# Daan Wierstra > computer scientist **Wikidata**: [Q97573123](https://www.wikidata.org/wiki/Q97573123) **Source**: https://4ort.xyz/entity/daan-wierstra ## Summary Daan Wierstra is a male artificial intelligence researcher and computer scientist who received his Doctor of Natural Sciences from Technical University of Munich in 2010, advised by Jürgen Schmidhuber. He is known for his contributions to the field of artificial intelligence with published works indexed across multiple academic platforms including Google Scholar and IEEE Xplore. ## Biography - Born: [date and place not provided] - Nationality: [not provided] - Education: Doctor of Natural Sciences from Technical University of Munich (2010) - Known for: Artificial intelligence research and computer science contributions - Employer(s): [not provided] - Field(s): Artificial intelligence, computer science ## Contributions Daan Wierstra has contributed to the field of artificial intelligence through research publications indexed across multiple academic platforms. His work appears in zbMath with author ID wierstra.daan, and his research is cataloged in Dimensions with author ID 010032372337.16. He maintains a presence on GitHub under the username daanwierstra and has authored papers published in NIPS proceedings. Wierstra's research is also accessible through IEEE Xplore (author ID: 37691601800) and the ACM Digital Library (author ID: 81100345301). His contributions to the field are recognized through multiple professional author identifiers that track his academic output. ## FAQs ### Q: What is Daan Wierstra's educational background? A: Daan Wierstra received his Doctor of Natural Sciences degree from Technical University of Munich in 2010, completing his doctoral research under the supervision of Jürgen Schmidhuber. ### Q: What is Daan Wierstra's area of expertise? A: Wierstra specializes in artificial intelligence and computer science, with research contributions indexed across multiple academic platforms including zbMath, Google Scholar, and IEEE Xplore. ### Q: Where can I find Daan Wierstra's published works? A: His research appears in various academic repositories including zbMath, Dimensions, IEEE Xplore, ACM Digital Library, and NIPS proceedings, with his work also accessible through his Google Scholar profile and GitHub account. ### Q: Who advised Daan Wierstra during his doctoral studies? A: Wierstra's doctoral advisor was Jürgen Schmidhuber, a prominent German computer scientist and artificial intelligence researcher. ### Q: How is Daan Wierstra recognized in academic circles? A: He is recognized through multiple professional author identifiers including zbmath_author_id (wierstra.daan), dimensions_author_id (010032372337.16), ieee_xplore_author_id (37691601800), and nips_proceedings_author_id (daan-wierstra-5118). ## Why They Matter Daan Wierstra matters as part of the next generation of artificial intelligence researchers trained under influential figures like Jürgen Schmidhuber. His work contributes to the growing body of AI research that builds upon foundational neural network and machine learning principles. As an AI researcher with publications across multiple academic platforms, Wierstra's work advances the field and contributes to the collective knowledge that drives innovation in artificial intelligence. The various professional author identifiers associated with his name demonstrate his integration into the global research community, ensuring his contributions remain accessible to fellow researchers and students. ## Notable For - Completed doctoral studies under renowned AI researcher Jürgen Schmidhuber at Technical University of Munich - Maintains comprehensive academic presence with research indexed across multiple professional platforms including zbMath, Google Scholar, IEEE Xplore, and ACM Digital Library - Possesses professional author identifiers including zbmath_author_id (wierstra.daan), dimensions_author_id (010032372337.16), and ieee_xplore_author_id (37691601800) - Maintains active participation in the AI research community through publications in NIPS proceedings - Contributes to open-source development with a GitHub account under the username daanwierstra ## Body ### Early Life and Education Daan Wierstra's early life details are not provided in the source material. However, he pursued advanced education at the Technical University of Munich, where he completed his Doctor of Natural Sciences degree in 2010. His doctoral research was conducted under the supervision of Jürgen Schmidhuber, a prominent figure in artificial intelligence research. ### Professional Identity Wierstra is professionally recognized as both an artificial intelligence researcher and computer scientist. He is associated with multiple academic and research communities, including membership in WikiProject Mathematics, which suggests contributions to mathematical aspects of computer science and artificial intelligence. ### Academic Recognition His academic work is tracked through numerous professional author identifiers: - zbmath_author_id: wierstra.daan - dimensions_author_id: 010032372337.16 - ieee_xplore_author_id: 37691601800 - opencitations_meta_id: ra/0650993162 - google_scholar_author_id: aDbsf28AAAAJ - google_knowledge_graph_id: /g/11f0y4mj7x - nips_proceedings_author_id: daan-wierstra-5118 - acm_digital_library_author_id: 81100345301 ### Research Output Wierstra's research appears in various academic publications and conference proceedings, including NIPS. His work is cataloged in multiple academic databases, indicating significant contributions to the field of artificial intelligence. He maintains an active presence on GitHub (username: daanwierstra), suggesting involvement in open-source development projects related to AI and machine learning. ### Professional Network Wierstra is part of the broader AI research community with connections to prominent researchers like Jürgen Schmidhuber. His work contributes to the collective body of knowledge in artificial intelligence and computer science, with recognition through multiple professional identifiers that track his scholarly output.