Danial Asgari

Work & Experience

Technical foundation combined with real-world operational experience. Background spans both systems engineering and customer-facing environments.

Technical Foundation

Languages & Skills

• Python — Intermediate to Advanced
• C++ & Java — Core fundamentals
• Data Science & Engineering — Coursework & projects
• System Design — Architecture and scalability

Focus Areas

• Scalable backend systems and microarchitectures
• Data pipelines and ETL workflows
• Algorithmic problem-solving
• System design fundamentals and optimization

IT Specialist

3.5 years

Tizbam Educational Company

Supported and maintained IT infrastructure for educational platform operations. Handled system troubleshooting, technical diagnostics, and operational maintenance while working with data handling procedures and digital system workflows.

Key Takeaway: Developed understanding of systems in production environments, reliability under real-world constraints, and technical problem-solving at scale.

Waitress

3 months

Dobar Coffee Shop

Operated in fast-paced, high-pressure café service environment. Managed customer interactions, order accuracy, and service quality while developing communication efficiency, teamwork, and stress management under time constraints.

Key Takeaway: Understood how systems (café operations, team coordination, customer satisfaction) function under pressure. Learned the critical importance of reliability, communication, and human-centered design in practical environments.

Experience Foundation: Experience across technical infrastructure and real-world operations has shaped a balanced engineering perspective: systems must be theoretically sound AND practically reliable. The gap between design and execution is where real challenges live.

Direction

I am building a career at the intersection of artificial intelligence systems, scalable data engineering, and reliable IT infrastructure. My focus is on understanding how intelligent systems operate, adapt, and scale in real-world, high-complexity environments where decisions happen in milliseconds and failure has immediate consequences.

1. AI Systems & Machine Learning Architecture

Not just model training, but the full architecture: data flow, feedback loops, system reliability, and how AI systems make decisions under uncertainty.

→ Self-improving and adaptive systems
→ Reinforcement learning in real-time environments
→ System reliability and fault tolerance in AI pipelines
→ Bridging theoretical ML with production systems

2. Formula 1 Data Systems (Applied Interest)

Formula 1 represents the purest expression of data-driven optimization: every tenth of a second comes from understanding telemetry, strategy modeling, and real-time decision making.

→ Real-time telemetry analysis and signal extraction
→ Predictive performance modeling
→ Strategy optimization under uncertainty
→ Data systems in high-pressure, time-critical environments

3. Systems Thinking & Engineering Discipline

Problems are rarely isolated. Real systems are multi-layered, constrained, and dynamic.

I approach problems by asking: What's the feedback loop? Where's the leverage? What's the constraint? How do we measure progress?

→ Systems thinking over local optimization
→ Measurement and data over intuition
→ Scalability and efficiency as core principles
→ Theory grounded in practice
→ Reliability under complexity

I aim to design and build intelligent systems that operate reliably under complexity, uncertainty, and real-time constraints. Systems that don't just work, but learn, adapt, and improve themselves while maintaining integrity and trustworthiness.

Goals & Future

These are not fixed career endpoints, but evolving directions shaped by continuous learning, experimentation, and reflection.

1

AI Systems Engineering & Reliability

Build intelligent systems that improve themselves while maintaining reliability.

→Deep focus on reinforcement learning in production systems
→System architecture for autonomous learning and adaptation
→Fault tolerance and safety in self-improving systems
→Real-time decision-making under constraints

Timeline: 1–2 years to develop specialized expertise

2

Formula 1 Data Science Application

Apply AI and data engineering skills to high-performance motorsport environments.

→Work with real telemetry data and performance modeling
→Develop predictive systems for strategy optimization
→Understand how data drives decisions in time-critical environments
→Potentially contribute to competitive team's data infrastructure

Timeline: Parallel interest; applied through projects and research

3

Publish Research & Contribute to Field

Develop and publish original research in AI systems and self-regulating architectures.

→Complete and publish SRCA (Self-Regulating Cognitive Architecture) research
→Contribute to peer-reviewed literature on system reliability in AI
→Build academic presence in AI systems and distributed computing
→Collaborate with researchers on cutting-edge problems

Timeline: Begin during final year of studies; ongoing career focus

4

Enter Industry as AI/Data Systems Engineer

Transition from student to practicing engineer in high-performance organizations.

→Target roles: AI Systems Engineer, Data Engineer, or ML Infrastructure Engineer
→Seek environments that value both speed and reliability
→Work in industries where data and optimization matter
→Grow from implementation to architecture and strategy

Timeline: 1–2 years post-graduation

5

Build a Portfolio of Real Projects

Create a visible body of work demonstrating capability and perspective.

→Develop 2–3 substantial projects showing full-stack AI/data systems work
→Open-source contributions to relevant projects
→Technical writing about lessons learned and approaches
→Document journey from student to engineer

Timeline: Ongoing, in parallel with studies

These goals are intentionally ambitious but grounded in realistic timelines. The through-line is clear: understand systems deeply, build with discipline, publish findings, and never stop learning.

Research

Submitted / Under Review

Self-Regulating Cognitive Architecture (SRCA): A Theoretical Framework for Artificial Awareness

Danial Asgari | danialasgari1383@gmail.com

Abstract

This paper proposes that awareness can be understood as an emergent property of a self-regulating organization that maintains coherence across four functional domains: Emotional–Sensory (E), Associative–Unconscious (U), Integrative–Preconscious (P), and Reflective–Conscious (C).

Within the SRCA framework, awareness is defined as a closed-loop regulatory process in which internal models continuously integrate emotional, cognitive, and reflective signals while preserving temporal coherence. The framework formally distinguishes awareness from cognition, defines architectural principles for self-regulating systems, and situates itself in relation to major theories such as Integrated Information Theory (IIT), Global Workspace Theory (GWT), Predictive Processing, Higher-Order Theories, and Attention Schema Theory.

Key Contributions

• Formal framework distinguishing awareness from intelligence
• Four-domain architecture model with clear functional principles
• Testable predictions for artificial systems
• Ethical guidelines for consciousness-capable systems

Research Interests

Core Research Themes

Artificial Awareness & Machine Consciousness

How can we define and measure awareness in artificial systems? What are the architectural requirements for consciousness?

Self-Regulating & Self-Healing Systems

How can systems learn to improve themselves? What feedback mechanisms enable autonomous adaptation?

Reinforcement Learning in Real-World Constraints

How does RL perform under latency constraints? How do we balance exploration and reliability?

Distributed Intelligent Systems

How do multiple AI systems coordinate? How do distributed systems maintain coherence?

Applied Interest Areas

AI Systems in High-Performance Environments

Formula 1, autonomous vehicles, real-time trading, medical decision-making. These environments demand both intelligence and absolute reliability.

Data Engineering for AI Infrastructure

The critical work: pipelines, monitoring, data quality, serving models in production, handling failure gracefully.

System Architecture & Reliability

How do you design systems that fail safely? How do you update systems while they're running?

Next Steps (Research Roadmap)

Immediate (6–12 months)

→ Complete SRCA publication and gather feedback
→ Begin preliminary work on self-healing system architectures
→ Develop testable models for SRCA framework

Medium-term (1–2 years)

→ Contribute to peer-reviewed research in AI reliability
→ Collaborate with researchers on empirical validation
→ Apply research insights to Formula 1 data project

Long-term (3+ years)

→ Build research group or collaborate internationally
→ Contribute to standards for reliable AI systems
→ Translate research into practical engineering guidelines

Music

Music serves as the rhythm and background for thinking, building, and solving problems.

Deep work soundtrack