Research & Engineering Portfolio

Breakthrough Achievement: Developed soil-specific Random Forest calibration achieving 34% accuracy improvement for sandy textures in SAR-based soil moisture retrieval.

Scientific Impact:

• Analyzed 677 paired SAR-soil moisture observations across 5 soil textures
• Discovered counterintuitive vegetation enhancement effect (r=0.743 vegetated vs r=0.380 bare soil)
• Developed information proxy framework predicting calibration success
• Publication: Submitted to Remote Sensing Letters (2024)

Innovation: Combined physics-based understanding with machine learning to solve operational remote sensing challenges.

Dataset Contribution: Curated and analyzed 10-year, 15-minute resolution soil moisture and temperature observations from semi-arid agricultural catchment.

Key Discoveries:

• Identified systematic increase in thermal inversions (+2.41%/year)
• Documented 79.3% percolation probability across 916 rainfall events
• Revealed “dry-soil advantage” for deep infiltration
• Detected emerging water stress trends (+4.6%/year)

Impact: Provides critical tropical land surface data filling gap in global soil moisture networks.
Publication: In preparation for Scientific Data (2025)

System Integration: Coupled 1D crop models (C/C++) with 3D Functional-Structural Plant Models (Fortran/CPlantBox), enabling realistic simulation of soil-plant-atmosphere dynamics.

Technical Achievement:

• Implemented loose-coupling data exchange with timestep synchronization
• Created mechanistic sink-term for AgroC using dynamic root architecture
• Built cross-platform PyQt5 GUI for model configuration
• Packaged workflows with Docker for HPC deployment

Scientific Communication:

• Led monthly project coordination
• Authored review paper on model coupling & digital twins (In Silico Plants, under review)
• Contributed book chapters on LLMs and UAVs in agriculture

Reproducible Science: Created containerized environments for plant modeling frameworks, dramatically improving accessibility and reproducibility.

CPlantBox GUI Docker:

• Integrated VNC viewer for 3D root visualization
• Bundled scientific packages (NumPy, SciPy, VTK)
• DockerHub: satraox/cplantbox-gui

DuMuX-ROSI-Jupyter:

• Pre-configured Jupyter environment for soil-plant simulations
• Coupled multi-phase flow (DuMuX) with root models (ROSI)
• DockerHub: satraox/dumux-rosi-jupyter

Thesis Title: “Investigation of signatures of plant roots from non-invasive geo-electrical measurements”

Research Problem: How do plant roots influence soil electrical properties? Can we “see” roots without digging them up?

Breakthrough Achievement:
Created world’s first coupled hydro-geophysical framework linking root architecture, water uptake, and electrical signatures. This enables non-invasive root phenotyping at scales from centimeters to field plots.

Why This Matters:

• 🌾 Agriculture: Real-time root monitoring without destructive sampling
• 🔬 Plant Breeding: Rapid phenotyping for drought tolerance
• 🌍 Climate: Quantify carbon sequestration in root systems
• 💧 Water Management: Optimize irrigation based on root activity

Funding: Belgian FNRS (Fonds National de la Recherche Scientifique) - Grant T.1088.15
Defense: 2020, UCLouvain (during COVID-19 pandemic)
Supervisors: Prof. Mathieu Javaux (UCLouvain), Prof. Frédéric Nguyen (ULiège), Prof. Sarah Garré (Gembloux)

Impact:

• 4 peer-reviewed papers (135+ citations)
• 5 international conference presentations
• Multiple international collaborations (Germany, Austria, Israel)
• Established new research field: Computational Root Geophysics

Consulting Applications: Precision agriculture, breeding programs, environmental monitoring, AgTech startups

Expand below to explore 5 thesis components →

Research Question: Can we build a mechanistic model incorporating BOTH root architecture and water dynamics?

Innovation - Coupled Framework:

• Integrated R-SWMS (root water uptake) with PyGIMLi (electrical modeling)
• 3D finite element models with 500,000+ tetrahedral elements
• Separated direct (root conductivity) vs indirect (moisture) electrical effects
• First model to achieve this level of physical realism

Key Discovery:
Roots impact petrophysical relations - The standard Archie’s Law doesn’t work when roots are present! We quantified exactly how roots modify the soil’s electrical behavior.

Technical Achievement:

• Custom mesh generation pipeline (Gmsh + Python automation)
• Forward ERT simulations validated against rhizotron experiments
• Computed apparent vs. effective electrical conductivities
• Developed upscaling methodology for field applications

Validation:
Synthetic measurements matched real rhizotron data for maize and lupin under controlled conditions.

Commercial Value:
Framework can be adapted to ANY crop-soil system. Companies can use this to:

• Design better root monitoring sensors
• Interpret field ERT data correctly
• Predict irrigation needs from electrical measurements

Publication: Vadose Zone Journal (2019), 35 citations
Thesis Chapter: 3
Tools: Python, PyGIMLi, Gmsh, R-SWMS, EIDORS

Research Question: Does electrical anisotropy (direction-dependent conductivity) contain information about root architecture?

Breakthrough Discovery:
YES! Electrical anisotropy is a fingerprint of root organization. This was the first mechanistic proof that geoelectrical measurements encode 3D structural information.

Methodology:

• Generated synthetic root architectures using C-Rootbox (monocots vs. dicots)
• Computed direction-dependent conductivity tensors
• Extracted geometrical indices (convex hull, depth, width, tortuosity)
• Applied machine learning (PCA + k-NN classification)

Key Results:

• Magnitude component (low frequency): Water uptake patterns
• Phase component (high frequency): Root architecture directly
• Anisotropy factor: Strong correlation with root geometry indices
• Species discrimination: 95% accuracy using k-NN on electrical signatures alone!

Machine Learning Integration:

• Principal Component Analysis for dimensionality reduction
• K-Nearest Neighbor classifier for species identification
• Statistical validation (ANOVA, Tukey HSD)
• Proven discriminatory power of anisotropy metrics

Why This Is Revolutionary:
You can identify crop species and quantify root traits without seeing the roots. Just measure electrical anisotropy!

Commercial Applications:

• Automated root phenotyping for breeding trials
• Early species detection in mixed cropping
• Root architecture quantification for precision agriculture
• Patent-worthy algorithms for AgTech companies

Publications: 2 conference papers (Geophysical Research Abstracts)
Thesis Chapter: 4
Collaboration: Prof. Andreas Kemna (Bonn)

Research Question: Can ERT discriminate between plant species in real field conditions?

Field Experimental Campaign:

• Multi-season ERT surveys across 6 grassland species (alfalfa, red clover, chicory, plantain, ryegrass, fescue)
• Controlled water deficit experiment (ForDrought project)
• Integration with TDR sensors for soil moisture validation
• Repeated 3D ERT measurements during drying cycles
• Weather station data for ET₀ calculations

Novel Methodology - “Model-Informed ERT Interpretation”:
Problem: Field ERT is noisy. How do you know if changes are real or artifacts?
Solution: Run synthetic forward models to test what SHOULD happen, then compare to observations.

Analytical Innovation:

• Fitted Gaussian temporal curves to quantify water uptake timing
• Computed spatial variability of transpiration demand
• Used numerical models to validate that changes were plant-driven
• Developed statistical framework to detect species-specific signatures

Major Finding:
Successfully discriminated 5 grass species based on their electrical-hydraulic fingerprints! Species-specific depletion zones were clearly visible and statistically significant.

What Made This Difficult:

• Soil heterogeneity (noise)
• Atmospheric variability (rain, temperature)
• Measurement artifacts (electrode contact)
• Small signal-to-noise ratio for subtle differences

How We Solved It:

• Advanced data filtering (Adrián Flores Orozco, TU Vienna)
• Model-based artifact detection
• Temporal curve fitting to extract patterns
• Statistical validation against destructive sampling

Practical Impact:
This proves ERT works for operational field phenotyping. Breeding programs can now:

• Screen hundreds of varieties non-destructively
• Monitor root activity continuously
• Select for drought tolerance in-field
• Reduce phenotyping costs by 10x

Publications:

• Plant and Soil (2020), 29 citations
• Field data supported by Région Wallonne (ForDrought D31-1341)

Thesis Chapters: 5 & 6
Collaborations: Prof. Sarah Garré (field access), Dr. Florian Wagner (RWTH Aachen), Dr. Nolwenn Lesparre (Strasbourg)

Comprehensive Review: State-of-the-art in geoelectrical methods for soil-root studies.

Coverage:

• Theoretical background (lossy dielectrics, polarization mechanisms)
• Measured electrical properties of plant tissues (resistive & capacitive)
• Overview of ERT and EIT methods for root investigation
• Petrophysical transfer relations (Archie’s Law, vegetation impact)
• Need for explicit root modeling (limitations of mixing models)

Experimental Work:

• Electrical measurements on root segments (DC resistance, polarization signatures)
• Laboratory characterization of rapeseed root electrical properties
• Time-Domain Induced Polarization (TDIP) experiments
• Spectral Induced Polarization (SIP) analysis

Key Insight:
Existing “mixing models” (averaging soil + roots) don’t capture physics correctly. You need explicit 3D representation of root architecture.

This Review Became Highly Cited:
Vadose Zone Journal (2020), 71 citations - Most cited paper of PhD!

“Sensing the electrical properties of roots: A review”
Comprehensive synthesis establishing foundation for thesis work.

Thesis Chapter: 2
Laboratory Collaborations:
Dr. Solomon Ehosioke (ULiège) for root electrical parameterization experiments

Objective: Create reproducible, modular framework for root-soil electrical modeling.

Architecture - Full Pipeline:

Root Architecture (C-Rootbox)
        ↓
Water Uptake (R-SWMS)
        ↓
3D Mesh Generation (Gmsh + Python)
        ↓
Electrical Forward Model (PyGIMLi)
        ↓
ERT Inversion & Analysis (PyGIMLi + SciPy)
        ↓
Visualization (ParaView, Matplotlib)

Key Components:

• Automatic mesh generation: Converts root architectures to FEM-ready meshes
• Coupled hydro-electrical: Bidirectional data exchange
• Scalability: Rhizotron → Pot → Field
• Parallelization: HPC-ready for large simulations
• Quality control: Automated convergence checks

Software Stack:

• PyGIMLi: Core ERT modeling and inversion library
• Gmsh: 3D tetrahedral mesh generation
• R-SWMS: Root water uptake (C++ backend)
• C-Rootbox: Root architecture generator
• EIDORS: Effective property calculations (MATLAB)
• Python: Pipeline orchestration (NumPy, SciPy, Pandas)
• ParaView: 3D visualization and rendering

Computational Challenges:

• Models with 500,000+ elements require HPC
• Memory management for large meshes
• Numerical stability in coupled simulations
• Parallelization for parameter sweeps

Open Science Contribution:

• Shared datasets for benchmarking
• Contributed code to PyGIMLi community
• Documented workflows for reproducibility
• Trained 5+ students in framework usage
• Active in PyGIMLi GitLab (Dr. Thomas Günther, Dr. Carsten Rücker)

Consulting Value:
This is a ready-to-deploy commercial framework. Companies can license/adapt for:

• Custom crop monitoring solutions
• Sensor design and validation
• Digital twin development for agriculture
• Real-time root activity estimation

Technical Depth:
Full grasp of: Finite elements, inverse problems, geophysics, plant biophysics, scientific computing, HPC

Repository: Available for collaboration/consulting (contact for details)

Computational Physics: Extended 2D electrostatic Particle-in-Cell plasma code into fully functional 3D electromagnetic simulation framework.

Achievements:

• Developed Helmholtz coil field generation modules for plasma thruster digital twins
• Implemented MPI/OpenMP parallelization for HPC scaling
• Captured wave-particle interactions and plasma instabilities

Funding: NSF grant ATM0647157
Publications: 3 papers in Physics of Plasmas (69 citations)

Research Project: Numerical modeling of light propagation in semiconductor optical systems using coupled Maxwell-Bloch equations.

Physical System:
Exciton-polaritons in semiconductors - quasi-particles resulting from strong coupling between light (photons) and matter excitations (excitons). These systems are crucial for developing optical switches, modulators, and quantum information devices.

Theoretical Framework:

• Maxwell Equations: Governs electromagnetic wave propagation
• Optical Bloch Equations: Describes two-level system dynamics (excitonic resonances)
• Coupling: Material response modeled as collection of two-level systems responding to optical fields

Technical Challenge:
Solving coupled nonlinear partial differential equations (Maxwell) with ordinary differential equations (Bloch) requires sophisticated numerical methods and deep understanding of both quantum mechanics and electromagnetism.

Background Preparation:

• Self-studied advanced quantum mechanics (second quantization, many-body theory)
• Learned solid-state physics and semiconductor optics (band structure, interband absorption, excitons, polaritons)
• Mastered optical Bloch equations for two-level systems
• Studied light-matter interaction theory from research literature

Numerical Implementation:

• FORTRAN code development for Maxwell-Bloch solver
• 4th-order Runge-Kutta method for temporal integration
• Finite-difference methods for spatial derivatives
• Adaptive time-stepping for numerical stability
• Handled stiff differential equations common in quantum-optical systems

Research Contribution:
Developed working numerical solver capable of simulating:

• Pulse propagation through semiconductor media
• Excitonic resonance effects
• Polariton formation and dynamics
• Nonlinear optical phenomena (self-induced transparency, solitons)

Technical Skills Acquired:

• Quantum mechanics (Wannier equation, hydrogen atom solutions, raising/lowering operators)
• Many-body quantum mechanics and second quantization
• Semiconductor physics (optical excitations, band structure)
• Advanced numerical methods (Runge-Kutta, finite-difference, FDTD)
• High-performance scientific computing in FORTRAN

Supervision: Prof. Torsten Meier (Theoretical Physics)
Funding: DFG (German Research Foundation) optical signal processing project

Consulting Relevance:
Expertise in coupled physics-based simulations, numerical stability, and quantum-classical interfaces applicable to:

• Optical device simulation
• Quantum computing systems
• Multiphysics modeling
• Scientific software development

Experimental & Theoretical Physics: Investigated laser-induced photodegradation of dye molecules (Rhodamine 6G).

Laboratory Skills:

• Performed photo-patterning and bio-deposition in cleanroom
• Operated high-value laser systems and AFM with nanometer precision
• Analyzed biomolecule deposition patterns
• Teaching Assistant for Physics 101

Academic Performance: GPA 4.0/4.0

Science Communication: Produce educational videos simplifying computational science, data analysis, and modeling for students and professionals.

Content Focus:

• Computational modeling fundamentals
• Data science workflows
• Technical writing best practices
• Research methodologies

Production Skills: Video editing (Final Cut Pro, DaVinci Resolve), 2D animation (Pencil2D), visual storytelling

Watch on YouTube

Published Monograph: Comprehensive exploration of digital twin technology evolution from aerospace engineering to modern agriculture.

Coverage:

• Historical development (Apollo missions → Industry 4.0)
• Agricultural applications and precision farming
• Integration of IoT, AI, and simulation technologies
• Future directions in digital agriculture

ISBN: 979-8900231457
Available on Amazon

Research Output: 9 peer-reviewed journal articles (204+ citations), 2 book chapters, 5 conference presentations.

Recent Publications:

• Remote Sensing Letters (2024, under review): SAR soil moisture calibration
• In Silico Plants (2024, under review): Digital twin architectures
• Scientific Data (2025, in prep): Decade-long soil dataset

Book Chapters:

• Climate-Resilient Agriculture with LLMs (2025)
• UAVs as Data Feeders for Agricultural Digital Twins (2025, submitted)

Most Cited Papers:

• “Sensing electrical properties of roots” (71 citations, Vadose Zone Journal)
• “Plasma waves in helicon magnetic nozzle” (39 citations, Physics of Plasmas)

Project Website: Developed comprehensive documentation platform for agricultural modeling tools integration.

Features:

• Interactive documentation for crop modeling workflows
• Scientific visualizations and animations
• Hugo-based static site with custom SCSS/JavaScript
• Integration guides for multiple modeling frameworks

Visit Project Website

Financial Technology: Developed algorithmic trading strategies and execution systems for clients using Python.

Technical Implementation:

• WebSocket integration for real-time tick data
• Trading strategies based on moving averages
• Integration with broker APIs (Angel Broking, Zerodha)
• Automated stock selection and order execution

Period Context: Maintained active skill-building during COVID-19 pandemic while pursuing academic opportunities.

Machine Learning Consulting: Advised on ML library selection and developed MVP for healthcare application using Gradio.

Deliverables:

• ML pipeline architecture design
• Interactive UI prototypes with Gradio
• Time-series modeling for medical data
• Technical documentation and training