I update measurement systems and experiment design to maximize machine learning benefits, closing the gap between scientific literature and real-world practice.
While research papers work in clean lab conditions, your breakthrough products need systems that work in reality - with device variation, contamination, and operational constraints.
Start Your Breakthrough ProjectBridging the gap between perfect lab conditions and practical breakthrough products
I analyze current research to understand what's possible, then identify what it takes to make it work in real-world conditions with device variation and contamination.
Smart stratification and experimental planning that accounts for real-world variation while capturing the data ML models need to succeed.
Custom AI architectures designed to work with messy real-world data - handling device differences, operator variation, and contamination issues.
Update measurement protocols and systems to maximize ML performance - like "sweep over sample" insights that dramatically improve data quality.
Structured approach from initial analysis to breakthrough results
Literature review, current setup analysis, and identification of breakthrough opportunities in your specific domain.
Custom AI architecture design, smart experimental planning, and protocol optimization strategies tailored to your goals.
Development of custom models, protocol refinement, and iterative optimization with your team's collaboration.
Complete documentation, team training, and methodology handover so your team can continue and build upon the work.
Real breakthrough projects where I designed and built measurement systems that didn't exist before
Challenge: Build system for 20+ bacterial species with device variation and contamination issues
Innovation: Advanced stratification planning + smart experimental space mapping for deep learning
Result: Reduced required experiments by 10-1000x through intelligent planning
Created data science plan for bacterial identification with AI. System handles real-world contamination and device variance. Methodology still used today.
Challenge: Material identification from sparse 3D data for autonomous systems
Innovation: Novel AI architecture: PointNet + VAE + contextual fusion
Result: Solved "impossible" sparse-data problem with state-of-art performance
Designed and trained custom deep learning system for per-pixel material prediction from 3D point clouds. Combined latest technologies to create scalable system.
Challenge: Maximize signal of Raman spectroscopy system for deep learning
Innovation: Adapt protocol to create 1000's of unique spectra suitable for deep learning
Result: Breakthrough measurement system from standard equipment
Transformed existing spectrometer into AI-powered diagnostic tool through protocol optimization and custom models.
From strategic analysis to custom AI implementation
I make cutting-edge ML work in real-world conditions, not just perfect lab environments
I take what works in clean research environments and make it work with real device variation, contamination, and operational constraints.
I update measurement systems and experiment design specifically to maximize machine learning performance and reliability.
Smart stratification and planning that can reduce required experiments by 10-1000x while ensuring comprehensive real-world coverage.
Custom AI architectures designed to handle messy data and variation - making breakthrough results possible in production environments.