Sanya Ray — Portfolio

Projekte

Zertifiziert

Selbstgesteuertes Projekt

AI-Assisted Code Modernization with IBM

Junior Software Engineer · Softwareentwicklung · September 2026

96/ 100

Basierend auf einer öffentlichen Fallstudie von IBM · nicht mit IBM verbunden

Built a complete modernization and bug-fix pass for the KM-Waechter service: corrected the wear math, handled missing service readings safely, fixed the nightly report’s average and crash path, and repaired a hidden mileage-conversion bug in the helpers. Also added the missing regression test, produced a data-driven breakdown-risk analysis from fleet_history.csv, and verified the repo against all 11 acceptance checks before handing it in.

Bewertet nach

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

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Caught and fixed the hidden fleet_utils.km_to_miles conversion bug that no test directly covered.
Preserved the required 15000 km interval and 80% threshold while fixing the wear calculation and missing-reading behavior.
Added the missing regression test for a car with no reading and verified the full checklist with verify.py.
Wrote an analysis that explicitly rejects the misleading odometer/age assumption and ranks risk using the real predictors.
Leakage-Free Bug Diagnosis
Directed AI Code Review

Das Ergebnis

Zum CodeSanyaR2908 / proov-km-waechter-fix

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 2.4k Zeichen · 8 Textantworten · 97 Wörter

Division Predict75 Zeichen
Diagnose Line130 Zeichen
Diagnose Why151 Zeichen
Scope Prompt66 Zeichen
Audit Findings158 Zeichen
Audit Why168 Zeichen
Acceptance Checklist214 Zeichen
Template Repo Click82 Zeichen
Bob Trial Click384 Zeichen
Bob Ready60 Zeichen
Verify Report70 Zeichen
Exec Pitch794 Zeichen
Division Predict1 Wort
Diagnose Line10 Wörter
Diagnose Why25 Wörter
Scope Prompt1 Wort
Audit Findings1 Wort
Audit Why24 Wörter
Acceptance Checklist34 Wörter
Template Repo Click1 Wort

Bewusst zusammengefasst — der eingereichte Code und die Texte bleiben privat, damit diese Seite nicht als Lösungsvorlage dient. Die vollständige Einreichung steht hinter dem verifizierten Zertifikat.

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Amazon Robotics - Black-Friday Fleet Coordination

Softwareentwicklung · August 2026

96/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a full Black-Friday fleet coordination solution end to end: modeled the warehouse as a typed grid, implemented optimal single-agent A* with Manhattan heuristic, extended it to cooperative multi-agent planning with a centralized reservation table that prevents both vertex and edge collisions, and selected realistic charging/idle policies to keep the fleet moving. In simulation, the candidate identified the throughput sweet spot, achieved zero collisions and zero deadlocks, and packaged the result into an executive-ready pilot recommendation.

Bewertet nach

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

Bestanden · Bestehensgrenze 55/100

Was herausstach6

Implemented A* with an admissible Manhattan heuristic on a 4-connected grid and verified it beat Dijkstra in node expansions.
Built a reservation table keyed by cell and tick, and explicitly prevented head-on swaps with directed edge reservations.
Chose a realistic fleet policy: centralized coordination, priority/watchdog deadlock handling, staggered charging, and dispersed perimeter idle parking.
Used simulator outputs to lock onto the concave throughput optimum at 35 robots and justified the choice with wait-time and collision data.
Leakage-Free Cooperative Planning
Honest Simulator-Based Evaluation

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 6.4k Zeichen · 8 Textantworten · 248 Wörter

Role And Mission115 Zeichen
Amazon Robotics Context459 Zeichen
Grid World Theory119 Zeichen
State Representation Task341 Zeichen
Phase1 Recap348 Zeichen
Astar Theory124 Zeichen
Astar Implementation Cell3.8k Zeichen
Astar Solution Correct4 Zeichen
Single Unit Challenge465 Zeichen
Phase2 Recap374 Zeichen
Multi Agent Problem111 Zeichen
Reservation Table Theory180 Zeichen
Role And Mission1 Wort
Amazon Robotics Context37 Wörter
Grid World Theory1 Wort
State Representation Task1 Wort
Phase1 Recap23 Wörter
Astar Theory1 Wort
Astar Implementation Cell171 Wörter
Single Unit Challenge13 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Machine Learning for Automotive

KI / ML · Juli 2026

78/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built an end-to-end used-car pricing analysis for VW and Audi listings: combined the datasets, explored price and mileage patterns, and compared Linear Regression with Random Forest using holdout evaluation. The work also translated model performance into a business-facing pricing exposure estimate and recommended a data-driven pricing workflow for the CPO team.

Bewertet nach

Exploratory Data Analysis20%
Feature Engineering25%
Model Building & Evaluation35%
Business Communication20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Combined VW and Audi listings into a single analysis table with a brand indicator and used it consistently across EDA.
Used multiple EDA views: histograms, correlation heatmap, scatter plots, and a brand boxplot to compare market behavior.
Identified the strongest business drivers in the model output and translated MAE into a pricing exposure estimate for the CPO team.
Compared Linear Regression against Random Forest and reported both R²-style accuracy and MAE differences in the business summary.
Exploratory Data Analysis
Leakage-Free Feature Preparation

Meine eingereichte Arbeit10 Aufgaben

5 Aufgaben · 6 Zeilen · 5 Textantworten · 196 Wörter

VW Sprint1 Complete56 Zeichen
VW Sprint2 Complete61 Zeichen
VW Sprint3 Complete59 Zeichen
VW Business Insights1.4k Zeichen
Jupyterlite Code2 Zeilen
VW Sprint1 Complete1 Wort
VW Sprint2 Complete1 Wort
VW Sprint3 Complete1 Wort
VW Business Insights192 Wörter
Jupyterlite Code1 Wort

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV