https://theminiceo.ir/en/research/ (Open Access • No Authentication or Login Required)
Conducting foundational and empirical research on Dynamic Matching, Stochastic Optimization, Multi-Agent Coordination, and Foundation Model-Assisted Operational Decision Making.
Confirmation of institutional leadership, academic roles, verified scholarly citations, and research oversight for computational grant and API evaluation boards.
This institutional registry certifies that Amir Pazhooh (Amir Hossein Hosseini) serves as the Principal Investigator (PI) and Director of the Applied AI & Operational Decision Systems Research Laboratory within TheMiniCEO. In this capacity, he defines the scientific research agenda, oversees mathematical modeling and algorithmic dispatch frameworks, supervises empirical evaluations, and holds primary administrative authority for institutional partnerships, academic researcher access programs, and foundation model computational resource allocations.
Biography & Scholarly Profile: Amir Pazhooh is an applied researcher and systems architect specializing in dynamic matching mechanisms, causal inference, behavioral mobility models, and operational decision making under uncertainty. His peer-reviewed and preprint contributions focus on real-time bipartite matching algorithms, stochastic dispatching optimization, and multi-attribute pricing engines for large-scale two-sided platforms. He also directs empirical investigations evaluating how generative foundation models and autonomous multi-agent systems interact with complex operational market environments.
Grounded in empirical operations research, stochastic optimization, and modern generative AI architectures.
Formulating real-time bipartite matching algorithms (e.g., Hungarian variants, Kuhn-Munkres extensions) for two-sided platforms. Mitigating spatial-temporal demand friction, driver idle time, and multi-agent dispatch interference under non-stationary order arrivals.
Applying econometric and causal inference frameworks to quantify network spillover effects, supply-demand elasticity, and interference in dense operational ecosystems. Testing randomized and synthetic control experiments on pricing sensitivity.
Investigating adoption barriers in shared urban transit and carpooling. Modeling behavioral shifts and decision friction using spatiotemporal optimization to accelerate user transitions from single-occupancy vehicles to shared transport networks.
Evaluating frontier Large Language Models (LLMs) and autonomous agents in executing multi-criteria operational decisions, automated dispatching policies, and complex resource allocation under conflicting real-world constraints.
Research papers authored by the PI, indexed on Google Scholar with persistent digital object identifiers (DOIs) and open-source code repositories.
10.5281/zenodo.22956619
10.5281/zenodo.22065899
Active computational testbeds utilizing foundation model APIs and empirical simulations.
Multidisciplinary researchers focusing on operations research, AI architectures, and causal inference.
Our empirical research actively involves large-scale Monte Carlo agent rollouts, long-horizon decision simulations, and foundation model evaluations across non-stationary supply-demand environments. High-rate-limit programmatic access to frontier reasoning models (such as the OpenAI Researcher Access Program, Google Cloud Research Credits, and Anthropic Academic Access) directly powers our reproducible experiments and open benchmark suites.
Commitment to Non-Commercial Open Research: Subsidized API credits and computational grants awarded to our group are strictly dedicated to non-commercial academic research, open-source software releases on GitHub, and peer-reviewed scientific dissemination.
Comprehensive institutional directory data for grant review panels, academic committees, and API access evaluators.
| Parent Organization | TheMiniCEO (Digital Innovation & Applied Advisory Institute) |
|---|---|
| Research Group / Lab | Applied AI & Operational Decision Systems Research Laboratory |
| Laboratory Director & PI | Amir Pazhooh (Amir Hossein Hosseini) — Confirmed Principal Investigator |
| Verified Google Scholar Profile | scholar.google.com/citations?user=G0ADtx0AAAAJ |
| Public Web URL | https://theminiceo.ir/en/research/ (Open, Public, No Login Required) |
| Institutional Contact Email | [email protected] |
| Principal Investigator Direct Email | [email protected] |
| Physical Location | Tehran, Iran (Hybrid Distributed Research Team) |
| Open Source Code Repositories | https://github.com/amirHHP |
| Verification Status | ● Confirmed Institutional Profile (Publicly Accessible • No Login Required) |