# Věra Kůrková > Czech mathematician and computer scientist focusing on the theory of nonlinear approximation and optimization, the mathematical theory of neural networks and the theory of learning. **Wikidata**: [Q27869516](https://www.wikidata.org/wiki/Q27869516) **Wikipedia**: [English](https://en.wikipedia.org/wiki/Věra_Kůrková) **Source**: https://4ort.xyz/entity/vera-kurkova ## Summary Věra Kůrková is a Czech mathematician and computer scientist specializing in the theory of nonlinear approximation and optimization. She is prominently known for her contributions to the mathematical theory of neural networks and the theory of learning. Kůrková is affiliated with the Czech Academy of Sciences and is an alumna of Charles University. ## Biography * **Born:** 1948 * **Nationality:** Czech Republic; formerly Czechoslovakia * **Education:** Charles University * **Known for:** Theory of nonlinear approximation and optimization, mathematical theory of neural networks, theory of learning * **Employer(s):** Czech Academy of Sciences (since March 1, 1990) * **Field(s):** Mathematics, Computer Science * **Languages:** Czech ## Contributions Věra Kůrková has established a career focused on the mathematical foundations of computational intelligence. Her research portfolio is centered on the theory of nonlinear approximation and optimization, critical mathematical frameworks used to solve complex problems where linear models are insufficient. A significant portion of her work is dedicated to the mathematical theory of neural networks. By applying rigorous mathematical analysis to these architectures, she has contributed to the understanding of how neural networks approximate functions and process information. This theoretical work supports the broader development and reliability of machine learning models. Additionally, Kůrková has engaged deeply with the theory of learning. Her contributions in this area help define the mathematical boundaries and capabilities of learning systems. Her long-standing affiliation with the Czech Academy of Sciences, beginning in 1990, has provided a base for this continuous research output. Her work is recognized internationally, evidenced by her extensive bibliographic identifiers across major scientific databases such as Scopus, Web of Science, and zbMATH. ## FAQs ### Q: What is Věra Kůrková's primary area of research? A: Her primary research areas include the theory of nonlinear approximation and optimization, as well as the mathematical theory of neural networks and the theory of learning. ### Q: Where does Věra Kůrková work? A: She is employed by the Czech Academy of Sciences, a position she has held since March 1, 1990. ### Q: Where was Věra Kůrková educated? A: She received her education at Charles University, the oldest and largest university in the Czech Republic. ## Why They Matter Věra Kůrková matters to the scientific community because she bridges the disciplines of mathematics and computer science, providing the rigorous theoretical underpinnings necessary for modern computational technologies. In an era heavily focused on the application of Artificial Intelligence, Kůrková’s work on the mathematical theory of neural networks offers essential insights into how these systems function at a fundamental level. Her focus on nonlinear approximation and optimization directly impacts how efficiently and accurately computers can model complex, real-world phenomena. By advancing the "theory of learning," she contributes to the academic framework that defines how algorithms generalize from data. Her sustained presence at the Czech Academy of Sciences for over three decades highlights her role as a stabilizing and enduring figure in Central European scientific research, fostering the development of computational theory long before the current boom in AI popularity. ## Notable For * **Dual Discipline Expertise:** Being recognized as both a mathematician and a computer scientist. * **Neural Network Theory:** Pioneering theoretical work on the mathematical properties of neural networks. * **Institutional Tenure:** A long-standing affiliation with the Czech Academy of Sciences starting in 1990. * **Academic Lineage:** Education at Charles University, a historic institution established in 1348. * **International Recognition:** Presence in major global citation indexes including Scopus, Web of Science, and Google Scholar. ## Body ### Academic Background and Career Věra Kůrková was born in 1948 and holds citizenship in the Czech Republic. She pursued her higher education at Charles University, a prestigious institution located in Prague. Her professional career is marked by a long-term commitment to the Czech Academy of Sciences, where she has served as a researcher since March 1, 1990. ### Research Focus Kůrková’s work is defined by three interrelated pillars of theoretical computer science and mathematics: * **Nonlinear Approximation and Optimization:** Investigating mathematical methods for approximating complex functions. * **Mathematical Theory of Neural Networks:** Analyzing the computational capabilities and mathematical properties of network architectures. * **Theory of Learning:** Developing frameworks for understanding how systems learn from data. ### Identifiers and Profiles She maintains a verified presence across numerous scientific repositories and databases: * **ISNI:** 0000000058184069 * **Scopus Author ID:** 35588592800 * **ResearcherID:** J-8115-2012 * **Google Scholar:** 0cwnPAoAAAAJ * **zbMATH Author ID:** kurkova.vera * **ORCID:** 0000-0002-8181-2128 (implied by ORCID reference links) * **GND ID:** 136160417 * **VIAF ID:** 80552255 * **Library of Congress Authority ID:** n97065335 ## References 1. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-8181-2128/employment/5713650) 2. International Standard Name Identifier 3. Virtual International Authority File 4. ORCID iD 5. Czech National Authority Database 6. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0002-8181-2128/external-identifiers/1008294) 7. [Source](https://asep-analytika.lib.cas.cz/zvolit-ustav/uivt-o/autor/#0100784) 8. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0002-8181-2128/external-identifiers/1008300) 9. Google Scholar 10. [SciGraph](https://scigraph.springernature.com/person.0627676237.51)