Colleges and universities are doubling down on efforts to add data science programs and to open up institutes to support research and development in the burgeoning field. While some are building massive centers to house researchers and students exploring these topics, others are teaming up with industry to better align curriculum with workforce needs.
To examine the trend, we looked at a handful of universities making big investments in programs to address data skills needs.
CATALOG, a Boston company co-founded by a recent UW–Madison Ph.D., is preparing to demonstrate the world’s fastest, densest DNA-based data-warehouse.
In a meeting at the Weinert Center for Entrepreneurship at the Wisconsin School of Business, Hyunjun Park said the device will hold digital information in DNA – life’s evolution-perfected “data storage” molecule.
Princeton University has teamed up with Microsoft to collaborate on the leading edge of microbiology and computational modelling research.
In this project, Microsoft is helping Princeton to better understand the mechanisms of biofilm formation by providing advanced technology that will greatly extend the type of research analysis capable today. Biofilms — surface-associated communities of bacteria — are the leading cause of microbial infection worldwide and kill as many people as cancer does. They are also a leading cause of antibiotic resistance, a problem highlighted by the World Health Organization as “a global crisis that we cannot ignore.” Understanding how biofilms form could enable new strategies to disrupt them.
A Government-sponsored Science and Innovation Audit has revealed that the area of London comprising King’s Cross, Bloomsbury, Angel and Euston contains one of the highest densities of knowledge-based businesses, cultural and scientific organisations anywhere in the world.
This area, known as London’s “Knowledge Quarter” (KQ), contributes significantly to the UK economy with an economic output similar to that of the City of London.
The National Science Foundation gave the University of North Texas College of Information a three-year, $359,879 to develop curriculum in data analytics.
The University of Cambridge is establishing a new research centre bringing together expertise from across academic departments and industry to drive research into the analysis, understanding and use of big data.
An understanding of statistics* is required to properly design, analyze, and interpret scientific studies. While most studies would thus benefit from the involvement of an experienced statistician*, almost everyone seems to agree that there aren’t enough of them to meet this need. So shouldn’t we just fund more statisticians? To unpack this question, I think it’s important to first clarify what kinds of statisticians we probably need more of, and to consider the relative merits of consultation vs collaboration.
AI Business School is non-technical and designed to get executives ready to lead their organizations on a journey of AI transformation, according to [Mitra] Azizirad.
Nick McQuire, an analyst who covers artificial intelligence for CCS Insight, said more than 50 percent of the companies his firm has surveyed are already either researching, trialing or implementing specific projects with AI and machine learning, but very few are using AI across their organization and identifying business opportunities and problems that AI can address.
“That’s because there’s limited understanding in the business community about what AI is, what it can do and, ultimately, what are the applications,” he said. “Microsoft is trying to fill that gap.”
Braving temperatures in the mid-20s °F, a bundled group of data enthusiasts gathered in Cadman Plaza in Brooklyn last week to compete for the charity of their choice. Participants solved clues using DATA2GOHEALTH.NYC, a mobile interactive data tool, tweeting images of truck traffic, Citibike stations, and stray broccoli along with tid-bits of data about the neighborhood. It was an all-around good time capped by happy hour drinks.
This was our first data-driven, social media-aided scavenger hunt — really, our first scavenger hunt, period — and it was a success. Measure of America is hoping to make this an annual event, we only wish Open Data Week were held in slightly warmer months. If you’re in the downtown Brooklyn area and want to explore the data and do the scavenger hunt on your own time, here are the clues we provided. Or dig into the data in your own neighborhood and come up with your own clues.
Innovation in higher education is increasingly fueled by data. From financial aid applications, to online classes, to student success initiatives, college students provide an extraordinary amount of data to schools, companies, and the government. This data provides unprecedented insights into student behavior, and colleges are using it to shape curricula, processes, and services to meet students’ needs.
In this week’s edition of FPF at 10, Sara Collins explains how data continues to transform the higher ed landscape, and why sound privacy practices are needed to ensure a safe, enriching academic experience.
Artificial intelligence will utterly transform medicine. Better diagnoses, but also privacy concerns. But one doctor says if done right, AI could put the “care” back in health care. … Dr. Eric Topol, author of the new book “Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.” Cardiologist and executive vice president at Scripps Research. Director and founder of the Scripps Research Translational Institute. (@EricTopol) [audio, 46:35]
Climate change is already affecting oceans, fisheries, and the livelihoods that depend on them. Some local fish stocks are declining, while other fish populations are shifting their distributions, forcing fishers to travel farther to make their catch.
It stands to reason that fighting climate change would also help protect fisheries and the fishing economy. Now, researchers have quantified these benefits, calculating just how much fish stocks, fishers, and seafood consumers worldwide would gain from meeting the climate benchmarks set out in the Paris Agreement.
In a study published 27 February in Science Advances, the researchers combed through U.N. Food and Agriculture Organization and other databases for information on fisheries catch levels and prices from 2001 to 2010. They gathered data on the 10 most valuable marine fisheries in each country – a total of 381 different species worldwide.
Artificial intelligence, or what passes for it, can be found in practically every major tech company and, increasingly, in government programs. A joint Harvard-MIT program just unloaded $750,000 on projects looking to keep such AI developments well understood and well reported.
The Ethics and Governance in AI Initiative is a combination research program and grant fund operated by MIT’s Media Lab and Harvard’s Berkman-Klein Center. The small projects selected by the initiative are, generally speaking, aimed at using technology to keep people informed, or informing people about technology.
Washington, DC March 29, starting at 10:30 a.m., Brookings Institution. “Jeroen van den Hoven, professor of ethics and technology at Delft University of Technology in The Netherlands, will give keynote remarks. He will then be joined for a panel discussion by Cameron Kerry, former general counsel at the U.S. Department of Commerce and distinguished fellow at Brookings, and Bilyana Petkova, assistant professor of international and European law at Maastricht University and fellow-in-residence at the Electronic Privacy Information Center. Nicol Turner Lee, fellow in the Brookings Center for Technology Innovation, will moderate the discussion.” [registration required]
New York University, Center for Urban Science and Progress; New York, NY
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Brooklyn, NY March 15, starting at 12:30 p.m., NYU Center for Urban Society + Progress. “Discussion on NSF’s Cyber-Physical Systems and Smart and Connected Communities Programs.”
Melbourne, Australia May 8-9. “This forum brings together senior industry leaders, investors, influential technologists, founders, CEOs, CTOs, data scientists, roboticists and digital leaders from all over the world, for a high profile gathering of refreshing, new thought leadership, collaborative discussion, new breakthroughs sharing to reshape the future.” [$$$$]
Gradient descent is often quite easy to implement and computationally affordable, which probably explains (at least some of) its popularity. One thing people tend to do with gradient descent is to “stop it early”, by which I mean: people tend to not run gradient descent until convergence. Why not? Well, it has been long observed (by many, e.g., here, here, and here) that early-stopping gradient descent has a kind of regularizing effect—even if the loss function that gradient descent is run on has no explicit regularizer; see the two figures above, for a bit more of this kind of motivation. In fact, it has been suggested (see, e.g., here and here) that the implicit regularization properties of optimization algorithms may explain at least in part some of the recent successes of deep neural networks in practice, making implicit regularization a very lively and growing area of research right now.
In our recent paper, we precisely characterize the implicit regularization effect of early-stopping gradient descent.
In dealing with huge datasets, it is also common to distribute deep learning over multiple GPUs in parallel. Achieving cost-effectiveness in these clusters relies on efficiently sharing resources between multiple users.
Naturally, this can lead to a host of problems and inefficiencies – modern GPU hardware, deep learning frameworks, and cluster managers are not designed for efficient, fine-grained sharing of GPU resources at either the micro or macro scale. Memory and computation power are regularly wasted at the level of individual GPUs and at that of an entire GPU cluster.
Prof. Mosharaf Chowdhury and his students, Juncheng Gu and Peifeng Yu, are working to overcome both of these shortcomings, multiplying the number of jobs a cluster can finish in a set amount of time and streamlining methods of sharing resources on the fly.
You can’t become a great writer without becoming a great reader.
Reading opens up the adjacent possible. Think of the adjacent possible like a new door opening in your brain — the house. Each time you read something new, you can make new connections in your writing.
Reading good writing also helps you become a better writer yourself.