Tag Archives: Data61

Highlights from the Spark Festival’s Spin on Spinouts

This week, the Spark Festival’s ‘The Spin on Spin-Outs’ event showcased five spinout founders who have commercialised research from CSIRO’s Data61.

As founders of new technology-based spin-outs, they discussed what it took to transition from researchers to entrepreneurs, and offered advice on accessing the funding and support required to commercialise research.

We’ve brought you the top tips on launching a successful spinout, from both spinout founders and investors at the event.

 

Meet the spinout founders;
  • Dr Silvia Pfeiffer is CEO and cofounder of Coviu, a platform that provides universal access to healthcare.
  • Dr Stefan Hrabar is CEO and cofounder of Emesent, a company formed to commercialise cutting edge drone technology developed by CSIRO’s Data61 Robotics Group, providing access to critical data in challenging underground environments.
  • Dr Anna Liu is Head of Public Sector Partnerships at Amazon Web Services. She founded and was CEO of Yuruware, the world’s first Disaster Recovery platform designed to simplify the migration, replication and disaster recovery process.
  • Pete Field is founder of Ayvri, a company using 3D virtual world technology to enable participants in major sporting and adventure events like RedBull’s Wings For Life to preview their adventure and to visualise their race in 3D through live tracking.
  • Matt Barbuto founded Ynomia, a platform that digitises realworld construction projects enabling builders to have visibility over what is moving in and out of the construction site.

 

Advice from the Founders: How to make the leap into a spinout.

 

  • Join accelerator programs;

CSIRO’s On Accelerator, and Data 61’s many platforms exist to prepare researchers and their research projects for commercialisation. The programs offer mentors, advisors, and the opportunity to gain entrepreneurial skills. Accelerators “focus you on on your business model,” says Pfeiffer, and “help sort out who are the people that are ready and willing to go out and start a business.”

Accelerators can also be the place to make the right connections. “We got to know some amazing investors and advisors through the ON program,” says Hrabar. “They had seen the journey we’d gone through and understood where we had come from and lined up well from the investment point of view.”

 

  • Choose the right people to build business relationships.

Get support from others. “You don’t need to be an expert in everything,” says Barbuto. “Knowing your strengths and knowing where you need other people to come in and help – that’s your job as a CEO,” Field agrees.

Seek mentorship from your Board members, and choose investors for what they can bring to benefit you, says Liu. “I wasn’t interested in taking just money because I was looking for business growth advice.”  

 

  • Get comfortable giving a sales pitch.

Speaking sales can be quite a shift in mindset for a researcher. Be prepared to transition from ‘precise’ researcher, to ‘predictive’ sales person.

“Researchers are taught to be very thorough in everything you do. Creating a start-up is more about predicting the future and there is no hard data. You are making things up,” says Pfeiffer. Be assured that investors understand that it is always a guess when you are talking about markets. You need to be comfortable selling your best guess.

 

Advice from the Investors: How to impress with your pitch.

In the second half of the event, the panel was joined by investment managers Martin Duursma from Main Sequence Ventures, and Natasha Rawlings from Uniseed.

Here are their best tips for researchers wanting to impress an investor;

 

  • Know your customers;

Investors want to see that you understand your customer, says Duursma. “Please go out and network in the industry, and ask about their big problems. Go to trade shows, walk the floor, cold call, join associations. Figure out if you’re solving a problem the customer actually has, and if they want your solution.”

 

 

  • Speak about your business model;

Talk to investors about the business side of your start-up, not just the tech. “What we really like hearing about is money,” says Rawlings. “Who is your customer? What is your product? How much are you selling it for? What is the margin on that? How many of these things do you think you can sell?” These are the ‘boxes to tick’ to secure a second meeting with an investor.

 

 

  • Follow up and follow through;

Investors are looking for signals about you, so be sure to follow through on any promises you make. “If the researcher says ‘yes I’ll get this thing to you by the end of the week, if that thing doesn’t happen, that’s a bad signal,” said Duursma.

“You’ve got to be easy to work with,” said Rawlings. “If you are not easy to work with we probably can’t invest in you no matter how good your tech is.”    

– Carmen Spears

 

This event was hosted by Inspiring Australia as part of the 2018 Spark Festival.

To see more events from the Spark Festival program click here

To learn more about Inspiring Australia’s activities and achievements click here.

Blockchain insights with Data61 researcher Dr Mark Staples

Dr Mark Staples is a blockchain researcher at Data61, which is part of Australia’s federal science organisation, CSIRO. Being both a scientist and a blockchain expert, he has rare insights into how blockchain can propel research.

On my last trip to Brisbane I caught up with Mark for a drink at the Plough Inn and asked him to answer some of science’s most burning blockchain questions.

In this interview, we take a look at the challenges scientists face in managing their data, how blockchain can help, and where we’re at when it comes to issues of confidentiality, scalability, cybersecurity and policy.


First of all Mark, could you tell us a bit about your background?

My background is in computer science, cognitive science, and then eventually I got into formal methods and software engineering. But these days, I’m mostly looking at a lot of work around blockchain. I do blockchain research at Data61 — mainly around software architectures for blockchain-based applications.

And can you tell us what’s happening in Australia on the blockchain front?

Australia is doing quite a lot of work around blockchain. The Commonwealth Bank has had some world firsts around the use of blockchain for the trade and also for bond issuance. Companies like AgriDigital have also had some world firsts for use of blockchain to track the agricultural supply chain. Australia’s leading the standardisation process — the international standardisation work on blockchain and distributed ledger technology. So Australia is quite present in blockchain internationally and leading in some areas.

What areas of blockchain research are Data61 focused on?

The area where we’ve been leading in research has been using blockchain as a way of executing business processes. So, taking business process models and turning them into smart contracts to execute multi-party business processes on blockchain.

We’ve also been thinking about ways to take legal logics to represent contracts or regulation, and turning those into smart contracts. We do some work in the Internet of Things for blockchain as well. And supply chain integrity.

So, there’s a variety of different pieces of research, and then we work with companies; we develop technology, and we participate in the international standardisation of blockchain.

Being a scientist yourself, how do you see blockchain propelling science?

The key thing that blockchain supports is data sharing and data integrity. Both of those are critical for science.

Normal blockchains are not so good for confidentiality, but they’re great for publishing stuff; they’re great for publicity. One of the barriers for the adoption of blockchain in enterprises includes challenges around managing commercial confidentiality. But for a lot of science publications — both low-risk data and papers — they want to be public and blockchain is good for that.

Not only will it be public, but you also get this trail of what’s happened to the data. You get some sort of evidence about the integrity or authenticity of the records that are being created as well, by relying on the cryptographic techniques inherent in blockchain.

So I think that’s the key potential for blockchain for science — better publishing of scientific datasets and publications with better support for integrity.

Is data management a big issue?

Yes, we’re not very good yet at managing data integrity or sharing datasets or getting recognition or citation for datasets that we’ve collected or used. Not only from a professional point of view — scientific impact analysis and the like — but also in understanding data integrity from a scientific validity standpoint.

We need to be able to answer questions like: What operations have been done to your dataset before you start doing your own operations on it? How was the data that you’re working with collected? Has it been cleaned or not? All those questions are important when you’re doing an analysis of the data.

What issues have you observed in your time as a scientist in terms of how the scientific data is managed and applied?

There are a lot of data description challenges. Have you described what are all the important characteristics of a dataset? How do you describe those? There’s a variety of standards for metadata for datasets.

How do you describe the history of the provenance for data? What steps were taken in the collection or the analysis of a dataset and derived datasets? All of those are not really completely solved problems. We don’t have standard solutions for a lot of them, so that’s one challenge.

Do you think blockchain’s support for data integrity might actually help reinstate or build better trust in scientific evidence?

Yes, potentially, it could create more evidence for the trustworthiness of data and more evidence that data has been analysed if we used it in the right way.

And what particular difficulties are there in actually getting these systems adopted by universities or research institutes?

Blockchain is good if you want to make datasets public. But there are certainly a lot of datasets in science that are not public for various reasons — especially in the medical research area. So, they present much more of a challenge; you can’t necessarily just publish those datasets through a blockchain.

You might still be able to use a blockchain and other kinds of digital fingerprinting techniques to provide evidence about the integrity of data that you’re using without compromising official privacy, but it gets complicated to manage that kind of thing. So that would be one of the main challenges.

Could you put metadata or de-identified data on the blockchain as a solution to the confidentiality problem?

If you just have high-level metadata on the blockchain that can be be okay. If you have aggregate statistics in there, then you need to start worrying about the version you’re releasing as well. But a very high-level purely descriptive dataset is less likely to be a problem.

De-identified datasets are difficult. It’s a real challenge to effectively de-identify data. We’ve seen so-called de-identified data sets that have been susceptible to re-identification attacks, so that’s a difficult problem. We have a couple of teams in Data61 looking at private data release and private data analytics.

Are there any particular challenges for scientists on an individual level, when it comes using blockchain-based systems?

One practical challenge is that all the public blockchains and most of the private blockchains rely on public-private cryptography, which means public-private key management. In order to create a transaction to report some data on a public blockchain, you need to be managing a private key to be able to digitally sign the data that you’re transacting with.

There are various bits of software that can help to manage that. There’s wallet software, for example. But still, it’s a new thing scientists will need to do to manage keys and to have good cybersecurity in key management. Because these blockchains allow people to enact things themselves on the blockchain — to directly interact with the blockchain.

Blockchain creates a responsibility for people to be able to manage their cryptographic identities with integrity as well. The integrity of your data can come down to how good you are at cybersecurity and how you protect yourself against cyber-attacks. It requires effective cryptographic key management by people who are not used to doing it. So, that becomes another barrier to using blockchain.

Is scalability still a problem?

That’s an inherent problem with blockchain. Blockchain is meant to be a distributed database where you might have thousands of copies of data all around the world. Big data in terms of big volume is just inherently hard to move over the network. So it’s inherently hard to replicate around the blockchain nodes all around the world.

But I think we already know the solution from a big data point of view. The blockchain-based system is never implemented just with blockchain alone. It’s always implemented with a variety of other auxiliary systems — whether that’s just key management or maybe also user interfaces or off-chain databases for private data or big data. So, I think that’s the solution; just kick the big data off-chain.

Apart from big data, there are other scalability challenges for blockchain in terms of transaction latency. These things are being worked on, so I don’t see them being a huge problem in the medium term.

Are there other ways that blockchain will need to develop before it can support large-scale research?

Another big challenge is governance of blockchain-based systems. So, normal IT governance assumes there’s a single source of authority that’s in control of an IT system, and so the adoption of that control and the evolution of that system can be controlled from the top through that source of authority.

But with many blockchain-based systems there’s no single source of authority. It might be a collective that’s operating it, or the collective might be random groups in the public.

So, how to control the evolution and management of the blockchain-based system can be a difficult problem. Some blockchains are implementing governance features directly on the blockchain, but it’s not clear yet what the best way to go is, and it’s still an active area of innovation.

Is there scope for greater funnelling of clinical data and consumer data back into research?

I think the biggest challenges there are policy challenges, not so much technical challenges.

What does a good security policy model involve for clinical information sharing? Who should be allowed to see what data, for what purpose and when, under what consent model? Even that is not very clear at a policy level at the moment.

So in terms of research ethics applications, there’s a huge variety of different consent models that are supported by specific ethics approvals. You can implement technical controls for any of those, but knowing what you should be implementing is, I think, the hardest part of the challenge. There’s a lot of variability, especially for clinical information.

Have you seen movement towards giving the individual control over their data over the long term?

There are some interesting things happening in that space. Are you familiar with the Consumer Data Right that the government recently announced? The first incarnation of it is something called open banking, where the government creates a right for consumers to direct their bank to share information about their personal accounts with a third-party. The individual has to give consent to the third-party to use their data for a particular purpose, but then they give an authorisation and direction to the bank to authorise the third-party to access their data.

That’s an interesting model for giving consumers more right to direct where their data goes on a case by case basis. It’s quite different to most of the other models I’ve seen for giving consent to share data. Normally, an organisation holds data about a consumer, and the consumer is trying to keep up with all the various consents to access it—derived and delegated consent, emergency accesses and whatever other accesses are made to their information.

But when it comes to clinical information, I think policy is complicated by a lot of different interests. I don’t know if we have a good answer to that.

It’ll be interesting to see how it all unfolds Mark. Stellar insights. Thanks for your time!


To find out more about blockchain research at Data61 and to read their reports on how can be applied to government and industry, click here.

– Elise Roberts

This article was originally published on Frankl Open Science via Medium. Frankl works on solving issues around data sharing and data integrity in science, using blockchain and other technologies.

Core code

Featured image above: Gernot Heiser. Credit: Quentin Jones

We trust computer systems every day – but trusted systems are rarely entirely trustworthy. Laptops can crash, servers can freeze, and personal details can be stolen. Even pacemakers can be hacked.

“The complexity of the systems we’re building has grown much faster than our ability to deal with it,” says Gernot Heiser, a professor of operating systems at UNSW and chief research scientist at Australia’s digital research network, Data61, a division of the national science agency CSIRO. “The result is an appalling lack of dependability.

“As critical tasks like controlling medical devices, mobile phones, industrial plants and airplanes become ever more technology-dependent, trust should not be taken for granted,” he adds.

Is it even possible to write truly trustworthy code? Heiser thinks so – which is why he has spent the past decade developing secure microkernels, the core on which dependable operating systems can be built. By itself, a microkernel does not provide useful services, but contains the core mechanisms on which to build them.

Working with UNSW colleagues Gerwin Klein and Kevin Elphinstone, Heiser sparked excitement among experts when the team proved that all 7,500 lines of C code in his seL4 microkernel were mathematically correct. May not sound like much, but this is incredibly difficult to achieve.

“It is hard to comment on this achievement without resorting to clichés,” quipped Lawrence Paulson, a noted leader in theorem proving and a professor of computational logic at the University of Cambridge.

June Andronick, a principal research scientist at Data 61, who specialises in the verifiability of software systems, adds: “What Heiser and his team have done, and keep doing, is to strengthen the guarantees that can be provided about software by orders of magnitude, while maintaining very good performance for real-world use.”

A big test of Heiser’s seL4 microkernel came in 2015, when the US Defense Advanced Research Projects Agency gave hackers unfettered access to the on-board computer of an autonomous Boeing AH-6 helicopter gunship. Their task was to hijack the microkernel and take control. While hackers easily commandeered the helicopter when it hosted other software, they could not crack the on-board computer when it ran on seL4 .

A predecessor of the secure seL4 software – known as OKL4 – may already be in your pocket. Heiser set up Open Kernel Labs in 2006 to commercialise his OKL4 microkernel. The company was later bought by General Dynamics, after which “our technology ended up in the pockets of billions of consumers,” says Heiser. OKL4 is now on the security processor of all Apple iOS devices.

But there are still important weaknesses. “Observing exact timings of actions can leak secrets, via so-called ‘timing side channels’, giving attackers the ability to eavesdrop on communication or even masquerade their malicious code as legit services,” says Heiser. His team is now working to prevent such failures by blocking any given process from unduly influencing the execution speed of another process – and eventually proving that this works.

The second weakness is price. The development cost of the seL4 microkernel was about three times that of comparable unverified, vulnerable software. But Heiser thinks he can make the software affordable for everyone.

“If we manage to eliminate this factor-three cost gap to standard software, we’re totally changing the world of software systems.”

– Ben Skuse

For more stories at the forefront of engineering research, check out Ingenuity magazine.