Contribution:

Dr. Ahi's work is at the forefront of defining and addressing the critical challenges posed by Large Language Models (LLMs) and Generative AI to platform integrity, cybersecurity, and privacy. This research provides comprehensive surveys, strategic roadmaps, and actionable blueprints to mitigate dual-use risks, combat AI-generated threats, and ensure responsible innovation in the digital landscape. The focus is on developing a secure and trustworthy AI environment for critical applications, from healthcare diagnostics to digital platforms.

Publications & Patents:

• K. Ahi, "Risks & Benefits of LLMs & GenAI for Platform Integrity, Healthcare Diagnostics, Cybersecurity, Privacy & AI Safety: A Comprehensive Survey, Roadmap & Implementation Blueprint," *arXiv preprint arXiv:2506.12088*, 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This paper provides a foundational survey and strategic roadmap for navigating the complex risks and benefits of LLMs and Generative AI, crucial for securing future digital platforms and promoting AI safety.
• K. Ahi, "Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies," in *Proc. IEEE*, 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This work rigorously analyzes the escalating dual-use risks of LLMs in cybersecurity, offering insights into AI-generated malware and proposing advanced defensive strategies for robust digital security.
• K. Ahi, "LLMs and Generative AI for Platform Integrity: A Survey and Strategy Roadmap for Automating Review and Moderation, Detecting Abuse and Fraud, Enforcing Compliance, and…," presented at the 6th Silicon Valley Cybersecurity Conf. (SVCC), 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This research delivers a strategic roadmap for leveraging LLMs and Generative AI to enhance platform integrity through automated content review, fraud detection, and compliance enforcement.

Contribution:

Dr. Ahi's work drives the cutting edge of Artificial Intelligence by harnessing the immense computational power of GPUs to transform computer vision and image processing. This research develops innovative algorithms and systems for autonomous image segmentation, unsupervised clustering, and smart pattern recognition, achieving dramatic performance enhancements for real-time AI processing. This work is pivotal for high-performance computing (HPC) scalability in big data environments, significantly boosting efficiency in critical applications like semiconductor manufacturing and product lifecycle management.

Publications & Patents:

• K. Ahi, "GPU-Accelerated Feature Extraction for Real-Time Vision AI and LLM Systems Efficiency: Autonomous Image Segmentation, Unsupervised Clustering, and Smart Pattern Recognition for…," presented at the 36th Annual SEMI Advanced Semiconductor Manufacturing Conf. (ASMC), 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This research pioneers methods for significantly accelerating Vision AI and LLM systems through GPU-powered feature extraction, crucial for real-time applications and enhanced efficiency.
• K. Ahi, "GPU-Accelerated Feature Extraction for Vision AI: Autonomous Image Segmentation & Smart Pattern Recognition for Scalable Real-Time AI Processing with 6.6× Faster Performance…," presented at the IEEE ASMC, 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This publication details a groundbreaking approach to achieve 6.6x faster real-time Vision AI processing through GPU-accelerated autonomous image segmentation and smart pattern recognition.
• K. Ahi, "AI-Powered End-to-End Product Lifecycle: Boosting Reviewer Productivity by 80%+ and Accelerating Decision-Making via Real-Time Anomaly Detection and Data Refinement with GPU…," presented at SPIE Silicon Valley, 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This work introduces an AI-powered framework that leverages GPU acceleration to drastically enhance product lifecycle management, achieving over 80% increase in reviewer productivity through real-time anomaly detection.
• K. Ahi, "Advancing AI-Driven Computer Vision and Image Segmentation via Pattern Recognition, GPU-Accelerated Unsupervised Clustering, and Edge AI for HPC-Scalable Big Data Processing…," presented at SPIE Silicon Valley, 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This paper presents advancements in AI-driven computer vision, utilizing GPU-accelerated unsupervised clustering and edge AI for scalable big data processing and image segmentation.
• K. Ahi, "Self-supervised deep learning neural network for CD-SEM image denoising using reduced dataset," *Metrology, Inspection, and Process Control XXXVII*, vol. 12496, pp. 365-378, 2023. Cited by: 7. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This research introduces a self-supervised deep learning neural network for efficient image denoising, critical for high-precision metrology in semiconductor manufacturing.

Contribution:

Dr. Ahi's research pushes the boundaries of digital image processing and computer vision by developing sophisticated algorithms for tasks such as robust image denoising, precise contour detection, and advanced defect identification. This work integrates machine learning, deep learning, and unsupervised clustering to enable highly accurate and automated visual analysis across diverse applications, including high-variability images in manufacturing metrology and the nuanced interpretation of visual data for broad scientific and industrial uses.

Publications & Patents:

• K. Ahi, "Unsupervised machine learning based SEM image denoising for robust contour detection," in *Proc. Int. Conf. Extreme Ultraviolet Lithography 2021*, vol. 11854, pp. 88-102, 2021. Cited by: 14. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This work introduces an unsupervised machine learning approach for highly effective denoising of SEM images, critical for achieving robust contour detection in semiconductor manufacturing.
• K. Ahi, "Wafer image denoising and contour extraction for manufacturing process calibration," U.S. Patent App. 17/823,741, 2023. Cited by: 2. [Link Placeholder].
  Highlight: This patent describes a system and method for enhancing manufacturing process calibration through advanced image denoising and precise contour extraction on wafer images.
• K. Ahi, "AI-Powered Defect Detection using Deep Learning: A Pattern-Agnostic Faster R-CNN Approach for SEM Images with GPU Acceleration," presented at the 36th Annual SEMI Advanced Semiconductor Manufacturing Conf. (ASMC), 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This paper introduces a robust deep learning-based Faster R-CNN for pattern-agnostic defect detection in SEM images, significantly advancing automated quality control in semiconductor fabrication.
• K. Ahi, "AI-Powered Anomaly Detection: A Robust, Pattern-Agnostic Faster R-CNN with Scalable GPU-Accelerated Deep Learning for High-Fidelity Computer Vision Defect Identification (mAP…)," presented at IEEE ASMC, 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This research details a scalable, GPU-accelerated deep learning framework for highly accurate, pattern-agnostic anomaly and defect detection in computer vision applications.
• K. Ahi, "Contour extraction of images with selection-based auto tuning," U.S. Patent App. 18/476,559, 2025. Cited by: (New patent application). [Link Placeholder].
  Highlight: This patent application presents an innovative method for automated and highly adaptable contour extraction from images, enhancing precision in various imaging applications.
• K. Ahi, "Methods and systems for developing silhouettes, traditional-looking silhouettes, sunset silhouettes, removing shadows and objects, and improving the quality of sunset in images," U.S. Patent App. 17/163,568, 2023. Cited by: (New patent application). [Link Placeholder].
  Highlight: This patent application offers novel methods and systems for sophisticated image manipulation, including artistic silhouette generation and quality enhancement for diverse visual effects.
• K. Ahi, "Unsupervised, Scalable Clustering, Pattern Recognition, and Graphics Processing Unit (GPU)-Accelerated Contour Extraction from Challenging High-Variability Images Using Edge…," U.S. Patent App. PCT/US25/027,065, 2025. Cited by: 4. [Link Placeholder].
  Highlight: This patent pending work describes a system for high-performance, GPU-accelerated image processing, enabling robust contour extraction and pattern recognition in complex and high-variability visual data.

Contribution:

Dr. Ahi's work pioneers the application of artificial intelligence and machine learning to complex biomedical data, particularly focusing on Electroencephalography (EEG) signal processing. This research develops novel AI frameworks for analyzing neural time-series patterns, enabling deeper insights into brain activity and facilitating advancements in neurodiagnostics. Furthermore, Dr. Ahi's research explores the unique capabilities of advanced imaging modalities like Terahertz (THz) for medical applications, including the early detection of disease symptoms, bridging the gap between cutting-edge physics and clinical utility.

Publications & Patents:

• K. Ahi, "A review on machine learning for EEG signal processing in bioengineering," *IEEE Rev. Biomed. Eng.*, vol. 14, pp. 204-218, 2020. Cited by: 466. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This highly-cited review provides a comprehensive overview of machine learning's transformative role in EEG signal processing, setting a benchmark for future research in bioengineering and neurodiagnostics.
• K. Ahi, "Survey of terahertz photonics and biophotonics," *Opt. Eng.*, vol. 59, no. 6, pp. 061629-061629, 2020. Cited by: 28. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This survey explores the burgeoning fields of terahertz photonics and biophotonics, highlighting the potential for advanced imaging and sensing in biological and medical applications.
• K. Ahi, "Increased cerebrovascular sensitivity to endothelin-1 in a rat model of obstructive sleep apnea: a role for endothelin receptor B," *J. Cereb. Blood Flow Metab.*, vol. 35, no. 3, pp. 402-411, 2015. Cited by: 28. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This fundamental biomedical research elucidates a critical mechanism in obstructive sleep apnea, identifying a key pathway for cerebrovascular dysfunction.
• K. Ahi, "A machine learning framework for analysis of resting-state eeg in patients," Technical University of Denmark, 2022. Cited by: 2. [Link Placeholder].
  Highlight: This work proposes a novel machine learning framework for analyzing resting-state EEG, offering a powerful tool for patient diagnostics and neurological research.
• K. Ahi, "Developing Novel Machine Learning Approaches for Distinguishing Neural Time-Series Patterns," Icahn School of Medicine at Mount Sinai, 2023. Cited by: (New publication, citations building). [Link Placeholder].
  Highlight: This research focuses on developing sophisticated machine learning methods to differentiate subtle neural time-series patterns, advancing the precision of brain activity analysis.
• K. Ahi, "Mentality: Amamba-based approach towards foundation models for eeg," 2022. Cited by: 1. [Link Placeholder].
  Highlight: This paper explores an Amamba-based approach for creating foundation models specifically designed for EEG analysis, pushing the frontier of generalizable neural signal processing.
• K. Ahi, "Transformers for EEG classification: architectures, pre-training, and applications to epileptic seizure forecasting," 2022. Cited by: 1. [Link Placeholder].
  Highlight: This work investigates the application of Transformer architectures for EEG classification, demonstrating their potential for highly accurate epileptic seizure forecasting.
• K. Ahi, "Method and system for enhancing resolution of terahertz imaging and detection of symptoms of COVID-19, cold, and influenza," U.S. Patent 11,510,586, issued 2022. Cited by: 1. [Link Placeholder].
  Highlight: This patent introduces a groundbreaking method for utilizing high-resolution terahertz imaging to non-invasively detect symptoms of respiratory illnesses, offering a novel diagnostic tool.

Contribution:

Dr. Ahi's pioneering research is centered on harnessing advanced imaging modalities, particularly Terahertz (THz) technology, to establish new paradigms in security, quality control, and anti-counterfeiting. This work develops sophisticated THz imaging and spectroscopic techniques for the non-destructive inspection, characterization, and authentication of complex electronic components. The research spans from mathematical modeling of THz systems and enhancing image resolution through deconvolution to developing robust nano-signatures and comparing THz capabilities with other modalities like X-ray, providing critical solutions for supply chain integrity and combatting global counterfeiting.

Publications & Patents:

• K. Ahi, "Quality control and authentication of packaged integrated circuits using enhanced-spatial-resolution terahertz time-domain spectroscopy and imaging," *Opt. Lasers Eng.*, vol. 104, pp. 274-284, 2018. Cited by: 228. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This seminal paper presents novel methods for high-resolution terahertz imaging and spectroscopy, enabling superior quality control and authentication of complex integrated circuits, a critical advancement for semiconductor security.
• K. Ahi, "Mathematical modeling of THz point spread function and simulation of THz imaging systems," *IEEE Trans. Terahertz Sci. Technol.*, vol. 7, no. 6, pp. 747-754, 2017. Cited by: 161. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This fundamental work provides a critical mathematical framework for understanding and simulating THz imaging systems, laying the groundwork for optimizing system design and enhancing image fidelity.
• K. Ahi, "Review of GaN-based devices for terahertz operation," *Opt. Eng.*, vol. 56, no. 9, pp. 090901-090901, 2017. Cited by: 128. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This comprehensive review assesses the capabilities of Gallium Nitride (GaN)-based devices for terahertz applications, guiding the development of next-generation THz technologies.
• K. Ahi, "Terahertz characterization of electronic components and comparison of terahertz imaging with x-ray imaging techniques," in *Terahertz physics, devices, and systems IX: advanced applications in…*, 2015. Cited by: 90. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This research details the distinct advantages of terahertz technology for characterizing electronic components and provides a crucial comparison with established X-ray methods, highlighting THz's unique material sensitivity.
• K. Ahi, "Advanced terahertz techniques for quality control and counterfeit detection," in *Terahertz Physics, Devices, and Systems X: Advanced Applications in Industry…*, 2016. Cited by: 87. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This paper introduces advanced terahertz methodologies specifically designed for robust quality control and the proactive detection of counterfeit electronic parts, enhancing supply chain security.
• K. Ahi, "Developing terahertz imaging equation and enhancement of the resolution of terahertz images using deconvolution," in *Terahertz physics, devices, and systems X: advanced applications in industry…*, 2016. Cited by: 80. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This work establishes a novel terahertz imaging equation and demonstrates significant resolution enhancement through deconvolution, pushing the boundaries of THz image clarity.
• K. Ahi, "A method and system for enhancing the resolution of terahertz imaging," *Measurement*, vol. 138, pp. 614-619, 2019. Cited by: 76. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This method and system introduce practical advancements for improving terahertz image resolution, providing clearer insights for non-destructive evaluation and security applications.
• K. Ahi, "Review of THz-based semiconductor assurance," *Opt. Eng.*, vol. 60, no. 6, pp. 060901-060901, 2021. Cited by: 47. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This comprehensive review synthesizes the state-of-the-art in THz-based techniques for ensuring semiconductor authenticity and reliability throughout the supply chain.
• K. Ahi, "Modeling of terahertz images based on x-ray images: a novel approach for verification of terahertz images and identification of objects with fine details beyond terahertz…," in *Terahertz Physics, Devices, and Systems X: Advanced Applications in Industry…*, 2016. Cited by: 38. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This novel approach leverages X-ray data to model and verify terahertz images, enabling the identification of intricate details in objects beyond the intrinsic resolution limits of THz.
• K. Ahi, "Terahertz based machine learning approach to integrated circuit assurance," in *Proc. IEEE 71st Electron. Compon. Technol. Conf. (ECTC)*, pp. 2235-2245, 2021. Cited by: 12. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This paper integrates terahertz technology with machine learning to create an intelligent framework for robust integrated circuit assurance, enhancing automated detection capabilities.
• K. Ahi, "Thz techniques: a promising platform for authentication of electronic components," presented at CHASE Conf. Trust. Syst. Supply Chain Assur., Storrs, Connecticut, USA, 2015. Cited by: 12. [Link Placeholder].
  Highlight: This work highlights terahertz techniques as a powerful and promising platform for the authentication of electronic components, addressing a critical need in global supply chain security.
• K. Ahi, "A novel approach for enhancement of the resolution of terahertz measurements for quality control and counterfeit detection," presented at Diminishing Manufacturing Sources and Material Shortages (DMSMS), 2015. Cited by: 12. [Link Placeholder].
  Highlight: This innovative approach significantly enhances the resolution of terahertz measurements, providing finer detail crucial for precise quality control and effective counterfeit detection.
• K. Ahi, "Fabrication of robust nano-signatures for identification of authentic electronic components and counterfeit avoidance," *Int. J. High Speed Electron. Syst.*, vol. 26, no. 03, p. 1740006, 2017. Cited by: 11. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This research details the creation of durable, embedded nano-signatures that serve as unique identifiers for authentic electronic components, offering a powerful anti-counterfeiting solution.
• K. Ahi, "Lithography, spectroscopy, and super resolution terahertz imaging for quality assurance and authentication," University of Connecticut, 2017. Cited by: 10. [Link Placeholder].
  Highlight: This doctoral work demonstrates the combined power of lithography, spectroscopy, and super-resolution terahertz imaging for comprehensive quality assurance and authentication of materials.
• K. Ahi, "Encrypted electron beam lithography nano-signatures for authentication," *Int. J. High Speed Electron. Syst.*, vol. 26, no. 03, p. 1740017, 2017. Cited by: 8. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This paper introduces a method for creating encrypted nano-signatures using electron beam lithography, providing an advanced layer of security for authentication purposes.
• K. Ahi, "A survey on GaN-based devices for terahertz photonics," in *Wide Bandgap Power Devices and Applications*, vol. 9957, pp. 24-36, 2016. Cited by: 8. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This survey provides insights into the rapidly evolving field of GaN-based devices for terahertz photonics, a key area for future high-frequency applications.
• K. Ahi, "Growth dependent optical properties of ZnMgO at THz frequencies," in *Terahertz Physics, Devices, and Systems IX: Advanced Applications in…*, 2015. Cited by: 6. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This research explores the optical properties of ZnMgO at THz frequencies, contributing to the fundamental understanding of materials critical for developing advanced THz devices.
• K. Ahi, "A novel mathematical approach for converting X-Ray images to terahertz images," presented at Connect. Symp. Microelectron. Optoelectron, pp. 1-5, 2016. Cited by: 3. [Link Placeholder].
  Highlight: This novel mathematical approach enables the conversion of X-ray images into terahertz images, offering a unique method for cross-modality verification and analysis of hidden structures.
• K. Ahi, "A Novel Approach for Extracting Embedded Authentication Engineered NanoSignatures," presented at Diminishing Manufacturing Sources and Material Shortages (DMSMS), 2019. Cited by: 2. [Link Placeholder].
  Highlight: This paper presents an innovative method for the non-destructive extraction of embedded nano-signatures, bolstering the integrity of authentication processes for valuable components.
• K. Ahi, "Terahertz Imaging of ICs Using K-means Clustering for Scale-Invariant Feature Transform Feature and Fusion Model of Wavelet Transform," *Sensors & Materials*, vol. 33, 2021. Cited by: 1. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This work introduces a sophisticated terahertz imaging technique for ICs combining K-means clustering, SIFT features, and wavelet fusion for enhanced defect detection.
• K. Ahi, "Materials for Electronics Security and Assurance," Elsevier, 2024. Cited by: 2. DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This comprehensive work delves into the critical role of materials science in ensuring the security and assurance of electronic components, addressing fundamental challenges in hardware integrity.

Contribution:

Dr. Ahi actively contributes to the vital discourse on AI safety, regulation, and governance, recognizing the profound societal implications of advanced artificial intelligence. This research critically examines the dual-use risks of AI, particularly Large Language Models (LLMs), addressing concerns around cybersecurity, privacy, and potential misuse. Dr. Ahi develops comprehensive strategies and blueprints for establishing robust governance frameworks that promote transparency, accountability, and ethical deployment of AI across various sectors, from platform integrity to healthcare diagnostics, ensuring that AI development aligns with human values and societal well-being.

Publications & Patents:

• K. Ahi, "Risks & Benefits of LLMs & GenAI for Platform Integrity, Healthcare Diagnostics, Cybersecurity, Privacy & AI Safety: A Comprehensive Survey, Roadmap & Implementation Blueprint," *arXiv preprint arXiv:2506.12088*, 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This comprehensive blueprint provides a critical assessment of AI's societal impact, offering a strategic roadmap for navigating the ethical, safety, and governance challenges of LLMs and Generative AI across diverse applications.
• K. Ahi, "Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies," in *Proc. IEEE*, 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This paper meticulously surveys the escalating cybersecurity and privacy risks posed by LLMs, proposing robust defensive strategies and emphasizing the crucial need for explainability in AI systems for enhanced safety and trust.
• K. Ahi, "LLMs and Generative AI for Platform Integrity: A Survey and Strategy Roadmap for Automating Review and Moderation, Detecting Abuse and Fraud, Enforcing Compliance, and…," presented at the 6th Silicon Valley Cybersecurity Conf. (SVCC), 2025. Cited by: (New publication, citations building). DOI: [DOI Placeholder]. [Link Placeholder].
  Highlight: This research delivers a forward-looking strategy for integrating LLMs into platform integrity systems, addressing governance challenges by proposing automation solutions for content moderation, fraud detection, and compliance enforcement.

Contribution:

Beyond academic publications, Dr. Ahi's commitment to innovation extends to tangible contributions that shape the technological landscape. The patent portfolio reflects a dedication to solving real-world problems, from enhancing critical imaging capabilities to developing novel consumer devices and AI-powered IoT systems. These patents underscore Dr. Ahi's ability to translate cutting-edge research into practical, defensible intellectual property that impacts various sectors. Dr. Ahi also engages in sharing insights through invited talks and contributes to public understanding through news mentions.

Patents:

• K. Ahi, "Method and system for enhancing resolution of terahertz imaging," U.S. Patent 10,783,612, issued 2020. Cited by: 4. [Link Placeholder].
  Highlight: This patent provides a robust method and system for significantly improving the clarity and detail of terahertz images, offering enhanced capabilities for industrial inspection and security applications.
• K. Ahi, "Mobile phone holder," U.S. Patent 10,900,608, issued 2021. Cited by: 7. [Link Placeholder].
  Highlight: This patent introduces an innovative design for a mobile phone holder, showcasing practical engineering and user-centric design.
• K. Ahi, "Unsupervised, Scalable Clustering, Pattern Recognition, and Graphics Processing Unit (GPU)-Accelerated Contour Extraction from Challenging High-Variability Images Using Edge…," U.S. Patent App. PCT/US25/027,065, filed 2025. Cited by: 4. [Link Placeholder].
  Highlight: This pending patent covers a groundbreaking system for high-performance, GPU-accelerated image processing that enables highly robust and scalable contour extraction and pattern recognition in complex image data.
• K. Ahi, "Method and system for enhancing resolution of terahertz imaging and detection of symptoms of COVID-19, cold, and influenza," U.S. Patent 11,510,586, issued 2022. Cited by: 1. [DOI Placeholder]. [Link Placeholder].
  Highlight: This patent outlines a novel method leveraging terahertz imaging for non-invasive detection of respiratory illness symptoms, providing a unique diagnostic tool for public health.
• K. Ahi, "Anti-fog medical face mask and a device for preventing formation of fog on eyewear while wearing face cover," U.S. Patent 11,119,340, issued 2021. Cited by: 1. [Link Placeholder].
  Highlight: This patent addresses a common challenge in healthcare and daily life by inventing an effective anti-fog solution for medical face masks and eyewear, enhancing usability and safety.
• K. Ahi, "Wafer image defect detection and characterization for manufacturing process calibration," U.S. Patent App. 17/823,235, filed 2024. Cited by: 3. [Link Placeholder].
  Highlight: This patent pending work details an advanced system for automatically detecting and characterizing defects in wafer images, which is vital for precise calibration in semiconductor manufacturing.
• K. Ahi, "Artificial Intelligence Based IoT System for Managing and Optimizing Shopping Lists, Purchased Items, Shopping Visits, Delivery Schedules and Reducing Food Waste," U.S. Patent App. 17/959,289, filed 2023. Cited by: 2. [Link Placeholder].
  Highlight: This patent application introduces an intelligent IoT system that optimizes consumer logistics and significantly contributes to reducing food waste through AI-driven management.
• K. Ahi, "Artificial Intelligence Based IoT System for Managing and Optimizing Shopping Lists, Purchased Items, Shopping Visits, Delivery Schedules and Reducing Food Waste," U.S. Patent 11,461,825, issued 2022. Cited by: 2. [Link Placeholder].
  Highlight: This granted patent describes an AI-powered IoT system designed to enhance efficiency in household management, specifically by optimizing shopping and minimizing food waste.
• K. Ahi, "Cell phone connector," U.S. Patent 11,428,366, issued 2022. Cited by: 2. [Link Placeholder].
  Highlight: This patent details an innovative design for a cell phone connector, demonstrating ingenuity in electronic component design.
• K. Ahi, "System for Distributing Advertisements and Pay the Viewers," U.S. Patent App. 62/809,757, filed 2019. Cited by: (New patent application). [Link Placeholder].
  Highlight: This patent application proposes a novel system for advertisement distribution that incentivizes viewership, potentially reshaping digital advertising models.
• K. Ahi, "Contour extraction of images with selection-based auto tuning," U.S. Patent App. 18/476,559, filed 2025. Cited by: (New patent application). [Link Placeholder].
  Highlight: This pending patent describes an advanced system for automating and fine-tuning contour extraction from images, providing critical precision for automated visual analysis.
• K. Ahi, "Methods and systems for developing silhouettes, traditional-looking silhouettes, sunset silhouettes, removing shadows and objects, and improving the quality of sunset in images," U.S. Patent App. 17/163,568, filed 2023. Cited by: (New patent application). [Link Placeholder].
  Highlight: This patent application outlines innovative image processing techniques for artistic and practical enhancements, from generating various silhouette styles to improving photographic quality.
• K. Ahi, "Method and System for Enhancing Resolution of Terahertz Imaging," U.S. Patent App. 17/023,356, filed 2021. Cited by: (New patent application). [Link Placeholder].
  Highlight: This patent application details a method and system for significant resolution enhancement in terahertz imaging, providing clearer insights for non-destructive testing and research.

Talks & News:

• K. Ahi, "AI-powered end-to-end product lifecycle: UX-centric human-in-the-loop system boosting reviewer productivity by 82% and accelerating decision-making via real-time anomaly…," presented at SPIE Silicon Valley, 2025. [Link Placeholder].
  Highlight: This presentation showcases a powerful AI system designed to dramatically improve efficiency and decision-making in product lifecycle management through real-time anomaly detection.