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DATAMASS GDAŃSK SUMMIT

4.10.2019

call for papers

ABOUT CONFERENCE

Get ready for the biggest data conference in the Northern Poland

DataMass Gdańsk Summit 2018 DataMass Gdańsk Summit 2017

What is DataMass Gdańsk Summit?
It’s not just another conference. It’s an event created with passion – and targeted at those who use Big Data in practice in their everyday work. The main idea behind the conference is to promote knowledge and experience in designing and implementing tools for the analysis of big data volumes.

 

We connect the people who care
We believe that building a community of dedicated experts will help us share knowledge and exchange experience in shaping scalable and distributed computing solutions.
We value real-life expertise – is there any better way to learn than from actual specialists? Our speakers, true practitioners from top data-driven companies, will show you what they have learned and discovered about the Big Data world.

Answering your needs
Playing an active role in the Big Data world, we could see that a big technical conference focused on the exchange of knowledge and experience in this field was needed in Poland. The aim of DataMass Gdańsk Summit is to create a synergy between businesses focused on the creation and implementation of Enterprise-class solutions, together with experience and knowledge of the academic environment. The Data Science meetup in Gdańsk is a tangible proof that this is currently possible.

TECHNOLOGIES

SPEAKERS

Jacek Laskowski

IT consultant and Software Engineer

Jacek Laskowski is a freelance IT consultant, software engineer and technical instructor specializing in Apache Spark, Apache Kafka and Kafka Streams (with Scala and sbt). Jacek offers software development and consultancy services with hands-on in-depth workshops and mentoring. Reach out to him at jacek@japila.pl or @jaceklaskowski to discuss opportunities.

Michal Zylinski

Solution Architect

Google

Michał is a solution architect in Google, helping customers and partners utilize cloud to its fullest potential. He’s a true passionate about the impact of information on business and social processes and used to work as a consultant on many projects related to big data, machine learning and data science.

Jakub Szamalek

Author & Video Game Writer

Jakub is a writer and screenwriter for CD Projekt RED (the producer of games series The Witcher). A graduate of the University of Oxford, he received a PhD in Mediterranean Archaeology from the University of Cambridge and was awarded the Bill and Melinda Gates Foundation scholarship. He is the author of a crime trilogy about Leochares the Athenian detective. For the books in that series, he received the Readers’ Great Calibre Award (2011, When Athena Averts her Eyes / Kiedy Atena Odwraca Wzrok) and the Great Calibre Award (2016, Bone Reading / Czytanie z Kości). According to ResPublica, Google and the Financial Times, he is among the hundred young leaders of Central and Eastern Europe. His latest novel „Whatever you choose” (Cokolwiek wybierzesz) is the first part of the „Hidden network” trilogy, published by W.A.B.

Emily Sommer

Senior Data Engineer

Etsy

Emily Sommer is a senior data engineer at Etsy. She has worked on Etsy’s internal A/B testing and experimentation platform and is currently helping to build out Etsy’s streaming data analytics platform.

Ammar Akhtar

Founder & CEO

Final Rentals

Ammar Akhtar is an entrepreneur based in Poland and UAE since 2007 and has formed several successful initiatives since then mostly inclined towards mobility and technology.

Ammar is the brain behind the online booking systems for Budget, Thrifty, Dollar and many other companies in the UAE including Udrive.

After working for years for many big car rental companies the company has launched UAE’s flagship car rental comparison and booking portal known as Finalrentals.com that won the Gulf Capital SME Award as the “Digital Business of the Year” 2018.

Ammar’s dream is to make car rental reservations as easy as renting a movie from Netflix or ordering food from Deliveroo. Last but not the least he would like to eradicate poverty as much as possible across the world and would love aspiring entrepreneur find the right guidance and purpose of their organization.

Kamil Folkert

Member of the Board & CTO

3Soft

Responsible for the analysis of technological trends and the application of the latest technologies in projects implemented at the interface of IT and business. An expert.

He has many years of experience in designing and implementation of Big Data architectures in terms of automation of advanced analytics (including deep learning). A holder of a master’s degree from the Faculty of Computer Science at the Silesian University of Technology in Gliwice. In 2015, he was awarded a PhD from the same university. Specialization: agent systems. His doctoral dissertation concerned the possibility of combining the Big Data technology, vertical communication protocols and multi-agent systems to automate data analysis processes in industrial environments. In 2013-2016 he worked as a lecturer at Høgskulen på Vestlandet in Førde, Norway. An author of scientific publications.

Privately, he never stops looking for new opportunities to grow and learn. Holds regular consultations with architects from the Silicon Valley, USA.

Responsible for the analysis of technological trends and the application of the latest technologies in projects implemented at the interface of IT and business. An expert.

He has many years of experience in designing and implementation of Big Data architectures in terms of automation of advanced analytics (including deep learning). A holder of a master’s degree from the Faculty of Computer Science at the Silesian University of Technology in Gliwice. In 2015, he was awarded a PhD from the same university. Specialization: agent systems. His doctoral dissertation concerned the possibility of combining the Big Data technology, vertical communication protocols and multi-agent systems to automate data analysis processes in industrial environments. In 2013-2016 he worked as a lecturer at Høgskulen på Vestlandet in Førde, Norway. An author of scientific publications.

Privately, he never stops looking for new opportunities to grow and learn. Holds regular consultations with architects from the Silicon Valley, USA.

Balazs Gaspar

Solution Engineer

Cloudera

Balázs Gáspár is a Sales Engineer at Cloudera, covering the countries of Central and Eastern Europe, helping customers identify use-cases and find the right technical solution to turn their data into business value. Balázs has eight years international presales experience in enterprise IT solutions.

Dmytro Tkanov

Head of AI at Sciforce

Co-founder of EyeAI

Graduated from Kharkiv National University in 2011. MSc in theoretical physics. Started solving problems using machine learning more than 8 years ago. Went a road from software engineer to head of AI department. Currently leading a team of several machine learning researchers at Sciforce, outlining R&D directions. Co-founder of beaconless indoor analytics startup — EyeAI. Main area of expertise is deep learning in spoken language applications: automated speech recognition, articulatory features modeling, speech segmentation etc. Extensive experience in adapting of-the-shelf models, building custom training and inference pipelines from scratch, conducting research in such domains as speech, vision, text, fintech, sensors data. Early adopter of Tensorflow and Tensor Processing Units.

Piotr Rosiak

Head of Data

AirHelp

Piotr Rosiak, as a Head of Data has created cross-functional Data Science Team at AirHelp, a core team in a whole company.

His 5 teams (22 team members) are responsible for creating insights, defining KPIs, digital tracking, predictions with machine learning models and stable infrastructure with data pipelines.

With 8+ years of experience working with data, he became “Analytics Translator” – new role in analytics, and a key factor in the failure of analytic programs when the role is absent. With technical knowledge, business and analytical skills, he’s translating wide spectrum of problems into data science projects, turning different opportunities into real impact.

He loves teaching, so he works with infoShare academy, creating data driven culture.

Lukasz Grala

MVP AI, CEO

TIDK

Data enthusiast, architect and designer of BI and Big Data solutions. Together with the TIDK team – Data Scientist as a Service, where he is a president, he implements projects related to advanced analytics and data science, in particular machine learning and artificial intelligence. Since 2010, he has been awarded by Microsoft MVP in the Data Platform category. From July 2018, he is one of the 50 MVPs in the world in the AI category. Scientifically connected with the Faculty of Computer Science at the Poznan University of Technology. Member of the board of PTI Wielkopolska and Polish Society of Artificial Intelligence.

Damian Rodziewicz

Data Scientist & Co-Founder

Appsilon Data Science

Damian likes to consider himself a technology maniac, which is apt, considering he is our Cofounder and Lead Data Scientist. He has a Masters in Computer Science and postgraduate in managerial law. Before founding Appsilon he worked at Accenture, UBS, Microsoft and Domino Data Lab.

Konrad Sloniewski

Data Scientist

Atos

Konrad Sloniewski is a Data Scientist in Atos where he is currently working on building prescriptive security and user behavior analytics capabilities. Prior to Atos he was a founder of a web startup for HR industry backed by VC where he used machine learning for real-time job offers to candidates matching. He is a graduate of University of Warsaw where he finished Computer Science with Artificial Intelligence specialization.

Anton Holovachenko

Founder & CEO

UniExo

Anton Holovachenko is the inventor, as well as the founder and CEO of
medical startup UniExo. Since he am a pioneer of exoskeleton technology in the medicine industry, was able to achieve breakthrough technological results to improve the health of patients, and for the
European community to provide more accessible robotic technology that
was built from scratch to a working product.

 

The main interests are medicine, robotics, artificial intelligence,
nanotechnology and futurism (life of the future).

 

Have studied degree by building an unmanned aerial vehicles, he studied in Ukraine, Hong Kong and Washington, now works in Germany and Ukraine.

Tomasz Sosinski

BigData Consultant

Scalac

Tomasz has been exploring the data landscape for a few years now. He has been the witness to technologies growth, adoption and (in some cases) failure.Working through all layers of data processing pipelines, registering odds of software engineering industry, always seeking a better alternative to current solutions.

Scala geek, functional programming disciple, join him in discovering new seas of data-oriented world.

Roman Khotyachuk

PhD Research Fellow

NORCE Norwegian Research Centre AS

Roman is currently working on a PhD project in BigData analysis of scientific data. His activity focused on developing analytical applications using modern BigData technologies
(Hadoop, Spark) to analyze large amounts of data from different fields like fluid dynamics,
climate modeling etc. Another Roman’s task is creating predictive models for these fields
using Machine Learning techniques.

He graduated from National Technical University of Ukraine (Kyiv) in 1998, MSc in electrical
engineering.

10+ years of experience in Business Intelligence/Data Analytics.

Alexey Shaternikov

CEO and Founder

DSLab

Alexey Shaternikov is Founder and CEO at DSLab. Alexey has 10+ years of practical experience in applying data analysis, machine learning and artificial intelligence to solve business problems. He currently focuses on forecasting demand and optimizing replenishment strategies to help retailers reduce the number of write-offs and out-of-stocks. Alexey received his degree from Saint Petersburg State University of Engineering and Economics.

Adrian Boguszewski

Deep Learning Engineer

Linux Polska

Adrian is currently working as Deep Learning Engineer in Linux Polska. He has over 3 years of experience in image processing and artificial intelligence algorithms. He specializes in classification, object detection and semantic segmentation using convolutional neural networks. He is an open source and clean code enthusiast. He travels in a free time.

Natalia Ziemba-Jankowska

Data Science Engineer

Linux Polska

Data Science Engineer at Linux Polska, Deep Learning enthusiastic, self-styled Computer Vision hero. I consider myself a mathematician – algorithms and analytical thinking are my everyday routine. I am particularly interested in visualization, predictive modeling, and efficient machine learning techniques.

Natalia Szostak

Leader of Data Science Team

TIDK

Natalia holds PhD in Computer Science. Currently she is the Leader of Data Science Team at TIDK. She is involved in coordinating and implementing projects in the areas of Data Science, Big Data and AI. Qualified in building IT models and analyzing data. Author of publications in top ISI journals. Natalia gained experience at the Poznań University of Technology and the Polish Academy of Sciences. This year, Natalia was among the most talented young Polish scientists awarded by FNP.

Mateusz Zając

Senior Data Engineer

Kainos

Mateusz Zając is a Senior Data Engineer in Kainos with over seven years of commercial experience developing large scale cloud projects from the drawing board to continuously delivered, scalable and cost-efficient solutions. Specializes in performance monitoring and improvement of software especially those based on JVM stack. Skilled in working with on-premises Big Data solution stacks thanks to experience gained in operational services while working as full-stack engineer.

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After Party

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Agenda

 

Big Data, Big Responsibility 9:30-10:00 We are at a cusp of a technological revolution, brought about by Big Data and Artificial Intelligence (AI). The novel ways of gathering, analyzing and interpreting data offer great advances in productivity, and will likely reshape the global economy. When properly harnessed, Big Data can be a force for good, poignant examples include in healthcare or crises management.

However, there is a darker side to this technological advancement. Big Data can also be used to control and manipulate people on an unprecedented scale, whether for pecuniary or political goals. It is already exacerbating economic inequalities, both at a local and a global scale, by greatly multiplying the wealth of the already affluent and affecting global employment patterns. Furthermore, it can lead to entrenching existing biases and solidifying unjust power structures.

Data scientists and AI engineers are set to reshape the world. This is a boon, a privilege – but also a tremendous responsibility. This talk aims to highlight the potential pitfalls of the ongoing technological revolution and encourage attendees to actively grapple with the ethical dilemmas it poses.

Jakub Szamałek

Author & Video Game Writer

10:10-10:50 More information coming soon Jacek Laskowski

IT Consultant

Where streaming meets batch: Data Integrations in the real world 11:00-11:30 Most 3rd-party APIs still only support batch updates, so how do you leverage the capabilities of streaming internal data? We’ve used some great new features of Kafka Streams to limit the load of hydration requests upon our internal databases while writing our own streaming batch implementation to only send updates to external partners (Google, Facebook) at rates they support. This is helping us deliver more up-to-date data to our 3rd parties with less strain on our production databases.  Emily Sommer

Etsy

Introducing Cloudera Data Platform (CDP), the industry’s first enterprise data cloud
11:40-12:10 Cloudera Data Platform (CDP) combines the best of Hortonworks’ and Cloudera’s technologies, to deliver the industry’s first enterprise data cloud. CDP delivers powerful self-service analytics across hybrid and multi-cloud environments, along with sophisticated and granular security and governance policies that IT and data leaders demand. Balazs Gaspar

Cloudera

Serverless – the next big thing in data processing 12:20-12:40 For a number of years, microservices architecture has been growing to become a silver bullet for all the pains of the IT industry (some still believe that is true) but it has never found the way to become fully utilized in data processing domain. However, a new buzzword emerges from the ashes of expectations related to microservices, its name Serverless. But is it yet another iteration of the same pattern or the solution that brings enlightenment to the whole IT field? Is it better suited for data processing than old, rusty microservices? This talk is going to explore serverless architecture in the data-oriented world and hopefully, find the answer to some burning questions. Tomasz Sosiński

Scalac

Big Data simulation and analysis of numerical solutions from PDEs 12:50-13:20 In this work, the d3f software is used for numerical solving the PDEs describing the Elder problem. The author adapted this software to conditions of a Spark cluster. That allowed implementing the mass parallel runs of the d3f, as well as efficient analysis of the result data using BigData technologies. Some important run metrics/estimates calculated.

The author explored the data within the broad range of Rayleigh numbers (Ra) with different grid levels, time steps and the simulation time. Ra sub-ranges containing the bifurcation points explored in more details. The conditional probabilities of the steady states estimated. A method for automatic recognition of steady states described. Also, the author presented an approach on how to build a predictive model for the Elder problem.

Roman Khotyachuk

NORCE Norwegian Research Centre AS

Lunch 13:30-14:30
3 paramount drivers of AI projects 14:40-15:10 Artificial Intelligence offers a wide spectrum of possibilities that can be used not only in commercial projects but also in scientific initiatives as well as part of building new innovative products. But what are the drivers? Kamil Folkert, PhD, will talk about the most important factors of AI projects. Kamil Folkert, PhD

3Soft

15:30-15:50

Machine learning models for speech-based depression classification offer promise for health care applications. Despite growing work on depression classification, little is understood about how the length of speech-input impacts model performance. We analyze results for speaker-independent depression classification using a corpus of over 1400 hours of speech from a human-machine health screening application. We examine performance as a function of response input length for two NLP systems that differ in overall performance. Results for both systems show that performance depends on natural length, elapsed length, and ordering of the response within a session. Systems share a minimum length threshold, but differ in a response saturation threshold, with the latter higher for the better system. At saturation it is better to pose a new question to the speaker, than to continue the current response. These and additional reported results suggest how applications can be better designed to both elicit and process optimal input lengths for depression classification.

Tomasz Rutowski

Ellipsis Health

Smart Cities Data – Discovering true potential of IoT street observations with Azure 16:00-16:30 Instrumenting the city is vital to preserve and enhance urban living across the globe and the amount of investment in Smart Cities is set to leap forward in the next few years to an estimated $600bn globally by 2027. As we collect, share and monetize data from the urban environment there are fascinating technical challenges involved with building the associated data pipelines from performance techniques to data anonymization. Kainos is investing in innovation in this area and this session will walk through some of our findings. Mateusz Zając

Kainos

AI’s not as black as it is painted 16:30-17:00 As any new technology artificial intelligence brings new set of challenges. Fortunately, at least some of those fears can and should be mitigated already. During this session I’ll try to debunk some of the common threats and share (technical) solutions to others. Human’s future is bright! Michał Zyliński

Google

 

In the service of the history. AI in archivistics 10:10-10:50 1839 is a data generally accepted as the birth year of practical photography. Since then mankind produced about 10 quadrillions of photos including 1 quadrillion only last year. This huge amount of unlabeled an undescribed data is a problem if we want to obtain important information quickly and efficiently. Old photos are extremely valuable because they contain a lot of data about the past. However, some expertise and experience are needed to properly describe such images. What if we include all this knowledge into neural networks? Can AI become a friend of the 21st century archivist? Let’s talk about automatic image tagging and faces recognition in old photos. Adrian Boguszewski & Natalia Ziemba-Jankowska

Linux Polska

How to expand Data Team from 8 to 22 members in one year and do not go crazy? 11:00-11:30 What’s the role of analytics in start-up success? How to build a proper data-driven culture in a dynamic environment? What are the different setups for data teams?

We explored and formed different analytical roles together: analyst, business analyst, scientist, engineers, data quality specialists.

In this presentation, we’re going to share our lessons learned from this exciting journey called building Data Science Team.

We’re going to share all the challenges we faced with, as well as all achievements too.

Piotr Rosiak

AirHelp

How to efficiently use huge satellite imagery dataset with Machine Learning 11:40-12:10 The talk is about new possibilities arising from analyzing satellite imagery. Satellite data changes the game, as it allows to travel in time and reach information not available to business. Combined with the advances in image recognition and computing power, satellite data analysis offers possibilities to automate or streamline processes and design better products. Satellite data is huge and non-obvious. Thanks to currently available technologies you can access it, build forecasts and observe events that were undetectable before. I will show you what possibilities can be offered with the use of deep learning on satellite images and how our data science department has been successfully working with satellite data to build decision support systems for business.  Damian Rodziewicz

Appsilon

How Bigdata, AI & Blockchain can have a positive impact of upcoming economies by adding validation and value in their democratic process.   12:20-12:40 Countries like India, Pakistan and similar Asian economies have lot of talent, land and resources but still they do not perform as per their expectation on a global scale.  

The reason is lack of education, healthcare and last but not the least … corruption.  

There is a way that these countries can flourish and prosper by adapting Blockchain, AI into their governmental, democratic and administrative procedures.  

How is this possible and what could be the roadmap? And how companies who offer these services can benefit from it. 

Ammar Akhtar

Final Rentals

Introducing Data Science teams to Big Data environments 12:50-13:20

Every Data Scientist should know the theoretical foundations behind statistical models, their advantages, disadvantages and ways of using them to solve various types of problems. Analysis of data on one computation unit is not a problem for a typical analyst. However, Big Data environments have their own traps, training and running models operating in distributed environments must meet a much wider range of criteria.

In this talk I will present the path followed by a typical Data Scientist in order to learn and understand Big Data environments and how to use them efficiently. I will demonstrate comparison of tools such as Spark, Dask, TensorFlow and Ray and how their knowledge can help ordinary Data Scientist become Big Data Scientist.

This talk is dedicated for both Big Data engineers who are responsible for running statistical models prepared by Data Science teams, as well as for Data Scientists themselves who want to understand how to build solutions that can operate efficiently in distributed environments.

Konrad Słoniewski

Atos

Lunch 13:30-14:30
Robot technologies that will improve the quality of life over the next 5 years. 14:40-15:10 Robotics and exoskeletons can improve our life in the next 10 years. How does robotics already help us live now? Robotic technologies that will improve the quality of life over the next 5 years. How can the world of robotics and exoskeletons change in 10 years and its positive impact on people’s lives?

Anton Holovachenko

UniExpo

A story of a model 15:30-15:50 In this talk we will follow a machine learning (ML) model lifecycle. As an example, we will look at a chessboard position recognition system. We will discuss common tradeoffs and pitfalls that are encountered during data collection, training, deployment and live running phases. The talk would mostly focus on custom object detection model and computer vision techniques. Occasionally we will peek into other domains, e.g. spoken language processing, for complimentary examples.

The narration would be centered on Python/Tensorflow ecosystem. Yet described choices are general enough to be applicable to other frameworks.

Some of the topics that we will discuss:

  • ML vs conventional methods;
  • CPU vs GPU vs TPU;
  • TPU for inference (Google Coral);
  • dataset labeling;
  • research efficiency;
  • of-the-shelf model vs Cloud API vs custom model.

This talk may be interesting for business owners considering risks of incorporating deep learning tech into their product and for ML engineers looking for model training tips.

Dmytro Tkanov

SciForce/EyeAI

Improving Demand Forecasting with Artificial Intelligence: a Practical Case Study 16:00-16:30 Despite a wealth of well-established forecasting approaches, retailers lose billions of dollars worldwide each year due to overstocks and out-of-stocks. AI, however, can strike a balance between the two and help predict demand as accurately as possible. The presentation reveals the case study on AI-based demand forecasting solution for Lenta, one of the world’s biggest retailers and the second-largest retail chain in Russia. We will share results, challenges, and pitfalls of implementing automated forecasting system to existing business processes. Alexey Shaternikov

DSLAb

Predictive Maintenance, or AI in industry 16:30-17:00 Along with the automation of production, we have gained a new, valuable source of data. Collecting, storing and analyzing data slowly becomes a new standard. This phenomenon has been hailed as the fourth industrial revolution. Undoubtedly, this is one of the signs of global progress. However, production automation also brings new challenges. Due to the complexity and scale of production processes, the use of artificial intelligence becomes indispensable in order to analyze the multitude and variety of data. Predicting failure is one of the key tasks faced by the industry today. Predictive Maintenance is designed to help to estimate when maintenance should be performed based on the actual condition of the equipment. AI has a lot to offer in this area. Łukasz Grala & Natalia Szóstak

TDIK

CONTACT

DataMass strives to provide the best service possible with every contact!

We operate in a company based on trust. This can be achieved through communication and experienced support.

Want to become a speaker or support the conference? We are opened for cooperation, so just drop us a line! Our entire team ensures you’re receiving the best support and information possible.

ADDRESS:

Centrum Stocznia Gdańska
pedestrian entry:
Wałowa Street 27a
car entry:
ul.Lisia Grobla

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