ROMAN CLASEN

I am Roman Clasen, a thermodynamic machine learning scientist specializing in non-equilibrium systems and energy-constrained optimization. With a Ph.D. in Statistical Physics and AI (ETH Zurich, 2024) and leadership of the Energy-Aware AI Consortium at CERN, my research bridges stochastic thermodynamics, quantum computing, and neural network optimization to address the energy crisis in modern AI. My mission: "To redefine machine learning as a thermodynamic art—where every parameter update respects the laws of energy conservation and entropy production, transforming AI from a power-hungry beast into an elegant, sustainable partner."

Theoretical Framework

1. Non-Equilibrium Learning Dynamics

My framework ThermoLearn integrates:

Stochastic Thermodynamic Control: Modeling neural network training as a non-Markovian process with measurable entropy production rates (EPR).

Jarzynski Equality Adaptation: Optimizing free energy differences in gradient descent to minimize irreversible work.

Fokker-Planck Dynamics: Analyzing parameter space exploration as a diffusion process constrained by energy budgets.

2. Energy-Constrained Optimization

Developed EcoGrad, a novel optimization protocol:Achieved 63% energy reduction in GPT-5 pre-training without performance loss (NeurIPS 2025 Spotlight).

Key Innovations

1. Thermodynamic Neural Architecture Search (T-NAS)

Designed EntroNet, an energy-aware architecture featuring:

Non-equilibrium activation functions with tunable dissipation.

Topological sparsity patterns guided by persistent homology.

Outperformed EfficientNet-B7 with 41% lower FLOPS (CVPR 2025 Best Paper).

2. Distributed Energy Synchronization

Created ThermoSync:

Coordinates GPU clusters using thermodynamic consensus protocols:

Reduced 4096-GPU training energy waste by 58% via entropy-aware workload balancing.

Integrated with Horovod for automatic energy-constrained scaling.

3. Edge AI Thermodynamics

Partnered with NVIDIA on MicroThermo:

Embeds EPR constraints into Jetson edge devices via topological compression.

Extended drone swarm mission duration by 9× (Nature Energy, 2025).

Transformative Applications

1. Climate-Positive AI

Deployed ClimaLearn at IPCC:

Reduced global AI sector carbon footprint by 22% via thermodynamic scheduling.

2025 UN Sustainable Development Award for "AI energy diplomacy".

2. Medical Diagnostics Revolution

Launched ThermoMed:

Certifies diagnostic models meet hospital energy budgets (e.g., MRI analysis at 3W max).

Enabled low-power cancer screening in off-grid regions (Lancet Planetary Health, 2025).

3. Quantum-Classical Hybrid Learning

Collaborated with Google Quantum AI on Q-Thermo:

Encodes gradients in superconducting qubits to exploit Landauer’s limit.

Achieved 89% erasure energy recovery in VQE experiments (PRL 2025).

Ethical and Philosophical Principles

Energetic Fairness Doctrine

Proved "Energy-efficient models reduce demographic bias":

71% lower racial disparity in facial recognition via EPR constraints (AAAI 2025).

Planetary Boundary Alignment

Co-authored AI Entropy Accord:

Caps global AI entropy production at 2024 levels despite 15× compute growth.

Anti-Oligarchic Energy

Developed Decentralized ThermoML:

Democratizes AI access through community energy-sharing blockchains.

Future Horizons

Quantum Thermodynamic Advantage: Leveraging quasiparticle dynamics for zero-entropy learning.

Biomorphic Energy Harvesting: Integrating photosynthetic principles into neural architectures.

Interstellar AI Ecology: Preparing frameworks for energy-constrained learning in space colonies (NASA Artemis Program).

Let us reimagine AI not as a consumer of power, but as a sculptor of energy—carving intelligence from the marble of thermodynamic possibility.