nordics AI Summit 
Speakers

Machine Learning & Data Engineer

Handelsbanken

Building Your Deep Learning Platforms

Mikael is a Research scientist/Machine learning engineer with a strong background in scientific programming, parallel computing, data science and DevOps.

Founder

AI4Diversity Sweden

Smriti is a Data Science and AI engineer with experience from several industries including fintech, sustainable tech and healthcare. Beside her work at AI4Diversity, she is also a Data Science/ Data Engineer at Adage AB, a Nova member and a Google for startup mentor. She is also part of LinkedIn for Creators and was recently included in the list of Top 200 LinkedIn Creators worldwide by Favikon. 

Lead AI Engineer

Electrolux

Conversational AI for Customer Services.

Anna is a specialist in conversational AI, currently working with Electrolux building a new platform for Customer Services.

Innovation Manager in AI

UNICEF

Fundamentals of Responsible AI Models

We all know that technology amplifies the existing entanglement of social constructions, relations, and practices. If the perspectives of marginalised users are not considered in AI development, implicit biases and damaging practices will persist. But how do we implement responsible models in practice? In this session, let's talk about the fundamentals of responsible AI at all stages of development process, from data collection to model performance monitoring.

We all know that technology amplifies the existing entanglement of social constructions, relations, and practices. If the perspectives of marginalised users are not considered in AI development, implicit biases and damaging practices will persist. But how do we implement responsible models in practice? In this session, let's talk about the fundamentals of responsible AI at all stages of development process, from data collection to model performance monitoring.

We all know that technology amplifies the existing entanglement of social constructions, relations, and practices. If the perspectives of marginalised users are not considered in AI development, implicit biases and damaging practices will persist. But how do we implement responsible models in practice? In this session, let's talk about the fundamentals of responsible AI at all stages of development process, from data collection to model performance monitoring.

Irina has 15+ years of experience as a data scientist and policy expert and is a Professional coach, director, and mentor. In addition, she is a Professional speaker, presenter, and workshop facilitator with a PhD and a track of publications in political economy, investment analysis, and international relations.

She is proud to be an experienced manager of agile, multicultural teams.

Lead Data Scientist - AI

Husqvarna

Nasir Uddin' PhD is a Senior data scientist and AI/machine learning engineer with a multidisciplinary educational background who can combine research and business together.

He is good at programming in Python and R as well as working with various open sources AI frameworks and cloud computing platforms including Azure, Google cloud, AWS, IBM etc. He is also familiar with SQL, Javascript, Java, Swift as well as AI software/application development, data engineering and big data architecture within industry 4.0 concept.

Nasir worked at various agencies/enterprises/industries dealing with finance/banking, energy, infrastructure, real estate, environment, space/satellite, car manufacturing, Food-Pharma and process. He holds PhD in Genetics from Aarhus University in Denmark, MSc from Uppsala University in Sweden and vocational certificates in Software/Mobile Application Development from Nackademin in Sweden.

Nasir has a futurist mindset and clear vision of 'growth hacking’ with AI & Cognitive technologies.

Technical Lead - Machine Learning Operations

SEB

Training a great machine learning model isn’t enough – it isn’t useful until you deploy it. In this talk, the lead developer of SEB’s MLOps platform candidly shares how it was built, what worked well and less well, and how the shift to a cloud-based solution is changing things.

Manne's experience includes backends for high traffic web sites in the gaming and media industries, telecom systems and IPTV. He's talked at several software development conferences and has often worked as a mentor and teacher. He has deep knowledge in Java, Test-driven development, Domain-driven design and server architecture and is fascinated by the new possibilities offered by Big data and machine learning algorithms.

Living the IKEA Culture and Values in the Digital Age - A Values Based Approach to Algorithmic & AI Ethics Founded almost 80 years ago, IKEA has become the world largest home-furnishing retailer in the world, always led by its values. The values can be seen in the actions that are taken, the range offered and how IKEA strive to create a better everyday life for the many people. Being a values-led brand, we are guided by our values in a digital context and when making decisions around data and technology. One of the keys for success within Inter IKEA Group is the mindset and approach around values-based decision-making and focusing on creating a positive impact on people, the planet and society. Algorithmic and AI ethics therefore becomes a natural part of how we live IKEA culture and values in the digital age.

Geovanni is a cyberpunk evangelist for good, a happy rebel delivering value through driving change of processes, organisations and analytics for +20 years. His vision is a future where technology will be created and used with ethics in mind.

André specialized in designing and deploying ML solutions on resource-limited edge devices, currently working at Voi to make e-scooters safer. Prior to this he was a Machine Learning Advisor working with data collection, predictive maintenance and computer vision at various companies including Metso, Volvo GTT and CEVT.

15 Years of experience in Multimedia domain with vast experience especially in Camera and Image/Video solutions. Research & Development in Computer Vision and Machine Learning. Improving accuracy and performance, Robustness and Productization in memory and performance constraint devices. Extensive problem solving and coding skills, participating actively in HackerRank contests.

The Catalog Problem: Neural Ordered Clusters from Variable Sets

Prediction of a  varying number of ordered clusters from sets of any cardinality is a challenging task for neural networks, combining elements of set representation, clustering and permutation learning. This task arises in many diverse areas, ranging from medical triage, through multi-channel signal analysis for petroleum exploration to product catalog structure prediction. This presentation focuses on the latter, referred to further as the eponymous Catalog Problem, which exemplifies a number of challenges inherent to adaptive ordered clustering. These include learning variable cluster constraints, exhibiting relational reasoning and managing combinatorial complexity.

Mateusz is a Senior Machine Learning Engineer at Tjek A/S, a Danish company helping people with their shopping via their eTilbusavis app. His current focus is on deep learning solutions to structure prediction, set representation and neural clustering, which is also the subject of his Industrial PhD thesis. Prior to his current position he worked at Intel, WNS Global and Procter and Gamble. 

After a career as entrepreneur and data scientist, Jens turned to research and is currently pursuing a PhD at KTH within the WASP program. He believes that an important property that sets humans apart from other animals is that we have a sense of geometry and topology. Teaching computers a sense of geometrical recognition and reasoning is thus a promising direction if we want to develop more powerful AIs. To do so we must expand the mathematical toolkit underlying machine learning to include math from less well-known fields such as computational geometry and topology. Jens will discuss some of the results and challenges in doing so. 

By taking neural networks back to the school bench and teaching them some elements of geometry and topology we can build algorithms that can reason about the shape of data. Surprisingly these methods can be useful not only for computer vision – to model input data such as images or point clouds through global, robust properties – but in a wide range of applications, such as evaluating and improving the learning of embeddings, or the distribution of samples generated by GANs. This is the promise of the emerging field of Topological Data Analysis which we will introduce and review recent works at its intersection with machine learning.

Morten is currently the Head of Advanced Analytics at DNB Wealth Management, responsible for the machine learning models used for customer insight and prediction. He is also responsible for the operational data governance activities in our business area.

He studied at the Norwegian University of Science and Technology (NTNU) and received an M.Sc in applied mathematics in 2001 and started my career in Norsk Hydro, working with energy trading and estimating the value of oil fields. In 2007 he started as a quantitative portfolio manager for global equities in DNB. Since then, he’s had various positions within equity trading and portfolio management in DNB, always with a focus on programming, statistics and machine learning.

In 2018 he started the Advanced Analytics department in DNB Wealth Management. It started out as a small team initially and has grown in size as our responsibilities expanded. They are now a team of 10 data scientists, data analysts and data stewards

Co-Founder & Chief Product Officer

Zerve

Significant Roadblocks to Usefulness for Jupyter Notebooks and a Recipe to Fix Them

The most popular data science development tools have largely been developed by academics as scratch pads for interactive data exploration.  Jupyter notebooks, for instance, were developed 20 years ago at Berkeley (they were called iPython notebooks at the time).  Because of their flexibility and interactivity, these tools have become widespread amongst coding data scientists.  More recently, GUI-based tools have begun to be popular.  They reduce the technical load on the user, but typically lack much needed flexibility and interoperability.  Both avenues of innovation are wildly inadequate for modern data science development.  GUI-based tools are typically too expensive, too restrictive, and too closed.  The development of automated machine learning tools only made this problem worse, with dozens of software startups urging business analysts to start building machine learning solutions, often with questionable results and even more questionable customer retention metrics.  On the other hand, notebook-based solutions are typically too error-prone, too loose, and too isolated to be sufficient.  The result is intractable challenges around collaboration, communication, and deployment.  The most recent entrants into the notebook space have only marginally improved the experience without fixing the underlying flaws.  This talk discusses the fundamental flaws with the way these tools have been developed and how they currently function.  Advancement in this space will require reworking the architecture and functionality of these tools at some of the most basic levels.  These fixes include things like multiprocessing capabilities; real-time collaboration tools; safe, consistent code execution; easy API deployment; and portable communication tools.  Future innovation in the data science development experience will have to tackle these problems and more in order to be successful.

Greg Michaelson is Co-Founder and Chief Product Officer at Zerve, a young, stealthy startup that’s rethinking the data science development experience. Previously, Greg was an early joiner at DataRobot where he played many roles, including Chief Customer Officer. Prior to that, he worked as a data scientist in the financial sector after earning a PhD in Applied Statistics from the University of Alabama.  In his spare time, Greg manufactures a line of flavored breakfast cereal toppings called Cerup. He lives in Spring Creek, Nevada with his wife, four children, and two Clumber Spaniels.

Data Scientist Leader, multilingual, seeking to drive impact and build a world class data science team at ICA. A skilled communicator capable of explaining complex issues with clarity to diverse audiences. Interested in growing data talent while creating an inclusive environment and developing scalable processes to make data accessible across organizations, especially to non-technical stakeholders.

Anna’s ambition is to be an inspiring leader who empowers and builds trust within her team. Highly adaptable and result-driven, she has demonstrated valuable leadership skills in technically complex environments, through periods of rapid change and in high-pace environments. Strong analytical skills and fast processing of complex information enables me to stay calm and methodical in all situations.

Janne is a leader specialized in developing data driven organizations and strategies, with hands-on expertise in advanced analytics. A highly versatile professional, with experience from leading client engagements as well as corporate level transformation initiatives in complex matrix organizations. He is a recognised thought leader and frequent speaker around digitalization and AI within the industry.

Presentation: Dissecting Volvo Car’s Strategy to Effectively Adopting MLOps   

• How Volvo Cars successfully operationalised ML & the lessons learned along the way 
• What is the model for success on deploying ML with aspects of software engineering?  
• How can your organisation reap the benefits of ML engineering & operations at an enterprise scale?  

Autonomous vehicles are one of the most hot topics these days. The complexity required to solve autonomous driving makes it inevitable that in some parts of the problem, AI will be used. It can be used in many sections like perception, planning, prediction, may be even end to end.
At research and development at SCANIA we have been investigating the application of AI for heavy-vehicle autonomous transport, and in this presentation, we will have a 10000 foot view of some of works that have been done in the society and also a snapshot of some of the things we are doing within the area of perception using AI.

Shahab is a driven and ambitious person loves working on productive and diverse projects. His academic background and curiosity on the effect of science and technology on our society to have a positive and bright future in the world drove me towards passions on artificial intelligence (AI), machine learning (ML), deep learning, data science and computer vision.

Maddy is Experienced in leading multifunctional data science and machine learning teams and is experienced in advanced data, analytical and machine learning techniques, and supporting product development with data-driven decision making. She has a PhD in image processing, computer vision and machine learning with computing science background, experienced in both industry and academic research. Her main work is in mobile gaming, social network, image applications in both medical and industrial data.

CEO & Co-Founder

Green AI Cloud

How to Manage the Ever-increasing Mega Size AI Models in the Cloud

Today’s larger AI models demand both extreme compute capacity, time-consuming and costly pre-work. But this is not enough; in addition, you must make substantial trade-offs in splitting the models and dealing with the sparsity dilemma to run on today’s standard CSP structure – even with a GPU based cloud provider. Finally, the staggering energy consumption is far from sustainable, neither from a climate nor from a cost perspective.
To meet these challenges, Green AI Cloud will present a cloud solution based on the Cerebras Systems.

Jacob Boström has a master’s degree in computer science from the Stockholm Institute of Technology (KTH). He has more than 15 years of experience of building leading edge telecom/AI platforms for Ericsson globally. This includes extensive programming experience and track record, primarily within telecom, where he has delivered many complex projects. One of the larger projects was Ericsson’s login-system IAM which today is used by some 100 000 employees around the world. Also worth mentioning is the groundbreaking work for Ericsson research department developing an AI simulation platform with cloud architecture. Jacob is the founder of Green AI Cloud. His earlier venture – Fredenheim a high profile dataconsultancy company- was nominated” Super company” both 2017 and 2018, by one of Sweden’s biggest business magazines.

Machine Learning Researcher

RISE Institute

Daniel is a Machine Learning Researcher at the RISE Institute of Sweden where he focuses on applying machine learning and probabilistic models to develop intelligent autonomous systems for solving combinatorial optimization problems for networked systems.

Senior Machine Learning Engineer

Ahold Delahize

At Ahold Delhaize, we have multiple brands in different countries and many of them want to have similar ML models running on their websites. We would like to show, how we create a system that allows us to deploy the same model for different brands, without reinventing the wheel.

The components we developed for reusability:
- Configurable central python package for each model
- Cookiecutter template to create repositories with necessary components
- Reusable pipelines to deploy models
- Identical infrastructure& service user setups

 Maria is a Senior Machine Learning Engineer at Ahold Delhaize. Maria is bridging the gap between data scientists infra and IT teams at different brands and focuses on standardization of machine learning operations across all the brands within Ahold Delhaize.

During more than eight years in Data&Analytics, Maria tried herself in different roles, from data scientist to a machine learning engineer, was part of teams in various domains, and have built broad knowledge. Maria believes that a model only starts living when it is in production. For this reason, last six years, her focus was on the automation and standardization of processes related to machine learning.

Machine Learning Engineer

Ahold Delhaize

At Ahold Delhaize, we have multiple brands in different countries and many of them want to have similar ML models running on their websites. We would like to show, how we create a system that allows us to deploy the same model for different brands, without reinventing the wheel.

The components we developed for reusability:
- Configurable central python package for each model
- Cookiecutter template to create repositories with necessary components
- Reusable pipelines to deploy models
- Identical infrastructure& service user setups

Basak Eskili is a Machine Learning Engineer at Ahold Delhaize. She is working on creating new tools and infrastructure that enable data scientists to quickly operationalise algorithms. She is bridging the space between data scientists and platform engineers while improving the way of working in accordance with MLOps principles.
In her previous role, she was responsible for bringing models to production. She focused on NLP projects and building data processing pipelines. Basak also implemented new solutions by using cloud services for existing applications and databases to improve time and efficiency.

AI & Machine Learning Expert

anch.AI

Biases are inherent in us human beings. We cannot avoid them. However, we can try to do more to govern AI. Ungoverned AI such as data bias, misuse/over-use of data and AI and immature data can lead to costly reputational harm and lack of trust.  All organizations must be accountable for how their use of data and AI is affecting people and society. The European Union is expected to introduce a regulatory framework for Artificial Intelligence in 2024, which will require AI reporting and new governance structures. This means that organizations must consider ethical AI across all functions within the organization. Compliance and regulation alone cannot be relied on to do the right things. It is a starting point, and having a sound AI framework moving forward is essential. 

Jonathan Rebane is an AI and Machine Learning Expert at anch.AI where he brings technical expertise for developing sustainability oriented AI methods, solutions, and data insights. He is passionate about sustainable AI and believes that various stakeholders must work together to ensure responsible and trustworthy data driven innovation.

Jonathan has experience in multiple areas of academic research and the private sector working in Data Analytics. Jonathan completed his PhD in Data Science at Stockholm University. His research involves developing AI frameworks to predict future diagnoses for patients using Electronic Health Record Data. In addition, Jonathan has published works on ”Explainable and Interpretable AI”,  ”AI for Cryptocurrency Price Prediction”, ”Mathematical Modelling in Cellular Neuroscience” and ”Spatial Cognition.” A complete list of Jonathan’s publications can be found here.
Jonathan holds a Master’s degree in Health Informatics from Karolinska Institutet & Stockholm University and a Master’s degree in Neural Information Processing from the International Max-Planck Research School for Cognitive and Systems Neuroscience in Germany.

Head of Data Science

Babyshop Group

When we in the ML-space talk about scaling machine learning, the focus usually falls on the technical aspects such as memory requirements, latency and throughput. When a company grows, and its machine learning applications grow with it, another aspect of scaling arises - an organisational one. A growing number of use-cases for ML combined with a growing team requires an ML-Ops based infrastructure, enabling both technical scaling and efficient ways of working. At Babyshop Group, an e-commerce scaleup, we have 10+ productionised ML-based applications within a relatively small team, made possible by leveraging ML-Ops best practices. During this talk, I will share our infrastructure, key learnings, and how we can sustain a high rate of innovation at scale.

Marcus Svensson is the Head of Data Science at the Babyshop Group, where he is responsible for the data science team (3 people), leveraging data across all stages of analytics, from descriptive to predictive to prescriptive, in order to create value across the whole organisation. Use-cases for in-house ML-based solutions involve; customer insights & segmentation, CLV-predictions (for Google Ads), attribution modelling, recommender systems (on-site / email), smart sorting (listing pages & search), demand predictions as well as intelligent pricing. In order to support such a wide range of use-cases, the underlying tech-stack and infrastructure used are a combination of Kubernetes & Serverless hosted on GCP, with emphasis on containerisation. More specifically a combination of Kubeflow Pipelines (ML-pipelines running on Kubernetes), Cloud Run (serverless REST API’s), Cloud Functions (serverless jobs) as well as Vertex AI (distributed hyper-parameter tuning).