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SDS2021 was a lot of fun!

Another year, another successful SDS! In 2021 the data innovation alliance took the bold move to organize a hybrid event. This was a success: in-person participants were very happy to get the opportunity to meet each other in person for more than a year. SDS2021 took place in-person at KKL Lucerne, with the beautiful backdrop of the lake surrounded by the ever majestic Swiss Alps. After a few challenges and some creative solutions, we were happy to be able to host as many as 150 in-person participants and 130 participants online. We could increase the number of in-person participants by making two separate “covid-safe wings”. The participants of the different wings could then get together and interact mask-free by the Lake-side.

All participants could also take part in the conference through the online platform where they had access to the contacts of the other participants in order to  share, discuss and maybe even start new partnerships. As networking is an important part of the SDS experience, there were many possibilities for on-site and online participants to interact. For example the hallway discussions took place entirely online in order for the participants to engage there. There was also the opportunity for anyone to start jump-in discussions with a topic of their choice.

The Topics of Interest in 2021 were Smart Services, Learning from Little, Intelligent Data Management, Health and Industry 4.0 as well as many other relevant subjects. We were joined by speakers from Switzerland and Internationally. 

SDS2021 in numbers: We had 2 Keynote Speakers, 34 Talks, 6 Sparkle Discussions and 4 streams.  23% participants were from academia and 77% participants from business. 67.5% were alliance members and 32,5% non-members.

But none of this would have been possible by ourselves. A special thank you to our fantastic sponsors with a very special thanks to our presenting partner D ONE and our scientific partner Datalab of Zürich University of Applied Science.  And a big thanks to all of our members and volunteers who worked backstage and made sure the conference was running smoothly. Something unexpected is always sure to happen and this year a dog attended the conference. However, due to not having registered in advance and since dogs are not allowed at KKL, he took part mainly from the lake-side in front of the venue.

To get back into the SDS mood, see the flashback video here, check-out our photos – maybe you have been spotted! And to spark your memory of the talks – or maybe to see the ones you missed – have a look at the slides and videos.

And what about next year?  We hope to be able to use all the experience from the hybrid event of SDS2021 and reuse the most successful aspects.

Save the date for SDS 2022:  23rd of June 2022! follow us on LinkedIn and twitter to get the deadlines and to sign-up. See you in 2022 – Together we move faster! 

SDS2021 Staff

Interview with Speaker Lukas Widmer

Lukas Widmer is Senior Principal Statistical Consultant at Novartis Pharma AG.

Could you tell us a little bit about your professional background?

From young age I had an interest in computer technology. Initially that led me to work in software engineering while studying Computer Science at ETH Zürich, after which it became clear to me that some of the most challenging and impactful applications where I could contribute were in life science and healthcare – here some exposure to the field of medicine through my family definitely had an influence. This led to me to pursue an MSc degree in Computational Bioinformatics at ETH Zurich and UC Santa Barbara and a PhD degree at ETH Zürich in Basel. In my PhD I focused on more interdisciplinary work; interfacing statistical / computational modelling and simulation to further understanding of basic biology, with the long-term goal to improve treatments. The desire to have more immediate impact in life science brought me to the Advanced Exploratory Analytics group – part of Advanced Methodology and Data Science at Novartis – which I joined in 2019. I joined Novartis with the double mission to drive the use of innovative methodology – such as data science, modelling and machine learning – across drug development, to deliver science-based progress to our patients and to re-imagine medicine.

What will you be speaking about at the SDS2021?

I will be discussing the importance of developing, propagating and applying Good Data Science Practice in the Data Science community in general, and in healthcare and pharma in particular. There have been several recent examples that highlighted the need for this, such as introduction of unwanted (and potentially unnoticed) bias which could impact patients in an unintended manner in Covid risk prediction or bias introduced into melanoma recognition using deep learning when not accounting for surgical skin markings. That any holistic approach to human research must be built on a solid ethical foundation has also been a current point of discussion in Computer Science, through the banning of University of Minnesota from making any further Linux Kernel contributions and the following apology, highlighting our duty to protect human subjects in research. We will discuss the need for Good Data Science Practice from multiple perspectives in the pharmaceutical industry and beyond, and we look forward to your thoughts and questions.

Why is the SDS conference important?

The Swiss Conference on Data Science is a great platform for exchange on current developments and issues in the data science space in industry and academia across Switzerland. There is a lot of excellent research and development going on both at universities and companies, and I find that having a good and critical discussion and dialogue (for example at SDS) is a good seed for collaboration and innovation. Having a direct line to people on the ground – subject matter experts – seems to be one of the key success factors, so I am looking forward to a diverse conference program.

Interview with speaker Aleksandra Chirkina

Could you tell us a little bit about your professional background?

My professional background is in application of data science to finance and financial data. Recently I’ve been working on an NLP project for analysis of financial documents, a recommender system for financial advice, and adaptation of different data science techniques for KYC compliance.

What will you be speaking about at the SDS2021?

My presentation “Data Science for Uninterrupted KYC Compliance” is going to demonstrate how data science can boost the efficiency and quality of KYC (Know Your Client) and AML (Anti-Money Laundering) processes for financial institutions. Proper implementation of KYC and AML measures is crucial not just for the smooth operations of a financial institution, e.g., a private bank, but for the economy as a whole, preventing the inflow of ‘dirty’ untaxed money and combating criminal activities.

In the talk I will present our experience with two data science models being applied to transactions and KYC profiles. As a result, some non-trivial KYC violations were detected, which were missed by traditional rule-based approach. The talk is aimed at inspiring financial institutions explore intelligent data-driven solutions for detecting KYC violations, money laundering and fraud.

Why is the SDS conference important?

For me personally, the SDS conference is a yearly milestone, for which we always thoroughly prepare in our team. We reflect on our achievements and discoveries over the past year, select the most interesting client projects and internal research to share with the Swiss data science community.

Another important role of SDS, particularly in the current self-isolation times, is being the attraction point for data scientists from different companies and industries, where they can share their professional and personal experiences, exchange ideas and inspire each other.

How Confidential Computing and Decentriq can facilitate greater industry collaboration

Born in Zurich, David Sturzenegger is a mechanical engineer by training and obtained a PhD degree in electrical engineering from ETH Zurich in 2015. From his time at big-data company Teralytics, he has several years of experience working with highly-sensitive data and leading teams of senior data scientists and software engineers.

Now David is Head of Product at Decentriq, where he is leveraging privacy-preserving technologies to help organizations collaborate on sensitive data. At SDS2021, David will be talking about Confidential Insights, a confidential survey platform jointly developed by Decentriq and Swisscom’s Fintech unit.

Confidential Insights was announced in November 2020. It is the world’s first platform for provably confidential surveys and peer-group analyses. Built to make collaborations around sensitive data easy and secure, Confidential Insights allows combining survey answers from multiple participants and extracting insights while keeping the answers provably confidential from anybody – including all admins. With Confidential Insights there is no trade-off between data utility and data privacy anymore.

An application of Confidential Insight

Leveraging the additional confidentiality guaranteed by Confidential Insights, Swisscom’s market research department –  e.foresight  – recorded an increase in participation in their annual survey on online mortgages, conducted by 30 banks in Switzerland. Banks that would not have participated previously due to confidentiality concerns, now responded to the survey through Confidential Insights, providing e.foresight with greater data input and deeper insights into the online mortgage space in Switzerland.

The underlying technology platform

The confidentiality guarantee is achieved by leveraging a technology called confidential computing, which is also the underlying technology behind the Decentriq platform. This is a SaaS enterprise that allows anyone to easily collaborate on the most sensitive data without risk of exposure. Confidential computing ensures that all the data passing through Decentriq is completely secure and encrypted, end-to-end. Even Decentriq itself cannot see the raw data input by organizations into the Decentriq platform. Confidential Insights uses the Decentriq platform as a backend.

Applications in different industries

From customers’ financial information to patients’ health data, collaborating with industry partners on sensitive data can securely bring about significant benefits and value to organizations and their customers. Below are some examples of industries that can benefit from secure data collaboration:

  • Insurance: To provide better protection and service for customers by leveraging customer insights, or to enhance collaborative fraud detection by analyzing data with fellow insurers, without exposing sensitive customer and claims data.
  • Financial Services: To participate in collaborative credit risk scoring with other firms and improve credit risk modelling and scoring, without ever exposing customers’ confidential data.
  • Healthcare: To bring together patients’ highly-sensitive health data, often distributed across different hospitals and clinics, in an anonymized manner so as to allocate resources more efficiently and provide patients with more effective treatments.

With confidential computing powering Confidential Insights and Decentriq, more organizations and industries can now collaborate with each other on their most sensitive of data with minimal risk, unlock new business value and deliver products that best match their customers’ needs.

Interview with Speaker Jürg Meierhofer

Jürg Meierhofer is senior lecturer, researcher and project manager in smart service engineering at the Zurich University of Applied Sciences (ZHAW). He is the coordinator of the “ZHAW Platform Industry 4.0“and expert group leader for the Expert Group “Smart Services” in the data innovation alliance.

Could you tell us a little bit about your professional background?

The design and engineering of services is the common thread throughout my professional activities. I got my PhD from the Swiss Federal Institute of Technology in Zurich (ETHZ) as well as an executive MBA degree from the international institute of management in technology (iimt). For more than ten years I worked as a manager for service innovation and optimization in the telecommunications and insurance industry.

What will you be speaking about at the SDS2021?

Service customization is a key factor for value creation in socio-technical service ecosystems, enabled and fuelled by new data-driven approaches. I will address the question of how to design service customization within the provider-customer interaction and show a novel quantitative approach to model this. We will see that the optimum design of the customer journey is hard to find by heuristic approaches and that the latter are often sub-optimal.

Why is the SDS conference important?

The SDS is important because it brings together the leading actors in data driven innovation from all around the world as well as from the Swiss community. It allows for visibility of the various innovation streams and provides the chance to connect to create new potential.

Interview with Keynote Speaker Lothar Baum

Lothar Baum is Head of Engineering Cognitive Systems at Bosch. He holds a PhD in computer science. Before joining Bosch, he worked for Hewlett Packard in Germany and in a smaller company in the USA.

Could you tell us what you are doing at Bosch?

I joined Bosch in 2006, working in corporate research, where I built up a research group on connective systems. We were looking at robotics and machine learning applications. Then I moved to Data and started a project in Data Mining. I then got involved in the foundation of the Bosch centre for AI. Since 2017 – four years now – I have been with the business unit on automated driving. I am responsible for the department that develops smart algorithms. So, basically all the algorithms from perception to situation analysis, prediction and behaviour planning.

What will you be talking about at the conference?

I will be happy to give you an overview of what it takes to build autonomous driving cars; what the technical challenges and the approaches to tackle these challenges are. Ultimately, I want to give you an impression of where we stand and where we need to extend our technology.

What are the biggest challenges for Automated Driving? And what are the solutions?

There are of course a lot of challenges. Trying to summarize them in a short time is in itself a challenge.

One of the first challenges is performance. Especially the perception performance: how well does a car perceive its environment? This comes down to sensor variety and performance; the more sensors you have and the more different modalities of sensors you have, the better it is. Secondly, it comes down to computational performance. We need fast computers that require energy and space, which – in turn – means more costs.

The second challenge is what we call the “open world problem”. The boundaries of the driving task and the rules for behaviour are not, and cannot be, clearly defined. The problem is that there will always be situations out there that nobody has ever thought about. And how do we handle situations that no one has implemented a solution for? This calls for approaches that are data driven. This means that we train systems with data examples and hope that they are sufficiently able to abstract and generalize. This, in turn, means that we need a lot of data training, which is another big challenge.

The basic idea is to have an approach that is similar to how we humans learn to drive. We know a set of rules, and we have collected experience via driving lessons. We don’t have a clear plan for every possible situation, but we have, let’s say, some kind of abstract data set in our heads where we are able to transfer situations or solutions to other scenarios. That’s the data driven approach.”

Then there comes the long tail of unknown cases: for which scenarios do we capture data and what happens with cases where we haven’t captured enough data? How do we create a sound safety argumentation around this? Ultimately this leads to ethical questions: what are the guardrails allowing or not allowing systems on the street? And, let’s be clear about this:  there will always be accidents. You will never get a 100% safe system and the question is whether we accept this, and at which stage we accept that there are residual risks. And this is a question that is both for technologists and for the society at large to decide.  

The amount of responsibility when driving a car is big, and it can already be difficult for a human to anticipate all the possible things that can happen on the street. How does this translate to a self-driving car?

The basic idea is to have an approach that is similar to how we humans learn to drive. We know a set of rules, and we have collected experience via driving lessons. We don’t have a clear plan for every possible situation, but we have, let’s say, some kind of abstract data set in our heads where we are able to transfer situations or solutions to other scenarios. That’s the data driven approach.

Given all of these challenges, how do you see the future of Automated Driving? When can we expect to see automated cars?

This depends on what we expect. How much are we willing to pay for it? Probably, it’s technically possible already. It’s a question of cost, obviously, and it’s probably not achievable for the broad public right now. But it’s something that is interesting for everybody. And on the other hand, it’s a question of performance. Today we still see a lot of new “corner cases” where many of these fully automated cars fail. And the question is to which extent we accept this.

In general, these challenges can be approached from two different angles. There’s one approach that is building on the (rather rules-based) assistance systems we see already in many of today’s cars. We’re trying to develop these assistance systems that are not fully automated but are the first steps towards helping the driver. These systems help us collect data and experience and iteratively expand their functionality.

And then there’s the top-down approach that basically strives to directly build a fully autonomous car, neglecting economic constraints such as costs or compute power, hoping the ready solution later can be scaled down to reasonable setups. At some point, hopefully, these two approaches will meet.

What, in your opinion, are the organizational and legal effects in bringing an automated car to the streets?

Again, it depends on the level of automation. There is a classification by the Society of Automotive Engineers (SAE) which defines five levels of automation. Level five corresponds to the highest degree of automation where no human driver is involved. Level zero means no automation or assistance at all. From level three upwards, the autonomous system actually takes over the responsibility for driving, at least for a limited amount of time. For the fully autonomous level five car, there is currently no consensus on how to handle this legally. It’s not allowed in most countries.

There is, for example, the Vienna convention on road traffic from 1968 which many countries around the world have adopted. It basically says there has to be a driver in the vehicle in charge of driving at all times. Only recently some countries have taken measures to soften this regulation and taking steps towards making self-driving cars legally possible. In Germany, about five years ago, there were some changes to the laws, that made it possible for the driver not to have direct control of the car at every point in time.

“I’m pretty sure that changes in laws will happen. The question is when it will be fully accepted, that is, when it will have become normalized in the society at large.”

Many companies involved are lobbying for changed laws. We can already see that in certain areas in the world, primarily the U.S. and China, they are pushing for this kind of legislation. And there have already been some regional adjustments. For example, in the states of Nevada and California self-driving cars are allowed under certain conditions. I’m pretty sure that changes in laws will happen. The question is when it will be fully accepted, that is, when it will have become normalized in the society at large.

Fully autonomous cars will have other impacts as well. They may have consequences on different sectors of the economy, for example the car industry, because less cars would be needed. Most of our cars are just standing idle 90% of the time and that’s because they are waiting for us. If they could drive to different locations themselves, we could probably get away with fewer cars. That’s one thing. And obviously the whole businesses of taxis, business shuttles and so on would be in trouble.

And lastly: why is SDS important and what do these conferences bring to the community?

From my perspective the main benefit is that this kind of conference brings together researchers, practitioners and decision makers to exchange ideas. And I would like to say that especially with respect to data science, the exchange of ideas and also the exchange of data – to know what data is available where, how and what can be done with it is specifically important for the data science community.

“if we look at things like autonomous driving, it is not just a technical question, it’s also a question for the society. What do we expect and what kind of risks do we accept? And this means that there has to be a discourse in the society about this.”

And last but not least, if we look at things like autonomous driving, it is not just a technical question, it’s also a question for the society. What do we expect and what kind of risks do we accept? And this means that there has to be a discourse in the society about this. And that’s why it’s important to talk openly and come to an overall decision on how to cope with the challenges.

Interview with Keynote Speaker Joe Hellerstein

Joe Hellerstein is a Professor at the University of California, Berkeley, and founder of the software company Trifacta. He works broadly on computer science and in data management on everything from data systems to human-computer interaction, machine learning and networking.

Can you tell us a little about yourself and your background?

I grew up in Wisconsin, in the United States. My mom was a computing pioneer; she worked in computer science already in the late 50s, early 60s, so she’s had a big influence on me. And my dad as well – he was a mathematics professor. And I have big sisters one of whom is a computer scientist. So, there are many family influences on my choice of career.

I have been a Professor at Berkeley now for about 25 years. About nine years ago I co-founded the company Trifacta. The goal of Trifacta is to transform data into a shape for use without having to write code. This allows people who aren’t coders to do their own data preparation and it allows people who are coders to do things much more effectively. Trifacta came about as an extension of a research that I was doing with some colleagues at Stanford, and we then founded the company together.

For fun I play the trumpet.

What will you be talking about at the conference?

At the conference I’m going to talk about data engineering. I will talk about how important it is to the data science lifecycle and how the tasks for data engineering are shifting from being a burden on a small select few in IT departments to something that everybody in data science can and must take on.

What do you tell people when they complain about data cleaning before they can do the fun machine learning stuff?

First of all, I tell people that they never know their data as well as they do when they are in the middle of preparing it for use. That’s when you get the complete context of what is in the data and what to do to get it in the form you need in order for it to work. You’re in a very intimate relationship with the data. It’s like when you’re deeply in practice with a piece of music—you really are immersed. If you’re not engaged in this process, then you probably don’t actually know what’s going on. It is only at the point of preparation when you’re really intimate with the material.

“In the machine learning lifecycle, the point of maximum agency takes place when you’re doing the data preparation and featurization. That is when you as a person have the most influence on things”.

And I would actually take a little issue with the framing of the question because mostly with machine learning, all you’re doing is turning on a model and seeing what pops up, and there’s not a lot of agency in that. In the machine learning lifecycle, the point of maximum agency takes place when you’re doing the data preparation and featurization. That is when you as a person have the most influence on things.

But we don’t do enough experiential teaching on this in universities. We tend to give students pre-prepared data sets and then they don’t get the experience of preparation until they’re in the field.

I will also say that over the years many of the tools for data preparation have been very poor, which has made the task unpleasant. It often looks like programming and you’re in practice not immersed in the data, you’re immersed in some code. I think that has to change. That’s actually a big part of what we do at Trifacta.

One of our conference topics this year is Learning from Little. How different are the big and little data problems and their solutions?

It’s such a lovely question. Partly it’s a nice question because, of course, when you start by thinking about why data is so big, you really only focus on the aspect of scale and performance and you don’t really focus on the quality of the data: what’s in there and how to get it into shape to use it.

Scale can be a problem for the user even with small data, because we as humans cannot really work with large data sets—our heads don’t do that. We need computational aids to look at more than a screenful of data. So, when you look at a table that is spread over 20 screens—which is only a few kilobytes of data!—you will not be able to keep it in context in your head. So, all the problems on a human scale happen already with very small scales of data. And they, as much as big data sets, challenge us in a bunch of ways to be able to do what we want, raising the questions: how do I know what’s in here and how will I make sure that what’s in here is appropriate to my task? This happens even on a very modest scale. So those questions should be present in everyone’s mind.

“You always need to be asking what’s missing. And small data kind of drives us to that question right away, which I think is great.”

And when you’re working with small data you almost always ask the question: what am I missing? Which is the question that you may forget to ask if you have this giant dataset. Which is a problem, because no dataset covers all the data that could have been generated by a specific phenomenon in the world. Even with banking transactions you probably don’t have all the transaction scenarios from the beginning of time. So, you can take these very humble computerized tasks, and you can still not get the complete data. You always need to be asking what’s missing. And small data kind of drives us to that question right away, which I think is great.

And in Trifacta, we start by giving people a small sample of their data set, even if the full set is large, because they can then interact with the data quickly and hypothesize about what they will find. They can try out different transformations to see what they get. And all of that happens at the speed of thought rather than at the speed of some gigantic computing task. We have an architecture that is called “sample to scale”, where we give you a sample to work on and then, when you believe that the work you’ve done is the right task for the whole data set, we compile it down to a job you can run on a big data platform. That’s a computer science compiler problem that we’re handling for you, and then running the job is a back-end systems problem handled by infrastructure. But the hard part of your job is the exploration and transformation work you do on the sample, in order to get it into shape.

So, we’re very much on board with the idea that even with large data sets you want to “go little” in order to get that fluidity: experimentation and exploration. So, I think it’s a wonderful theme.

And lastly: why is SDS important and what do these conferences bring to the community?

I think it’s really important for practitioners, technologists and researchers to be together in a dialogue about what matters and what innovations can do to help. I think when research has been done in a vacuum, sometimes you get innovations that aren’t really great for people to use—people can’t adopt the technology because it is too hard to use or too generally focused. The feedback from practice to research is to let researchers understand what holds people back from getting value out of data and that’s critical to the research effort.

“And in my own work in particular I’m very informed by practitioners, with the idea that innovation in computer science may be about helping practitioners do their jobs better as opposed to creating things from scratch that nobody asked for.”

At the same time, there’s a lot of creative work that goes on in R&D in both universities and companies that practitioners can learn from. I see it very much as a two-way street. And in my own work in particular I’m very informed by practitioners, with the idea that innovation in computer science may be about helping practitioners do their jobs better as opposed to creating things from scratch that nobody asked for. That dialogue can be quite healthy.

Interview with Sedimentum

Could you shortly tell us what Sedimentum does?

Sedimentum is a healthcare and technology start-up that has developed a technological protection tool that ensures the physical safety of, for example, people in nursing homes, when they are on their own. This solution aims to support and relieve the burden of nursing staff in psychiatric clinics and in nursing homes for the elderly, as well as for relatives of the elderly. The caregivers are informed in real time about any unusual occurrences (e.g. falls), enabling them to take the necessary measures to protect the people living alone in a timely manner.

We develop the first contactless solution for fall and emergency detection in healthcare without compromising privacy.

What is Sedimentum’s background story?

Before co-founding Sedimentum, Sandro Cilurzo, CEO, worked in a Swiss psychiatric clinic as an information security officer. He was also a member of a think tank for management members and experts from medical and nursing fields. There he was responsible for contributing technological expertise. The representatives from the medical and nursing sector discussed problems and challenges in their day to day work and he proposed new technological solutions to these “real-world” problems. It was precisely after a meeting with the think tank that he had a sudden insight. Sedimentum is the outcome of this eureka moment:

One of the main challenges of any psychiatric institution is to ensure the physical safety of their patients 24/7. There are always periods of time when the patients are on their own, but especially at night they must get along without any caregiver for an exceptionally long time. During the night shift the available nursing staff is very limited – often there is just one single caregiver. Therefore, it is impossible for the nursing staff on duty always to be in the right place at the right time.

Additionally, the privacy and data protection requirements are extremely high in the healthcare sector. Camera-, and microphone-based systems are an absolute no-go. Besides that, already existing solutions such as wearables and watches, don’t work well enough in “real world” conditions. This is why Sedimentum developed the first contactless fall and emergency detection solution in healthcare, which processes fully anonymized data whilst protecting privacy.

Sandro Cilurzo, CEO

Why is it important that Sedimentum exists?

In Switzerland alone, 80,000 elderly people fall in their homes every year. Around 1400 of those affected die as a result of the fall. It is not only the people over 80 who are considered particularly at risk of falling. People with epilepsy, patients in stationary or ambulatory psychiatric clinics, younger seniors or even small children are affected. Many people are in need of protection, but as of yet no smart solutions exist to fulfil that need. Seamless support from third parties is resource-intensive and therefore costly, and in most cases not possible. The physical safety of vulnerable people who live alone cannot be guaranteed 24 hours per day. This problem exists in private homes and retirement homes, nursing and care institutions, psychiatric institutions and other healthcare organizations alike.

Sedimentum technological solution will make the lives of thousands of people safer and more independent in the future – and all of this in a completely automated way.

Who can profit from your services?

Initially, we focus on business customers. Our primary target groups are psychiatric clinics, nursing homes, ambulatory care organizations and institutions for assisted living.

You recently announced and exciting partnership with HOPR (a blockchain based data protection start-up), could you tell us more about this and how it came about?

The HOPR protocol ensures that everyone in a communications network has control over their privacy. HOPR and Sedimentum share the same values and we have been in contact with the HOPR team from the beginning. HOPR has developed a groundbreaking open source technology to be used by privacy-aware developers all over the world. Their protocol is integrated in our cutting-edge privacy-preserving technology to save lives without compromising privacy.

Can you give some further examples of your success stories?

We are a fast-growing startup with 7 full-time employees and are constantly pushing technological boundaries. In January this year (2020), we successfully closed a pre-seed financing round that enabled us to reach our set milestones. One of the most important milestones so far was to launch a proof-of-concept with a leading Swiss psychiatric clinic. Also, other proof-of-concepts with nursing homes and ambulatory care units are starting this fall and winter.

On the first of November 2020 Sandro Cilurzo got listed in the Forbes 30 under 30 list. We are very proud and happy for this recognition!

Also, in November we got the “Zuger JungUnternehmer Preis 2020 “, (the Zuger price for a young company).

What are your biggest challenges?

High regulatory requirements and different technical uncertainties. Our amazing and competent team finds solutions to these technical difficulties. We have massively improved our solution over the last couple of months. All this added value highly improves the customer experience.

The regulatory requirements have been challenging as well. We had to put a lot of effort into solving them. Meanwhile we have developed a mature regulatory strategy that is appropriate to our needs. Our main learning is: question everything (even experts) and above all, remain calm – there is always a solution!

How do you see the future of Sedimentum and what is your long-term goal?

We will officially enter the DACH market next spring. Our vision is to make living safe, especially for vulnerable people. We want to become the leading contactless and privacy preserving fall and emergency detection provider for healthcare worldwide.

First Event: Match-Making in Big Data with Academia and Industry 10.11.2020

The National Research Program, NRP embraces research projects that contribute to solving key issues of today. The Federal Council makes the final selection of topics, which it then refers to the Swiss National Science Foundation, SNSF, to address within the scope of an NRP. NFP 75 provides foundations for the effective and appropriate use of big data. The projects focus on computing and information technology but also deal with the effects on society as well as on big data applications in various areas of society.

In its aim to help companies develop new products and services the Swiss Alliance for Data-Intensive Services is also looking for joint workshops and conferences with partners who are active in similar areas. This is how the collaboration with the National Research Programme NRP 75 “Big Data” came about.

On 10 November 2020 a first match-making event took place. Short pitches of ten projects in two blocks focused on technology transfer and were followed by breakout sessions to answer questions, make contacts, and exchange ideas. Originally planned as physical events at three different locations in Switzerland, the series had to be moved into a virtual space due to the current Covid-19 situation. 

Beatrice Huber, head of knowledge and technology transfer at NRP 75, opened the event: “We believe that we are doing excellent research in our NRP. But that is not enough. Research must also be useful for the society, for the industry. That is why technology transfer is important and that is why NRP 75 looked for partners to promote it. Swiss Alliance for Data-Intensive Services was the ideal partner to this end.”

Gundula Heinatz, Managing Director of the alliance then welcomed the participants and speakers and introduced the alliance “We are an innovation network with members from Academia and Industry. Our mission is to provide a significant contribution for data-driven value creation. With events like these we create an inspiring ecosystem, even during special times like today. It is a pleasure to know that some of our academic members are presenters and some industrial members are participants today. Take this opportunity to exchange and discuss collaborations”.

Cedric Huwyler from FHNW held the first talk “Automatic analysis of solar eruptions” about the impact of solar flares and the huge amount of data that it produces when it’s being recorded. 

Then Antoinette Weibel, from the University of St Gallen, talked about “Big Brother in Swiss companies? Trust, data and personal privacy of employees”. More and more spying software is bought by companies. Weibel talked about the ethical handling of this and how the software is used in Switzerland.

Michael Lechner, from the University of St Gallen, talked about the “Causal Analysis with Big data.” 

Kristen Schmidt, Attorney-at-Law, talked about “Who owns data”. Personal data is key since it is useful for personalisation in sales.

Joseph Molloy from ETH talked about “Using data traces to improve transport systems”. He talked about mobility behaviour on the large scale for tourism, retail, marketing and public health (covid). For example, to understand travel patterns during the lockdown.

Thomas Brunschwiler, IBM Research,  talked about “ICU cockpit: computer assistance for intensive and emergency medicine”. There are a lot of alerts in the ICU that are sent from many different devices. They want to use all the signals and sources of data to reduce the amounts of false alerts.

David Bresch, from ETH, talked about “Combining theory with big data? The case of uncertainty in prediction of trends in extreme weather and impacts.”

Helmut Harbrecht, University of Basel, talked about “Big data for computational chemistry: Unified machine learning and sparse grid combination technique for quantum based molecular design.”

Then Mira Burri, from the University of Luzern talked about “The Governance of Big Data in Trade Agreements”. She talked about the largely ignored link between international trade law and the regulation of data.

Finally Zalan Borsos, ETH, talked about “Scaling Up by Scaling Down: Big ML via Small Core sets”.

The event on 10 November was only the first of three match-making events as NRP 75 has many more projects. Two more events will therefore take place: one on Thursday 19 November and the other one on Tuesday 24 November.

More information regarding the participating projects:

Active Activities with Autonomous Robots

What are the likes and dislikes of robots and what makes them act? On October 15, 2020 F&P Robotics, together with the Swiss Alliance for Data Intensive Services, organized an interactive Robotics workshop. F&P Robotics introduced their products and solutions in the field of professional personal robotics. Their applications can be used in healthcare; for example mobile assistants for elderly care, persons with disabilities and for people in rehabilitation centers. They also create gastronomy robots, i.e bar robotics 

The workshop was made up of four interactive talks, where the participants got to know the robots on a first-name basis. We made our acquaintance with the autonomous assistant robot, Lio, and shook hands with his collaborative robot arm, P-Rob. At the end of the day Barney, the robot bar, made us some drinks. 

Dr. Hansruedi Früh, Managing Director of F&P Robotics, opened the workshop and gave an introduction to cooperative and care robotics. The Care Robot Lio is not a medical device but aims at helping people with special needs while at the same time leaving space for autonomy. Its collaborative robot arm P-Rob can be used for a variety of tasks, for example in the kitchen: you teach him by moving him in accordance to your needs.

After our first introduction to Lio and his P-Rob arm, Rastislav Marko, working with Software and Lio System Development at F&P Robotics, introduced us to the integrated Python scripting language and we got to do some programming exercises via myPⓇ’s browser interface: a web interface that controls Lio. Like all of us, Lio has a calendar when he works, to remind himself of his daily tasks. Lio can recognize people and has a good memory, this means that he can remember that he has seen someone and report this. Lio then proudly demonstrated his skills, singing a song and giving us a quote of the week.

But can a Robot choose how to act? Frederik Zwilling, working with Software Development and Lio Project Management at F&P Robotics, told us about the principles of autonomous behaviour in a robot. They are programmed to make decisions through logical reasoning of knowledge-based systems and common-sense rules. For example, a robot knows that at night you should be silent and let us, the non-robots, sleep.

Dr. Justinas Miseikis, Head of AI at F&P Robotics, told us about the learning principles for voice and face detection. Robots detect faces, usually through pre trained neural networks that are then optimized further, if they are not performing well enough. Although Lio has some trouble understanding people with their face masks – Robots have their own challenges with covid-19!

At the end of the day we got to see demos of the robots. To round off the day, we moved to Baronics AG to have a drink at the Barney Robot Bar where we continued the discussions.

The Workshop was highly interactive and interesting, the participants got to try the coding and communicate with the robots, something most of us don’t get to do everyday. We got an understanding about how robots move and work and exist in the world. The participants agreed that it was a very exceptional opportunity.