I'm a biochemist by training, and software engineer by trade!
My background has equipped me with the following skills!:
- Software
- Programming Languages: Python, JavaScript, PHP, & HTML.
- Libraries: PyTorch, TensorFlow, Keras, NumPy
- Chemistry
- Spectroscopy/Imaging/Analytical: 1H-NMR, 13C-NMR, COSY, NOESY, HPLC, MALDI-ToF, gas chromatography, IR & UV.
- Methods: Solid phase peptide synthesis, organic synthesis.
- Biology/Biochemistry
- Cell culture, confocal microscopy, gel electrophoresis, immunofluorescence, mouse surgery (non-survival)
My current research interests lie in the machine learning discipline of computer vision, and the development of domain-randomized data augmentation strategies. The projects I am currently most excited about are:
- oct_vesselseg: A Label-Free and Data-Free Synthesis Engine and Training Framework for Vascular Segmentation of sOCT Data with PyTorch.
- SynthShapes: A realistic 3D shape generator for biomedical image augmentation.
| Journal/Conference | Type | Title | Date |
|---|---|---|---|
| Medical Imaging with Deep Learning | Conference/Short Paper | A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data | July 2024 |
| Journal of the American Chemical Society | Journal Article | Site-Specific Photochemistry along a Protonated Peptide Scaffold | April 2024 |
| Journal of Vascular Surgery | Abstract | Development of a Machine Learning Algorithm to Automate Occlusion Detection in Lower Extremity Conventional Angiograms | April 2023 |
| International Symposium on Molecular Spectroscopy | Conference | Uv Photofragment Spectroscopy And Electronic Energy Transfer On A Peptide Scaffold: The Case Of Near-degenerate Uv Chromophores | June 2022 |
| Preprint | Preprint | Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Data | ---- |