Nyx Institute for Computational Medicine
Research and methods in computational causation, reproducibility, and medical evidence integrity.
The Nyx Institute for Computational Medicine is a research and methods initiative focused on computational medicine, causal reasoning, reproducibility, health-equity modeling, and scientific evidence integrity.
Scope. This site presents research and methods only. Commercial services and product initiatives are maintained separately through Nyx Dynamics LLC and its distinct initiatives.
Research areas
Work is organized across four connected programs spanning prevention modeling, causal inference, healthcare AI, and computational neuromedicine.
Computational HIV Prevention
Modeling the implementation and kinetics of HIV prevention at population scale.
- LAI-PrEP bridge decision support
- Finite PEP windows
- Counterfactual prevention modeling
- Calibration-to-deployment mismatch
Causation and Evidence Integrity
Reliability of inference from populations to individuals, and the structural conditions that bias it.
- Population-to-individual inference
- Counterfactual reasoning
- Structural censoring
- Specific-causation reliability
Healthcare AI and Reproducibility
Whether clinical prediction models are reported, validated, and equitable enough to deploy.
- AI Readiness Framework
- TRIPOD+AI-aligned review
- Model reporting integrity
- Algorithmic bias epidemiology
Computational Neuromedicine
Signal and registry methods for neurometabolic states in HIV infection.
- HIV neurometabolic coupling
- MRS registry methods
- Temporal-coherence frameworks
Selected outputs
Peer-reviewed publications and preprints, with persistent identifiers. The full list, with code and data links, is on the publications page.
LAI-PrEP Bridge Period: a computationally validated clinical decision support tool
Computational validation across 21.2 million synthetic patients at UNAIDS global PrEP target scale, with a reconceptualized PrEP cascade and a 21-intervention evidence library.
Finite Prevention Windows for HIV Post-Exposure Prophylaxis
A formal framework deriving a finite, route-specific PEP prevention window from within-host viral kinetics, showing parenteral windows compress roughly threefold versus mucosal exposure.
Synergistic Barriers to Algorithmic Recourse and Structural Discrimination in Healthcare AI
A framework for quantifying synergistic algorithmic discrimination, where features equitable in isolation combine multiplicatively to produce discriminatory outcomes.