Title: Industrial mathematics at ITWM for the digital automotive factory --- perspectives for mathematics graduates

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Bio: In the 80s I studied physics and mathematics at the universities of Heidelberg and Kaiserslautern. After graduating at TU Kaiserslautern in Theoretical Physics I continued with PhD work in photonics on the simulation of optical solitons in active glass fibers. I joined ITWM in 1996, a few months after the foundation of the institute. In the first years I worked on applications of the simulation of iron casting processes in foundry industry. After that, I changed to the newly founded division “Mathematics for Vehicle Engineering” in 2003, where I am now heading the department “Mathematics for the Digital Factory”.

Title: Math meets industry – from mathematical analysis to business development – from increased oil recovery to green solutions and the soft skills needed to succeed  

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Bio: Kristin Flornes holds a dr.ing. (PhD) in industrial mathematics from NTNU and a Master of Technology Management from NTNU and NHH. With more than 25 years’ experience her background includes software development for the oil industry, market analysis in the Nordic power market and research and management positions in NORCE, the second largest R&D institute in Norway. Flornes has been project manager for many joint industry ventures including open innovation projects. With a strong passion for innovation and sustainable business development, Flornes joined Eviny as senior advisor in the innovation department in April 2021. Eviny is the largest renewable energy and technology company in Western Norway. Flornes works with digitalization, electrification and energy storage. So far she has been instrumental in the establishment of two new startups partially owned by Eviny.

Title: Industrial Mathematics: Lessons gained over 20 years

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Bio: Dr. Öktem received his Ph.D. in 1999 in Mathematics from Stockholm University and is now an Associate Professor in Numerical Analysis at the Department of Mathematics, KTH - Royal Institute of Technology, Stockholm, Sweden. 
Öktem has an extensive experience from industrial work. He started already during his graduate studies to work full time in industry applied mathematician and continued with this in various sectors (finance, biotechnology, engineering software) for 13 years before returning to academia in 2010. Prior to joining KTH, he held full professorial positions at Heriot-Watt University and Uppsala University. Research wise, he specialises in theory and algorithms for solving severely ill-posed inverse problems with emphasis on tomographic imaging. Much of his work is in the intersection of mathematical analysis, differential geometry, mathematical statistics, and machine learning. Focus lately has been on combining model based approaches with deep neural networks for uncertainty quantification and task adapted reconstruction in large scale inverse problems. Work is spearheaded by concrete challenges in imaging applications from various scientific fields, like 3D electron and fluorescence microscopy in bioimaging, low-dose clinical CT and spatiotemporal PET/CT, X-ray phase contrast tomography for bioimaging and material sciences, and lately seismic tomography for geophysical prospecting and single particle cryo-EM. 

Title: Statistics for the knowledge economy – experiences from an academic research and innovation center with industrial partners

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Bio: Ingrid K. Glad obtained her degree in Industrial Mathematics from NTNU in 1990, and earned a PhD in statistics from the same institution in 1995. After two years as a post doc researcher at the Sapienza University in Rome, she started in an associate professor position at the Department of Mathematics at the University of Oslo in 1997, and is since 2011 full professor in statistics there. Her main research interest is developing statistical and machine learning methods motivated by problems originating in the life sciences and other data intensive disciplines, in particularly high-dimensional regression methods and methods for change detection in high-dimensional data streams.

Ingrid K. Glad has been leading the research group of Statistics and Data Science at the Department of Mathematics, she is co-director of the Center for Research based Innovation (SFI) BigInsight and the research cluster DataScience@UiO. In addition to various international commissions of trust, she is supervising several PhD students and master students in statistics and data science. Since January 2022 she is acting as director for the Center for Computational and Data Science at the University of Oslo, dScience.

Title: Math, data and telecom

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Bio: Kenth Engø-Monsen, PhD, is Fellow of Telenor Research, where he currently is leading the Analytics and AI team, work package leader of STREAM in the SFI NorwAI, and leading Telenor Group’s initiative on big data for social good. With more than 20 years of experience in telecom, Dr. Engø-Monsen has extensive knowledge in the field of telecom data, social network analysis, and applied research using mobile data. He received his Master’s in 1995 in Industrial Mathematics from NTNU, Trondheim, Norway, and PhD in 2000 in Computer Science from University of Bergen, Norway.

Title: Jumping contact points: Mathematics beyond academic research

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Bio: Prof. Dr. Martin Arnold received his M.Sc. and Ph.D. in mathematics from Martin Luther University Halle-Wittenberg (Germany) in 1988 and 1990, respectively. From 1990 to 1997 he was assistant professor at the University of Rostock, where he obtained a habilitation degree in mathematics in 1997. He worked as research scientist and head of the Computational methods team at the Institute of Aeroelasticity of DLR German Aerospace Center and as Privatdozent at Munich University of Technology. Since 2003 he is full professor at Martin Luther University Halle-Wittenberg: from 2003-2007 as Professor of Numerical methods for nonlinear problems and since 2007 as Professor of Numerical mathematics. His research interests include the numerical solution of time-dependent coupled and constrained systems of differential equations, model-based simulation and optimization in science and engineering and numerical methods and scientific software for industrial computer aided engineering processes. Martin Arnold is co-organizer and member of the Scientific Committee of the NUMDIFF conferences and coordinates the European Training Network THREAD, a Marie Skłodowska-Curie Action within the EU Research and Innovation programme Horizon 2020.