Senior AI Researcher
Tannistha Maiti
DeepKapha / EarthScan
Tannistha leads applied research at EarthScan with a focus on subsurface AI, deep learning for geoscience, and bio-inspired materials. Her work spans reservoir characterisation, seismic facies analysis, and well-log digitisation.
Tannistha Maiti is a senior AI researcher at EarthScan and DeepKapha, building deep-learning systems for the upstream energy industry. Her work focuses on the long-tail data realities of subsurface analytics — noisy raster well logs, multi-vintage seismic acquisitions, and the practical challenges of getting models into production.
She has published extensively on:
- Reservoir characterisation — Bayesian / Hamiltonian Monte Carlo approaches to the inverse problem, integrating well logs with seismic to recover subsurface properties.
- Well-log automation — VeerNet AI, the convolutional architecture underpinning ES Raster Digitizer, plus traditional + FMI-log correlation pipelines.
- Seismic facies analysis — domain-adaptation methods that close the gap between annotated and unannotated 3D volumes.
- Bio-inspired materials & lignin AI — applying multimodal AI to the search for sustainable polymers from biomass.
Tannistha's research drops on EarthScan are written for practitioners — geoscientists, petrophysicists, and ML engineers who actually have to ship the work. She has a particular distaste for benchmark-only papers that don't survive contact with real field data.
