Open to opportunities

Ethan Tucker

@ethantucker

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Applied AI/ML researcher and engineer specializing in federated and adversarial ML for critical infrastructure.

United States

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What I'm looking for

I’m seeking AI/ML engineering and applied research roles at the intersection of rigorous methods and real-world deployment—especially work combining federated/adversarial ML for critical infrastructure and practical cybersecurity outcomes.

I’m an Applied ML researcher with 3+ years at a U.S. National Laboratory, specializing in federated and adversarial machine learning for critical infrastructure. My work focuses on rigorous methods that hold up in real-world deployment, progressing technology from concept to TRL 5.

I’m the sole inventor of a patent-pending framework (SOS pooling) and a co-inventor of multiple additional filed patents. I’ve co-secured over $1.85M in competitive research funding, authored peer-reviewed publications, and produced software disclosures to translate research into usable tools.

In my role as Staff Researcher - Artificial Intelligence for Cyber Security, I led AI engineering for CySAT (Cybersecurity Situational Awareness Tool for Hydropower), designing a two-stage transformer autoencoder pipeline for low-latency, multi-platform microservice deployment. I also developed FLAMES, an OS-agnostic, plug-and-play federated ML anomaly detection toolkit supporting multiple HFL/VFL architectures.

Currently, I’m an AI/ML Research Consultant & Founder at Pistach.io, continuing active collaboration on federated learning and power systems cybersecurity. I’m seeking AI/ML engineering and applied research roles at the intersection of rigorous methods and real-world deployment.

Experience

Work history, roles, and key accomplishments

Current

AI/ML Research Consultant

Current

Pistach.io

Aug 2025 - Present (10 months)

Developing federated and adversarial ML research for power-systems cybersecurity and grid control, including patent-pending work on VFL bandwidth optimization and a two-stage IT/OT state-graph transformer feeding a DRL agent for anomaly mitigation. Co-authored an NREL LDRD proposal that secured $600k in funding over two years, with multiple manuscripts in final and internal review stages.

Federated Learning Adversarial Machine Learning Power Systems Cybersecurity Graph Transformers Knowledge Distillation

Staff Researcher, AI Security

NREL (Now NLR)

Nov 2022 - Aug 2025 (2 years 9 months)

Co-secured over $1.85M in competitive DOE funding and progressed federated, adversarial AI for critical infrastructure from concept to TRL 5, including SOS pooling (attack-resilient VFL aggregation) and FLAMES (federated anomaly detection toolkit). Led CySAT transformer autoencoder pipelines for IT/OT anomaly detection and MLOps/DRL red-teaming for grid-inverter security using containerized AWS li

VFL Stochastic Order Statistic (SOS) Pooling Anomaly Detection AI Red Teaming MLOps Docker AWS FedAvg

Data Science Analysis Intern

WildFlower Education

Aug 2022 - Nov 2022 (3 months)

Built AWS-to-database ETL pipelines and developed interactive dashboards to identify at-risk students using confidence intervals and hypothesis testing. Supported data-driven analysis to help prioritize student interventions at an education startup.

ETL AWS PostgreSQL Dashboards Hypothesis Testing Data Analysis

Education

Degrees, certifications, and relevant coursework

U.S. Department of Energy (DOE) Energy I-Corps Cohort 20

Intensive Entrepreneurial Business Program, Entrepreneurship (Energy I-Corps)

Activities and societies: DOE WPTO & DOE CESER $40K scholarship; 80 customer discovery interviews; validated to TRL 5.

Completed the DOE Energy I-Corps Cohort 20 program as Entrepreneurial Lead, including a $40K scholarship and 80 customer discovery interviews for market validation.