From cutting-edge research in temporal context neural networks to production-ready AI platforms. Explore my portfolio of AI innovations that bridge academic breakthroughs with real-world applications.
Temporal Context Neural Networks for Microscopy
Revolutionary approach combining U-Net architecture with multi-head attention mechanisms to leverage extended temporal context in single-molecule localization microscopy. Achieves breakthrough precision by using up to 50 frames of temporal information.
First application of Transformer attention mechanisms to microscopy data, enabling neural networks to correlate emitter positions across extended time sequences for unprecedented localization accuracy.
Efficiency scores on CRLB dataset
Research tools and production systems spanning computer vision to sports analytics
Compressed Sensing + Deep Learning
Breakthrough neural network architecture combining compressed sensing with deep learning for confocal lifetime localization microscopy. Published in BMC Bioinformatics with state-of-the-art precision for irregular PSFs.
Sports Analytics Platform
Production-grade AI platform for endurance sports analytics. Full-stack application demonstrating ML-powered performance prediction, training optimization, and real-time analytics for triathlon and cycling.
Biological Structure Analysis
Automated analysis tool for line-shaped biological structures with sub-pixel precision. Recognizes filaments in microscopy data and computes orientation, position, and morphological parameters for quantitative biology.
3D Point Cloud Renderer
High-performance OpenGL point cloud implementation with Python interface. Real-time rendering and filtering of large 3D SMLM datasets with millions of points, optimized for interactive scientific visualization.
Revolutionary temporal context neural networks for microscopy
Convolutional layers extract spatial features from individual frames before temporal processing
Transformer attention learns temporal correlations across 50-frame sequences
Probabilistic localization with uncertainty quantification for each emitter
| Method | Efficiency Score | % Below CRLB | Context Frames |
|---|---|---|---|
| AttentionUNet | 94 | 76.6% | 50 |
| DECODE | 91 | 39.9% | 3 |
| ThunderSTORM | 23 | 17.5% | 1 |
* CRLB = Cramer-Rao Lower Bound (theoretical minimum uncertainty). Higher percentages below CRLB indicate better precision.
Modern tools and frameworks powering AI innovation
Python
PyTorch
TensorFlow
CUDA
OpenCV
OpenGL
Django
PostgreSQL
Docker
FastAPI
Git
GitHub
Making AI research accessible to the scientific community
All research projects include complete implementations, trained models, and simulation engines. This ensures other researchers can reproduce, extend, and build upon the work.
Let's discuss how these AI innovations can advance your research or solve your technical challenges