Vikas Pandey

I am a scientist working at the intersection of biomedical imaging hardware, data science, and deep learning. I specialize in building imaging systems and computational pipelines that turn complex optical measurements into clinically actionable insights, and I enjoy developing state-of-the-art biophotonics computational models alongside translational research that moves these tools from the lab into real-world use.

My focus is on physics-guided deep learning models with hardware-accelerated deployment for developing efficient, user-friendly, high-impact medical technologies (Imaging Technogies) that address critical challenges in oncology such as in image-guided surgery and infectious disease diagnostics.

Research Focus & Core Expertise

• Computational Biophotonics: Developing physics-guided neural networks, deep learning models, and advanced statistical frameworks to extract precise parameters from low-photon, high-noise functional data.

• Advanced Optical Modalities: Designing and deploying time-resolved optical imaging, single-pixel imaging, fluorescence molecular tomography, and mesoscale light-sheet platforms for paired Near-Infrared (NIR) and Shortwave-Infrared (SWIR) imaging.

• Translational Medical Devices: Translating complex laboratory instrumentation into certified, field-deployable clinical tools.

Recent Progress & Key Achievements

• Open-Source Software Development: Authored and published on PyPI, an open-source Python package designed for the unified, scalable processing and analysis of Fluorescence Lifetime Imaging (FLI) data.

• Amortized Bayesian Inference: Developed a breakthrough computational framework utilizing amortized inference for exceptionally fast and robust lifetime parameter estimation in low-photon FLI regimes.