Selected work

Representative Papers

Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design

Apr 15, 2026

High-fidelity multiphysics simulations are computationally expensive, while purely data-driven surrogate models often suffer from limited generalization capabilities. To address this challenge, this work proposes a non-intrusive physics-informed spatiotemporal surrogate modeling framework (PISTM) that leverages a Koopman autoencoder to learn the intrinsic spatiotemporal dynamics of the system. By embedding physical constraints without modifying the original simulation code, PISTM significantly enhances out-of-distribution generalization to unseen operating conditions. Integrating principles from physics-informed neural networks and spatiotemporal dynamical systems modeling, the method is validated on the two-dimensional incompressible flow past a cylinder, demonstrating its ability to accurately and efficiently replace high-fidelity simulations and substantially accelerate engineering design workflows.

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Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies

Dec 08, 2025

Cyberattacks increasingly exploit ambiguity to evade detection, yet existing security analytics lack models of attacker cognition. Method: This work pioneers the incorporation of “ambiguity aversion”—a well-established cognitive bias from psychology—into cybersecurity, formalizing it as a quantifiable, attacker-specific trait. Leveraging red-team experiments, we collect multimodal attack data and deploy a large language model–driven log parsing pipeline to automatically map unstructured system logs to the MITRE ATT&CK framework. We then design a sequence-based computational model that infers individual-level ambiguity aversion in real time from observed attack behavior. Contribution/Results: Our approach transcends conventional behavior-centric analysis by enabling interpretable, operationally actionable modeling of attacker cognition. It establishes both theoretical foundations and a technical framework for generating adaptive, cognitively informed defense strategies tailored to individual adversaries.

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Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis

Nov 26, 2025

Traditional cybersecurity defenses assume attackers are fully rational, overlooking their inherent cognitive constraints and biases. This paper proposes GAMBiT—the first proactive defense framework that explicitly models attacker cognitive states within adversarial game-theoretic interactions. GAMBiT embeds cognitive sensors and triggers to detect real-time deviations—including loss aversion, base-rate neglect, and sunk-cost fallacy—and dynamically intervenes in the attacker’s decision-making process. By shifting from passive protection to active cognitive manipulation, GAMBiT establishes the first systematic methodology for modeling and strategically exploiting psychological biases in cyber defense. Three controlled human-subject experiments (n = 61) demonstrate that GAMBiT significantly reduces attack task completion rates, induces substantial path deviation from optimal strategies, and increases behavioral detectability—collectively degrading attacker efficiency. The results validate the feasibility and efficacy of cognition-aware, game-theoretic active defense.

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Risk Psychology & Cyber-Attack Tactics

Oct 23, 2025

This study investigates whether individual cognitive traits predict cyberattack behavior. Using red-team operation data from cybersecurity experts in simulated enterprise networks—integrated with psychometric assessments and tokenized attack action logs—we construct a multilevel mixed-effects Poisson regression model (with technical usage frequency nested within participants) to examine how cognitive attributes influence attack technique selection. Results demonstrate significant heterogeneity in the predictive power of cognitive differences across distinct attack techniques; notably, cognitive traits explain variance beyond that accounted for by experience and training. Specific dimensions—including cognitive flexibility and risk preference—robustly predict the adoption of high-stealth or high-complexity attack techniques. This work provides the first empirical evidence of micro-level cognitive mechanisms exerting a dominant influence on cyberattack decision-making. It establishes a foundational theoretical and methodological basis for developing cognition-informed active defense strategies and cognitive threat profiling techniques.

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The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

Sep 02, 2025

Current AI development lacks foundational scientific guidance, while mathematical and physical sciences (MPS)—including astronomy, chemistry, materials science, mathematics, and physics—lack systematic integration of AI tools. Method: This project establishes a bidirectional co-evolution framework—“science-driven AI innovation” and “AI-accelerated scientific discovery”—grounded in interdisciplinary research paradigms, collaborative governance mechanisms, and integrated talent development. It combines theoretical modeling, algorithmic feedback from scientific challenges, and experimental validation across multiple MPS subfields. Contribution/Results: The study yields empirically grounded cross-disciplinary insights and culminates in the *2025 AI+MPS Consensus*, a policy-oriented document offering actionable strategic priorities for funding agencies, universities, and research institutions. It advances the relationship between fundamental science and AI beyond unidirectional tool adoption toward deep, mutually constitutive paradigm co-construction.

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Recent publications

Latest Papers

Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design

Apr 15, 2026

High-fidelity multiphysics simulations are computationally expensive, while purely data-driven surrogate models often suffer from limited generalization capabilities. To address this challenge, this work proposes a non-intrusive physics-informed spatiotemporal surrogate modeling framework (PISTM) that leverages a Koopman autoencoder to learn the intrinsic spatiotemporal dynamics of the system. By embedding physical constraints without modifying the original simulation code, PISTM significantly enhances out-of-distribution generalization to unseen operating conditions. Integrating principles from physics-informed neural networks and spatiotemporal dynamical systems modeling, the method is validated on the two-dimensional incompressible flow past a cylinder, demonstrating its ability to accurately and efficiently replace high-fidelity simulations and substantially accelerate engineering design workflows.

0 citationsRead paper

Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies

Dec 08, 2025

Cyberattacks increasingly exploit ambiguity to evade detection, yet existing security analytics lack models of attacker cognition. Method: This work pioneers the incorporation of “ambiguity aversion”—a well-established cognitive bias from psychology—into cybersecurity, formalizing it as a quantifiable, attacker-specific trait. Leveraging red-team experiments, we collect multimodal attack data and deploy a large language model–driven log parsing pipeline to automatically map unstructured system logs to the MITRE ATT&CK framework. We then design a sequence-based computational model that infers individual-level ambiguity aversion in real time from observed attack behavior. Contribution/Results: Our approach transcends conventional behavior-centric analysis by enabling interpretable, operationally actionable modeling of attacker cognition. It establishes both theoretical foundations and a technical framework for generating adaptive, cognitively informed defense strategies tailored to individual adversaries.

0 citationsRead paper

Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis

Nov 26, 2025

Traditional cybersecurity defenses assume attackers are fully rational, overlooking their inherent cognitive constraints and biases. This paper proposes GAMBiT—the first proactive defense framework that explicitly models attacker cognitive states within adversarial game-theoretic interactions. GAMBiT embeds cognitive sensors and triggers to detect real-time deviations—including loss aversion, base-rate neglect, and sunk-cost fallacy—and dynamically intervenes in the attacker’s decision-making process. By shifting from passive protection to active cognitive manipulation, GAMBiT establishes the first systematic methodology for modeling and strategically exploiting psychological biases in cyber defense. Three controlled human-subject experiments (n = 61) demonstrate that GAMBiT significantly reduces attack task completion rates, induces substantial path deviation from optimal strategies, and increases behavioral detectability—collectively degrading attacker efficiency. The results validate the feasibility and efficacy of cognition-aware, game-theoretic active defense.

0 citationsRead paper

Risk Psychology & Cyber-Attack Tactics

Oct 23, 2025

This study investigates whether individual cognitive traits predict cyberattack behavior. Using red-team operation data from cybersecurity experts in simulated enterprise networks—integrated with psychometric assessments and tokenized attack action logs—we construct a multilevel mixed-effects Poisson regression model (with technical usage frequency nested within participants) to examine how cognitive attributes influence attack technique selection. Results demonstrate significant heterogeneity in the predictive power of cognitive differences across distinct attack techniques; notably, cognitive traits explain variance beyond that accounted for by experience and training. Specific dimensions—including cognitive flexibility and risk preference—robustly predict the adoption of high-stealth or high-complexity attack techniques. This work provides the first empirical evidence of micro-level cognitive mechanisms exerting a dominant influence on cyberattack decision-making. It establishes a foundational theoretical and methodological basis for developing cognition-informed active defense strategies and cognitive threat profiling techniques.

0 citationsRead paper

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

Sep 02, 2025

Current AI development lacks foundational scientific guidance, while mathematical and physical sciences (MPS)—including astronomy, chemistry, materials science, mathematics, and physics—lack systematic integration of AI tools. Method: This project establishes a bidirectional co-evolution framework—“science-driven AI innovation” and “AI-accelerated scientific discovery”—grounded in interdisciplinary research paradigms, collaborative governance mechanisms, and integrated talent development. It combines theoretical modeling, algorithmic feedback from scientific challenges, and experimental validation across multiple MPS subfields. Contribution/Results: The study yields empirically grounded cross-disciplinary insights and culminates in the *2025 AI+MPS Consensus*, a policy-oriented document offering actionable strategic priorities for funding agencies, universities, and research institutions. It advances the relationship between fundamental science and AI beyond unidirectional tool adoption toward deep, mutually constitutive paradigm co-construction.

0 citationsRead paper