Researchers from Harvard, NYU and the University of California San Diego have come together to set up the “Open Humans Network,” in an effort to let individuals share their personal health data to accelerate medical breakthroughs.

Funding for the initiative comes from John S. and James L. Knight Foundation and the Robert Wood Johnson Foundation, in the form of $500,000 from each in separate grants.

The project’s director, Jason Bobe who also runs the project’s parent organization, PersonalGenomes.org, explains, “Think of it as open-sourcing your body.”

“There is tremendous potential for accelerating medical discoveries by helping individuals take their health and personal data out of data silos and making the data more broadly used.”

The project will allow “willing individuals” ease in access and sharing of data with researchers through an online portal that matches volunteers having specific health data with researchers who would benefit from access to more information.

“The premise is that more individuals will join scientific studies if they are empowered with the choice to share their data. And the greater availability of shared data will allow scientists to conduct more studies, and produce more robust and meaningful results,” explains the blog post making the announcement.

A data sharing framework allows researchers to join and share respective studies at openhumans.org, wherein individuals participate in studies that are part of the framework by importing their data into a profile on the Open Humans Network website.

Presently the portal has three studies that members can take part in:

• “American Gut” – exploring microbial diversity of the human body;
• “GoViral” – profiling viruses related to flu-like illness; and
• “Harvard Personal Genome Project” – collecting genomic, environmental and human trait data.

As for the privacy aspect of the project, members must pass a test as part of the consent process to ensure that they understand the potential risks of sharing personal health data, publicly.

Photo credit: Asja. / Foter / CC BY

According to Shikhar Ghosh, a senior lecturer at Harvard Business School, almost three-quarters of US venture-backed firms fail to return on investor’s capital. This week’s disheartening announcement from Fab proved that even the most promising cash cow is not a safe bet. Luckily, researchers at MIT may have come up with a mathematical model which can assess the “quality” of a startup- and help us understand why some startups go up in flames.

The model is based on an empirical study which projects the growth potential of tech startups with “new precision — and could help local or regional policymakers assess their growth prospects,” according to the MIT News Office. It determines the “quality” of startups, where “quality” denotes the chances of either landing an IPO or getting acquired within the first six-years.

“A central question in evaluating the impact of policies toward business creation, startups, and innovation, is simply how to measure the kinds of entrepreneurs who are likely to build growth businesses,” explains Scott Stern, the David Sarnoff Professor of Management at the MIT Sloan School of Management, the research lead.

The study summarized in Science, titled “Where is Silicon Valley?” explains how Stern and his colleague Jorge Guzman, a doctoral candidate at MIT Sloan, armed with an exhaustive list of new firms from California’s official business registry for the years 2001 to 2011, tracked a series of features that was common high-growth firms over the period from 2001 to 2006, for 70 percent of the firms.

With the results they tested their model with the outcomes of the other 30 percent of new firms in the same period. They also tested their model against new firms registered in the years 2007 to 2011.

“It is very difficult to manage the entrepreneurial ecosystem if we cannot measure the entrepreneurial ecosystem,” Stern said. “While our work is a first step, we believe that policymakers and practitioners and researchers would benefit from being able to assess … the combined entrepreneurial quantity and quality in a given region. What we want is to really create new real-time economic statistics.”

Some interesting findings from their research include:

• Companies without the name of the founder in the title had 70 percent more chances of either landing an IPO or getting acquired within the first six-years.
• Companies with shorter names had 50 percent more chances of success than the ones with longer names.
• Equally interesting is the fact that those companies with “high tech” names have 92% better chance of either landing an IPO or getting acquired.

The study also provides insight regarding geographies and locations.

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Steve Ballmer, former Microsoft CEO and Harvard alumnus; who announced a significant donation to the Computer Science Department at Harvard last week is advocating machine learning as the next era of computer science. Ballmer expressed his excitement about the ability of computer and IT to process huge amounts of data not only to see patterns but to suggest actions and understand human intent.

“I think it’s the dawn of an exciting new era of info and computer science,” Ballmer told Computerworld. “It’s a new world in which the ability to understand the world and people and draw conclusions will be really quite remarkable… It’s a fundamentally different way of doing computer science.”

His emphasis on the potential of machine learning and artificial intelligence (AI) in being able to shape our own future was evident throughout the discussion on the future of CS at Harvard with Harvard President Drew Faust and Cherry Murray, dean of the School of Engineering and Applied Sciences (SEAS).

“It’s not about just putting in input and getting an answer,” Ballmer stated. “Computer science evolves and changes. This is going to be a fundamental area. I’m not trying to pick [what Harvard focuses on] but we do share a passion for this being a leading edge over the next several years.”

Ballmer received a bachelor’s degree in applied mathematics and economics from Harvard College in 1977. He joined Harvard alum, Bill Gates, at Microsoft in 1980. Today, Ballmer is the owner of the Los Angeles Clippers basketball team.

November 12, Ballmer emphasized the importance of strengthening the faculty for “the next era of computing,” in fields such as machine learning and computational theory, which are key to propelling “the next wave of innovation and research.” Expanding the faculty cohort from 24 to 36 would “enable the Harvard computer-science department really to be built for the future” computing era.

In light of the recent criticism of artificial intelligence as an existential threat by Tesla and SpaceX CEO, Elon Musk, Ballmer’s statement appears quite contradictory.  “With artificial intelligence, we are summoning the demon,” Musk said in a rather radical statement last month.

Ballmer, to that retorted- “It doesn’t concern me,”. He said “At the end of the day, will we have to have other innovations that protect people from privacy and security [problems]? Of course we will… I don’t think being afraid of any innovation is a good thing.”

He added that he doesn’t think self-driving cars, which would require artificial intelligence and machine learning, will proliferate for another 10 years. “I won’t be getting in any of them any time soon, at least not in the streets of Cambridge,” he said.

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(Image Credit: Rain Rannu)

You’re an extremely talented engineering high school senior, and the future looks bright. You’re well on track to obtaining the sexiest profession in the world, and a job in Silicon Valley at one of the world’s most well-renowned companies is in your reach, perhaps with a 6-figure starting salary attached. Problem is, there’s a whole host of top engineering schools you could apply to; which one’s going to get you closest to your data science dream?

One individual took to Quora to ask “How does a star engineering high school senior choose among Carnegie Mellon, MIT, Caltech, Stanford and Harvard?” Luckily, Airbnb engineer and Stanford grad Christopher Lin took up the gauntlet, using one of the most famous schools in popular culture for comparison- the Hogwarts School of Witchcraft and Wizardry. He remarked:

“Going to Stanford is like being sorted into Gryffindor.
“Their daring, nerve, and chivalry set Gryffindor apart.”
Stanford Engineering places heavy emphasis on entreneurship and is deeply mired in Silicon Valley culture wherein taking a risk and hacking on a startup is considered nobler and more interesting than being a smart engineer but languishing in academia or a large corporate environment. Stanford is also the most socially normal of the four schools.

Going to Harvard is like being sorted into Slytherin.
“Power-hungry Slytherin loved those of great ambition.”
Harvard is known for social climbing and an atmosphere where interactions are perpetually shaded with professional networking. Many people who attend come from privileged backgrounds and expect success in traditional settings like finance, consulting, and large technology companies.

Going to Caltech is like being sorted into Hufflepuff.
Going to MIT is like being sorted into Ravenclaw.
“For Hufflepuff, hard workers were most worthy of admission.”
“For Ravenclaw, the cleverest would always be the best.”
This was a tricky one since both Caltech and MIT are reputed for having students who are wicked-smart and hard-working but perhaps at the expense of being socially well-adjusted. Yishan Wong’s answer claims that Caltech students are more likely to be weird and quirky, which is reminiscent of prime Ravenclaw Luna Lovegood, but I think the most salient distinction between the two is that Caltech students are more known for being very hard-working — see Adam D’Angelo’s answer to What is Caltech’s image in the CS industry? — while MIT is primarily known for valuing raw, academic intelligence above all else.

I just spent twenty minutes comparing top-tier engineering programs to Hogwarts houses. You’re welcome.”

Perhaps a little narcissitic for a Stanford grad to self-appoint Gryffindor as his house, but the overview does offer a high-level (if slightly stereotypical) overview of the US’ top engineering schools.

If you’re interested, the good people over at Quartz took it one step further and looked into the graduate employment breakdown of the four schools, to see if the stereotypes hold true. There seems to be mild differentiation between the employment prospects of all four institutions, with Google being the top employer for all four. So in short, hypothetical high school senior, if you’re looking to end up at one of the most renowned companies in the world, any of these schools could get you there- without the need for a sorting hat.

Melanie Mueller, Data Scientist from Harvard University, gives us an overview of her visit to Big Data TechCon conference in Boston. Melanie attended the conference from March 31 to April 2.

Big Data practitioners converged on Boston to attend Big Data TechCon , a Big Data training conference organized by BZ media. Topics covered both tools to deal with Big Data, such as current database and parallelization solutions, as well as analytics to derive insight from it.

For tools, hands-on tutorials were particular popular and often overcrowded, revealing the most coveted techniques. Classes to analyze social media streams were filled to the last chair, and a one-day crash course on Hadoop was booked out weeks before the conference.

In his tool-focused keynote, Sunil Venkayala from HP Vertica talked about Distributed R, an open source software that is still under development. Distributed R marries R and Hadoop, and promises to allow analysis of data that is too large for vanilla R. To this end, the Vertica team rewrites R routines to provide scalable high-performance on multiple nodes for distributed processing, while allowing users to use familiar GUIs and packages from R.

 Todd Cioffi from RapidMiner raised the question: Why every time we get a new data problem we start coding? He emphasized that we shouldn’t confuse the tools with the process, and that we need to focus on the questions rather than the tools in order to gain valuable insights. To ask the right questions, we need to move on from traditional business intelligence with its query and dashboard based reporting to modern advanced analytics with descriptive and predictive modeling. For example, a business intelligence question such as ‘Which packages are on which truck?’ should be replaced by an advanced analytics questions like ‘How can I optimally assign packages to trucks to minimize delivery time and usage of trucks?’

Scott Sokoloff from TEL and Will Ford from Alpine Data Labs illustrated common pitfalls when trying to leverage Big Data for business decisions. They emphasized that it is not enough to just follow the pattern sin the data and find actionable insights. The insights must also align with the company interests, and they must be actionable in the company’s particular environment. For example, proposing a strategy that will bring down product prices sounds good – but it might not go well with the sales team if their bonuses are based on the total dollar amount sold. Scott Sokoloff phrased his take home message as: Big data analytics is not about who is the smartest, it’s a relationship business.

Apart from the presentations, Big Data TechCon allowed for ample networking opportunities between the attendees– indeed, Big Data is a relationship business!

Melanie is a postdoc at Harvard University, where she is creating and munching data from biological experiments. Most of the time she she is trying to figure out what yeast cells have done while growing on a Petri dish. It turns out that automated data analysis with Matlab and Python can help a lot in this process! Before moving to Harvard University, Melanie obtained a PhD in Physics from the Max Planck Institute of Colloids and Interfaces in Potsdam, Germany, where she used mathematical modeling, computer simulations and analysis of experimental data to understand how molecular motors transport cargoes in cells.

Experfy, the Harvard-backed start-up, has introduced a new platform for data scientists to earn money. The company is launching a talent marketplace to match companies and their data science projects with people who have “strong quantitative skills…know data and statistics.”

According to a Wikibon report, there is an estimated $2.28 billion short-term big data service work available today. With a shortfall of 140,000-190,000 workers with analytical expertise and 1.5 million managers who lack the skills to understand and make decision on the analysis of big data, Experfy is targeting its efforts carefully.

Experfy intends to differentiate itself from other large freelancing websites like oDesk, Elance, and Freelancer, by vetting the best and highest quality freelancers. Freelancers will log into Experfy using their LinkedIn profiles, list their key strengths, and then wait for their profiles to be approved.

Harpreet Singh, the Co-founder of Experfy, commented on his stance towards big data freelancing:

“We have huge advantage that a similar high-end big data marketplace does not exist yet.  Our real competition is not Freelancer; rather it is the likes of McKinsey, KPMG and Accenture,”