Ayesha Farhath

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Ayesha Farhath

Computer science and engineering (Aiml) · CMR College of Engineering & Technology

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Projects

Certified

Self-directed project

Amazon Robotics - Black-Friday Fleet Coordination

Software Engineering · September 2026

96/ 100

A ProoV case study · educational project, not employment

Built an end-to-end warehouse fleet coordination prototype for Black Friday surge conditions: framed the operational bottleneck as aisle congestion and dock contention, implemented optimal single-agent A* on a 4-connected grid, extended it to centralized cooperative A* with vertex and edge reservations, and validated a 200-robot simulation with zero collisions and zero deadlocks. The final pitch tied the policy to throughput, wait time, and a scoped single-zone pilot ask for leadership.

Graded against

Problem framing & operational stakes20%
Algorithmic depth - A* and cooperative coordination30%
Coordination policy realism - deadlock, charging, idle25%
Throughput trade-off analysis15%
Executive pitch to leadership10%

Passed · pass mark 55/100

What stood out6

Correctly identified that the bottleneck was congestion and dock-queue collapse, not robot speed
Implemented A* with an admissible Manhattan heuristic and showed it beat Dijkstra on node expansions
Built a reservation table that explicitly blocks both vertex collisions and head-on edge swaps
Chose a realistic operating policy: priority-watchdog deadlock handling, staggered charging, and dispersed perimeter idle parking
Leakage-Free Feature Engineering
Honest Model Evaluation

The work I submitted20 tasks

12 tasks · 8.0k characters · Python · 8 written answers · 198 words

Role And Mission115 characters
Amazon Robotics Context459 characters
Program Overview133 characters
Grid World Theory104 characters
State Representation Task341 characters
State Representation Task ValidatorPython · 1.8k characters
Phase1 Recap348 characters
Astar Theory124 characters
Astar Implementation Cell3.8k characters
Astar Solution Correct4 characters
Single Unit Challenge467 characters
Phase2 Recap374 characters
Role And Mission1 word
Amazon Robotics Context37 words
Program Overview1 word
Grid World Theory1 word
State Representation Task1 word
State Representation Task Validator133 words
Phase1 Recap23 words
Astar Theory1 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

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 case, found the quiet km-to-miles helper bug, and added the missing regression test. Also delivered a data-driven breakdown-risk analysis that follows the fleet history data rather than the misleading mileage assumption, with clear acceptance checks and documented review notes.

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

Correctly identified the floor-division bug and explained why it turned a nearly worn car into 0% wear
Caught the agent’s attempt to change the 80% threshold and kept the business rules untouched
Verified the fix with all 11 checks passing in verify.py and documented the review process in NOTES.md
Built an analysis that explicitly rejects odometer_km and age_years as predictors and uses the data-supported factors instead
Leakage-Free Bug Diagnosis
Directed AI Agent Auditing

The deliverable

View the codeayeshafarhath / proov-km-waechter-fix

The work I submitted20 tasks

12 tasks · 1.9k characters · 8 written answers · 74 words

Division Predict71 characters
Diagnose Line130 characters
Diagnose Why122 characters
Scope Prompt66 characters
Audit Findings158 characters
Audit Why160 characters
Acceptance Checklist156 characters
Bob Trial Click74 characters
Bob Ready60 characters
Template Repo Click152 characters
Verify Report71 characters
Exec Pitch720 characters
Division Predict1 word
Diagnose Line10 words
Diagnose Why19 words
Scope Prompt1 word
Audit Findings1 word
Audit Why22 words
Acceptance Checklist19 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

2Projects completed

2Verified certificates

96Average score

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