Industry Experience

Comprehensive mathematical and computational solutions across multiple industries (references available on request)

Applied Physics

  • Data Processing and Imaging:Advanced Denoising, Imaging and Image Reconstruction Techniques.
  • Hydrodynamics: Mathematical modeling of fluid flow and transport phenomena in complex geometries.
  • Parameter Estimation and Inversion: Multi-parameter inversion techniques for attribute estimation in continous media.
  • Computational Physics: High-performance computing solutions (CUDA,MPI,OMP) for large-scale data processing workflows.
  • Uncertainty estimation: Stochastic techniques for uncertainty quantification and scenario building in complex systems.

Consumer Electronics

  • Touch Interface Optimization: Statistical inference methods for high-precision touch detection and gesture recognition in mobile devices and tablets.
  • Neuromotor & Wearable Technology: Real-time motion tracking algorithms, kinodynamic pose estimation, and sensor fusion for wearable health devices and fitness trackers.
  • Thermal Management Systems: Advanced control algorithms and optimization techniques for managing heat distribution in electronic and other consumer devices.
  • Computer Vision Applications: Machine learning algorithms for image processing, object recognition, and augmented reality features.
  • Biometric Authentication: Mathematical models for fingerprint recognition, facial recognition, and behavioral biometrics.
  • Signal Processing: Digital signal processing techniques for audio enhancement, noise reduction, and wireless communication optimization.

Medical Technology

  • Sensor Fusion: Mathematical algorithms for processing and interpretation of real-time signals from multiple medical probes.
  • Biostatistics: Data processing and statistical inference algorithms for analyzing biomedical data.
  • Medical Imaging: Computational methods for improving image quality and diagnostic accuracy in medical imaging systems.
  • Predictive Health Analytics: Machine learning and statistical models for early disease detection and treatment outcome prediction.

Scientific Computing

  • High-Performance Computing: Parallel algorithms and GPU acceleration using CUDA for solving large-scale computational problems.
  • Optimization Algorithms: Advanced mathematical optimization techniques including gradient-based methods, genetic algorithms, and stochastic optimization.
  • Machine Learning Infrastructure: Scalable ML pipelines for training and deploying complex models in production environments.
  • Numerical Methods: Development of robust numerical algorithms for solving differential equations, linear systems, and eigenvalue problems.
  • Statistical Computing: Advanced statistical methods for data analysis, uncertainty quantification, and probabilistic modeling.
Custom Solutions

Each project is approached with a tailored mathematical framework designed to address specific technical challenges. Our expertise spans from theoretical algorithm development to practical implementation and deployment. Contact us to discuss how we can apply advanced mathematical methods to solve your unique problems.