AliAkarma
I build agentic AI systems that know their own limits. My work focuses on the gap between autonomous capability and institutional accountability — designing architectures where AI agents can be stopped, audited, and corrected when they behave unexpectedly. I'm a 4th-year IT student at the Islamic University of Madinah and have published 20 peer-reviewed papers and manuscripts on AI governance, adversarial robustness, and constrained multi-agent systems. Read background →
I study how to make autonomous AI systems fail safely: designing governance architectures that prevent unintended actions before they propagate through real-world infrastructure.
Current Research Frontier
Autonomous Safety Governance
Safety-Critical Multi-Agent Systems
"Investigating cryptographic trust-anchors and constrained reasoning for large-scale agentic deployments."
Why This
Research
Matters
I study how to make autonomous AI systems fail safely: designing governance architectures that prevent unintended actions before they propagate through real-world infrastructure.
My work addresses the alignment problem in deployed agentic systems — exploring how we can build autonomous pipelines that remain safe and governable when exposed to adversarial inputs, distributional shift, or misaligned incentives. I approach this through the intersection of safety engineering, formal governance frameworks, and empirical failure-mode analysis.
Research Landscape
Recent Publications
Governance-Aware Autonomous Retail Coordination in Artificial Intelligence Cities Using Multi-Agent Reinforcement Learning, Blockchain Accountability, and Federated Learning
Toqeer Ali Syed, Ali Akarma, Shahid Kamal, Salman Jan, Ahmad B. Alkhodre, Arshad Jamal
Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks
Ali Akarma, Toqeer Ali Syed, Muhammad Khan, Qurat-ul-ain Mastoi, Adeel Ahmad
Agentic AI for Climate-Resilient Cities: A PRISMA-Guided Review and Digital Twin Framework
Toqeer Ali Syed, Ali Akarma, Muhammad Tayyab Naqash, Danial Hameed, Shahid Kamal, Antonio Formisano
Featured Research Systems
LagrangianCTDE — Constrained MARL for Disaster Response
Risk-aware multi-agent reinforcement learning framework coordinating Storm, Flood, and Evacuation agents under Lagrangian safety constraints, achieving 81.5 reward with only 2.3% safety violations across six baselines.
Agentic AI-Enhanced Digital Twin — Smart City Infrastructure
Physics-grounded simulation framework evaluating rule-based, digital twin, and agentic AI monitoring architectures for smart city civil infrastructure, with blockchain-anchored audit trails and Kalman-filtered state estimation.
FinNutriAgent — Household Budget & Nutrition Optimizer
Open agentic AI framework jointly optimizing household meal planning and financial budgets under nutritional, cultural, and economic constraints using MILP and LLM orchestration across multi-store price data.
Latest Research Notes
Governance-Aware Autonomous Retail Coordination in AI Cities: Multi-Agent RL, Blockchain Accountability, and Federated Learning
When autonomous systems take operational authority over urban commerce, accountability and human oversight matter as much as efficiency. Published in Frontiers in Artificial Intelligence, this paper presents AAIRM: lowering normalized inventory cost by 13.2% (95% CI 12.2–14.2) while proving that the defensible advantages of agentic coordination stem from hard feasibility and blockchain auditability rather than language-model reasoning.
Privacy Leakage in Federated Learning: Client Identity Inference and Defenses for Inertial Sensing in Vehicular Networks
Federated learning is widely heralded as privacy-preserving because raw sensor data never leaves the edge. This paper presented at IEEE VTC 2026 reveals that undefended weight deltas allow an honest-but-curious server to identify clients with near-perfect accuracy (≈1.000), and formulates rigorous clip-then-noise and ensemble defenses with formal (ε, δ)-DP guarantees.
Recent Milestones
Paper accepted: Governance-Aware Autonomous Retail Coordination in Artificial Intelligence Cities Using Multi-Agent Reinforcement Learning, Blockchain Accountability, and Federated Learning — Frontiers in Artificial Intelligence
Presented paper: Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks — IEEE VTC 2026
New paper: Agentic AI for Climate-Resilient Cities: A PRISMA-Guided Review and Digital Twin Framework — MDPI Sustainability
Building Safety-Aligned Autonomous AI
Currently open to research collaborations, academic exchanges, and graduate opportunities in AI safety, multi-agent systems, and trustworthy machine learning.