Responsible and Trusted AI Algorithms

My research focuses on engineering trustworthy systems and redefining the boundaries of identity protection. With 15 years of professional experience, I address the fundamental challenges inherent in AI-driven security, including algorithmic bias, adversarial threats, lack of explainability, and privacy risks. My technical expertise centers on designing AI algorithms tailored for complex multivariate data, spanning degraded DNA signals, hyperspectral data, biometric imagery and videos. By mitigating sophisticated vulnerabilities such as sensor spoofing and fraud, my work establishes rigorous new standards for secure, ethical, and equitable security technologies. I am a pioneer in the application of hyperspectral imaging and sweat analysis within the field of identity science.

"Be Curious!"

Curiosity drives Innovation, 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.

Forensics

Analysis of FBI fingerprint data shows that match performance varies with demographic covariates such as age and gender. Incorporating these factors via ROC regression provides a more accurate measure of discriminatory capacity.

DNA Profiling

DNA identification relies on 13 STR alleles, but detecting them in degraded samples is challenging with conventional or Rapid DNA systems. Our adaptive signal-processing algorithm effectively detects allelic peaks in degraded DNA.

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