Role Overview: Leading applied AI R&D focusing on deploying Vision Models (CV), Large Language Models (LLMs), and Vision‑Language Models (VLMs) in real‑world fintech and payment infrastructure environments.

Key Contributions:

• Designed and developed AI‑driven architectures for intelligent payment ecosystems, integrating Computer Vision and multimodal models for real‑time decision‑making.
• Built and optimized vision‑based systems for device intelligence: transaction monitoring, anomaly detection, operational analytics.
• Integrated LLMs/VLMs into fintech workflows for automation, contextual analysis, and intelligent reporting systems.
• Developed scalable AI pipelines for edge and embedded deployment in payment terminals and banking hardware.
• Contributed to AI‑enhanced security systems, improving fraud detection and behavioral anomaly recognition.
• Collaborated with cross‑functional R&D teams to transition AI models from research prototypes to production‑grade systems.

Role Overview: Senior technical lead responsible for research, development, validation, and certification of payment terminal and ATM operating systems, supporting Iran’s largest ATM infrastructure ecosystem.

Core Responsibilities:

• Led end‑to‑end R&D lifecycle for payment terminals, ATMs, POS systems, cashless platforms, and biometric/ID devices.
• Specialized in Security Platform (SP) architecture: secure boot processes, cryptographic key management, system integrity enforcement.
• Developed and maintained Java‑based OS‑level validation frameworks for functional, performance, and compliance testing.
• Managed multi‑OS certification and release engineering across Prolin, Monitor, PayDroid, and Android‑based systems.
• Designed and executed hardware‑software integration testing strategies for global vendor devices (PAX, NexGo, Kozen/XC Tech, Hyosung, GRG Banking, LKS).
• Optimized system‑level performance for embedded payment environments under strict latency and reliability constraints.
• Collaborated with international vendors and internal teams to ensure compliance, interoperability, and production readiness.
• Contributed to standardization and modernization of national‑scale ATM infrastructure (Tosan holds the leading market position in Iran).

Research Overview: Selected for an intensive, lab‑style program simulating graduate‑level computational neuroscience, focusing on neural and computational principles of decision‑making under uncertainty.

Focus: Brain‑wide representations of prior information and uncertainty, inspired by IBL 2024 study. Worked with Neuropixels electrophysiology and calcium imaging from behaving mice.

Key Contributions:

• Developed Bayesian and probabilistic models to characterize latent cognitive variables governing decision dynamics.
• Applied machine learning and representation learning to extract interpretable structure from high‑dimensional neural recordings.
• Designed large‑scale data preprocessing pipelines (signal denoising, alignment, multimodal validation).
• Analyzed neural population activity to decode encoding of prior beliefs, uncertainty, and behavioral adaptation.
• Collaborated in a multidisciplinary team spanning neuroscience, AI, and statistical modeling.
• Produced technical reports and presentations, communicating findings through peer review.

Impact: Strengthened ability to bridge neuroscience and AI, developing principled models of cognition grounded in real neural data.

Role Overview: Contributed to AI‑driven control systems for autonomous vehicles and intelligent transportation, supporting smart mobility and unmanned logistics in a scaling international environment.

Technical Scope:

• Integrated embedded AI modules into real‑time vehicle control architectures.
• Developed and validated ADAS and autonomous driving controllers under strict real‑time and safety constraints.
• Collaborated in hardware‑software co‑design across perception, control, and embedded systems teams.
• Worked with radar sensing, intelligent controllers, and in‑vehicle platforms.

Key Contributions:

• Designed and validated control logic and system‑level behaviors for intelligent driving systems.
• Optimized performance and reduced latency in real‑time embedded environments.
• Contributed to safety validation and robustness testing aligned with industry‑grade standards.
• Supported international deployment and adaptation across diverse regulatory environments.

Industry Exposure: Collaborated with major automotive and AI stakeholders: JAC Motors, China National Heavy Duty Truck Group, AXera Technologies, Tongxing Technology, National Intelligent Connected Vehicle, Ligong Tongyu.

Impact: Strengthened expertise in embedded AI, autonomous control, and real‑time system design, with hands‑on experience scaling intelligent vehicle technologies.

Role Overview: Designed and developed AI‑driven safety and driver‑assistance systems for heavy‑duty trucks and commercial vehicles at a leading Middle East manufacturer.

Technical Scope:

• Integrated machine learning models into embedded automotive platforms for intelligent driver‑assistance and safety.
• Developed and validated ECU firmware for safety‑critical vehicle systems under real‑time constraints.
• Participated in system‑level architecture design bridging control algorithms, embedded software, and vehicle electronics.

Key Contributions:

• Implemented and tested AI‑enhanced control logic within resource‑constrained embedded environments.
• Optimized system performance for low‑latency and real‑time execution on automotive hardware.
• Supported validation and reliability testing aligned with automotive safety and compliance standards.
• Collaborated across mechanical, electrical, and software teams to ensure robust system integration and production readiness.

Industry Exposure: Solutions supporting regional expansion in commercial transport and logistics markets across the Middle East.

Impact: Strengthened expertise in embedded AI, ECU development, and automotive safety systems.

Role Overview: Core contributor to the MRL Humanoid Soccer Team, developing AI‑driven perception and decision‑making systems for fully autonomous humanoid robots in dynamic, adversarial environments.

Achievements:

• Part of a top‑tier robotics team achieving multiple international RoboCup championships in Australia, Canada, Thailand, Russia, and Japan.

Technical Scope:

• Designed and implemented real‑time computer vision pipelines for object detection, ball tracking, localization, and field recognition.
• Developed multi‑agent AI systems for coordinated team behavior and distributed decision‑making.
• Applied reinforcement learning and adaptive control strategies to enhance autonomous tactical responses.
• Optimized algorithms for low‑latency execution on resource‑constrained robotic hardware.

Key Contributions:

• Engineered robust perception systems resilient to noise, occlusion, and rapid environmental changes.
• Improved real‑time performance and decision accuracy through efficient model design.
• Collaborated within a multidisciplinary robotics team integrating AI, control, and mechanical systems.

Impact: Built strong expertise in real‑time AI, embodied intelligence, multi‑agent systems, and robotic perception.