Felix Herrmann
Professor
Develops computational seismic imaging, inverse problems and machine learning with uncertainty quantification.
Felix Herrmann's lab at Georgia Tech works on probabilistic & causal inference and signal processing & information theory. 123 papers since 2023. 10 PhD students and 2 master's students. 14 alumni. Applications close .
At a glance
- papers, 2023–2026
- 32/ year
- at T1 venues
- 0%
- PhD students
- 10
- MS students
- 2
- alumni listed
- 14
Papers by yearfrom the lab homepage, 2023–2026
- 16 papers2026 · T1 0 · collecting
- 40 papers2025 · T1 0
- 29 papers2024 · T1 0
- 38 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- seismic29 papers
- Probabilistic & causal inference28 papers
- uncertainty quantification8 papers
- Signal processing & information theory0 papers
- earth0 papers
Paper list
From the lab homepage.
202616 papers
- —Amortized Full-Waveform Inference w/ Learned Summary Statistics
- —Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers
- —Bridging the Acoustic-Elastic Gap in Seismic Inversion via Robust Summary Statistics
- —Context- and uncertainty-aware Digital Twin for the Optimization of Underground Storage Operations
- —Decision Making and Control with Digital Twins for Underground Energy Storage
- —Foundation models for Full-Waveform Inference w/ Uncertainty Quatification
- —Improving reservoir state prediction in digital shadows using Radon-transformed subsurface common-offset gathers
- —JUDIAgent: a scientific coding agent for JUDI workflows in wave-equation imaging
- —Learned Multiphysics Inversion with Differentiable Programming & Machine Learning: An open-source path from wave physics to CO₂ digital twins
- —Neural Operators with Accurate Jacobian for High-Fidelity Image-Domain Seismic Inversion
- —OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI
- —SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
202540 papers
- —A reduced-order derivative-informed neural operator for subsurface fluid-flow
- —Advancing Geological Carbon Storage Monitoring with 3D Digital Shadow Technology
- —An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring (opens in a new tab)
- —ASPIRE: Iterative Amortized Posterior Inference for Bayesian Inverse Problems (opens in a new tab)
- —Attention Please: Getting the Most from CIGs with Learned Summary Statistics
- —Background-Conditioned Diffusion Inversion for Seismic Velocity Models
- —CO2 Enhanced Oil Recovery Through Digital Shadow Modeling
- —Context- and uncertainty-aware Digital Twins for Integrated Reservoir Management
- —Context-aware Digital Twin for Underground Storage
- —Context-Aware Digital Twin for Underground Storage Operations and Decision Making
- —Digital Shadow for CO2-based Enhanced Oil Recovery (CO2-EOR)
- —Efficient and scalable posterior surrogate for seismic inversion via wavelet score-based generative models
202429 papers
- —A Digital Twin for Geological Carbon Storage with Controlled Injectivity (opens in a new tab)
- —An Uncertainty-Aware Digital Twin for Geological Carbon Storage
- —BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows - a case study in optimal monitor well placement for CO2 sequestration (opens in a new tab)
- —Coupled Permeability Inversion from Time-Lapse Seismic Data
- —Digital Twins in the era of generative AI - Application to Geological CO2 Storage
- —DT4GCS –- Digital Twin for Geological CO2 Storage and Control
- —Enhancing Full-Waveform Variational Inference through Stochastic Resampling
- —Enhancing Full-Waveform Variational Inference through Stochastic Resampling and Data Augmentation
- —Enhancing Performance with Uncertainty Estimation in Geological Carbon Storage Leakage Detection from Time-Lapse Seismic Data
- —FNO-charged ASPIRE
- —Generative AI for full-waveform variational inference
- —Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations
202338 papers
- —3D seismic survey design by maximizing the spectral gap (opens in a new tab)
- —Act normal that's crazy enough — an overview of seismic inversion with normalizing flows and surrogate modeling
- —Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification
- —Amortized Bayesian Full Waveform Inversion and Experimental Design with Normalizing Flows
- —Amortized normalizing flows for transcranial ultrasound with uncertainty quantification
- —CO2 reservoir monitoring through Bayesian data assimilation
- —Coupled physics inversion for geological carbon storage monitoring
- —Derisking geologic carbon storage from high-resolution time-lapse seismic to explainable leakage detection (opens in a new tab)
- —Derisking geological storage with simulation-based seismic monitoring design and machine learning
- —Digital twins in the era of generative AI (opens in a new tab)
- —End-to-end permeability inversion from prestack time-lapse seismic data: a case study on Compass model
- —Enhancing CO2 Leakage Detectability via Dataset Augmentation
Where alumni went14 listed
Apply to Georgia TechFall 2027
Georgia Tech Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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