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Key Skills in AI:

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  • Image Segmentation: Proficiency in using U-Net and other CNN architectures for precise segmentation of microscopy images.

  • Feature Extraction and Analysis: Expertise in extracting and analyzing features from segmented images, such as cell morphology, nano- and micro-object parameters.

  • Classification and Clustering: Use of k-means clustering and decision trees for classifying uptake patterns and predicting transport rates.

  • Time-Series Analysis: Application of LSTM networks for analyzing time-dependent features and dynamic biological processes.

  • Model Training and Optimization: Experience in training, fine-tuning, and optimizing neural networks for specific tasks.

  • Data Augmentation: Use of data augmentation techniques to improve model robustness and generalization.

  • Integration of AI/ML with Experimental Systems: Combining AI/ML analysis with microfluidic systems and magnetic field studies for comprehensive biomedical research

Technical Proficiency

  • Programming Languages: Python, R, MATLAB.

  • Image Processing: OpenCV, ImageJ, FIJI.

  • Deep Learning Frameworks: TensorFlow, PyTorch, Keras.

  • Data Annotation: LabelMe, COCO dataset formatting.

  • Statistical Analysis: scikit-learn, R, SPSS.

  • Simulation Tools: COMSOL, ANSYS, custom FEM implementations.

AI

AI/ML Competencies
Generative Models for Tissue Engineering

Design of Generative Adversarial Networks (GANs) to simulate and optimize 3D tissue scaffolds or biomaterial structures.

Predictive modeling of cell growth patterns using recurrent neural networks (RNNs) to guide tissue regeneration strategies.

Nanoparticle Interaction Modeling

Molecular Dynamics-AI Integration: Combining AI with molecular dynamics simulations to predict nanoparticle-cell interactions, toxicity, and biodistribution.

Drug Delivery Optimization: Reinforcement learning (RL) frameworks to design nanoparticle coatings for targeted drug delivery and reduced immune response.

Multi-Modal Data Fusion

Integration of omics data (genomics, proteomics) with imaging data to correlate nanoparticle behavior with cellular responses.

Graph Neural Networks (GNNs) for analyzing biological networks (e.g., protein-protein interactions) in nanoparticle-mediated therapies.

Explainable AI (XAI) for Biomedical Insights

Application of SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to interpret AI predictions in tissue engineering and nanomedicine.

Visualization tools for tracking nanoparticle uptake and intracellular trafficking in real time.

Automated Experimental Design

Active Learning frameworks to prioritize high-impact experiments in tissue engineering (e.g., optimizing scaffold porosity).

Robotic Process Automation (RPA) for AI-guided nanoparticle synthesis and characterization.

Spatial Transcriptomics Analysis

AI-driven analysis of spatial gene expression patterns in tissues exposed to nanoparticles, enabling precision toxicity assessments.

© 2014 by Biodevicesystems sro

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