Martin Johnsen
About Martin Johnsen
Martin Johnsen is the Co-Founder and CEO of Subsets, where he has worked since 2022. He has a background in data science and consulting, with previous roles at PwC and Implement Consulting Group, and holds a Master's degree in Mathematical Modelling and Computation from the Technical University of Denmark.
Work at Subsets
Martin Johnsen serves as the Co-Founder and CEO of Subsets, a position he has held since 2022. Under his leadership, the company focuses on leveraging advanced technologies to enhance business value and improve decision-making processes. His role involves strategic oversight and the implementation of innovative solutions that align with the company's mission.
Professional Experience
Prior to his current role, Martin Johnsen accumulated diverse experience in various consulting and data science positions. He worked at Fonden DBK as a Junior Data Scientist for 11 months from 2017 to 2018. He then joined PwC Consulting as an Assistant Consultant from 2018 to 2020, followed by a role as a Management Consultant at Implement Consulting Group from 2020 to 2021. He later advanced to Senior Management Consultant at Implement Consulting Group from 2021 to 2022. Additionally, he worked as a Data Scientist at Ecotek in Hanoi, Vietnam, for 7 months in 2020.
Education and Expertise
Martin Johnsen holds a Bachelor of Science in Strategic Analysis and Systems Design from the Technical University of Denmark (DTU), where he studied from 2015 to 2018. He further pursued a Master of Science in Mathematical Modelling and Computation at DTU from 2018 to 2020, specializing in Machine Learning and Signal Processing. He also studied Data Science and Management as an exchange student at Boston University in 2017. His educational background includes language studies in English at Bloomsbury Institute London and Mandarin at Shanghai International Studies University.
Research Contributions
Martin Johnsen co-authored a research publication titled 'Population synthesis for urban resident modeling using deep generative models,' which was published in Neural Computing and Applications. This work reflects his expertise in machine learning and his commitment to advancing the field through research and practical applications.
Technical Skills
Martin Johnsen possesses in-depth experience with various deep learning techniques, including natural language processing, image recognition, and generative models such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN). His passion lies in utilizing prescriptive methods in Machine Learning and AI to assist human experts in making better decisions, demonstrating his commitment to integrating technology with human expertise.