Mr. Jordan Bernard | Spatial Smoothing Award | Best Researcher Award

Mr. Jordan Bernard, Alaska Department of Fish and Game, United States

Mr. Jordan Bernard, an M.S. graduate in Statistics from the University of Alaska Fairbanks, specializes in Biological Applications of Statistics, complemented by a B.S. in Mathematics from Colorado State University. 👨‍🔬 With a rich background as a Biometrician II at the Alaska Department of Fish and Game, he adeptly utilizes Bayesian statistics, predictive analytics, and spatial ecology methods to analyze marine mammal populations. Previously, as a Senior Staff Scientist at Geosyntec Consultants, he provided GIS support for mapping contamination, showcasing his versatility. 💻 His skill set spans Bayesian and spatial statistics, R programming, GIS, and data visualization, making him a formidable force in statistical analysis and data management across various domains.

Professional Profile:

Scopus

📚 Education:

Mr. Bernard holds an M.S. in Statistics from the University of Alaska Fairbanks, specializing in Biological Applications of Statistics, and a B.S. in Mathematics from Colorado State University.

👨‍🔬 Work Experience:

Mr. Jordan Bernard is an accomplished Biometrician with extensive experience in statistical analysis and data management, particularly in marine mammal populations. He has served as a Biometrician II at the Alaska Department of Fish and Game, where he calculated statistics related to marine mammal abundance, movement, and genetics, employing Bayesian statistics, predictive analytics, and spatial ecology methods. Previously, he worked as a Senior Staff Scientist at Geosyntec Consultants, providing GIS support and mapping contamination in soil and sediment. Additionally, he has worked as a Data Analyst at the Alaska Primary Care Association, managing healthcare data and calculating population-level health statistics.

💻 Skills:

His skills include expertise in Bayesian statistics, spatial statistics, time series statistics, R programming, JAGS, GIS, SQL, data visualization, and report writing.

Publications Top Notes :

A geostatistical model based on random walks to krige regions with irregular boundaries and holes
- Published in Ecological Modelling in 2024.
- Contributors: Barry, R.P., McIntyre, J., Bernard, J.
An empirical Bayesian approach to incorporate directional movement information from a forage fish into the Arnason-Schwarz mark-recapture model
- Published in Movement Ecology in 2021.
- Contributors: Bishop, M.A., Bernard, J.W.
- Cited by 5 articles.

Mr. Nikita Belyakov | Remote Sensing Imagery Analysis Award | Best Researcher Award

Mr. Nikita Belyakov, Moscow State University, Russia

Mr. Nikita Belyakov is a 6th-year student at Lomonosov Moscow State University, specializing in AI applications in satellite data analysis and remote sensing. 🛰️ His research focuses on deep learning and AI applications in spacecraft trajectory modeling and orbit parameter estimation, with expertise in cloud and snow segmentation using AI methods. He has contributed to projects improving spacecraft state vector estimation and developing autonomous systems for barcode detection, alongside involvement in space debris monitoring at MSU’s ballistic center. 📡 Published in journals like Advances in Space Research, his work showcases a blend of innovation in AI and space science.

🔬 Research Focus:

His scientific activities revolve around deep learning and AI applications in remote sensing, spacecraft trajectory modeling, and orbit parameter estimation. He has been actively involved in developing software for spacecraft maneuver control and space debris monitoring at the ballistic center of MSU.

🛰️ Projects:

He has also worked on improving the accuracy of spacecraft state vector estimation using TLE data from the International Laser Ranging System (ILRS), applying machine learning and AI techniques. Additionally, Mr. Belyakov has experience in natural language processing (NLP) tasks and developing autonomous systems for barcode detection using neural networks.

Publications Top Notes :

Cloud and Snow Neural Network Segmentation using the Electro-L No. 2 satellite low-resolution data

Published in Advances in Space Research in 2024.
Contributors: Nikita V. Belyakov; Sergey V. Kolpinskiy; Artem V. Vasiliev

Determining binocular lenses orientation using inertial sensors: problem solution

Published in Engineering Journal: Science and Innovation in 2022.
Contributors: V.V. Latonov; N.V. Belyakov; A.A. Petrov; T.A. Semenikhin