Alex R. Sathler

Models & Methods: Dermis & Epidermis • Predictive diagnostics • FLAME microscopy • Content-aware image restoration • Fluorescence lifetime imaging • Computer vision

Languages & Frameworks: Python • Tensorflow • UNet • ONNX • MLFlow • MLOps • OpenCV • WSL • Jupyter • Command-line

Impact:

• Developed a robust denoising computer vision model and associated command-line tool that reduced acquisition timelines by 75% in FLAME imaging of epidermal fluorescence for predictive skin diagnostics.
• Created de-novo ML pipelines for dataset curation, model training, model evaluation, model performance tracking, and model deployment on intranet-based server infrastructure.

Models & Methods: Mammalian bioreactors • Bioprocess engineering • Instance segmentation • Product development • Digital staining • Cell viability • quantitative phase imaging (QPI) • CHO cells • HEK293 cells • E. coli • S. cerevisiae • UX design

Languages & Frameworks: Python • C++ • Batch • Bash • Tensorflow • PyTorch • Detectron2 • TensorRT • ONNX • Vertex AI • Lightning AI • OpenCV • WSL • Qt

Impact:

• Trained a 90% accurate computer vision model enabling real-time live cell viability quantification in mammalian cell bioreactor, allowing real-time culture monitoring and informing feedstock delivery to reduce waste by 3-5% in pharmaceutical manufacturing pipelines.
• Integrated product development into venture capital pitches and customer outreach, driving investment opportunities in a $1.5 billion market while increasing marketing engagement by 70X and establishing 3 OEM partnerships.
• Reported on model performance in prediction of cell viability across three eukaryotic and two bacterial, driving receipt of SBIR funding for commercialization of QPI as a novel label-free imaging modality.

Models & Methods: AI/ML • Computer vision • Phase separation • Aging • Murine primary cortical culture • Murine DRG culture • Airyscan confocal microscopy • Live & fixed immunofluorescence • MTT assay • Extracellular flux analysis • High-performance computing • Plasmid amplification • HEK293 culture • R-Loops

Languages & Frameworks: Python • Tensorflow • Bash • HPC • Slurm • OpenCV • ImageJ / FIJI • Jython

Impact:

• Revealed redox-mediated separation of mitochondrial DNA into transcriptionally deficient phenotypes, suggesting a novel explanation for aging-associated neuronal energy crises and cognitive declines.
• Achieved 96% pixel-level classification accuracy and 86% intersection-over-union semantic segmentation ML model with a small dataset of six manually annotated images.
• Elucidated novel mechanistic hypothesis for aging-associated reductions in mitochondrial transcription and translation through literature review to reveal a novel experimental avenue for a senior NIH investigator.

Models & Methods: Glioblastoma multiforme (GBM) • U87 cell line • Western blotting • Crystal violet assay • Genetic code expansion • Redox biology • Protein engineering • Confocal microscopy • Synthetic biology • Python • Jupyter • OpenCV

Languages & Frameworks: Python • Jupyter • OpenCV • Matplotlib

Impact:

• Unveiled a metabolic switch in Glioblastoma Multiforme (GBM) caused by the post-translational nitration of specific tyrosine residues in heat shock protein 90 (HSP90) that mimic the Warburg effect by reducing oxygen consumption in tumor periphery and increasing glycolysis in the tumor core.
• Practiced protein purification for the isolation of HSP90 constitutively nitrated through genetic code expansion technology to unveil the effects of selective tyrosine nitration on tumor cell survival.
• Built a qualitative suite for protein distribution validation in any 3D culture model, enabling cost-effective visualization of spatio-temporal gene expression in cell biology, pathology, and tissue modeling.
• Established low-cost method to understand protein distrubution in three dimensions, reducing barriers to discovery given inacessibility of expensive software analysis suites.

Languages: Python • SQL • R

Frameworks: AWS • GCP • Jupyter • Pandas • RStudio • VCF

Domains: Bioinformatics • Oncology • Cloud Computing • Statistics • Visualization

Destription: Implemented genome fingerprinting and comparison into a custom tissue processing pipeline for patient-personalized CAR-T therapy in the nation's 11th largest health system. Integrated into existing AWS workflow as a quality measure to conclusively determine whether a tumor sample comes from a patient or not.

1. Created and fingerprints from the hundreds of tumor & normal tissue samples.
2. Assessed quality control efficacy with statistical and visualization methods.
3. Enabled integration into AWS pipeline for high-throughput quality control.