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Ekta Mulkalwar
Junior Machine Learning & Full-Stack Engineer specializing in applied AI systems
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About
Master’s thesis focused on building and deploying a gait-based biometric authentication system using mobile accelerometer time-series data as an alternative to passwords/2FA. Emphasized real-world robustness by addressing sensor noise and variability (phone placement, walking speed, footwear) and improving safety using biometric metrics like FAR/FRR and EER, while collaborating closely with a non-ML thesis advisor.
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Key Strengths
- Built and deployed an AI-powered gait authentication system (master’s thesis) using accelerometer time-series data
- Handled noisy/variable real-world sensor data via preprocessing (filtering, stride segmentation) and salience-based logic
- Applied biometric-specific evaluation metrics (FAR/FRR, EER) to reduce false acceptance and improve decision safety
- Structured AI workflow design with staged pipeline approach and edge-case testing
- Strong research-driven approach (reviewed 12–15 papers per step) to select methods and validate decisions
- Able to communicate ML/biometrics concepts to non-technical stakeholders (explaining why EER matters vs accuracy)
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