This book grew out of the graduate and undergraduate courses the authors built and taught at colleges and universities, refined over many semesters as the field changed underneath the syllabus. Both authors split their time between academia and applied research with industry partners; the material here reflects what survived the round trip between the two.
Alexander (Sasha) Apartsin, Ph.D.
Sasha holds a Ph.D. in Computer Science from Tel Aviv University, M.Sc. degrees from the Weizmann Institute and NYU Polytechnic, and a B.Sc. from the Technion.
Before returning to academia he led data science, AI, and research teams across the technology sector: automotive AI, telecommunications, and financial services. That industry stretch shaped a research program he now calls Pragmatic AI: designing methods that hold up when real-world data and conditions depart from clean benchmarks. The current work covers robust perception under degraded inputs, constraint-driven generation with verifiable specifications, and coordination mechanisms for multi-agent systems; the corresponding chapters in this book (Parts III, V, VI, and IX) draw directly on that line of work.
His teaching focuses on the practical and scientific foundations of modern AI systems. The undergraduate and graduate courses he designed and taught in large language models, AI agents, generative AI, NLP, computer vision, and deep learning are the immediate ancestor of this book, and many of the labs at chapter ends started life as those course assignments.
Yehudit holds a Ph.D. in Mathematical Economics from the Weizmann Institute and an M.Sc. in Game Theory from the Technion, with postdoctoral work in Financial Mathematics at Bar-Ilan University. She has been on the Afeka faculty since 2008.
She founded and directed the M.Sc. Program in Intelligent Systems at Afeka from 2016 to 2023, and in 2024 founded ICSGen.AI, the Afeka Interdisciplinary Center for Social Good and Generative AI. She has led collaborative research projects with defense organizations and industry partners, focusing on intelligent systems applied to healthcare, finance, and signal processing; the Part XIV chapters on industry applications draw on those collaborations.
Her teaching spans machine learning, data science, optimization, game theory, and decision-making under uncertainty. Her experience designing and leading academic programs in these areas shaped the educational structure and interdisciplinary orientation of this book, particularly the way each part walks the seam between technique and consequence.
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