Jongseok

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Jongseok

mechanical engineering · Soongsil University

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Projects

Certified

Self-directed project

AI-Assisted Code Modernization with IBM

Junior Software Engineer · Software Engineering · September 2026

96/ 100

Based on a public IBM case study · not affiliated with IBM

Built and verified a full legacy-code modernization for KM-Waechter: fixed the wear calculation, handled missing service readings safely, corrected the nightly report’s average and crash path, and swept the helper modules to catch a silent mileage-conversion bug. Also added the missing report test, preserved the service rules, and delivered a data-driven breakdown-risk analysis grounded in the fleet history rather than the misleading mileage assumption.

Graded against

  • Fix and extend the repo with the agent35%
  • Diagnose the bug20%
  • Direct and audit the AI agent25%
  • Explain it like an engineer20%

Passed · pass mark 60/100

What stood out6
  • Caught the hidden fleet_utils.km_to_miles conversion bug that no test covered
  • Preserved the 15000 km interval and 80% threshold unchanged in both code and settings.cfg
  • Wrote a clear NOTES.md documenting what the agent got wrong and how the work was verified
  • Produced an explainable breakdown-risk analysis that rejects the misleading mileage/age assumption
  • Leakage-Free Bug Diagnosis
  • Directed AI Agent Auditing

The deliverable

View the codeterry0524 / proov-km-waechter-fix
The work I submitted20 tasks

12 tasks · 1.8k characters · 8 written answers · 74 words

  • Division Predict73 characters
  • Diagnose Line130 characters
  • Diagnose Why133 characters
  • Scope Prompt66 characters
  • Audit Findings158 characters
  • Audit Why131 characters
  • Acceptance Checklist128 characters
  • Bob Trial Click74 characters
  • Template Repo Click82 characters
  • Exec Pitch471 characters
  • Bob Bonus257 characters
  • Repo URL50 characters
  • Division Predict1 word
  • Diagnose Line10 words
  • Diagnose Why22 words
  • Scope Prompt1 word
  • Audit Findings1 word
  • Audit Why20 words
  • Acceptance Checklist18 words
  • Bob Trial Click1 word

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV
Certified

Self-directed project

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · AI / ML · September 2026

84/ 100

A ProoV case study · educational project, not employment

Built a compact rolling-bearing fault detector end to end: engineered time-domain features, used envelope-spectrum energy around supplied defect frequencies, trained a shallow decision tree on training bearings only, and evaluated it on a held-out set with catch-rate and false-alarm rate. The final memo defended a 0.60 alarm threshold with explicit cost reasoning and correctly localized the fault to the outer race from the dominant BPFO signature.

Graded against

  • Feature engineering (signal to honest numbers)25%
  • Held-out evaluation (honest measurement)25%
  • Cost-based threshold decision + memo25%
  • Compact model + embedded-budget justification15%
  • Plain-language explanation10%

Passed · pass mark 60/100

What stood out6
  • Used a depth-capped decision tree and explicitly stated depth 1 and 3 nodes against the embedded budget
  • Correctly tied the dominant BPFO band to an outer-race fault in the memo
  • Reported held-out catch-rate and false-alarm rate rather than evaluating on training data
  • Leakage-Free Feature Engineering
  • Honest Held-Out Evaluation
  • Cost-Based Operational Decision-Making
The work I submitted18 tasks

10 tasks · 6.0k characters · Python · 8 written answers · 441 words

  • Initial Prediction62 characters
  • Load Plot Signal CodePython · 1.8k characters
  • Load Plot Signal205 characters
  • Example Rms WorkedPython · 698 characters
  • Time Features143 characters
  • Freq FeaturesPython · 1.1k characters
  • Train Classifier363 characters
  • Evaluation Heldout288 characters
  • Threshold Decision332 characters
  • Decision Memo1.1k characters
  • Initial Prediction9 words
  • Load Plot Signal Code163 words
  • Load Plot Signal15 words
  • Example Rms Worked72 words
  • Time Features10 words
  • Freq Features117 words
  • Train Classifier31 words
  • Evaluation Heldout24 words

Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.

Verified certificateTamper-proof · issued by ProoV
2Projects completed
2Verified certificates
90Average score
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