Zvi Lapp

About

I am Zvi Lapp, a strategic AI researcher and innovator specializing in the deployment of state-of-the-art technologies in healthcare and autonomous systems. My journey in the AI industry, backed by my ongoing Ph.D. studies in Computer Science, is characterized by leadership roles and a constant drive towards innovation. By introducing novel methodologies, my work combines deep learning paradigms and AI-empowered predictions for advanced analysis of medical images, contributing to greater diagnostic precision and efficiency.

In my current role as Senior Deep Learning Solutions Architect at Nvidia, coupled with my past experiences in AI-focused positions, I have honed expertise in managing multidisciplinary teams and steering strategic decision-making processes. My specialization lies in the integration of advanced AI solutions, particularly Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) applications, into various industries. I am adept at deploying enterprise-grade solutions and developing deep learning platforms that deliver actionable insights. My track record includes creating solutions projected to save millions annually, significantly influencing the operational methods of healthcare and data-driven decision-makers.

Away from the professional sphere, my life is grounded by my role as a husband and father of two wonderful daughters, coupled with a love for sports and athletics. A seasoned wrestler with numerous tournament victories and a devotee of kettlebell training and powerlifting, I apply the same discipline and strategic mindset in my personal pursuits as I do in my professional life. In essence, I am a problem-solver, strategist, athlete, and family man, who continuously strives for excellence.

Professional Journey, Expertise & Impact

My professional journey, spanning a diverse range of roles in AI research and strategy, is marked by a passion for startups, transformative solutions, and the thrill of creating high-impact products from conception to execution.

In the early stages of my career, as a Server-Side Software Engineer at Algotec Phillips Medical Imaging and a Software Engineer Intern at Israel Aerospace Industries, I developed a strong foundation in robust backend services and multicore architecture management. These experiences paved the way for my future endeavors in the exciting world of AI research.

As my career evolved, I found my stride in the startup environment, embracing the challenges and rewards it offers. At DeePathology, in my role as a Machine Learning Researcher, I developed novel approaches for classifying and retrieving predictive signatures from multiscale pathology images. Progressing to roles such as Lead AI Researcher at Cortica, Head of AI at Marpai, and currently, Senior Deep Learning Solutions Architect at Nvidia, I have been instrumental in bringing academic research to life, creating deep learning solutions for diverse applications, and seeing them through from ideation to deployment.

Throughout this journey, I have continually expanded my skill set, mastering strategic thinking, team leadership, data analysis, and a myriad of programming languages and tools.

Parallel to my professional pursuits, I have been committed to academic excellence. I hold a B.Sc. and an M.Sc. in Computer Science from Bar Ilan University (BIU), both with a focus on Artificial Intelligence. Currently, I am a Ph.D. candidate at BIU, conducting groundbreaking research that blends deep learning Generative AI for advanced multi-modal medical analysis.

In essence, my journey is a fusion of professional experiences, academic rigor, startup dynamism, and a deep-seated desire to drive high-impact transformations using AI.

Publications & Patents

LBA: Online Learning-Based Assignment of Patients to Medical Professionals: A novel methodology that leverages online learning for optimal patient-medical professional assignment

Certainty Pooling for Multiple Instance Learning: Introduced a novel strategy for handling multiple-instance learning scenarios to enhance prediction accuracy.

PathRTM: Real-time prediction of KI-67 and tumor-infiltrated lymphocytes: Developed a real-time prediction model for two crucial biomarkers in cancer diagnostics, thus enhancing the speed and accuracy of diagnosis.

Autonomous Enriching Reference Information (Patent Filed – US · Apr 5, 2022): Created an autonomous system that enriches reference data, contributing to enhanced accuracy of AI systems.

Unsupervised Method For Anomaly Detection in Manufacturing (Patent Filed – US · Feb 28, 2022): Developed a novel unsupervised anomaly detection technique aimed at improving the efficiency and quality of manufacturing processes.