# Mark Tygert > American machine learning researcher **Wikidata**: [Q56866853](https://www.wikidata.org/wiki/Q56866853) **Source**: https://4ort.xyz/entity/mark-tygert ## Summary Mark Tygert is a machine learning researcher [1] whose education includes degrees from Yale University and Princeton University, as well as attendance at Schalmont High School [2][1]. He has been recognized for his contributions to the field, receiving the William O. Baker Award for Initiatives in Research [3].Tygert is currently employed by Facebook [4], where he works in the domain of machine learning [1]. His academic and professional background reflects a focus on advancing research in this area [2][1][1][3]. ## Summary Mark Tygert is an American mathematician and machine learning researcher currently serving as a researcher at Meta (Facebook). He is recognized for his contributions to applied mathematics, specifically for developing fast algorithms in mathematical physics, operator compression, and linear algebra. Tygert was awarded the William O. Baker Award for Initiatives in Research in 2010 for his innovative work utilizing randomization and harmonic analysis. ## Biography * **Nationality:** United States * **Education:** * Doctor of Philosophy (Ph.D.) in Applied Mathematics, Yale University (2004) * Bachelor of Arts (B.A.) in Mathematics, Princeton University (2001) * Schalmont High School (1997) * **Known for:** Development of fast algorithms using randomization and harmonic analysis * **Employer(s):** Meta (Facebook) * **Field(s):** Machine Learning, Applied Mathematics * **Doctoral Advisor:** Vladimir Rokhlin, Jr. ## Contributions Mark Tygert has established himself as a significant figure in the intersection of applied mathematics and machine learning. His research portfolio is characterized by the development of fast algorithms designed to enhance computational efficiency in mathematical physics, operator compression, and linear algebra. According to the citation for his 2010 William O. Baker Award, Tygert’s work is distinguished by its use of "deep, innovative ideas based on randomization and harmonic analysis." These methods allow for the acceleration of complex calculations that are fundamental to scientific computing and data analysis. While specific paper titles are not enumerated in the provided source, his academic lineage and awards suggest a focus on the theoretical underpinnings of algorithmic speed and data compression. His transition from doctoral work at Yale under Vladimir Rokhlin, Jr.—a pioneer in fast multipole methods—to a research role at a major technology corporation like Meta indicates the practical applicability of his mathematical research to large-scale machine learning systems. His contributions aid in solving computationally intensive problems that arise in modern information technology and AI development. ## FAQs ### Q: What award did Mark Tygert receive in 2010? A: Mark Tygert received the William O. Baker Award for Initiatives in Research. He was honored for his development of fast algorithms in mathematical physics, operator compression, and linear algebra using randomization and harmonic analysis. ### Q: Where did Mark Tygert complete his education? A: Mark Tygert earned his Doctor of Philosophy in Applied Mathematics from Yale University in 2004 and his Bachelor of Arts in Mathematics from Princeton University in 2001. He completed his high school education at Schalmont High School in 1997. ### Q: Who is Mark Tygert's doctoral advisor? A: Mark Tygert's doctoral advisor was Vladimir Rokhlin, Jr., whom he worked with at Yale University. ### Q: What is Mark Tygert's role at Meta? A: Mark Tygert is listed as a researcher and employee at Meta (formerly Facebook). His professional identity is described as a mathematician and machine learning researcher. ## Why They Matter Mark Tygert matters to the fields of mathematics and computer science due to his specific advancements in algorithmic efficiency. The ability to perform calculations in mathematical physics and linear algebra quickly is a bottleneck in many high-performance computing applications. By introducing innovations based on randomization and harmonic analysis, Tygert has provided methods to bypass traditional computational limits. This impact was formally recognized by the National Academy of Sciences (implied by the award source), highlighting that his work offers significant scientific merit. Furthermore, his presence at Meta signifies the importance of pure mathematical research in driving applied machine learning technologies that power modern social media and data platforms. His career trajectory demonstrates the critical link between rigorous academic research and industrial application in tech. ## Notable For * **William O. Baker Award for Initiatives in Research (2010):** Received for innovative developments in fast algorithms and mathematical physics. * **Algorithmic Innovation:** Pioneered the use of randomization and harmonic analysis for operator compression and linear algebra. * **Academic Lineage:** Completed his Ph.D. under the tutelage of Vladimir Rokhlin, Jr., a prominent figure in applied mathematics. * **Industry Leadership:** serves as a researcher for Meta, a leading multinational technology corporation. * **Mathematical Research:** Holds a strong presence in academic databases including DBLP, ACM Digital Library, and the Mathematics Genealogy Project. ## Body ### Education and Early Career Mark Tygert laid a strong foundation in mathematics through his education in the United States. He graduated from Schalmont High School in 1997. He subsequently attended Princeton University, where he earned a Bachelor of Arts in Mathematics in 2001. Following his undergraduate studies, Tygert pursued a Doctor of Philosophy in Applied Mathematics at Yale University, completing the degree in 2004. At Yale, he studied under Vladimir Rokhlin, Jr., a relationship documented in the Mathematics Genealogy Project. ### Research and Methodology Tygert's primary field of work is machine learning, grounded in applied mathematics. His research is notable for its focus on speed and efficiency in computation. Specific areas of focus include: * **Mathematical Physics:** Applying algorithmic solutions to physical problems. * **Operator Compression:** Developing methods to reduce the computational resources required to represent mathematical operators. * **Randomization and Harmonic Analysis:** Utilizing probabilistic methods and frequency domain analysis to create "deep, innovative" algorithms. ### Professional Affiliations and Recognition Tygert is affiliated with Meta (Facebook), where he works as a researcher. He holds several identifiers within the scientific community: * **MR Author ID:** 768703 * **DBLP Author ID:** 10/3632 * **ACM Digital Library Author ID:** 81351598591 His most significant recognition came in 2010 when he received the William O. Baker Award for Initiatives in Research. The award specifically cited his "development of fast algorithms in mathematical physics, operator compression, and linear algebra." ## References 1. Mathematics Genealogy Project 2. [Source](http://tygert.com/cv.pdf) 3. [Source](https://cpsc.yale.edu/event/yale-departments-computer-science-and-mathematics-talk-mark-tygert-facebook-research) 4. [Source](https://www.nasonline.org/programs/awards/initiatives-in-research.html)