I work as a backend developer in Korea and am preparing for ICML 2026 as a networking and research-direction milestone, not as a paper author.

My current transition is:

Backend Engineer -> XAI / Agent Explainability Researcher-in-Progress

I am interested in research questions that connect practical engineering constraints with explainability evaluation:

• Medical XAI: evaluating whether heatmaps or explanation maps are faithful to the model's decision process, not only visually plausible.
• Uncertainty & Calibration: combining uncertainty signals with explanations so users can judge when an explanation should not be trusted.
• Agent Explainability: identifying which parts of an LLM agent trace are actually useful for debugging, accountability, and calibrated trust.
• AI-assisted Software Engineering: evaluating AI coding-agent output through maintainability, testability, and failure analysis, not only pass/fail results.

My engineering background is in enterprise web systems, database-heavy services, and deployment workflows. I try to keep the research transition grounded in systems that have real constraints: legacy code, operational risk, unclear requirements, and verification cost.

• Backend: ASP.NET Core, ASP.NET Web Forms, Spring Boot, Java, C#
• Database: MSSQL schema design, stored procedures, views, query optimization
• Infrastructure: IIS, Windows virtual servers, Nginx reverse proxy
• Frontend: React, Vite, JavaScript/TypeScript
• AI-assisted workflow: agentic planning, code review, refactoring support, test generation, QA checklists, failure analysis

The part I want to carry into research is not the claim that AI makes development faster. It is the habit of asking: what evidence would make this output trustworthy?

1. Explanation Faithfulness
   When a heatmap looks reasonable, how do we know whether it reflects the model's actual decision process?

2. XAI + Uncertainty
   Can calibration or uncertainty estimation help users decide when an explanation should not be trusted?

3. Agent Trace Evaluation
   Which intermediate steps of an AI agent are useful for debugging, accountability, and calibrated trust?

4. AI-assisted Software Engineering
   Can real engineering tasks become small benchmarks for evaluating AI coding agents beyond pass/fail tests?

These repositories are being reorganized as evidence for the research direction above:

• HRClaw: Windows-first local multi-agent runtime with approval, audit, provider routing, bounded connectors, and worker execution records. Relevant to agent trace, accountability, and operator-facing explanations.
• orchestrator: plan-driven media production harness with explicit artifacts, schemas, validation gates, and quality bars. Relevant to reproducible agent workflows and artifact-level evaluation.
• TravelSearcher: full-stack flight-search service using Next.js, TypeScript, Supabase, Redis caching, API integration, and QA reports. Relevant to backend systems, data flow, and validation under real application constraints.
• ARIS-research-tool-: forked research-workflow reference for autonomous research loops. Useful as a study/reference repo; it should be pinned only if I add my own adaptation notes or experiments.

More context is tracked in research-direction.md.

• Email: jsjsjsjsjs1019@naver.com
• Portfolio: Notion Portfolio
• GitHub: github.com/dingmon1019