Gurjeet Singh: Shaping the Future of Data [Entire Talk] | Summary and Q&A

February 10, 2014
Stanford eCorner
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Gurjeet Singh: Shaping the Future of Data [Entire Talk]

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This video features an interview with the co-founders of Ayasdi, Gunnar Carlsson and Gurjeet Singh. They discuss the journey of turning mathematical research into a successful company that specializes in turning data into knowledge using automated methodologies. They explain how topology, the study of shapes, plays a significant role in their technology and how they have applied it to various industries such as pharmaceuticals, finance, and healthcare.

Questions & Answers

Q: What problem is Ayasdi trying to solve?

Ayasdi aims to solve the problem of turning data into knowledge using automated methodologies.

Q: What is the standard process for turning data into knowledge?

The standard process involves a smart analyst coming up with a hypothesis or idea, converting it into a query or code, running it against a database, and analyzing the results.

Q: What are the problems with the standard process?

The first problem is the need for specialized people with advanced degrees in mathematics, statistics, computer science, and domain knowledge. The second problem is that there are too many hypotheses to consider, especially with large datasets.

Q: How does Ayasdi's methodology differ from the standard process?

Ayasdi uses a more automated methodology by applying hundreds of machine learning algorithms to large datasets. Their algorithms combine the results based on research conducted at Stanford, allowing users to have some answers from the start.

Q: What is Gunnar Carlsson's background and how did he start working on applied mathematics?

Gunnar is a mathematician who had a background in algebraic topology. He started exploring the application of mathematical concepts to real-world problems in the mid '90s.

Q: How did Gurjeet Singh end up in the Ph.D. program at Stanford?

Gurjeet, originally from India, had a passion for mathematics and wanted to learn more to expand his career opportunities. He found a program called Scientific Computing at Stanford and applied, hoping to combine his knowledge of mathematics and computer science.

Q: How did Gurjeet and Gunnar start working together?

Gurjeet saw an email from Gunnar, who was talking about using algebraic topology to understand large complex datasets. He saw an opportunity to apply his math and machine learning skills and reached out to Gunnar. He became a student of Gunnar's and started working together on research projects.

Q: What was different about Gurjeet compared to other students?

Gurjeet not only had a deep understanding of the theory but also had the drive to implement the ideas and solve real-world problems. He was able to quickly prototype solutions and show practical applications of the research.

Q: Did Gunnar ever feel like a sellout for starting a company instead of pursuing pure mathematical research?

Gunnar initially anticipated feeling like a sellout, but his colleagues and peers were actually supportive and appreciative of his entrepreneurial endeavors. He believes that applying math to real-world problems is a valuable contribution.

Q: How did Ayasdi transition from theoretical research to building a company?

After completing their research and realizing the potential impact of their findings, Gurjeet and Harlan Sexton left academia and started building Ayasdi. They focused on meeting with potential clients and gaining feedback on use cases that Ayasdi could address. They were able to secure funding after demonstrating the value and impact of their technology.

Q: What are some of the use cases Ayasdi has worked on?

Ayasdi has worked on various use cases, including fraud detection in the banking industry, triage models in healthcare, and analyzing the characteristics of successful doctors in hospitals.


Ayasdi's technology aims to automate the process of turning data into knowledge by applying topology and machine learning algorithms to large datasets. They have successfully addressed problems in multiple industries and have demonstrated significant improvements in data analysis and decision-making. The transition from academia to building a company required a shift in focus and a strong drive to solve real-world problems. Overall, Ayasdi's approach highlights the potential for mathematics and data analysis to revolutionize various industries.

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