Physic-based machine learning methods are pivotal in addressing numerous bioimaging challenges:

  1) Physics-driven computational approach

     We employ a novel computational strategy that incorporates physics principles to enhance deep tissue imaging capabilities. 

  2) Image reconstruction

   Biophotonic imaging often grapples with incomplete data. Through machine learning, we're able to reconstruct high-resolution images, including phase and spectral details of biological entities, enabling us to achieve single-shot 3D and hyperspectral imaging. 

  3) Image classification

     Machine learning aids in the automatic categorization of different cells and their organelles, paving the way for rapid diagnostics. We're actively working on designing compact healthcare devices that come with an integrated optical imaging and classification system.

Mobile healthcare, often termed "mHealth", improve public health and healthcare services:

  1) Artificial Intelligence and Machine Learning approach

   Deep learning, reinforcement learning, and transfer learning are advancing various sectors including healthcare, finance, and automotive industries. We're actively working on designing algorithm to resolve various helath issuses such as Remote Patient Monitoring, Diagnosis, Treatment Recommendations, Mental Health, Personal Health assistants, Health ducation. The combination of AI, ML, and mobile technology holds significant promise for the future of healthcare, making health management more accessible, personalized, and efficient for users worldwide.

Rapid advancements in microfluidics, optics, and machine learning have given rise to an exciting domain where wearable devices can revolutionize the way we monitor and diagnose health conditions:Through cutting-edge technology and deep research, we aim to redefine the future of health monitoring and disease diagnosis. 

  1) Smart contact lens for diabetes management 

  Smart contact lenses for diabetes monitoring represent an innovative direction in non-invasive glucose detection and health tech. Smart contact lenses are designed to monitor glucose levels in tears as an alternative to traditional blood tests for people with diabetes. 

  2) Optofluidics-based Blood diagnostic technology

  Optofluidics merges the manipulation of light (optics) and liquids (microfluidics) at the microscale. When applied to blood diagnostics, it allows for precise, rapid, and on-chip analysis of blood samples. 

Biophotonics and light modulation plays a pivotal role in revolutionizing numerous areas in biology, medicine, and diagnostics: The use and manipulation of light in the biomedical field, particularly in therapies and deep tissue applications, is a testament to the versatility and potential of biophotonics. 

  1)  Low level laser theraphy (Photobiomodulation)

    Low-Level Laser Therapy (LLLT), also known as photobiomodulation, is a non-invasive procedure where low-level (low-power) lasers or light-emitting diodes (LEDs) are used to alter cellular function. We aim to focus on wound healing, pain management, and cell proliferation with visible, near-infrared (NIR) light, and SLM and DMD for selective stimulation. 

  2) Deep tissue light delivery

       Getting light deep into biological tissues is challenging due to scattering and absorption. However, it's crucial for many biomedical applications like deep tissue imaging and therapy