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DATASET2050 goodbye


After the hundreds of days (36 months!) working hard in the project + corresponding proposal…

After the 3 successful events specifically organised by the project (London, Madrid, Belgrade)…

After the more than 30 DATASET2050 posts tackling mobility-related topics…

After the tens of scientific papers, deliverables and even a book chapter written around door-to-door and mobility topics…

After 3 always supportive European Commission project officers (Ivan, Mindaugas, Andreas)…

After the massive efforts dealing with the endless lists of mobility datasets reviewed, used and implemented in our model… (http://visual.innaxis.org/mobilityDataSETs/)

After hundreds of millions of passengers being modelled/measure in our door-to-door model (http://visual.innaxis.org/dataset2050/d2d-time-distribution/)

After interesting results about what is European door-to-door “reachability” in a certain amount of time (http://visual.innaxis.org/dataset2050/d2d-time-distribution/)

After interesting results in the “reachability” metric looking at the door-to-door price (http://visual.innaxis.org/dataset2050/d2d-price-map/)

our beloved CSA DATASET2050 have reached to its end!

But this is not the end! For future reference: our website with the public deliverables, presentations/videos during events, visualizations

and somehow a DATASET2050 continuation: H2020 CAMERA CSA kicked-off last month with a very similar consortium

PS: All the research done would not be feasible without the incredible team. In alphabetical order: Andrew, Annika, Dave, David, Gerald, Graham, Inés, Luis, Pete, Patricia, Paula, Samuel, Seddik, Ulrike and myself (Hector). Apologies for those missing in the pictures below!



How long?

With the imminent publication of the DATASET2050 project results, this seems an ideal moment to compare a recent trip with one of the key project outcomes, the average door-to-door travel time.

DATASET2050 modelling of passenger journeys within Europe has found the average door-to-door time to be 6 hours, some way off the Flightpath 2050 target of 90% of travellers being able to complete their journey within 4 hours. Of this 6 hour average, the time passengers spend at the departure airport is almost as long as the flight itself.

Out of interest, I timed each phase of a recent work trip between south London and central Madrid – from the front door of my home to the final destination. The journey took place on a weekday without undue disruption affecting any part of it.

Time taken for each phase journey:

  • Door-to-kerb: 64 minutes from my front door to the airport, travelling by bus and train, including walking and waiting time.
  • Kerb-to-gate: 78 minutes spent within the departure terminal, including check-in and security processes, plus walking, refreshments and waiting time.
  • Gate-to-gate: 175 minutes taken from aircraft boarding at Gatwick to alighting at Barajas. Of this, 109 minutes was in the air, the remaining 66 minutes on the ground (i.e. boarding, taxiing-out, taxiing-in and alighting).
  • Gate-to-kerb: 43 minutes taken from the arrival gate, through immigration and customs processes, plus walking time (note carry-on baggage only, so no waiting around to reclaim luggage).
  • Kerb-to-Door: 40 minutes from the airport to the hotel by metro, including walking and waiting time.

The overall door-to-door time comes out at 6 hours 40 minutes – worse than average! 27% of this time was spent in the air, with a further 34% spent at the departure airport (i.e. kerb-to-gate plus the ground portion of gate-to-gate at Gatwick). Admittedly some of the time spent in the departure terminal was unused door-to-kerb ‘buffer’ time (to allow for problems travelling to the airport), however a good proportion of the kerb-to-gate time was there ‘just in case’.


The new report to the Club of Rome: Come On!

The human footprint is increasing fast and will —if not reversed— eventually lead to a collapse of the global economy. So say the authors of the new book Come On! which proposes an overhaul in the way that governments, businesses, financial systems, innovators and families interact with our planet.



About the book Come On!

About the Club of Rome

Now, in cooperation with more than 30 members from the Club of Rome, authors Ernst Ulrich von Weizsäcker and Anders Wijkman, co-presidents of the Club, suggest possible solutions to the global ecological and social crises. At the core is the suggestion to develop a new Enlightenment for a "Full World": we can no longer depend on a societal model that was developed for a world of less than one billion people.

Humans and farm animals constitute 97 percent of the bodyweight of all living land vertebrates on earth so it’s not surprising that the remaining 3 percent of wildlife struggles to compete for land and for survival. Alongside an environmental crisis are social, political and moral crises. Billions of people no longer put trust in their governments, poverty has deepened in many countries, in the US the middle-class is rapidly shrinking.

Measuring our success on GDP growth has proven inadequate to the task and it also masks a growth in inequality between rich and poor. New indicators such as a Genuine Progress Indicator could more accurately measure economic welfare.

The present model of development is seriously flawed. Profit maximization – under the principle of shareholder value first – and saving the planet are inherently in conflict. The new Enlightenment must be characterized by a vastly improved balance between humans and nature, between markets and the law, between private consumption and public goods, between short-term and long term thinking, between social justice and incentives for excellence.

Carlos Alvarez Pereira (President of Innaxis and member of the Club of Rome) contributed to the report with a chapter on the Digital Revolution, highlighting that advances in technology will be crucial in order to cope with environmental degradation. However technological disruption must be analyzed beyond the current hype that digitization is clean and exponentially opening up new possibilities. Instead the effects on resources depletion, climate change, and employment have to be carefully considered and addressed for a true sustainable and inclusive technological disruption.

This book comprises many practical examples, success stories and opportunities for the “Full World”. A move towards a circular economy can help overcome mineral scarcity, significantly lower carbon emissions and increase the number of jobs. Regenerative agriculture will help stop soil erosion, enhance yields and build carbon in the soil. Efforts have to be made to rein in the financial sector by increasing capital reserves and control of money creation. Some insights can come from the Hopi tradition in North America, which developed sustainable agriculture and maintained a stable population size while avoiding wars.

Civil society, the communities of investors, and the research and education communities should become strong players in the necessary transformation.


SafeClouds presentation at the IATA ADS

On November 15-16, 2017, IATA organised the first Aviation Data Symposium in Miami, FL USA. This event covered different angles of the application of engineering and data analytics to airline safety, operations, passenger distribution, sales, and air freight. These three areas were complemented by a technology track, which covered techniques and tools to support data activities in airlines. The safety and operation tracks discussed how big data is helping airlines to optimise operations while maintaining safety, and also presenting the upcoming main challenges.

The event also covered a review of the benefits from the various global information sharing and exchange networks, including the Global Aviation Data Management programmes coordinated by IATA. During the Symposium, Mr. Quevedo presented IATA data connect, the database of aviation accidents, IATA FDX, the GDDB and STEADES. ASIAS, the US data exchange programme was also presented by Mr. Madar, Managing Director of Operation Safety of American Airlines. Then, Mr. Hernández-Coronado, Director of Safety Analysis and QM of the Spanish Aviation and Security Agency (AESA) presented the European programme Data4Safety, that was recently launched by EASA in Europe.

Concerns regarding privacy remain very strong, as often, the privacy protocols are strict and de-identification could make data challenging to use, as explained by the programme representatives. Mr. Madar stressed new techniques and technologies that allow to progress on data privacy, together with new tools that allow to move from descriptive to predictive technologies, like machine learning, as an area that will help the programmes evolve, as the descriptive analysis done in the last decade, as done with ASIAS.

Mr. Hernández-Coronado presented SafeClouds in detail. AESA participates in the SafeClouds project and helps the team understand how different technologies researched in the project can help aviation data exchange programmes overcome some of the presented challenges. These challenges include data fusion and integration, data protection and privacy, and computing infrastructures. SafeClouds also investigates predictive analytic concepts and techniques to help aviation stakeholders make decisions, even during the operations.

Mr. Hérnandez-Coronado also covered the activities performed by the Spanish Aviation and Security Agency, particularly the Spanish SSP, State Safety Programme. This system receives and collects around 300-400 safety events per week. He also presented the RIMAS system, showing the capability of providing a complete risk assessment picture of the national safety status by combining a variety of data sources; ultimately providing analytical support for AESA so that they may focus their attention on those areas that require supervision.

Jet-bridges: The gateway to time-wasting?

Author: Pete Hullah

So you walked for what seems like miles to get to your gate. You've just queued for an age to have your boarding-card scanned and your passport checked. "Bon voyage" says the attendant. Welcome to the jet bridge, or Passenger Boarding Bridge (PBB) in the jargon. A claustrophobic metal box, often not air-conditioned, where you can now stand in another queue, at the front of which a business-class passenger is slowly trying to place too much luggage into the overhead bin while simultaneously talking into a telephone, apparently oblivious to the world behind. When you land, you'll have another interminable walk from your gate to passport-control/luggage-reclaim/exit.

And ever was it so, and ever will it be so.

But why is it like that? This walking and queuing is a constraint on mobility and on the EU goal of having 90% of intra-EU32 air passengers undertaking their journey in less than four hour, door-to-door. There must be something we can do to reduce this.

Why do we walk so far in airports?

n the early days of civil aviation, passengers were led on foot from the terminal to the plane and climbed a mobile staircase to board it; the reverse process applied upon landing. This is still the case in some smaller airports. As the number of flights increased, it became difficult to park planes close to the terminal, thus the walk (sometimes in the rain!) lengthened and more staff were needed to marshal the passengers. From the early 1960s, airports started installing piers and PBBs that made marshalling easy and kept passengers dry. With the development of the hub, PBBs helped passengers transfer between flights within a short time.

But PBBs meant that the gates at an airport have to be spaced far-enough apart to allow aircraft to park at them safely; at least an aircraft wingspan between them therefore. (Some airports reduce the distance a bit by curving the piers of having circular satellites - Paris CDG Terminal 2F and CDG Terminal 1 are examples of this).

Aircraft wingspans can range from some 25m for regional jets like the E170 and CRJ and around 35m for B737s and A320s, to more than 65m for B777s and B747s and even nearly 80m for A380s. The spacing, or combination of spacings, used at a given airport depends on that airport's traffic but it is fair to consider an average 40m walk or travellator from one gate to the next. With a gate either side of the pier we have an average of 20m walk per gate - plus any additional walk (usually a shopping mall) from security/border-control/etc. to the first gate.

Why so much queuing?

160 passengers or so have to wait at the gate while an A320 is prepared to accommodate them and they can board, generally through just one PBB attached to the front door. In order to improve the time it take to actually seat passengers on the plane, airlines often request people to pass through the gate as a function of the "zone", or group of rows, of the aircraft they're seated in - generally starting from the rows at the back. However, it is impossible to impose such a sequence and additionally, business class passengers (seated at the front) are generally advised that they can "board at [their] convenience" thereby blocking other passengers while they stow their cabin luggage. If passengers boarded in the correct sequence boarding time could be massively reduced.

So why not scrap the jet bridge?

Scrapping the jet bridge could be a solution to both these problems and enable a real reduction in time wasted. Isn't it time we re-thought about buses?

Buses are already a feature of airports.

  • Because airlines are charged more for parking at the gate and for using PBBs than for parking further away and using buses some, particularly low-fare airlines, tend to prefer this solution. This is especially the case if the plane has overnighted.
  • A plane could have docked at the wrong section of the airport - an incoming international flight parked at the international terminal will be a domestic outbound whose gate is at the domestic terminal; the domestic passengers are bussed to the airside of the international gate.
  • At Washington Dulles, "mobile lounges" that rise to the aircraft door take international passengers directly to immigration thereby saving time and heightening security by avoiding "losing" passengers on the air-side of the controls.

Now if you ask anyone about buses or mobile lounges at an airport they will cringe! But given that they are uncomfortable - most passengers have to stand in them - that there's no distinction between business and economy classes, and that airport policy seems to be to cram as many passengers as possible into one of them before it moves off, that dislike is understandable. The reason for this overcrowding is mostly economic - why employ 3 drivers when you can squeeze all of the passengers into 2 buses. With the advent of automated transport, this argument is removed.

If aircraft parked at stands by runways, less taxiing would be needed than for getting back to a terminal (especially from the new runways at Frankfurt or Schiphol, for example) and there would be no need for taxiing aircraft to cross runways, which brings a risk of runway incursion accident. Buses use much less fuel to carry the same number of passengers (and they could be electric) and they can use simple tunnels to cross runways.

Airport buses v2

If bus loading concourses were designed like a train station under the pier with several buses per flight lined up perpendicular to the pier, the entire width of bus-train, pavement and escalator would be some 5-6 metres per A320. 30 gates would therefore require 180m as opposed to the 600m required today.

Using multiple buses allows embarkation and disembarkation from both the front and rear doors. This can therefore speed up these processes, provided the departing passengers have been assigned to buses according to their seat zone on the plane (rarely the case today). Access to each bus of a bus-train could be controlled by automatic gates opened by scanning a boarding card, thus ensuring correct zoning of the passengers; the business-class bus could be more comfortable than the economy-class ones. Additionally, the buses could be available well before the plane was ready for boarding, taking the place of waiting areas - no pier seating required - and enable the gate process to be executed smoothly at the passenger's convenience and finished on time. Additional seating could be placed on an upper floor in the shopping area.

Once the bus-train has arrived at the aircraft, doors can be opened in sequence to allow passengers to board smoothly. As with the mobile lounges at Dulles, buses can take passengers directly to where they need to be - the central immigration/transfer/luggage-reclaim area - rather than their having to walk down long piers.

A well-designed bus transfer system could reduce walking, boarding and taxiing time at an airport and considerably help reach Europe's 4-hour door-to-door target.

Web Summit 2017

We are pleased to announce that Carlos Alvarez Pereira (President of Innaxis) will be participating as a speaker at the Web Summit taking place 6th-9th November 2017 in Lisbon.

The Web Summit is dedicated to connecting the technology community with a range of people from across the global technology industry, as well as with politicians, scientists and influencers. The Web Summit has grown to become the “largest technology conference in the world” with more than 6000 attendees participating this year.


Carlos will participate at the panel discussion ”Reducing carbon-intensive activity: Will we always have Paris?” on 8th November.

Other panelists will include Javier Garcia-Martinez (University of Alicante), Mohan Munasinghe (Planetiers), Femke Groothuis, (The Ex’tax Project) and Michael Kuhndt (Collaborating Centre on Sustainable Consumption and Production). The panel discussion will be moderated by Sam Geall (China Dialogue).

The panel will investigate the challenge of how to ensure the success of the Paris Climate Agreement, and how to effectively reduce carbon emissions when it conflicts with business interests. Incentives and strategies will be examined in order to re-align business interests with the urgent humanitarian need to address climate change and its disastrous consequences. For this, the role of behavioral change, technologies, political and economic incentives will be part of the discussion


Innaxis is currently seeking for a Data Engineer (Entry Level/Junior ) to join its deployment team, Tadorea. We are based in Madrid, Spain. We look for talented and highly motivated data engineers who want to pursue and lead a career outside of the more mainstream, conventional alternatives. Individuals with a great dose of imagination, problem solving skills, flexibility and passion are encouraged to apply.

As a Data Engineer, you will help the team to design and integrate complete solutions for Big Data architectures; from data extract, load and transform processes until data storage, life cycle, management and delivery for analysis. Always making use of the latest technologies and solutions for the ultimate performance.

Skills wanted
Data Engineers at the Innaxis spin off, work very closely with the rest of the Data Science team, so a broader knowledge and a varied skillset will be very much appreciated.Candidates would be evaluated according to the following items (fulfilling the complete list is not a mandatory requirement)

  • University degree on Computer Science
  • MSc or PhD not required but positively evaluated
  • Professional experience is not a must, although it might be positively evaluated.
  • Proficient in a variety of programming languages, for instance: Python, Scala, Java, R or C++ and up to date on the newest software libraries and APIs, e.g. Tensorflow, Theano.
  • Experience with acquisition, preparation, storage and delivery of data, including concepts ranging from ETL to Data Lakes.
  • Knowledge of the most commonly used software stacks such as LAMP, LAPP, LEAP, OpenStack, SMACK and similar.
  • Familiar with some of the IaaS, PaaS and SaaS platforms currently available such as Amazon Web Services, Microsoft Azure, Google Cloud and similar.
  • Understanding of the most popular knowledge discovery and data mining problems and algorithms; predictive analytics, classification, map reduce, deep learning, random forest, support vector machines and such.
  • Continuous interest for the latest technologies and developments, e.g. blockchain, Terraform.
  • Excellent English communication skills (written and oral). It is the working language at Innaxis.
  • And of course, great doses of imagination, problem solving skills, flexibility and passion.
The successful candidate will be offered a position as a Data Engineer, including a unique set of benefits:

  • Being part of a young, dynamic, highly qualified, collaborative and heterogeneous international team.
  • Flexible working environment, schedule and location.
  • A horizontal hierarchy, all researchers’ opinions matter.
  • Long term and stable position. Innaxis is steadily growing since its foundation ten years ago.
  • Salary adjusted to skills, experience and education.
  • The possibility to develop a unique career outside of mainstream: academics, private companies and consulting.
  • No outsourcing whatsoever, all tasks will be performed at Innaxis offices.
  • An agile working methodology; Innaxis recently implemented JIRA/Scrum and all the research is done on a collaborative wiki/Confluence.
IMPORTANT: Interested candidates should send their CV, together with a interest letter (around 400 words) and any other relevant information supporting their application to recruitment@innaxis.org .You will be contacted further and a personal selection process will start. We deal personally with all candidates.

Mobility metrics and indicators rethought

Performance is about comparing some output of a system with some level of expectations. The issue of setting the right level of expectations is certainly a major issue by itself, but choosing the right metrics to measure is probably even more difficult.

This difficulty comes from the fact that Key Performance Areas (KPAs) live in a different world than Key Performance Indicators (KPIs). KPAs live in a qualitative world, where general ideas are thought to be important for human beings. For instance, ‘safety’. KPIs on the other hand belong to a quantitative world of ‘cold values’ — floats, integers — observed on the real world. Matching these two worlds is like getting into Mordor: first you think that it will be obvious, then you think that it will be impossible, and you finally pick a way because it is pretty much the only one available.

Indeed, the potential KPIs that one could imagine are fortunately severely restrained by reality and what we can observe in the system. For instance, in DATASET2050 we were trying to define an indicator for the ‘seamlessness’ of a trip, something which is important for all travelers without a doubt. Important, ok, but what is it exactly?

Seamlessness is about the perception of travellers. As a consequence, it is highly subjective, which by definition cannot be part of an indicator, because an indicator is meant to be objective. So instead of a top-down approach where we use the question ‘What would be the best metrics to measure in order to represent seamlessness?’, we are left with a bottom-up approach consisting in ‘Among the ones I can measure, what are the metrics which would be related somehow to seamlessness?’.

So, what can we measure? For many years now, sociologists and psychologists use the ‘cognitive load’ to have a measure of the effort needed by a brain to accomplish a given task. Seamlessness is about being able to forget the trip itself and not actively be forced to take decisions or looking for information for the continuation of the journey. We thus defined a first indicator, which is the total cognitive load of a given trip for the passenger as a measure of seamlessness. Ok, but how do you measure cognitive load in reality?

Well, you don’t, as least not on a large scale. And here comes the second step of the search for a good indicator: can we find something easily measurable which is an approximation for what would be a perfect indicator?

In the case of seamlessness, we have to go back to how the travel unfolds. For instance, what is the difference between:

1) depart from home, take a taxi, take a train, take a taxi, arrive at destination.vs:

2) depart from home, take a taxi, take a train, take another train, take a taxi, arrive at destination.

Easy: there is one train more. Ok, but what makes you choose the first option over the second if both have the same travel time, price, etc.? Well, the first is easier, right? You do not have to think about getting off the train, find the next one, wait, get in train, possibly struggling to find a spot to seat, etc. So the idea that the first one is easier than the second one comes ultimately from the ‘continuation’ property of the actions you are taking, which is associated with a low cognitive load dedicated to the journey. In other words, taking different actions during a trip is more annoying that taking only one action.

Following this idea, DATASET2050 defined the journey as a series of ‘phases’ and ‘transitions’. ‘Phases’ are typically long with a low cognitive load dedicated to the journey, whereas ‘transitions’ are short and require the active participation of the passenger in order to continue the journey. A simple indicator can then be defined as the number of transitions taken in a single journey, which is trivial to compute for nearly any journey, with very little data input.

A slightly more advanced indicator is to consider the time spent within the transitions — for instance, queuing times — compared to the total travel time. For instance, a small 45 minutes trip where one has to take three buses is quite tiring compared to a single-bus journey. This indicator requires more data, as the specific times in each of the segments are required. However, it is largely feasible to compute it with modern methods of data collection (e.g. GPS tracking). Giving a good balance between the measuralibity and its concetpual proximity with the initial KPA, this indicator is the one which has been selected as key performance indicator for seamlessness in DATASET2050.

In DATASET2050, we have gone through the exercise of finding the right indicator for all of the KPAs defined by ICAO, including safety, flexilibity, efficiency, etc. These concepts are sometimes too vast and need to be broken down into sub-KPAs, called “Mobility Focus Areas”. For all of them, several indicators have been defined, but we selected only one final KPI in the end per KPA. For instance, the KPA “flexibility” has been subdivided into “diversity of destinations”, “multimodality”, and “resilience”. Only on key indicator has been selected in the end, weighting the travel options by the distance between the potential destinations. All this work can be found in the public deliverable 5.1 of DATASET2050, soon available here

To conclude, the choice of a good indicator is thus dictated by the balance between the measurability of the metrics and its relationship with the overall concept. This is an important issue, as the indicators are then used by the policy makers to drive the system is a certain direction. And the quality of the indicator decides whether it is the right one or not.

Author: Gérald Gurtner (University of Westminster) as part of DATASET2050 post series

Blockchain and other data science applications for aviation digitalization

For the 5th consecutive year, Innaxis organized the Data Science in Aviation Workshop with much positive feedback. This 2017 edition took place last September at EASA HQ in Cologne, Germany, sponsored by the SafeClouds.eu project.

This series of annual workshops was created in 2013 to promote data science techniques applied to the aviation field. Initially, this was a breakthrough idea as data analytic initiatives in the sector were very scarce. On the other hand, the potential benefit of applying these techniques to aviation, with relatively limited investment, greatly supported the effort of pushing this paradigm shift. Now, only 5 years later, the number of ongoing initiatives of data science applications in the aviation sector has continuously increased; demonstrating that the effort was really worth it.

Data has become the key driver of change all across aviation: from maintenance to training, from fuel efficiency to safety. There are on-going examples, with different levels of maturity, in nearly every layer of the aviation sector. This ranges from manufacturing to operations, both from the industry as well as the academia. The last DSIAW brought together this wide variety. Knowledge discovery and Data Mining (KDD) will be, is currently being, a key enabler of the digitalization of our industry.

The entire Horizon2020 transport research programme is driven by the overall objective of making “European transport greener, safer, more efficient and innovative“. These challenges were precisely the 4 pillars of the 2017 DSIAW, showing how data can play a key role in achieving them through the application of data science (DS) techniques. The presentations were distributed among these 4 sessions: DS4Environment, DS4Safety, DS4Predictability and innovative DS techniques and supporting tools, illustrating the audience with these initiatives:

DS4Environment: While the development of greener technologies (engines, aerostructures, components, etc) require several coordinated initiatives, data science offers cost-effective solutions based on real figures of fuel burnt and noise pollution. Applying data analytics techniques to these datasets enhances our knowledge of fuel consumption and noise emission patterns, which supports efficient resource use, thus resulting in a emissions reduction to minimize environmental impact. For this theme, Boeing Global Services – Fuel Dashboard solution and the Technical University of Madrid initiatives related to environmental and noise emissions studies.

DS4Safety: The aviation sector’s requirement for high safety levels has always been the main reason to avoid ‘radical’ changes in this industry or, at least, follow a very slow adoption path. Nevertheless, aviation safety has recently become a pioneering area in data science applications. We can’t neglect to mention the significant challenges in this line of research, such as data protection, data merging, pattern detection in rare events, secure data infrastructures, etc, but nonetheless there are very promising initiatives such as: the SafeClouds project coordinated by Innaxis, the EASA Data4Safety programme, or the activities from SafetyData in NLP applied to Occurrence Reports. All projects were presented at the workshop.

DS4Predictability: In air transportation, efficiency is very linked to predictability, and predictability in turn, is highly dependent on data. Improving predictability reduces uncertainty which avoids losses and enables a more efficient aviation system from reducing delays to predicting systems failures. Ongoing studies, such as those presented by the University of Westminster or Atos, are good examples on how data can provoke a deep transformation of common airline procedures, like disruption management or maintenance scheduling.

DS techniques and supporting tools: Different KDD application techniques require appropriate infrastructures as well as supporting techniques that ensure various requirements are met. This includes: data protection, security, computation efficiency, flexibility, scability, etc. During this last workshop, we learned from the Eurocontrol experience in using cloud-based infrastructures. We also learned about the Innaxis spin-off, TADOREA, which shared knowledge on crypto-economics as a potential solution for enabling secure data analytics, while maintaining data privacy.

Still not convinced? Wanting to learn more? Visit the event page to watch the presentations and videos.

10 years later… and so much to come!


This year marks Innaxis’ 10th Anniversary. A most remarkable date that we are very happy to celebrate and share with you. This decade -and the 30 projects developed so far- have provided us the opportunity of creating solid relationships with trusted partners and strengthening those links through successful collaborative stories. We consider you as part of this trusted network of partners, colleagues and friends and we feel very grateful for it.

As you surely know, Innaxis was founded with the objective of finding applications of Complexity Science to address problems of real socio-technical systems. From that (quite abstract) idea, we have done our (exciting and challenging) way to become a reference research organization at the confluence of Complexity Theory, Data Science and Societal Challenges, mainly in the Aviation and Mobility sector. This rapid evolution has been possible, and even more stimulating, thanks to people like you and organizations like yours, who have accompanied us in this journey.

Addressing real-life problems through breakthrough innovation requires a clear focus on applied research and a close collaboration with end-users to ensure the solutions meet users´ expectations and help in solving their needs. To effectively apply some of the research results obtained, we launched some time ago a new venture called Tadorea as a spin-off of Innaxis. Tadorea focuses on applying Knowledge Discovery and Machine Learning solutions to the aviation sector, leveraging on massive data analytics. We strongly believe on the potential of this promising area, and so David Pérez has been appointed as General Manager of Tadorea to take the lead of our spin-off efforts. David will nevertheless stay very well connected to Innaxis by being nominated to its Board of Trustees.

And it is also time to give new responsibilities to people who have been with us for a long time and have shown an outstanding capacity and performance, combined with personal styles which are quite unique. Both Paula López-Catalá as Programme Director and Samuel Cristóbal as Science & Technology Director, are newly appointed to these most relevant functions. Together with them, David, Arantxa Villar as Finance Director and Carlos Álvarez Pereira as President, will integrate the Management Team to pursue our -even more- ambitious goals in this new era which we are much willing to share, explore and enjoy with you.

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