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Gurjeet Singh

Gurjeet Singh is a co-founder of Ayasdi, has a Bachelor’s degree from Delhi University, and a Master’s and Ph.D. degree in Computational Mathematics from Stanford University. Gurjeet first met Gunnar Carlsson at Stanford during his Ph.D. and this led to Gurjeet’s graduate work and thesis on the theory, algorithms and applications of Topological Data Analysis (TDA), as well as, Gurjeet’s participation on Gunnar’s DARPA funded research project to study how topology can be applied to solve real world problems. During this time, Gurjeet designed and developed various TDA algorithms to bring out the shape and meaning of data. This led to the creation of Ayasdi’s core technology and machine-learning algorithms. Gurjeet has published academic papers in top mathematics journals and computer science conferences, previously worked at Google and Texas Instruments. He lives in Palo Alto with his wife and son, and develops multi-legged robots in his spare time.

Posts by Gurjeet Singh


Machine learning is generating a tremendous amount of attention these days from the press as well as the practitioners. And rightly so – machine learning is a transformative technology. But despite the references to the topic, the money raised from venture capitalists, and the spotlight that Google is bringing to the subject, machine learning is […]

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Today we announced a key partnership with Intermountain Health where they will deploy our Clinical Variation Management software to accelerate the process of developing, deploying and measuring care process models. The reason that the partnership is so significant is that Intermountain is the undisputed leader in the clinical variation space – and for them to […]

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“Is the model a black-box?” This is a question that many a data scientist struggle with in communicating with business. In all fairness, there are plenty of business situations which require the models to be transparent, such as: Regulations – in many industries such as financial services and healthcare, the models used to make predictions […]

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Last week on the Alphabet earnings call Sundar Pichai made several explicit references to machine learning. This is meaningful on several levels and is also a warning shot for entire industries who don’t view Google as a competitor (but should). Sundar noted early in his prepared remarks: “I also want to point out that our […]

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Today is both exciting and humbling for the team here at Ayasdi. This morning, the World Economic Forum (WEF) announced that Ayasdi has been selected as a member of the 2015 class of Technology Pioneers, considered by many to be the most prestigious honor that an emerging company can garner. The WEF is an extraordinary […]

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Today we announced two things of significance for Ayasdi that will help to define the future of the company. First off, we announced significant year over year growth in customers, revenues, bookings and annual contract value.  These growth milestones don’t just tell the story of a rapidly growing enterprise –  they tell a story of […]

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The Fed released its first of two pronouncements yesterday on the health of the financial services sector.  The first was the Dodd-Frank mandated Stress Test for qualifying financial institutions.  The goal is to determine how resilient those organizations are to potential financial shocks. To achieve this the Federal Reserve oversees a number of scenarios (company-run […]

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This morning we were named one of Fast Company’s Most Innovative Companies in the world for Big Data.    This is a remarkable achievement for a company our size and age and we are both flattered and humbled at our inclusion alongside such innovators as Apple, Google, Netflix, Tesla Motors, and 2015 Winner Warby Parker. […]

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2014 has been a landmark year for Big Data. The most spectacular example of this was the Hortonworks IPO – a success by any measure. As we look forward to 2015, it is clear that while big data technologies still have a long ways to go in terms of enterprise adoption, its ultimate adoption is […]

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How do we learn new things from Data? Yann LeCun recently said:  “Seriously, I don’t like the phrase “Big Data”. I prefer “Data Science”, which is the automatic (or semi-automatic) extraction of knowledge from data. That is here to stay, it’s not a fad. The amount of data generated by our digital world is growing […]

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Today, every business is generating — and attempting to leverage — massive amounts of complex data which is why discussions about “Big Data” seem to be everywhere. Companies and research organizations understand that valuable insights hidden in their data can unlock new efficiencies, revenue opportunities, or even life-altering breakthroughs. The main challenge today is finding […]

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Last week in GigaOM, I wrote an article that asked the Big Data industry to think differently about how it gathers insights from data. It’s imperative that we shift our thinking because the query-based approach that we’ve used for the last 40 years is not going to scale. Even though gathering and storing data is […]

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