Artificial Intelligence & Machine Learning Conference 2022

Bernd «Benno» Blumoser is Co-founder and Head of the Siemens AI Lab, which serves as co-creation platform for exploration of Industrial AI. Being organizationally embedded in Siemens’ Munich-based Technology division, where around 100 experts drive its «Data Analytics & AI» research hub, it acts as Open Innovation platform where business stakeholder and domain experts are matched with AI researchers to jointly gain further orientation in the vast field of AI opportunities.

Veronika Haderlein-Høgberg is Senior Researcher with the Semantics & Reasoning (SMR) Team at Siemens AG. The SMR-team serves as a strategic consulting and sounding board for the various Siemens Business Units and their respective CTO-offices regarding the development of end2end-services in Data Management, Data Architecture, Data Readiness and Machine Learning. In this role, the SMR works on projects, tools and infrastructure to help the Siemens Business Units enable their customers in leveraging their data for increased automation and efficiency.

Yongyos Kaewpitakkun is a senior data scientist in the Digital production team at Yara International. He has a Ph.D. in AI / Machine learning and many years of hands-on experience leveraging machine learning, computer vision, and natural language processing models to solve challenging business problems.

Alexander leads the Digital Production unit in Yara International ASA. Yara established the unit to further improve the operations across Yara’s global production site network. Today, Digital Production is building an industry leading enterprise data platform (on AWS). On top of the platform, the team develops or buys & integrates digital products in close collaboration with production sites – always focused on solving real operational problems.

Andreas works as Managing Director of the software company Idletechs AS that specializes in solutions for the process industry that combine data-rich sensors, such as IR cameras, with transparent machine learning and domain knowledge. By combining these three it is possible to gain a much deeper understanding of process states and product quality than before, and thus these solutions support near real time information that can be used for better control and optimization.

Peder Aursand is a senior data scientist who leads a multi-disciplinary team developing, deploying and maintaining machine learning models for interpreting subsurface data as part of Aker BPs digital umbrella EurekaX. In this role he is responsible for the full life cycle of machine learning from prototype to everyday use, including implementing and executing a pragmatic but effective approach to MLOps.

• Studied MSc (siv.ing.) in Chemical Engineering at NTNU 2010-2015, with specialization in Process Systems Engineering.
• Master thesis on the topic of “Modelling and Optimization of a Two-Stage Refrigeration Cycle” with Prof. Johannes Jäschke as supervisor
• PhD in the same group at NTNU 2015-2020
• PhD thesis: “Control Degrees of Freedom for Optimal Operation and Extending Remaining Useful Life”
• Work in Cybernetica AS as Senior Control Engineer from 2019-present, developing model-predictive control applications
• Mostly worked with various applications for AkerBP and NMPC installations on their Ivar Aasen platform

I’m a senior data scientist at Cognite AS, Oslo. I’m a part of the Strategic Customer Services organization responsible for delivering value to the customers through our trademark product cognite data fusion (CDF). I’ve background in communication systems & signal processing and possess rich experience over a decade in analyzing data and generating insights from various industrial domains such as wind, aviation and manufacturing.