“StormGeo employs people of different backgrounds with high expertise in various fields, and my team is a particularly great example of that. Working with this variety of expertise means I'm constantly learning, and I really enjoy that.”
Alla has been involved with the fields of machine learning and data science since 2009. She helped create Artificial Intelligence Machine Learning (AI/ML) solutions in the renewable energy sector, specifically for electricity consumption prediction and forecasting. Her work has also been used in industries such as fishing, shipping and logistics, as well as in the health sector and for fish farming biology.
In 2018, she received funding from the Research Council of Norway (RCN) as the inventor and primary investigator of two digitization and data science innovations for fish farming and renewable energy. This was the second time she received support from the RCN for innovations within the fish farming sector.
“I’m enjoying working in a cross-disciplinary environment for engineering and data exploration,” says Alla. “I love getting insight from data and then planning the information flow starting from data acquisition to communicating the model’s output to customers.”
Alla keeps abreast of current mathematical advances in the optimization of machine learning. She is fascinated by how data science and machine intelligence is penetrating deeper into society, helping people make better decisions, reducing workloads and enhancing the security of operations. “I’m interested in understanding the dynamics of trust development between human and intelligent machines,” she adds.
PhD in Physics and Mathematics from M.V.Lomonosov Moscow State University
Publications (most recent)
Alla Sapronova, Vladimir Bystrov, Machine learning application for molecular modeling at nanoscale: a current state and the way forward. Accepted for presentation at the joint IEEE ISAF-EMF-ICE-IWPM-PFM meeting, Lausanne, Switzerland, July 2019.
Thongtra, Sapronova, Time-series data analytics using spark and machine learning. Lecture Notes in Computer Science 2017. s.509-515
Sapronova, Johannesen, Meissner, Mana, Deep learning for short term wind power forecast, NOBIDS 2016, 2016.
Sapronova, Graham, Wind-power prediction. NORCOWE-RR-C-16-WP2-006, 2016.
Sapronova, Use of numerical weather predictions in short-term wind-power forecasts with artificial intelligence. NORCOWE-RR-C-16-WP2-007, 2016.
Sapronova, Machine learning predictive modeling for short term wind power forecast, NORCOWE newsletter August 2016.
StormGeo's lead forecaster, Chris Hebert provides the forecast for the 2023 Northwest Pacific Typhoon Season.Shipping | Oil & Gas | Renewables & Energy Markets | Other Industries
In this regularly updated forecast, StormGeo's Hurricane Forecasting team looks at current conditions across the Atlantic and Pacific as well as long-range patterns to identify...Shipping | Oil & Gas | Renewables & Energy Markets | Other Industries