Professional Profile
Dmitry S. Efremenko is a Doctor of Science and a leading researcher in the fields of computational physics, remote sensing, and artificial intelligence. His academic and research work focuses on advanced methods for analyzing physical processes, mathematical modeling, and the application of AI technologies to solve complex scientific and engineering problems.
He has extensive international experience in both research and teaching and actively contributes to interdisciplinary developments at the intersection of physics, mathematics, and data science.
Professional Experience
- Professor, Nairi International University
- Research Scientist, Head of the Research Group “Mathematical and Physical Foundations of Remote Sensing,” German Aerospace Center (DLR), Munich (since 2011)
- Leading Researcher, Faculty of Computer Science, National Research University Higher School of Economics (since 2024)
Education & Academic Qualifications
Doctor of Science (Technical Sciences) (2017)
Technical University of Munich / Moscow Power Engineering Institute
Dissertation:
“Fast Interpretation Technology of Optoelectronic System Signals for Retrieving Atmospheric and Solid-State Parameters”
PhD in Physics and Mathematics (2011)
Lomonosov Moscow State University
Dissertation:
“Solving Inverse Problems of Radiative Transfer and Particle Scattering Theory for Multilayer Structure Analysis”
Teaching Experience
Technical University of Munich (2017 – present)
- Electrodynamics
- Nonlinear Optimization
- Inverse Problems in Atmospheric Remote Sensing
- Radiative Transfer and Light Scattering
- Big Data Processing in Remote Sensing
NUST MISIS (2020 – present)
- Introduction to Data Science
- Applied Digital Projects in Data Science
- Linux in Data Science
- Web Services and SaaS Development
- Databases and Data Warehouses
Moscow Power Engineering Institute (2009 – 2011)
- Mechanics
- Thermodynamics
- Optics
Research Interests
His research covers a broad range of interdisciplinary areas:
- Numerical modeling of radiative transfer in the atmosphere and ocean (1D, 3D, and stochastic models)
- Electromagnetic scattering theory (T-matrix methods, null-field method, discrete dipole approximation, integral methods)
- Machine learning applications in physics (physics-informed neural networks, neural operators, hybrid models)
- Development of accelerated physical simulators and AI-based surrogate models for climate modeling and weather forecasting
- Multi-agent systems based on LLMs for automating scientific research, code generation, and hypothesis validation
Achievements & Recognition
- Corresponding Member of the Russian Academy of Electrical Engineering (since 2024)
- Author of more than 80 peer-reviewed scientific publications, including 2 monographs
- Recipient of the Elsevier/JQSRT Richard Goody Award for contributions to atmospheric radiative transfer
- Supervisor and scientific advisor of undergraduate, graduate, and PhD research projects
- Active contributor to educational and science outreach activities