03Selected experience

The technical experience behind Tensor State Group.

This page describes prior roles and research by the principal. It is not presented as Tensor State Group client work. Organization names, metrics, and confidential implementation details are omitted until they are cleared for publication.

Attribution standard

Experience is separated from company-client history. Production state and results are described only to the level that can be supported and disclosed.

Distributed ML infrastructure

Infrastructure + enablement

Problem context: Research teams need easier, more reliable access to distributed training and shared compute environments.
Why it is difficult: Cluster scheduling, usability, resource policy, reproducibility, and researcher workflows have to work as one system.
Relevant experience: Experience spanning distributed-training tooling, Slurm-backed compute environments, resource-allocation work, and technical enablement for ML researchers.

Generative systems for visual workflows

Model systems + workflows

Problem context: Creative and production teams need generative capabilities that respect workflow, control, and output requirements.
Why it is difficult: Model choice alone does not solve control, routing, consistency, evaluation, latency, or integration with existing tools.
Relevant experience: Experience with image generation, inpainting, outpainting, diffusion systems, model routing, and retrieval-supported creative applications.

Computer vision and neural media

Vision + applied research

Problem context: Visual data must be transformed, segmented, recognized, or reconstructed under imperfect production conditions.
Why it is difficult: Dataset design, visual artifacts, rare failure cases, throughput, and subjective quality all shape the engineering answer.
Relevant experience: Experience across deblurring, segmentation, object detection, synthetic-data generation, and neural-rendering research.

Evidence standard

Case studies should earn trust, not borrow it.

Detailed case studies follow a consistent structure: context, technical difficulty, intervention, evaluation, result, and production state. Prior-employer work is clearly labelled.

Additional names and technical detail can be discussed under the appropriate confidentiality conditions during engagement discovery.