Abdullah Rahim Hussain

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Abdullah Rahim Hussain

robotics · Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology

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Projects

Certified

Self-directed project

Predictive Maintenance: Industrial ML for Fault Detection

Condition-Monitoring Engineer · AI / ML · September 2026

92/ 100

A ProoV case study · educational project, not employment

Built a working rolling-bearing fault detector end to end: engineered RMS, kurtosis, crest factor, and defect-frequency envelope features; localized the fault to the outer race via BPFO; trained a compact depth-2 decision tree on training bearings only; and evaluated it on held-out bearings with reported catch-rate and false-alarm rate. The candidate also wrote a maintenance memo that justified a 0.60 alarm threshold using their own cost numbers and explained the decision in plain language.

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
  • Computed the requested envelope-spectrum band energies and correctly identified BPFO as the dominant defect frequency.
  • Trained a depth-capped decision tree on training bearings only and stated both depth and node count against the embedded budget.
  • Used held-out metrics and a cost argument in the memo, rather than optimizing for raw accuracy alone.
  • Included a clear maintenance-facing explanation tying the 236.4 Hz outer-race peak to the alarm decision.
  • Leakage-Free Feature Engineering
  • Physics-Grounded Fault Localization
The work I submitted19 tasks

11 tasks · 6.4k characters · Python · 8 written answers · 393 words

  • Initial Prediction50 characters
  • Load Plot Signal CodePython · 1.9k characters
  • Load Plot Signal205 characters
  • Example Rms WorkedPython · 698 characters
  • Time Features143 characters
  • Freq FeaturesPython · 929 characters
  • Fault Location239 characters
  • Train Classifier363 characters
  • Evaluation Heldout287 characters
  • Threshold Decision332 characters
  • Decision Memo1.3k characters
  • Initial Prediction6 words
  • Load Plot Signal Code163 words
  • Load Plot Signal15 words
  • Example Rms Worked72 words
  • Time Features10 words
  • Freq Features82 words
  • Fault Location14 words
  • Train Classifier31 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
Certified

Self-directed project

Machine Learning for Automotive Safety

Perception Validation Engineer (ADAS Safety Sign-off) · AI / ML · August 2026

84/ 100

A ProoV case study · educational project, not employment

Built a perception validation dossier that defined detection rigorously with IoU≥0.5, computed aggregate and VRU-specific metrics from a scored synthetic table, and surfaced the night/occluded recall collapse that drives safety risk. The final release recommendation was evidence-based and internally consistent, with a real-world case analysis that linked the Uber Tempe crash to the same vulnerable-road-user night-miss failure mode.

Graded against

  • Detection metrics correctness (precision, recall, VRU false-negative rate)30%
  • Failure-mode analysis & per-condition rigour25%
  • SOTIF safety case quality (ISO 21448 reasoning)25%
  • Ship verdict — evidence-based & internally consistent20%

Passed · pass mark 60/100

What stood out6
  • Correctly treated VRU false-negative rate as the safety metric rather than relying on headline aggregate recall
  • Exposed the dangerous night and occluded slices, including the worst night+occluded recall collapse
  • Produced an internally consistent GO-WITH-CONDITIONS verdict tied to the dossier’s own numbers
  • Mapped the Uber Tempe crash to the same night VRU failure mode with a concrete mechanism
  • Leakage-Free Detection Definition
  • Safety-Critical Metric Selection
The work I submitted19 tasks

11 tasks · 13.6k characters · Python · 8 written answers · 846 words

  • Sensor Match553 characters
  • Example Iou Demo1.5k characters
  • Task Definition Code1.1k characters
  • Task Definition377 characters
  • Example Metrics DemoPython · 2.2k characters
  • Scored Metrics RunPython · 2.3k characters
  • Scored Metrics355 characters
  • Ttcs Example SlicePython · 1.3k characters
  • Ttcs Triage SlicePython · 1.6k characters
  • Ship Verdict1.6k characters
  • Realcase Analysis807 characters
  • Sensor Match27 words
  • Example Iou Demo169 words
  • Task Definition Code149 words
  • Task Definition42 words
  • Example Metrics Demo154 words
  • Scored Metrics Run143 words
  • Scored Metrics19 words
  • Ttcs Example Slice143 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
88Average score
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