Deep knowledge of artificial intelligence can be hard to come by, which is why large tech companies routinely poach the best minds from top universities to develop their products. The result is that the work of data scientists benefits one corporation instead of democratizing something that could benefit many.

That, at least, is the way Anil Kamath sees it. As vice president of technology at Adobe, Kamath is trying to bridge that gap, by gathering academic experts in data science and artificial intelligence with his company’s developers to share experiences and ideas.

The Data Science Symposium sponsored each year by Adobe is an opportunity for data science and artificial intelligence researchers in academia to share ideas with company developers.
The Data Science Symposium sponsored each year by Adobe is an opportunity for data science and artificial intelligence researchers in academia to share ideas with company developers.

The event is the Data Science Symposium, which Adobe hosted last week, at which data science researchers present ideas that could help businesses deliver better experiences to consumers.

“We started this program to create awareness in the academic community about the work we do and the kind of problems that we are interested in,” Kamath explains. “The symposium is aimed at educating professors that are working in the field of data science about digital marketing and the kind of problems it presents that is relevant to data scientists, such as machine learning and artificial intelligence.”

Approximately 50 professors from different fields of data science and different business schools attend the symposium. “We present what we are doing in the field of digital marketing, and these professors present on the work they are doing that is related to Adobe and to digital marketing in general.”

“Half of the audience is professors – they come from local schools such as Stamford and Berkeley – but also from MIT, Columbia, Maryland, etc. They also come from different fields – computer science, statistics, industrial engineering, operations research and business,” Kamath explains.

Kamath noted that “since 2009 we have been expanding the products we provide to our customers to include a lot of solutions that include everything from analytics, to social marketing, to managing audience profiles, video, content management, etc. – we provide a pretty wide suite of tools for digital marketers to reach their brands. As we have expanded the suite of tools, we have seen that more of them of are being used not just for marketing but for the whole delivery of experience. So our products get used not just to acquire customers but to engage with them.”

“We collect a large amount of data on behalf of our brands,” Kamath continues. “Our goal is obviously analytics and insights, but also to use it for design and deliver better experiences. We share our data. Our team talks about our products and what we do for these digital brands and data science problems that are relevant to be solved with them.”

The other part of the program is that Adobe awards grants to select professors to help them further their research.

“We invite professors to send us proposals for research awards that we give out twice a year,” Kamath says. “Every six months we get about 60 to 70 proposals, and we work with seven to 10 that are related to the work that we are doing. We have been doing this for three years. Some of the professors that present are among those that have received these grants. We talk about the work that they’ve done in collaboration with us.”

Adobe has been an innovator in various fields, Kamath notes, “but we were not as well known for the work that we were doing in digital marketing. This is something that we wanted to do to create awareness of our program. But we also recognized there was a lot of innovation going around digital marketing in academia and we wanted to help researchers to help them understand what the real problems are where data science could be used.”

“For us it was a way for us to get into the research on what is happening in data science and academia in the context of machine learning, and see how we could use it. The academic community gets access to real data and real problems, and we benefit from the research that they have been doing in the data science and machine learning context,” Kamath concludes.

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