan's super-aging society context.
The Problem Statement
People with Parkinson's disease (PD) face complex, intertwined risks involving medication mismanagement, sleep disturbances, and unpredictable motor fluctuations (e.g., freezing of gait, on/off periods). Existing remote monitoring solutions are either highly intrusive (such as video cameras) or fail to interpret raw data meaningfully, presenting charts that lack actionable context for family caregivers.
The "Semantic Gap"
This research addresses the critical disconnect between raw numerical sensor outputs and the actionable, context-aware insights required by caregivers. A system is needed that preserves privacy, decodes behavioral context, and translates data into human-readable care narratives.
Core Research Questions
Data & Ground Truth Verification
How can we reliably correlate multi-modal sensor anomalies with actual PD symptoms (e.g., freezing of gait, off-periods) using real-time, high-fidelity annotation by a resident caregiver?
Spatio-Temporal & Contextual Reasoning
Can reasoning models reveal hidden causal relationships between environmental stressors (e.g., ambient temperature fluctuations) and subsequent daily symptom fluctuations?
HCI & Generative AI Semantics
How can Large Language Models (LLMs) effectively bridge the "semantic gap" between raw digital signal processing and empathetic, human-readable care reports?
Proposed 3-Layered AI Architecture
A privacy-preserving, hybrid approach combining Edge Computing, Symbolic Reasoning, and Generative AI.
Privacy-First Sensing
Non-intrusive collection of telemetry via edge-computing environments:
- Medication: Smart pillbox / NFC logging
- Sleep & Rhythm: Under-mattress sensors & Passive Infrared (PIR) motion sensors
- Environment: Ambient Temp/Humidity sensors
Intelligent Modeling
Mapping deviations from established baseline circadian rhythms using hybrid inference models:
- Statistical anomaly detection against personal baselines
- Spatio-Temporal Symbolic Reasoning to identify contextual outliers
Generative AI Interface
Translating mathematical and temporal data into structural clinical narratives:
Longitudinal Methodology Plan
High-Fidelity "Ground Truth" Study (Japan Context)
Initial deployment in the researcher's home environment. Crucial Methodology Advantage: The researcher's dual role as primary resident caregiver enables real-time, high-quality ground-truth annotation of every detected sensor anomaly, generating a gold-standard baseline dataset of exceptional reliability for PD research.
Global or Regional Living Laboratory Deployment (Overseas / Japan)
Adapting and validating the architecture within broader settings, selectable between overseas environments and extended cohorts in Japan. This phase proposes converting real-world residential homes and local communities into decentralized "Living Laboratories," evaluating model generalizability and validating cross-environmental behavior recognition algorithms beyond the constraints of a traditional static laboratory setting.
Scientific
A validated framework for mapping non-intrusive sensor data directly to specific Parkinson's Disease metrics, backed by context-rich ground truth labels.
Technical
A novel architecture fusing Spatio-Temporal Symbolic Reasoning (deterministic logic) with Generative AI (semantic abstraction).
Societal
A human-centered "Care-Supportive AI" model tailored to alleviate caregiver burden in super-aging societies through real-world ecosystem scaling.
Academic Foundations & Credentials
Education Matrix
Georgia Institute of Technology
M.S. in Computer Science (OMSCS) | Machine Learning Specialization
University of the People
M.S. in Information Technology (MSIT)
Cloud & Methodological Validation
Anonymized Technical CV
Review full professional history stripped of personal identifiable information (PII).
TECHNICAL PROGRAM MANAGER · CLOUD · GENAI
Candidate ID: VEAI-PROFILE-01 // Location: Japan (Open to Relocation & Remote)
Professional Summary
Technical program manager and cloud engineer with over 10 years of software delivery experience and 10+ years of hands-on family caregiving for a Parkinson's disease patient. Architects secure, serverless AWS systems and works as a senior-level GenAI data-quality analyst evaluating the factuality of large language models. Holds an M.S. in Information Technology, with admission to Georgia Tech's OMSCS program.
Work Experience
Confidential global enterprise (under NDA), Remote
- Senior-level (Level III) analyst evaluating the factuality and alignment of large language models on a large-scale GenAI / knowledge platform.
- Performs multilingual (Japanese/English) model validation and safeguards structural data integrity to reduce hallucinations.
VEAI LAB. (Remote)
- Architects secure, serverless AWS systems (Lambda, DynamoDB, API Gateway, Cognito, CDK) and owns end-to-end technical delivery across a suite of production AgeTech products.
- Designs for tenant isolation, health-data privacy boundaries, and high availability, with CI/CD and automated testing.
Longitudinal Home Environment
- Managing 24/7 care logistics, medication schedules, and mobility risk assessment for a patient over a 10-year progression cycle.
- Acquired deep tacit knowledge of motor fluctuations, directly inspiring PhD-level architecture for ambient non-intrusive sensor data capture.
Core Tech Stack
Python, JavaScript/Node.js, SQL; AWS serverless (Lambda, DynamoDB, API Gateway, Cognito, CDK), CI/CD; LLM evaluation (factuality, multilingual); IoT/ambient sensing (MQTT, edge concepts).
Methodologies
AWS serverless architecture, DevOps CI/CD, human-centered design, data-quality QA, Agile/Scrum, version control (Gitflow).