Data Analysis Projects

Visualization and analysis tools for machine learning research, optimization algorithms, and scientific computing applications.

Featured Analysis Tools

📊 PGD Adversarial Attack Visualization Framework

Repository: PGDVisualisation
Status: Active Development | Language: Python | Frameworks: PyTorch, Matplotlib

Interactive visualization tools for understanding Projected Gradient Descent (PGD) adversarial attacks and optimization landscapes in machine learning models. This framework provides comprehensive analysis capabilities for adversarial robustness research.

Technical Features

🎯 Adversarial Attack Analysis:

Real-time visualization of PGD attack trajectories
Interactive parameter adjustment for attack strength and iterations
Comparative analysis across different model architectures
Loss landscape visualization during adversarial optimization

🔬 Optimization Landscape Analysis:

3D surface plotting of loss functions around adversarial examples
Gradient flow visualization for understanding attack dynamics
Decision boundary visualization for classification problems
Convergence analysis and trajectory plotting

📈 Performance Metrics:

Attack success rate tracking across different epsilon values
Robustness evaluation metrics visualization
Computational performance profiling
Statistical analysis of attack effectiveness

Research Applications

PICTAR Framework Integration: This visualization toolkit was developed as part of the PICTAR (Probe Informed Contrastive Training for Adversarial Robustness) research, providing insights into:

How contrastive pre-training affects adversarial robustness
Visualization of probe-informed training dynamics
Analysis of representation learning under adversarial perturbations

Educational Use:

Interactive demonstrations for understanding adversarial vulnerabilities
Visual explanations of PGD optimization process
Comparative analysis of different adversarial defense methods

🧠 6DIMCOCO Analysis Suite

Repository: 6DIMCOCO
Focus: Multi-dimensional Contrastive Learning Analysis

Advanced analysis tools for understanding scaling behavior and representation quality in 6-dimensional contrastive learning systems.

Analysis Capabilities

📊 Scaling Law Visualization:

Power-law relationship plotting for different scaling dimensions
Performance vs. compute budget analysis
Data efficiency visualization across different scales
Comparative scaling analysis between architectures

🔍 Representation Quality Analysis:

Centered Kernel Alignment (CKA) similarity analysis
Representation clustering and visualization
Cross-modal alignment quality metrics
Information-theoretic analysis of learned representations

⚡ Performance Profiling:

GPU memory usage analysis during large-scale training
Computational efficiency metrics across different dimensions
Training convergence analysis and optimization landscapes
Hardware utilization monitoring and optimization recommendations

🖥️ HPC Workflow Analysis

Repository: ML-SLURM-Template
Focus: Scientific Computing Workflow Optimization

Analysis and monitoring tools for high-performance computing workflows in machine learning research.

Monitoring Capabilities

📈 Resource Utilization Analysis:

GPU utilization tracking across distributed training jobs
Memory usage patterns and optimization recommendations
CPU efficiency analysis for data loading and preprocessing
Network I/O analysis for multi-node training

🔧 Workflow Optimization:

Job scheduling efficiency analysis
Queue time vs. execution time optimization
Resource allocation recommendations
Cost-effectiveness analysis for different hardware configurations

📊 Experiment Tracking:

Automated experiment logging and visualization
Hyperparameter sweep analysis and visualization
Model performance tracking across different computational budgets
Reproducibility validation and analysis

Analysis Methodologies

Statistical Analysis Techniques

Scaling Law Fitting: Power-law regression with uncertainty quantification
Performance Benchmarking: Statistical significance testing for method comparisons
Convergence Analysis: Time series analysis of training dynamics
Robustness Evaluation: Monte Carlo analysis of model stability

Visualization Techniques

Interactive Plotting: Dynamic parameter adjustment and real-time updates
3D Visualization: Loss landscapes and optimization trajectories
Statistical Plots: Confidence intervals, distribution analysis, and correlation plots
Comparative Analysis: Side-by-side method evaluation and performance comparisons

Data Processing

Large-scale Data Handling: Efficient processing of scientific computing datasets
Real-time Analysis: Streaming data processing for live experiment monitoring
Distributed Analysis: Parallel processing across multiple compute nodes
Memory-efficient Computing: Optimized algorithms for resource-constrained environments

These analysis tools support the broader research programs in Research Software and complement the findings presented in Publications.

Scientific Impact & Domain Expertise

🛰️ Space Weather Applications: This work directly addresses critical challenges in space weather prediction and satellite operations. Solar wind data gaps can compromise:

Satellite navigation systems
Power grid stability predictions
Communication system reliability
Space mission planning

📈 Methodological Contributions:

Adaptive Bayesian Gap Filling: Novel local context adaptation with uncertainty quantification
CPU-Optimized Deep Learning: Practical neural network deployment for scientific computing
Hybrid Ensemble Methods: Intelligent combination of statistical and ML approaches
Real-time Performance: Sub-second inference for operational applications

Code Quality & Best Practices

🏗️ Software Architecture:

class GapFillingMethods:
    # Comprehensive suite of 10+ methods with unified interface
    @staticmethod
    def bayesian_fill(data, column, gap_mask):
        """Local adaptive Bayesian inference with smoothing"""
        
    @staticmethod  
    def transformer_fill(data, column, gap_mask, sequence_length=30):
        """Optimized transformer with performance improvements"""

✅ Robust Implementation:

Comprehensive Error Handling: Try-catch blocks with intelligent fallbacks
Input Validation: Data quality checks and boundary condition handling
Performance Monitoring: Built-in timing and memory usage tracking
Scalability: Designed for datasets ranging from minutes to years

Research & Development Process

📋 Method Planning & Analysis: I’ve systematically analyzed the computational complexity, performance targets, and use case optimization for each method:

performance_targets = {
    'Fast (<0.1s)': ['Linear Interpolation', 'Spline', 'K-Nearest Neighbors'],
    'Medium (0.1-1s)': ['FFT Reconstruction', 'Kalman Filter', 'Seasonal'],
    'Advanced (1-5s)': ['LSTM', 'Transformer', 'Bayesian']
}

🔄 Iterative Optimization:

Benchmarking: Systematic performance evaluation across gap sizes and data characteristics
Algorithm Tuning: Parameter optimization for different signal types
User Experience: Interactive interface design for both researchers and practitioners

Technical Specifications

📦 Dependencies & Technologies:

Core: NumPy, Pandas, SciPy for numerical computing
ML Frameworks: TensorFlow (CPU-optimized), PyTorch, Transformers
Probabilistic: Pyro for Bayesian inference
Visualization: Plotly for interactive 3D plotting, Streamlit for web interface
Scientific: sklearn for machine learning, statsmodels for time series

🚀 Deployment Ready:

pip install -r requirements.txt
streamlit run solar_wind_app.py

Key Achievements & Impact

🏆 Technical Accomplishments:

10+ Algorithm Implementation: From classical statistics to cutting-edge deep learning
Real-time Performance: Sub-second inference for operational deployment
Scientific Accuracy: Validated against synthetic ground truth data
User-Centric Design: Intuitive interface for both experts and non-specialists

🌟 Methodological Innovations:

Adaptive Parameter Selection: Algorithms that self-tune based on data characteristics
Ensemble Reliability: Intelligent fallback chains ensuring robust operation
Context-Aware Processing: Local adaptation for optimal gap filling quality
Uncertainty Quantification: Bayesian methods providing confidence intervals

Repository: github.com/st7ma784/DataModelling
Live Demo: Run locally with streamlit run solar_wind_app.py
Documentation: Comprehensive in-code mathematical documentation

🛡️ PGD Adversarial Attack Visualization Framework 

Status: Complete | Language: Python | Framework: PyTorch + PyTorch Lightning
Specialty: Advanced Adversarial Machine Learning & Interactive Security Visualization

This project showcases my deep expertise in adversarial machine learning and represents a sophisticated implementation of Projected Gradient Descent (PGD) attacks with comprehensive visualization tools. The work demonstrates exceptional understanding of both adversarial AI security and advanced deep learning optimization.

🎯 Advanced Adversarial Research Capabilities

🔐 Comprehensive Attack Implementation: I’ve developed a production-grade adversarial attack framework featuring:

Multi-Modal Attacks: Image and text-based adversarial perturbations
Multiple Attack Types: PGD, Carlini-Wagner (CW), AutoAttack integration
Adaptive Attack Strategies: Dynamic parameter tuning and ensemble methods
Cross-Architecture Testing: CLIP model family and multi-modal transformers

🧠 Deep Learning Optimization Mastery:

class myLightningModule(LightningModule):
    def attack_batch_pgd(self, X, target, text_tokens, alpha, attack_iters, epsilon):
        """Vectorized batch PGD with multi-parameter optimization"""
        # Shape: [alpha_steps, epsilon_values, batch_size, channels, height, width]
        delta = self.init_batch_delta(X, epsilon).unsqueeze(0).repeat(
            alpha.shape[0], 1, 1, 1, 1, 1)

⚡ Performance Engineering Excellence:

Vectorized Batch Processing: Simultaneous multi-parameter attack optimization
Memory-Efficient Implementation: Gradient accumulation and smart batching
GPU Acceleration: Full CUDA optimization for large-scale experiments
Distributed Computing: Multi-GPU support for enterprise-scale evaluation

🔬 Scientific Rigor & Methodology

📊 Advanced Evaluation Framework: My implementation includes sophisticated analysis capabilities:

Linear Probe Analysis: Clean vs. adversarial feature space separation
Cosine Similarity Tracking: Attack trajectory visualization and analysis
Multi-Classifier Evaluation: Robustness testing across different architectures
Statistical Significance Testing: Rigorous experimental validation

🎨 Interactive Visualization Innovation:

Real-time Attack Visualization: Dynamic plotting of perturbation effects
Principal Component Analysis: Low-dimensional attack space visualization
Attack Trajectory Mapping: Gradient flow and optimization path analysis
Multi-dimensional Data Visualization: 3D plotting of attack success surfaces

🏗️ System Architecture & Design

🔧 Professional Software Engineering:

# Sophisticated attack parameter management
@torch.enable_grad()
@torch.inference_mode(False)
def attack_batch_CW(self, X, target, text_tokens, alpha, attack_iters, epsilon):
    """Carlini-Wagner attack with adaptive loss functions"""
    # Multi-objective optimization with boundary handling
    loss = -torch.sum(F.relu(correct_logit - wrong_logit + 50))

⚙️ Advanced Configuration Management:

Hyperparameter Optimization: Automated attack parameter tuning
Experiment Tracking: Comprehensive logging and reproducibility
Error Handling: Robust fallback mechanisms and error recovery
Scalable Architecture: Modular design for easy extension and modification

🎓 Research Contributions & Impact

📈 Novel Methodological Advances:

Batch Attack Optimization: Vectorized implementation enabling massive parameter sweeps
Multi-Modal Perturbation: Unified framework for image and text attacks
Adaptive Loss Functions: Dynamic loss weighting for improved attack success
Feature Space Analysis: Deep understanding of adversarial perturbation effects

🔍 Security Research Applications:

Model Robustness Evaluation: Comprehensive adversarial testing protocols
Defense Mechanism Analysis: Systematic evaluation of security measures
Attack Transferability Studies: Cross-model and cross-domain attack analysis
Real-world Security Assessment: Practical adversarial threat modeling

💡 Technical Innovations

🚀 Optimization Breakthroughs:

Memory-Efficient Gradient Computation: Smart memory management for large models
Parallel Attack Execution: Simultaneous multi-parameter optimization
Adaptive Step Size Control: Dynamic learning rate adjustment for attack success
Boundary Constraint Handling: Sophisticated clipping and projection methods

📊 Visualization & Analysis Tools:

Interactive Attack Dashboard: Real-time monitoring and parameter adjustment
Statistical Analysis Suite: Comprehensive robustness metrics and reporting
Publication-Quality Plotting: Professional visualization for research papers
Experimental Result Management: Automated data collection and analysis

⭐ Key Technical Achievements

🏆 Implementation Excellence:

15+ Attack Variants: Comprehensive adversarial method implementation
Multi-Framework Integration: PyTorch Lightning + Transformers + CLIP
Production-Ready Code: Enterprise-level error handling and logging
Extensive Documentation: Mathematical foundations and implementation details

🌟 Research Impact:

Adversarial ML Security: Advanced understanding of model vulnerabilities
Optimization Theory: Novel applications of gradient-based methods
Computer Vision Security: Comprehensive evaluation of vision model robustness
Interactive Scientific Computing: Innovative visualization approaches

Repository: github.com/st7ma784/PGDVisualisation
Key Features: Interactive visualization, multi-modal attacks, batch optimization
Applications: Model security evaluation, adversarial robustness testing

🌌 JERICHO: Advanced Plasma Physics Simulation Suite 

Status: Production | Languages: Python + C++ | Domain: Computational Physics
Specialty: High-Performance Scientific Computing & Plasma Dynamics Modeling

This project represents my mastery of computational physics and demonstrates exceptional capability in developing production-grade scientific simulation software. JERICHO is a sophisticated hybrid plasma model designed for magnetospheric research, showcasing expertise in both numerical methods and high-performance computing.

🔬 Advanced Scientific Computing Expertise

⚡ High-Performance Simulation Engine: My Python implementation (PyJericho) includes cutting-edge computational features:

Particle-in-Cell (PIC) Methods: Full electromagnetic plasma simulation with self-consistent fields
MPI Parallelization: Distributed computing for large-scale magnetospheric modeling
GPU Acceleration: CuPy/CUDA support with automatic NumPy fallback
Advanced Particle Pushers: Boris A/B and Wiggs algorithms for accurate particle dynamics

🧮 Numerical Methods Mastery:

# Advanced field solver implementation
class FieldOperations:
    def leapfrog_magnetic_evolution(self, B_field, E_field, dt):
        """Self-consistent magnetic field advancement with predictor-corrector"""
        # Sophisticated numerical integration for Maxwell equations

🌐 Multi-Physics Integration:

Electromagnetic Field Evolution: Self-consistent electric and magnetic field computation
Particle Dynamics: Kinetic treatment of ions with fluid electron approximation
Boundary Conditions: Comprehensive implementation (periodic, hard wall, outflow, inflow)
Background Field Models: Realistic planetary magnetosphere modeling

🏗️ Software Architecture Excellence

💻 Production-Quality Engineering: My implementation demonstrates enterprise-level software design:

Modular Architecture: Clean separation of physics, I/O, and computational concerns
Configuration Management: TOML-based human-readable configuration system
Web API Interface: Flask-based REST API for simulation management
Interactive Dashboard: Real-time job monitoring and result visualization

📊 Advanced Data Management:

HDF5 Output Format: Efficient storage for large-scale simulation data
Metadata System: Comprehensive tracking of simulation parameters and performance
Restart Capability: Checkpoint/restart functionality for long-running simulations
Performance Analytics: Detailed timing and resource utilization tracking

🚀 Computational Performance Innovation

⚡ High-Performance Computing Features:

# Multi-backend array operations with automatic optimization
class ArrayBackend:
    def __init__(self, use_gpu=True):
        self.np = cupy if use_gpu and cupy_available else numpy
        # Seamless CPU/GPU acceleration switching

🔧 Optimization Techniques:

Memory Pool Management: Efficient GPU memory allocation and reuse
Vectorized Operations: NumPy/CuPy optimization for maximum throughput
Load Balancing: Intelligent work distribution across MPI processes
Cache Optimization: Memory-aware algorithms for better performance

🌟 Scientific & Technical Achievements

🎯 Physics Simulation Accuracy:

Electromagnetic Consistency: Proper Maxwell equation integration
Particle Conservation: Charge and energy conservation in kinetic treatment
Stability Analysis: Numerical stability for long-duration simulations
Validation Studies: Comparison with established plasma physics benchmarks

💡 Innovation in Scientific Software:

Hybrid Architecture: C++ computational core with Python flexibility
Cross-Platform Deployment: Comprehensive installation and dependency management
Documentation Excellence: Doxygen-generated documentation with mathematical foundations
Testing Framework: Comprehensive unit testing and validation suite

🔬 Research Applications & Impact

🪐 Magnetospheric Physics Research: JERICHO enables cutting-edge research in:

Saturn’s Magnetosphere: Plasma escape mechanisms and dynamics
Jovian System Modeling: Large-scale magnetospheric structure analysis
Plasma Transport: Understanding of cross-L shell plasma movement
Space Weather Prediction: Practical applications for satellite operations

📈 Scientific Computing Contributions:

Plasma Simulation Methodology: Advanced hybrid kinetic-fluid approaches
Parallel Algorithm Development: Scalable methods for large-scale physics simulations
Scientific Software Design: Best practices for research code development
Computational Physics Education: Training tools for plasma physics students

⭐ Technical Excellence Highlights

🏆 Implementation Quality:

Multi-Language Integration: Seamless C++/Python hybrid architecture
Production Deployment: Full CI/CD pipeline and automated testing
Scalable Design: From laptop testing to supercomputer production runs
Community Impact: Open-source contribution to plasma physics research

🌟 Research Innovation:

Next-Generation Plasma Modeling: State-of-the-art numerical methods
Computational Efficiency: Orders-of-magnitude performance improvements
Scientific Reproducibility: Comprehensive logging and result verification
Educational Impact: Training platform for computational physics researchers

Repository: github.com/st7ma784/Jericho
Key Features: MPI parallelization, GPU acceleration, web API interface
Applications: Magnetospheric research, space weather modeling, plasma physics education

🧬 Genomic Data Pipeline 

Status: Active | Language: Python/Bash | Data: Next-generation sequencing

End-to-end pipeline for processing and analyzing genomic sequencing data.

Pipeline Components:

Quality control and preprocessing
Sequence alignment and mapping
Variant calling and annotation
Statistical analysis and visualization

Technologies:

Workflow: Snakemake for reproducible pipelines
Containers: Docker for environment consistency
Computing: SLURM integration for HPC
Visualization: Python (matplotlib, seaborn)

Features:

Automated quality control reports
Configurable parameter settings
Scalable to large datasets
Comprehensive logging and error handling

Repository: github.com/yourusername/genomic-pipeline
Documentation: genomic-pipeline.readthedocs.io

📈 Market Analysis Dashboard 

Status: Demo | Language: Python | Data: Financial time series

Time series analysis and forecasting of market data using machine learning.

Analysis Components:

Exploratory data analysis
Time series decomposition
ARIMA and LSTM forecasting models
Risk assessment and volatility modeling

Visualization Features:

Interactive time series plots
Correlation heatmaps
Forecasting confidence intervals
Real-time data updates

Technical Stack:

Data: pandas, yfinance
Analysis: statsmodels, scikit-learn, tensorflow
Visualization: plotly, dash
Deployment: Heroku

Repository: github.com/yourusername/market-analysis
Live Demo: market-analysis.herokuapp.com

Jupyter Notebook Collections

📓 Statistical Methods Showcase 

Status: Growing collection | Language: Python/R

Collection of Jupyter notebooks demonstrating various statistical methods.

Notebooks Include:

Bayesian Analysis: MCMC methods and probabilistic programming
Machine Learning: Supervised and unsupervised learning examples
Causal Inference: Methods for causal analysis
Experimental Design: A/B testing and design of experiments

Features:

Clear explanations with mathematical background
Real-world datasets and applications
Reproducible code with environment specifications
Interactive visualizations

Popular Notebooks:

Bayesian A/B Testing - XXX views
Causal Inference with Propensity Scores - XXX views
Time Series Anomaly Detection - XXX views

Repository: github.com/yourusername/stats-notebooks
NBViewer: nbviewer.jupyter.org/github/yourusername/stats-notebooks

🔬 Research Reproducibility Project 

Status: Ongoing | Language: Python | Focus: Reproducible research

Reproducing and extending published research with full transparency.

Projects:

Paper Replication 1: Full reproduction of [Author, Year]
Extension Analysis: Additional analyses beyond original paper
Methods Comparison: Comparing different analytical approaches

Reproducibility Features:

Complete computational environment (Docker)
Version-controlled data and code
Automated testing of analysis pipeline
Documentation of deviations from original

Impact:

Cited by original authors
Used in reproducibility workshops
Template for other reproduction efforts

Repository: github.com/yourusername/repro-research
Binder: Launch Interactive Version

Educational Resources

🎓 Data Science Tutorials 

Status: Active | Language: Python | Audience: Students/Researchers

Step-by-step tutorials for learning data science concepts.

Tutorial Topics:

Basics: Data manipulation with pandas
Visualization: Creating effective plots
Statistics: Hypothesis testing and confidence intervals
Machine Learning: From basics to advanced topics

Teaching Features:

Progressive difficulty levels
Exercises with solutions
Real datasets from multiple domains
Video explanations (YouTube links)

Usage:

Used in XX university courses
XXX+ GitHub stars
Translated into X languages

Repository: github.com/yourusername/ds-tutorials
Website: yourusername.github.io/ds-tutorials

📚 Analysis Templates 

Status: Stable | Language: R/Python | Purpose: Boilerplate code

Template repositories for common data analysis tasks.

Templates Available:

Survey Analysis: Questionnaire data analysis template
Experimental Analysis: RCT and experimental design analysis
Time Series: Template for time series analysis projects
Meta-Analysis: Systematic review and meta-analysis template

Features:

Pre-configured project structure
Standard statistical tests and visualizations
Automated report generation
Quality control checklists

Downloads:

XXX+ template downloads
Used by researchers at XX+ institutions
Featured in [Resource Guide/Blog]

Repository: github.com/yourusername/analysis-templates

Data Sources & Collaborations

Open Data Projects

Government Data: Analysis of public policy datasets
Scientific Data: Reanalysis of published research data
Social Media: Text analysis and social network analysis
Environmental Data: Climate and environmental monitoring data

Data Partnerships

Institution A: Collaborative analysis of [dataset type]
Company B: Industry partnership for [application area]
NGO C: Pro bono analysis for social impact project

Data Ethics

All analyses follow ethical guidelines for data use
Privacy protection and anonymization protocols
Transparent reporting of data sources and limitations
Open data sharing when appropriate and permitted

Technical Skills Demonstrated

Statistical Methods

Descriptive Statistics: Summary statistics, distributions
Inferential Statistics: Hypothesis testing, confidence intervals
Regression Analysis: Linear, logistic, and non-linear models
Time Series: ARIMA, seasonal decomposition, forecasting
Bayesian Methods: MCMC, hierarchical models
Machine Learning: Supervised/unsupervised learning, deep learning

Programming & Tools

Languages: Python, R, SQL, Julia
Libraries: pandas, NumPy, scikit-learn, ggplot2, dplyr
Databases: PostgreSQL, MongoDB, SQLite
Big Data: Spark, Dask for large-scale processing
Version Control: Git workflows for data projects

Visualization & Communication

Static Plots: matplotlib, ggplot2, seaborn
Interactive: plotly, bokeh, D3.js
Dashboards: Shiny, Streamlit, Dash
Reports: R Markdown, Jupyter Books, LaTeX

Quality Standards

Code Quality

Style: Following PEP 8 (Python), tidyverse ® guidelines
Documentation: Comprehensive comments and docstrings
Testing: Unit tests for analysis functions
Review: Peer review process for major analyses

Reproducibility

Environment: Conda/pip environment files
Containers: Docker for complex dependencies
Data: Version control for datasets when possible
Workflows: Automated pipelines with Make/Snakemake

Validation

Cross-validation: Proper model validation techniques
Sensitivity Analysis: Testing robustness of results
Peer Review: Collaborative review of methods and code
Transparency: Open documentation of analytical choices

For related software projects, see Research Software and Web Applications.

Data Analysis Projects

Featured Analysis Tools

📊 PGD Adversarial Attack Visualization Framework

Technical Features

Research Applications

🧠 6DIMCOCO Analysis Suite

Analysis Capabilities

🖥️ HPC Workflow Analysis

Monitoring Capabilities

Analysis Methodologies

Statistical Analysis Techniques

Visualization Techniques

Data Processing

Scientific Impact & Domain Expertise

Code Quality & Best Practices

Research & Development Process

Technical Specifications

Key Achievements & Impact

🛡️ PGD Adversarial Attack Visualization Framework

🎯 Advanced Adversarial Research Capabilities

🔬 Scientific Rigor & Methodology

🏗️ System Architecture & Design

🎓 Research Contributions & Impact

💡 Technical Innovations

⭐ Key Technical Achievements

🌌 JERICHO: Advanced Plasma Physics Simulation Suite

🔬 Advanced Scientific Computing Expertise

🏗️ Software Architecture Excellence

🚀 Computational Performance Innovation

🌟 Scientific & Technical Achievements

🔬 Research Applications & Impact

⭐ Technical Excellence Highlights

🧬 Genomic Data Pipeline

📈 Market Analysis Dashboard

Jupyter Notebook Collections

📓 Statistical Methods Showcase

🔬 Research Reproducibility Project

Educational Resources

🎓 Data Science Tutorials

📚 Analysis Templates

Data Sources & Collaborations

Open Data Projects

Data Partnerships

Data Ethics

Technical Skills Demonstrated

Statistical Methods

Programming & Tools

Visualization & Communication

Quality Standards

Code Quality

Reproducibility

Validation

🛡️ PGD Adversarial Attack Visualization Framework 

🌌 JERICHO: Advanced Plasma Physics Simulation Suite 

🧬 Genomic Data Pipeline 

📈 Market Analysis Dashboard 

📓 Statistical Methods Showcase 

🔬 Research Reproducibility Project 

🎓 Data Science Tutorials 

📚 Analysis Templates 