Jason Chia-Sheng Lin

Doctoral Researcher, Artificial Intelligence in Medical Imaging / Signal Analysis Lab National Yang Ming Chiao Tung University

I build trustworthy AI systems for real-world, security-critical, and regulated environments. My work brings together AI systems, speech intelligence, LLM/RAG pipelines, medical cybersecurity, and investigation-informed thinking to design systems that remain useful when evidence, reliability, and deployment constraints matter.

Professional Profile

Area	Details
Current role	Doctoral Researcher
Lab	Artificial Intelligence in Medical Imaging / Signal Analysis Lab
Institution	National Yang Ming Chiao Tung University
Location	Taiwan
Website	jasonln0711.github.io
LinkedIn	jason-lin-1a648813b
Email	cre062400@gmail.com

Summary

• I am a doctoral researcher in the Artificial Intelligence in Medical Imaging / Signal Analysis Lab at NYCU, where I work across trustworthy AI, AI Software as a Medical Device (SaMD), speech and language pipelines, and security-aware evaluation.
• Before doctoral research, I worked in cybercrime investigation. That background continues to shape how I think about evidence, adversarial behavior, failure analysis, and the difference between a strong model demo and a system that can actually be trusted in practice.
• My current work focuses on trustworthy AI systems, medical cybersecurity governance, ASR + LLM + RAG workflows, and deployable AI for high-stakes environments.
• I am especially interested in collaborations that value technical depth, careful evaluation, human review, and realistic deployment conditions.

Core Focus Areas

Trustworthy AI Systems

Designing AI systems where reliability, evaluation, human review, and traceability are built into the architecture rather than treated as afterthoughts.

Speech, Language, and Evidence Pipelines

Building ASR + LLM + RAG workflows for conversational analysis, structured extraction, and evidence-aware reasoning over long-form audio and transcripts.

Security and High-Stakes Deployment

Studying privacy, leakage, adversarial risk, and governance constraints that shape AI systems used in regulated or security-sensitive environments.

Medical Cybersecurity and AI Governance

Current work includes cybersecurity risk management and governance for AI software medical devices, with attention to threat modeling, vulnerability and attack-surface analysis, Zero Trust implementation, and the interpretation of regulatory frameworks such as the U.S. FDA and Taiwan TFDA. I am particularly interested in translating standards and guidance into structured, verifiable engineering processes for cross-disciplinary teams.

Selected Projects

Speech Evidence Intelligence Pipeline

An evidence-aware ASR + LLM workflow for turning long-form conversational audio into structured, reviewable outputs for high-stakes analysis. The pipeline emphasizes traceability between generated outputs and source transcript evidence.

Fraud Conversation Analysis with RAG

A retrieval-augmented workflow for analyzing fraud-related conversations while keeping language-model outputs grounded in transcript evidence. The project focuses on investigator-friendly review, grounding quality, and hallucination control.

Federated Learning Leakage Study

An AI security study centered on leakage risk and privacy trade-offs in federated learning for sensitive collaborative training settings. The work compares realistic threat models instead of treating federated learning as privacy-safe by default.

Selected Writing

• Designing Speech Evidence Pipelines with ASR and LLMs
• From Cybercrime Investigation to Trustworthy AI

Speaking

CYBERSEC 2026 Official Session

Title: AI 軟體醫材的資安實戰：從美國 FDA 524B 規範到 Threat Modeling 與 Patch SLA 的完整落地

• Event: CYBERSEC 2026
• Track: Medical Cybersecurity Forum
• Format: Breakout Session
• Schedule: May 6, 2026, 16:15-16:45
• Venue: Taipei Nangang Exhibition Center Hall 2, 4F Conference Room 4A
• Speaker page: CYBERSEC 2026 Speaker Profile
• Talks page: jasonln0711.github.io/talks

This session focuses on cybersecurity design for AI software medical devices, using FDA 524B as a practical anchor for threat modeling, SBOM, Zero Trust design, and auditable risk governance in heavily regulated environments.

CISC 2025 English Conference Papers

1. Evolution and Defense Challenges of Ransomware-as-a-Service in the AI Era
Technical and strategic analysis using Medusa and CrazyHunter as a case study.

• Event: Cryptology and Information Security Conference 2025 (CISC 2025)
• Schedule: May 28-29, 2025
• Venue: Feng Chia University
• Format: Conference Paper, English
• Conference site: CISC 2025

This paper analyzes how AI-era RaaS operations evolve through BYOVD, LOTL, covert C2, and adaptive tradecraft, then connects those threats to a ZTAID-grounded zero-trust defense strategy for practical containment and response.

2. Integration of Threat Pulse Modeling into the ZTAID Zero Trust Maturity Assessment Model
An analytical framework for continuous intelligence-driven assessment.

• Event: Cryptology and Information Security Conference 2025 (CISC 2025)
• Schedule: May 28-29, 2025
• Venue: Feng Chia University
• Format: Conference Paper, English
• Conference site: CISC 2025

This paper proposes Threat Pulse Modeling (TPM) as a way to transform live cyber threat intelligence into ZTAID pillar-level maturity signals, combining pulse-event mapping, severity triage, and time-series forecasting to accelerate the intelligence-to-assessment-to-response loop.

Experience Snapshot

Current

Doctoral Researcher, NYCU Artificial Intelligence in Medical Imaging / Signal Analysis Lab
Researching trustworthy AI systems, medical cybersecurity, speech intelligence, grounded LLM workflows, and security-aware evaluation for real-world deployment.

Previous

Cybercrime Investigation
Worked on digital evidence, online fraud analysis, OSINT, and operational reasoning in high-stakes investigative settings.

Ongoing

Research and Technical Communication
Developing case studies, technical writing, and speaking material around trustworthy AI, speech systems, and deployment risk.

Toolkit

ML / LLM Systems

PyTorch, Transformers, Whisper, LLM Pipelines, RAG Systems

Speech / Language

ASR, Speech Intelligence, Transcript Processing, Evidence Extraction, Conversation Analysis

Security / Operations

Cybersecurity, Digital Forensics, OSINT, Fraud Analysis, Federated Learning Security

Research / Evaluation

Experiment Design, Evaluation Frameworks, Reproducible Workflows, Python, GitHub Actions

Collaboration

I welcome thoughtful conversations around research collaboration, trustworthy AI, speech and language systems, and AI deployment in security-sensitive or regulated environments.

• Research: jasonln0711.github.io/research
• Projects: jasonln0711.github.io/projects
• Contact: jasonln0711.github.io/contact

About This Repository

This repository contains the Astro source for my personal website and research portfolio, covering trustworthy AI, speech intelligence, cybersecurity, and regulated AI systems through research pages, project case studies, writing, and talks.

Recent SEO and Analytics Updates

• Standardized the site's shared SEO pipeline through the Astro layout and head components instead of adding a second SEO framework.
• Added unique, route-specific titles, meta descriptions, canonical URLs, Open Graph tags, and Twitter tags for the main public pages and detail pages.
• Normalized canonical URL handling around the production domain: https://jasonln0711.github.io.
• Refined structured data so the home page uses Person, WebSite, and WebPage; blog posts use BlogPosting; project pages use ResearchProject; and other public pages use WebPage.
• Kept the 404 page accessible but marked it noindex, follow, and verified that it does not appear in the sitemap.
• Verified that robots.txt points to the generated sitemap index at /sitemap-index.xml.
• Added RSS discovery in the shared head as an absolute alternate feed link.
• Improved factual image metadata for the shared headshot by adding dimensions and clearer alt text without changing the visual design.
• Added a centralized analytics layer in the shared layout so tracking logic is defined once and reused across pages.
• Added optimized high-value events for:
  • cta_click
  • project_open
  • blog_open
  • contact_click
  • external_link_click
  • scroll_depth
  • language_switch
  • 404_recovery
  • navigation_click
  • research_direction_open
  • talk_open
  • content_filter_use
• Added section-level location metadata so analytics can distinguish hero, navbar, footer, grid, card, and detail-page interactions.
• Added optional Microsoft Clarity support for heatmaps and session replays without changing the site design.
• Confirmed that the site remains visually unchanged and that npm run build passes after the refactor.

Files Updated for This Refactor

• .env.example
• src/lib/site.ts
• src/components/seo/Head.astro
• src/layouts/BaseLayout.astro
• src/components/layout/LanguageSwitcher.astro
• src/components/layout/Navbar.astro
• src/components/layout/Footer.astro
• src/components/home/Hero.astro
• src/components/home/ResearchThemes.astro
• src/components/home/FeaturedProjects.astro
• src/components/home/WritingPreview.astro
• src/components/project/ProjectCard.astro
• src/components/project/ProjectLayout.astro
• src/components/blog/BlogCard.astro
• src/pages/contact.astro
• src/pages/links.astro
• src/pages/blog/index.astro
• src/pages/projects/index.astro
• src/pages/research.astro
• src/pages/research/[slug].astro
• src/pages/404.astro

Analytics Setup

1. Copy .env.example to .env.
2. Paste the exact Plausible "Review Installation" snippet into PUBLIC_PLAUSIBLE_SNIPPET.
3. Optionally add PUBLIC_CLARITY_PROJECT_ID for Microsoft Clarity.
4. Optionally add PUBLIC_GOOGLE_SITE_VERIFICATION_TOKEN for Search Console.
5. Run npm run build or redeploy.

Dashboard Notes

• scroll_depth is intentionally tracked at 50 and 90 percent to keep the signal useful and lower-noise.
• Plausible is the primary event layer; Clarity is optional and intended for replay/heatmap debugging rather than KPI reporting.
• If you use the custom external_link_click event as your main outbound-link metric, avoid duplicating that with another outbound-link event source in your analytics dashboard.