Applied research at the intersection of AI and the physical world.
From trustworthy AI evaluation to embodied systems that operate in clinical, industrial, and molecular environments. Independent research with open methodology.
Our ResearchIndependent research, compounding infrastructure
Our research spans two converging frontiers: ensuring AI systems are trustworthy and safe, and applying AI to physical systems that interact with the real world.
H.H.A. Applied Research Institute is an independent California nonprofit. From adversarial robustness testing to magnetically guided microrobots, we pursue problems where rigorous methodology and interdisciplinary expertise produce verifiable results.
Every finding is verifiable. Every tool is public.
Principal investigators
Haedar Hadi
MS Computer Science (Boston University, Information Systems). ML model development, reinforcement learning architectures, and evaluation methodology. Leads benchmark development, multi-agent system architecture, and reproducible evaluation pipelines. In embodied AI: autonomous navigation controller development, sim-to-real transfer, and safety-constrained policy optimization.
Hass Dhia
MS Biomedical Sciences, medical school background. AI infrastructure architect with production systems at scale. Leads experimental design, adversarial evaluation methodology, and analysis. In embodied AI: biomedical domain expertise in anatomy, physiology, and pharmacokinetics. Regulatory strategy and clinical problem framing.
Ahmed Dhia
Director of Manufacturing. Dataset curation, longitudinal safety constraint analysis, and experimental coordination. In embodied AI: design for manufacturability (DFM), production scaling, and quality systems (ISO 13485). Bridges laboratory prototypes to production-ready systems.
Published research
Open-source reinforcement learning environments for safety-critical medical applications. All packages include comprehensive test suites and are available on PyPI.
Research originally published by Smart Technology Investments Research Institute (smarttechinvest.com/research). Licensed to H.H.A. Applied Research Institute under a non-exclusive research license for R&D and grant pursuit. Commercial exploitation rights retained by STI.
Research Briefs
Latest opportunity assessments
Each brief evaluates an embodied AI research direction for scientific merit, technical readiness, team fit, and funding viability.
Autonomous Soft Tissue Surgical Systems with Learned Dexterity
Level 4 surgical autonomy via imitation learning. Zero commercial products. $4.1B addressable market in US laparoscopic surgery.
Closed-Loop Bioelectronic Implants for Inflammatory Disease
RL-optimized vagus nerve stimulation for autoimmune conditions. FDA-validated mechanism. $6.2B addressable market.
Magnetically Guided Microrobots for Targeted Drug Delivery
Sub-millimeter autonomous navigation for precision chemotherapy. Zero commercial products at the intravascular scale. $2.5B addressable market.
Core research programs
Spanning AI safety evaluation, adversarial robustness, embodied systems, and governance.
Evaluation & Adversarial Testing
Existing AI benchmarks evaluate models in isolation under static conditions. We develop reproducible evaluation frameworks with granular, binary-testable criteria that capture real-world failure modes, combined with red-team methodologies and multi-perspective stress-testing protocols that systematically discover vulnerabilities before deployment.
Multi-Agent Safety & Governance
How do safety failures propagate across networks of cooperating AI agents, and how should governance frameworks respond? We study emergent risks invisible to single-agent evaluation — failure cascades, unintended coordination, and constraint erosion — while producing empirical evidence on compliance gaps and democratic oversight mechanisms.
Embodied AI Systems
Research across five scale tiers — from orbital platforms to molecular machines. 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.
RL for Medical Decision-Making
Open-source reinforcement learning environments for safety-critical medical applications — anesthesia dosing, ventilation control, glucose management, radiation therapy, and cardiac electrophysiology. Eight published simulation platforms with over 1,150 combined tests, all available on PyPI.
How we work
Verifiable results through structured methodology.
Criteria-Driven Evaluation
Research questions decompose into discrete, binary-testable criteria before investigation begins.
Multi-Perspective Analysis
Each finding is examined through independent analytical lenses to surface blind spots.
Adversarial Stress Testing
Results undergo structured red-team review through automated pipelines at scale.
Open Reproducibility
Every benchmark, dataset, and framework ships as open-source software.
Collaborate with us
We welcome partnerships with researchers, institutions, funding agencies, and organizations working on AI safety, governance, and evaluation.