Selected work

Representative Papers

Search and Rescue on the Plane

Aug 12, 2026

This study addresses an online search problem in the plane where an agent, starting from an arbitrary position and orientation, must locate an unknown target on the positive x-axis and transport it to the origin. Through competitive analysis, geometric modeling, and optimization theory, the work reveals a strategy phase transition induced by a critical angle θ* ≈ 15.6°: when the initial heading angle is below θ*, the optimal strategy involves first moving to a specific checkpoint before searching along the x-axis; otherwise, direct search is optimal. The paper provides an explicit formula for the checkpoint location as a function of the initial angle, derives a closed-form expression for the competitive ratio, and fully characterizes the structure of the optimal competitive algorithm for any initial orientation, thereby achieving theoretically optimal performance.

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Low cost, easily manufactured, highly flexible strain and touch sensitive fiber for robotics applications

Jun 11, 2026

This work proposes a low-cost, multifunctional sensing fiber for soft robotics, addressing limitations of existing approaches that often rely on expensive materials and complex fabrication processes. The fiber integrates commercially available conductive yarns within silicone tubing and enables dual-modal sensing—resistive strain and capacitive touch/proximity detection—through a simple manual threading process (e.g., 20 cm in under two minutes). Exhibiting high flexibility, ease of fabrication, and reparability, the fiber was successfully integrated into diverse soft robotic systems, including pneumatic grippers for trigger control, flexible bands for pose estimation, soft structures for deformation monitoring, and robotic arms for touch interaction and gesture following. These demonstrations highlight its promising applicability in knitted architectures for wearable and soft robotic applications.

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Remarks on the Relevance of Privacy Expectations for Default Opt-out Settings

Mar 16, 2026

Over the past few years an increasing number of states in the US have adopted new privacy laws. The majority of these laws require compliance with universal opt-out mechanisms (UOOMs), which allow consumers to send legally binding opt-out signals. However, a number of laws generally do not allow UOOMs to be enabled by default. While some laws exempt privacy-protective software from this prohibition, the exemption does not apply to pre-installed software, e.g., a privacy-protective web browser bundled with an operating system. The reason for not allowing default opt-out settings for pre-installed software is to ensure that settings reflect consumers' "affirmative, freely given, and unambiguous choice," as, for example, the Colorado Privacy Act (CPA) is putting it. However, prohibiting vendors of privacy-protective software from turning on UOOMs by default can force them into committing unfair or deceptive acts or practices under the FTC Act and equivalent state laws. Thus, whether UOOMs can be turned on by default on pre-installed software should depend on consumers' privacy expectations. For pre-installed software that is creating a reasonable expectation for consumers that their privacy will be protected, the simple use of such software should be considered a valid choice for enabling UOOMs. In such software a turned-on UOOM is not a "default setting" but rather the software's inherent behavior that a consumer expects and chooses through its use. This interpretation of consumer choice is preferable under the CPA and similar laws as it grounds the notice and choice principle in the privacy expectations of consumers and enables companies to compete on better privacy for consumers.

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Nonogram: Complexity of Inference and Phase Transition Behavior

Jul 09, 2025

This work investigates the computational complexity and phase-transition behavior of deterministic logical inference—i.e., uniquely deducing the solution without guessing—in Nonogram puzzles. Focusing on how cell fill density governs inference difficulty, we propose an efficient CNF encoding based on regular expressions, enabling the first systematic complexity analysis and large-scale empirical study of Nonogram inference. Our results demonstrate that inference hardness is predominantly determined by fill density, exhibiting a sharp phase transition in the critical density range ≈0.3–0.5: instances below this range are efficiently solvable via deduction alone, whereas those above require extensive backtracking. This transition aligns closely with human solvers’ perceived difficulty shift. We formally establish the NP-completeness of the deterministic inference problem for Nonograms and, for the first time, uncover its intrinsic phase-transition mechanism. This work thus provides both theoretical foundations and empirical evidence for understanding the computational structure and cognitive demands of logic puzzles.

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TRIP: A Nonparametric Test to Diagnose Biased Feature Importance Scores

Jul 09, 2025

Permutation feature importance (PFI) suffers from unreliable interpretations under feature correlations, as it generates unrealistic out-of-distribution samples that induce model extrapolation. To address this, we propose TRIP—a nonparametric hypothesis testing framework for diagnosing whether PFI scores are compromised by extrapolation. TRIP imposes no strong distributional assumptions, scales to high dimensions, and ensures robustness via theoretical guarantees and an adaptive resampling strategy. Extensive experiments on synthetic and real-world datasets demonstrate that TRIP accurately identifies misleading PFI estimates, thereby substantially improving the credibility of feature importance assessments. This work constitutes the first systematic approach to validating PFI reliability in extrapolatory regimes, providing a critical diagnostic tool for interpretable machine learning.

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Recent publications

Latest Papers

Search and Rescue on the Plane

Aug 12, 2026

This study addresses an online search problem in the plane where an agent, starting from an arbitrary position and orientation, must locate an unknown target on the positive x-axis and transport it to the origin. Through competitive analysis, geometric modeling, and optimization theory, the work reveals a strategy phase transition induced by a critical angle θ* ≈ 15.6°: when the initial heading angle is below θ*, the optimal strategy involves first moving to a specific checkpoint before searching along the x-axis; otherwise, direct search is optimal. The paper provides an explicit formula for the checkpoint location as a function of the initial angle, derives a closed-form expression for the competitive ratio, and fully characterizes the structure of the optimal competitive algorithm for any initial orientation, thereby achieving theoretically optimal performance.

0 citationsRead paper

Low cost, easily manufactured, highly flexible strain and touch sensitive fiber for robotics applications

Jun 11, 2026

This work proposes a low-cost, multifunctional sensing fiber for soft robotics, addressing limitations of existing approaches that often rely on expensive materials and complex fabrication processes. The fiber integrates commercially available conductive yarns within silicone tubing and enables dual-modal sensing—resistive strain and capacitive touch/proximity detection—through a simple manual threading process (e.g., 20 cm in under two minutes). Exhibiting high flexibility, ease of fabrication, and reparability, the fiber was successfully integrated into diverse soft robotic systems, including pneumatic grippers for trigger control, flexible bands for pose estimation, soft structures for deformation monitoring, and robotic arms for touch interaction and gesture following. These demonstrations highlight its promising applicability in knitted architectures for wearable and soft robotic applications.

0 citationsRead paper

Remarks on the Relevance of Privacy Expectations for Default Opt-out Settings

Mar 16, 2026

Over the past few years an increasing number of states in the US have adopted new privacy laws. The majority of these laws require compliance with universal opt-out mechanisms (UOOMs), which allow consumers to send legally binding opt-out signals. However, a number of laws generally do not allow UOOMs to be enabled by default. While some laws exempt privacy-protective software from this prohibition, the exemption does not apply to pre-installed software, e.g., a privacy-protective web browser bundled with an operating system. The reason for not allowing default opt-out settings for pre-installed software is to ensure that settings reflect consumers' "affirmative, freely given, and unambiguous choice," as, for example, the Colorado Privacy Act (CPA) is putting it. However, prohibiting vendors of privacy-protective software from turning on UOOMs by default can force them into committing unfair or deceptive acts or practices under the FTC Act and equivalent state laws. Thus, whether UOOMs can be turned on by default on pre-installed software should depend on consumers' privacy expectations. For pre-installed software that is creating a reasonable expectation for consumers that their privacy will be protected, the simple use of such software should be considered a valid choice for enabling UOOMs. In such software a turned-on UOOM is not a "default setting" but rather the software's inherent behavior that a consumer expects and chooses through its use. This interpretation of consumer choice is preferable under the CPA and similar laws as it grounds the notice and choice principle in the privacy expectations of consumers and enables companies to compete on better privacy for consumers.

0 citationsRead paper

Nonogram: Complexity of Inference and Phase Transition Behavior

Jul 09, 2025

This work investigates the computational complexity and phase-transition behavior of deterministic logical inference—i.e., uniquely deducing the solution without guessing—in Nonogram puzzles. Focusing on how cell fill density governs inference difficulty, we propose an efficient CNF encoding based on regular expressions, enabling the first systematic complexity analysis and large-scale empirical study of Nonogram inference. Our results demonstrate that inference hardness is predominantly determined by fill density, exhibiting a sharp phase transition in the critical density range ≈0.3–0.5: instances below this range are efficiently solvable via deduction alone, whereas those above require extensive backtracking. This transition aligns closely with human solvers’ perceived difficulty shift. We formally establish the NP-completeness of the deterministic inference problem for Nonograms and, for the first time, uncover its intrinsic phase-transition mechanism. This work thus provides both theoretical foundations and empirical evidence for understanding the computational structure and cognitive demands of logic puzzles.

0 citationsRead paper

TRIP: A Nonparametric Test to Diagnose Biased Feature Importance Scores

Jul 09, 2025

Permutation feature importance (PFI) suffers from unreliable interpretations under feature correlations, as it generates unrealistic out-of-distribution samples that induce model extrapolation. To address this, we propose TRIP—a nonparametric hypothesis testing framework for diagnosing whether PFI scores are compromised by extrapolation. TRIP imposes no strong distributional assumptions, scales to high dimensions, and ensures robustness via theoretical guarantees and an adaptive resampling strategy. Extensive experiments on synthetic and real-world datasets demonstrate that TRIP accurately identifies misleading PFI estimates, thereby substantially improving the credibility of feature importance assessments. This work constitutes the first systematic approach to validating PFI reliability in extrapolatory regimes, providing a critical diagnostic tool for interpretable machine learning.

0 citationsRead paper