Applied Research Institute

Applied AI for the physical world.

About the Institute

Independent research, compounding infrastructure.

Independent California 501(c)(3). Two frontiers: trustworthy AI, AI for physical systems. Research publishes open source. Reference designs license to commercial partners. Every artifact re-derivable.

Two Paths

Research and reference designs.

Research.

Grant-funded investigation. Open publication. RFPs and federal grants.

Reference Designs.

Licensable form factors. Commercial deployment via licensed partners.

Team

Principal investigators.

Haedar Hadi

Lead Principal Investigator

MS Computer Science (Boston University). ML model development, reinforcement learning architectures, and evaluation methodology. Leads benchmark development, multi-agent system architecture, and reproducible evaluation pipelines.

Hass Dhia

Co-Principal Investigator

MS Biomedical Sciences. AI infrastructure architect with production systems at scale. Leads experimental design, adversarial evaluation methodology, and analysis. Biomedical domain expertise in anatomy, physiology, and pharmacokinetics.

Ahmed Dhia

Key Team Member

Director of Manufacturing. Dataset curation, longitudinal safety constraint analysis, and experimental coordination. Design for manufacturability (DFM), production scaling, and quality systems (ISO 13485).

Published Research

Open-source research papers.

Our published work spans clinical reinforcement learning, neural interfaces, vascular intervention, and molecular engineering. All papers, code, and tooling are freely available on PyPI and GitHub.

01AnestheSim — RL for anesthesia dosingClinical RL

02VentiSim — RL for mechanical ventilationClinical RL

03GlucoSim — RL for glucose managementClinical RL

04OncoSim — RL for radiation therapy planningClinical RL

05CardioSim — RL for cardiac electrophysiologyClinical RL

06NeuroSim — Brain-computer interface environmentsNeural Interface

07VascularSim — Microbot vascular navigationEmbodied AI

08PeptideGym — Peptide design environmentsMolecular

All papers →

Opportunity Assessments

Where AI meets unmet need.

We identify and evaluate opportunities where AI-driven control, manufacturing engineering, and domain expertise converge to bridge the gap between laboratory prototypes and deployable systems. Each assessment maps technology readiness against market need.

TRL 6Autonomous soft-tissue surgical systems with learned dexterity

TRL 5Closed-loop bioelectronic implants for inflammatory disease

TRL 3Magnetically guided microrobots for targeted drug delivery

TRL 8Wearable on-device LLM inference for brand integration

All briefs →

Research Areas

Core research programs.

Spanning AI safety evaluation, adversarial robustness, embodied systems, and governance.

01

Evaluation & Adversarial Testing

Reproducible evaluation frameworks with binary-testable criteria. Red-team methodologies. Multi-perspective stress-testing.

02

Multi-Agent Safety & Governance

Failure cascade propagation. Unintended coordination. Constraint erosion. Policy translation for democratic oversight.

03

Embodied AI Systems

Five scale tiers — orbital to molecular. AI-driven control, manufacturing engineering, deployable system bridges.

04

RL for Medical Decision-Making

Eight open-source RL environments for clinical applications. 1,150+ tests. PyPI-distributed.

05

Methodology Infrastructure

Cross-domain pipelines from research to deployable systems. Reference designs per category.

Methodology

How we work.

Verifiable results through structured methodology. Every claim resolves to an artifact a third party can re-derive.

01

Criteria-Driven Evaluation

Research questions decompose into discrete, binary-testable criteria before investigation begins.

02

Multi-Perspective Analysis

Each finding is examined through independent analytical lenses to surface blind spots.

03

Pre-Registered Falsification

Hypotheses, endpoints, and stopping rules locked before data collection. RFC 3161-stamped pre-registration on OSF.

04

Cross-Toolchain Reproduction

Independent verifier in a different language and framework reproduces every primary numeric result.

Brand Integration

Reference designs, licensed deployments.

On-device AI reference designs across wearable form factors. Brands license the integration; HHA stewards the underlying IP. Revenue funds research.

In development

On-body inference modules
Embedded sensor cassettes
Edge-AI accessories

Each form factor licensable per vertical. Licensing, co-development, or partnership. NDA on request.

Commercial deployment proceeds through licensed entities. Conflict-of-interest review applies.

[email protected] →