We are experts in laser-tissue interactions and biomedical optical imaging, providing extensive experience in the research and industrial applications of related technologies. Our competences include, but are not limited to

• Optical responses of tissues, including the following phenomena
  • Absorption
  • Fluorescence
  • Elastic scattering
  • Raman scattering
  • Multi-photon scattering
• The propagation of light in tissues modelled in ballistic, diffusion, and hybrid regimes
• Bio-optical imaging modalities based on
  • Spectrally- and temporally-resolved fluorescence
  • Raman spectroscopy
  • Hyperspectral and multispectral imaging
• High-power pulsed laser-tissue interactions, including features of
  • Dynamic optical properties
  • Laser generated heat transfer
  • Thermodynamic response of tissues
  • Thermo-mechanical properties of tissues
  • Laser ablation of tissues
  • Laser ablation plume dynamics
  • Photoacoustic effects
  • Plasma dynamics
  • Photochemical effects
• Laser devices based on solid-state, fiber, and semiconductor active media
• Optical system design, including
  • Illumination sources
  • Detection devices
  • Light delivery and collection optics

We assist our clients in clarifying their R&D goals and highlight various technical and commercial aspects of optical and laser-based tools relevant to clients’ specific R&D challenges. Based on the problem definition and available resources, we produce conceptual solutions and practical designs of proof-of-concept devices. This phase may be followed by building a prototype and a practical demonstration. We can aid further by helping to translate accepted prototype solutions to our clients’ development and production teams.

We also specialize in data science within R&D based on physical sciences and engineering. Dealing with data in these settings poses additional challenges compared to standard data workflows for two main reasons.

First, the data flowing from experimental measurements to data models is subject to uncertainties inherent to the R&D process. Data veracity, data structuring, and noise characterization are nontrivial and require knowledge of experimental intricacies to successfully undertake. Second, an R&D process is iterative by nature, so data analytics and predictive analysis are usually not ends in themselves but instead are tied to a recursive experimental design. For these reasons, such an effort requires comprehensive expertise in both experimental science and data science methods. Therefore, we work closely with our clients to establish a firm grasp on experimental specifics before proposing and implementing a data modelling process. Our tools of the trade include the following:

• Statistical analysis of experimental data, including
  • Statistical design of experiments
  • Error analysis
  • Statistical tests
  • Regression analysis
  • Multivariate statistics
• Machine learning models, including
  • Classification algorithms
  • Clustering algorithms
  • Artificial neural networks
  • Decision trees
  • Support vector machines
  • Genetic algorithms
  • Bayesian networks