My Experience and Interests:

Data Science & Machine Learning
Leveraging cutting-edge statistical methods and machine learning algorithms to extract meaningful patterns and insights from complex datasets. I have applied these skills in industrial settings, designing and optimizing machine learning pipelines using LangChain and OpenAI for tasks such as XML processing, intelligent data generation, and automated decision-making. I also developed AI models for step-by-step math solutions, improving accuracy and user engagement by enhancing interpretability and adaptive learning. My expertise spans deep learning, probabilistic modeling, and reinforcement learning, enabling robust and scalable AI-driven solutions.

Theoretical Physics
Utilizing methods from fluid dynamics, non-equilibrium thermodynamics, quantum mechanics, and statistical physics to investigate fundamental mechanisms underlying critical phenomena and self-organized systems in nature. My expertise includes stochastic processes, random walks, percolation theory, renormalization group methods, and Ising models. I apply these concepts to study emergent properties in complex systems, from quantum to classical regimes.

Biological Systems Modeling
Applying mathematical and computational techniques to study biological dynamics, from individual behavior to large-scale ecological interactions. My research includes agent-based modeling of pheromone-guided navigation, adaptive evolutionary strategies, and self-organized biological systems. By integrating statistical physics and machine learning, I analyze biological datasets to uncover fundamental principles governing collective behavior, pattern formation, and resilience in natural systems.

Network Modeling
Exploring the structure, dynamics, and evolution of complex networks, with applications ranging from communication and transportation systems to social, ecological, and artificial intelligence networks. My work involves developing graph-based algorithms and statistical models to understand emergent behaviors, cascading failures, and robustness in interconnected systems.

Industrial & Research Development
Bridging academic research and industrial applications by developing computational models that drive innovation. I thrive in interdisciplinary environments, applying scientific problem-solving to real-world challenges in sectors such as finance, healthcare, and environmental science. My experience includes collaborating with cross-functional teams to integrate AI, simulation, and optimization techniques for practical implementation.

Educator & Communicator
Passionate about teaching and knowledge-sharing, I have experience in academic instruction, mentoring, and presenting complex technical concepts to diverse audiences. I enjoy translating mathematical and computational insights into accessible narratives, fostering collaboration between researchers, engineers, and decision-makers. My ability to convey technical knowledge effectively has been instrumental in both academia and industry, helping bridge the gap between theory and application.

EXPERIENCE & EDUCATION

• University of Melbourne: Senior Researcher

  • Machine Learning for Complex Systems: Applying deep learning, probabilistic modelling, and AI-driven simulations to analyze amorphous materials, including polymers, glasses, and granular systems.

  • AI in Non-Equilibrium Thermodynamics: Developing ML frameworks to model non-equilibrium behaviours, uncover new material properties, and improve predictive capabilities.

  • Molecular Dynamics & AI Integration: Enhancing molecular dynamics simulations with ML-based models, improving accuracy, scalability, and efficiency.

  • Supervision & Mentorship: Guiding PhD students and postdocs in applying machine learning to computational physics and materials science, fostering skill development.

  • Cross-Disciplinary Collaboration: Partnering with physicists, chemists, and material scientists to translate high-dimensional data into practical insights.

  • Custom Algorithm Development: Designing scalable ML models for physics-informed learning, data-driven discoveries, and real-world applications.

• Outlier (USA): AI Math Tutor & TESTIFI (Germany): Machine Learning Specialist

  • Developed AI models for step-by-step math solutions, improving accuracy and user engagement.

  • Optimized ML models through content evaluation, enhancing real-world applicability.

  • Built knowledge graphs from Jira data, automating workflows.

  • Applied A/B testing to refine recommendation accuracy and model performance.

• University of Queensland: Senior Researcher 

  • Multi-agent random walks in artificial/ecological systems

  • Statistical Modeling and Stochastic Processes

  • Developed non-equilibrium simulation algorithms and theoretical frameworks 

  • Supervised projects in machine learning applications

• Korea Institute of Energy Technology: Research & Development, Machine Learning & Data Science 

  • Applied complex network theory and graph neural networks to renewable energy studies

  • Developed the necessary technologies, focusing on technologies for analysing, predicting, operating, protecting, and controlling future grids

  • Done research on carbon-free power generation, power conversion, electric machinery, and power systems

• Korea Institute for Advanced Study: Research Fellow, Statistical Modelling & Data Science

  • Worked on stochastic processes, statistical models, and grid technologies

  • Applied machine learning to investigate advanced energy materials

  • Worked on Quantum Hall plateau transition projects

  • Participated in the supervision of research postgraduate students 

• University of Tehran & Institute for Research in Fundamental Sciences: Research & Development

  • Developed codes: Head of a team working on computational physics, which involves converting the FORTRAN codes to C++ and making them object-oriented, optimized, and parallelized

  • High-performance computing server manager and developer, web designer

  • Research: Self-Organized Critical phenomena, Growth Processes

  • Teaching: Advanced computational physics - Advanced solid state - Disordered systems

  • Supervised graduate students: Simulation of self-organized critical models on the human brain networks - Fractional Brownian motion in the surface growth problems

• Sharif University of Technology: PhD, Theoretical & Computational Physics

  • Thesis: Classification of two-dimensional Surface Growth Models using Schramm-Loewner Evolution

  • Supervisor: Sahin Rouhani

• Sharif University of Technology: MSc, Theoretical Physics

  • Thesis: Calculation of structure coefficient in conformal field theory (CFT) by AdS/CFT correspondence

  • Supervisor: S. Moghimi-Araghi

• Sharif University of Technology: BSc, Theoretical Physics

SKILLS

• Machine Learning: Traditional ML, Neural Networks, CNN, RNN GAN, Autoencoders, Prompt engineering, Reinforcement Learning, LLM, RAG

• Frameworks: PyTorch, TensorFlow, Scikit, Pandas, NetworkX, Git

• Programming: Python, C++, C#, Fortran, Shell Script, SQL, MPI, OpenMP, Octave, Matlab

• Algorithms: Data Structures, Graph Algorithms, Dynamic Programming

• Software Development: GitHub

• Mathematical Tools: Mathematica, MathKernel

• Statistical Expertise: Extreme Value Statistics, Entropy Functional, Stochastic Evolution, Growth Processes

• Data Analysis: Time Series, Statistical Modelling

• Simulation Techniques: Molecular Dynamics, Monte Carlo, Cellular Automaton, Quantum Annealing, Simulated Annealing, Markov Chains

• Knowledge: Statistical Physics, Critical Phenomena, Conformal Field Theory, Complex and Self- Organized Systems, Chaos, Non-Linear Dynamics, Turbulent, Granular Materials, Quantum Systems