Responsible and Trusted AI Algorithms

My research focuses on building trustworthy systems. I address key challenges in AI-driven security technologies—such as bias, adversarial threats, lack of explainability, and privacy risks—to strengthen identity protection and prevent unauthorized access and fraud. With over a decade of professional experience and a deep passion for my work, I have developed technical expertise in designing and developing AI Algorithms to process complex multivariate data, including 1D degraded DNA signals, RGB and greyscale biometric images, 3D hyperspectral data, and RGB videos.

"Be Curious!"

Curiosity drives Innovation and Discovery, leading to a better understanding of complex problems. It inspires us to ask questions and passionately seek answers.

Machine Unlearning

This research develops efficient machine unlearning methods to selectively remove sensitive data—such as identity traits or biased text. It advances fairness and compliance in biometric and language models.

Security in Virtual Reality

This research enhances identity verification in Mixed Reality by utilizing iris recognition with synthetic data for spoof resistance and multimodal fusion for improved accuracy.

Hyperspectral Imaging

This technology combines imaging and spectroscopy to capture rich spectral data across hundreds of wavelengths, enabling deeper analysis for advanced recognition, detection, and decision-making in defense contexts.

Inclusive Cybersecurity

This project explores optics-informed AI to develop a contactless biometric security system that reduces skin tone bias through explainable models, enhancing mobile security.

Mobile Security

This project enhances the detection of presentation attacks in finger photo recognition technologies, combining various color space representations.

Explainable AI (XAI)

This research examines XAI to promote transparency, enhance control over AI systems, and foster public trust by clarifying the decision-making process.

Chemical Biometrics

This exploratory study investigates human sweat as a non-invasive, spoof-resistant biometric by developing and evaluating protocols that address factors impacting biomarker reliability such as sampling site, timing, and individual variability.

Usable Biometric MFA

The project focuses on developing robust multi-factor authentication using biometric factors; I developed FingerPIN, which combines sequential finger scans with PIN entry to enhance security —pilot results show strong usability.

Video Analysis

Video data offers richer information than single images, enhancing accuracy and attack resilience. This research develops deep learning algorithms for robust identity matching from short video sequences.

Capture Bias Mitigation

Device diversity has a significant impact on the accuracy of security algorithms. Addressing this variability is essential for reliable and trustworthy decision-making systems.

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