Control · Optimization · AI · Energy · Digital Health
Intelligent optimization and control for complex engineered systems.
I am Daniel Rajabi, a control and optimization researcher with an M.Sc. in Electrical Engineering (Control Systems), working across multi-objective and distributed optimization, computational intelligence, power & energy systems, and interdisciplinary digital-health applications.
Research focusDistributed Optimization
Multi-objective Optimization
Computational Intelligence
Power Systems
Digital Health
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Optimal ControlDistributed OptimizationMetaheuristicsComputational IntelligencePower SystemsSmart GridsMicrogridsDigital HealthBiomedical Signal ProcessingIndustrial Automation
Optimal ControlDistributed OptimizationMetaheuristicsComputational IntelligencePower SystemsSmart GridsMicrogridsDigital HealthBiomedical Signal ProcessingIndustrial Automation
ACADEMIC SNAPSHOT
My research profile at a glance.
A visual overview of my education, research identity, technical skills, engineering experience, honors, and interdisciplinary research directions.
PROFILE
Optimization, control, and computational intelligence — grounded in real systems.
Control and optimization researcher with a strong foundation in optimal control, systems theory, multi-objective and distributed optimization. My academic path has progressed from modeling and analyzing power-system dynamics in MATLAB to solving real-world non-convex optimization problems using Python and computational-intelligence methods.
This work has strengthened my experience in metaheuristics, data-driven modeling, machine learning, uncertainty-aware decision-making, and scientific computing. My research spans intelligent power and energy systems, microgrids, industrial automation, and digital health, with particular interest in combining optimization, control, and AI for reliable, efficient, and sustainable engineered systems.
Looking ahead, I aim to develop scalable, data-driven, and resource-aware methods for complex cyber-physical and biomedical applications.
RESEARCH INTERESTS
Core methods, intelligent systems, and application domains.
The portfolio is organized around a methodological core in optimization and control, extended through computational intelligence and applied across energy and digital-health systems.
Optimization & Control
Multi-objective optimization; Metaheuristics; Distributed optimization; Optimal control; Consensus & Multi-agent systems; Game-Theoretic Decision-Making; Reliable & Sustainable Control.
Artificial Intelligence
Machine learning; Deep learning; Computational intelligence; Intelligent decision-making; Data-driven modeling & Analytics; Uncertainty modeling & Forecasting.
Power & Energy Systems
Power-system optimization; Smart grids & Microgrids; Electricity markets; Renewable energy integration; Energy management.
Digital Health
Wearable health monitoring; Biomedical signal processing; Biosensors & Medical Instrumentation; AI for Healthcare; Health Data Analytics for Detection and Prediction.
EDUCATION
Electrical engineering — from power systems to control.
Oct 2021 – Jul 2024
M.Sc. in Electrical Engineering (Control Systems)
Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
- Final Rank: 3ʳᵈ out of 36 students in program; GPA: 3.88/4.00 (17.23/20).
- Thesis: “Solving the Combined Economic–Emission Dispatch Problem under Realistic Conditions and Proposing an Optimization Algorithm.”
- Relevant Coursework: Optimal Control Systems, System Identification, Neural Networks, Multivariable Control, Nonlinear Control, Fuzzy Control, Economy & Energy Management, Power System Utilization.
Sep 2016 – Sep 2020
B.Sc. in Electrical Engineering (Power Systems)
Imam Khomeini International University, Qazvin, Iran
- Final Rank: Top 9% of 135 students in program; GPA: 3.15/4.00 (15.74/20).
- Thesis: “Solving the Economic Load Dispatch Problem Using a Novel Approach.”
- Relevant Coursework: Signals and Systems, Linear Control Systems, Electric Circuits I & II, Modern Control Systems, Fundamentals of Operations Research, Engineering Probability, Engineering Mathematics, Power System Analysis I & II.
PUBLICATIONS & MANUSCRIPTS
Published research, preprints, and ongoing manuscripts.
Five current research outputs span wearable digital health, metaheuristic optimization, dynamic economic-emission dispatch, distributed microgrid energy management, and a wearable-technology review.
Design and prototyping of an AI-powered wearable device for continuous vital signs monitoring with intelligent alerting.
Huriye Sadat Sadeghi, Mohsen Ahmadi, Daniel Rajabi.
Scientific Reports 15, 45178 (2025). Published Nov 2025.
DOI: 10.1038/s41598-025-28772-2 ↗ Official article ↗Olympic Champion Algorithm (OCA): A new human-inspired metaheuristic algorithm for solving optimization problems.
Daniel Rajabi, HuriyeSadat Sadeghi, Mohammad Bagher Menhaj, Amir Abolfazl Suratgar, Seyedali Mirjalili.
Under review at the International Journal of Artificial Intelligence and Soft Computing (2026).
Preprint DOI ↗Data-Driven Multi-Objective Optimization for Dynamic Combined Economic Emission Dispatch (CEED): Application of the OCA and Benchmarking of Metaheuristic Algorithms.
Daniel Rajabi, AmirAbolfazl Suratgar, Mohammad Bagher Menhaj, Babak Safaei.
Preprint DOI ↗Computational Intelligence–Driven Distributed Multi-Objective Energy Management for Sustainable Renewable-Rich Hybrid Microgrids under Uncertainty: Machine Learning-Based Forecasting, Energy Storage, and Dynamic Economic-Emission Dispatch.
Daniel Rajabi, Mohammad Bagher Menhaj, Amir Abolfazl Suratgar.
Recent Advances in Wearable Technologies for Continuous Vital Sign Monitoring in Digital Health: Enabling Technologies, Artificial Intelligence, Clinical Translation, and Future Directions.
HuriyeSadat Sadeghi*, Daniel Rajabi*, Mohsen Ahmadi. *These authors contributed equally to this work and share first authorship.
FUNDED RESEARCH EXPERIENCE
From research concepts to funded interdisciplinary work.
From Research to Prototype | Interdisciplinary Development of an AI-Powered Wearable Vital-Sign Monitoring System
Digital Health · Research supported by Shahid Beheshti University of Medical Sciences
- Theoretical & Data-Driven Research: PPG-based cuffless BP/RR estimation, embedded AI, multi-vital-sign monitoring, and intelligent alerting.
- Prototype Development: Hardware integration, on-device implementation, system testing, and validation of a functional wearable prototype.
- Research Outcome: Peer-reviewed publication with continued development toward clinical validation and commercialization.
Research Contributor & Proposal Co-Author | Joint IIASA–INSF Postdoctoral Fellowship Project
International Institute for Applied Systems Analysis (IIASA) & Iran National Science Foundation (INSF)
Contributed to the development of the research proposal and technical framework integrating machine learning, distributed optimization, and metaheuristic methods for sustainable power-grid operation.
Grant record ↗HIGHLIGHTED RESEARCH PROJECTS
Research assistantships and selected graduate projects.
2022–Present
Research Assistant · Distributed and Intelligence Optimization Research Laboratory (DIOR)
Amirkabir University of Technology (AUT) · under the supervision of Prof. Amir Abolfazl Suratgar ↗
Energy trading and markets, power distribution and management, resource allocation, and distributed optimization in microgrids
Developed distributed optimization frameworks for energy trading, market clearing, resource allocation, and resilient microgrid coordination under realistic operational and network constraints.
DIOR Energy Systems ↗A Distributed Approach for Solving Multi-Objective Optimal Power Flow
Developed a distributed optimization approach for multi-objective optimal power flow, enabling coordinated decision-making across interconnected power-system components while handling conflicting objectives and operational constraints.
DIOR project index ↗2023–Present
Research Assistant · Advanced Learning Systems Laboratory
Amirkabir University of Technology (AUT) · under the supervision of Prof. Mohammad B. Menhaj ↗
Development and Benchmarking of an OCA-Based Python Optimization Framework for Derivative-Free, Non-Convex, and Discrete Optimization
Developed and benchmarked a Python-based implementation of the Olympic Champion Algorithm (OCA) for black-box, non-differentiable, non-convex, and constrained optimization, with support for binary and high-dimensional search spaces, and evaluated its convergence, robustness, and solution quality on data-driven and feature-selection problems as a gradient-free alternative for cases where conventional gradient-based optimization is difficult or unsuitable.
Selected Graduate Course Projects2021–2024
- Multi-Objective Optimization & Control for Microgrid P2P Energy Transactions: Modeled producer–consumer–prosumer energy transactions using multi-objective optimization, OCA, optimal-control concepts, and Pareto-front analysis.
- Scenario-Based Economic & Emission Dispatch with Metaheuristic Optimization: Implemented and benchmarked SMA, GWO, PSO, WOA, and SA for multi-objective economic dispatch under environmental and operational constraints, analyzing cost–emission trade-offs across multiple scenarios.
- Optimization in Electricity Markets: Developed and analyzed optimization models in MATLAB for electricity-market operation, profitability, resource allocation, system constraints, and reliability.
- CNN Hyperparameter Optimization for Sensor-Based Human Activity Recognition: Applied metaheuristic optimizers, including GWO, WOA, and OCA, to tune CNN hyperparameters for sensor-based human activity recognition.
- Machine Learning for Pediatric Length-of-Stay Prediction: Applied Decision Tree, CNN, KNN, and SVM models to pediatric respiratory-disease data for hospital length-of-stay prediction and comparative model evaluation.
TEACHING & ACADEMIC SERVICE
Teaching Assistant — 4 Courses
Amirkabir University of Technology ↗ · Electrical Engineering · 2022–2023
ACADEMIC SERVICE
Conference Reviewer — 3 Papers
ICROM · 2024
Reviewed three conference submissions related to control, optimization, automotive safety, vision-based precision landing, and robotic motion planning.
TECHNICAL SKILLS
Scientific computing, optimization, modeling, and automation.
Programming Languages
MATLAB; Python; C/C++
Scientific Computing & Machine Learning
Deep Learning: PyTorch, TensorFlow
Core Libraries: scikit-learn, NumPy, SciPy, pandas
Signal Processing: Real-Time & Biomedical Signal Processing
Optimization & Control
Metaheuristics, Multi-Objective Optimization, Optimal Control, Distributed Optimization, Consensus & Multi-Agent Systems, GAMS
Modeling, Simulation Tools & Automation
System Modeling: Simulink, Proteus, Fritzing, COMSOL Multiphysics
Embedded Systems: Arduino, Microcontrollers, AVR
Industrial Automation: PLC, Siemens TIA Portal, SCADA/BMS, HVAC
Documentation & Productivity
LaTeX, Microsoft Office Suite
Professional Competencies
Project Coordination, Technical Troubleshooting, Cross-Functional Collaboration, Leadership, Teamwork, Adaptability, Persistence, Independent Problem-Solving
INDUSTRIAL EXPERIENCE
Real-time monitoring and control in operational environments.
Sep 2025 – Present
Building Management System (BMS) Engineer
Zist Takhmir Pharmaceutical Company ↗
- Worked with large-scale sensor-based monitoring and control infrastructure, including BMS, SCADA, HVAC, and cleanroom automation, covering approximately 700 real-time operational parameters.
- Monitored and analyzed temperature, humidity, pressure, water, gas, electrical, steam, and utility-system variables, supporting fault detection, alarm management, data logging, and reliable system operation.
- Gained hands-on experience in industrial automation, real-time monitoring, systems integration, multidisciplinary engineering, leadership, and technical teamwork within a pharmaceutical manufacturing environment.
Jul 2019 – Sep 2019
Control Engineer (Intern)
Shahid Rajaee Power Plant · Qazvin, Iran
- Completed a three-month industrial internship in a large power-generation environment, gaining exposure to plant operations and electrical engineering practice.
- Collaborated closely with system operators to define precise defect criteria, translating practical needs into robust technical specifications.
- Validated system performance and accuracy through rigorous real-world testing, fine-tuning model parameters to meet industrial performance targets.
OTHER HONORS & ACADEMIC DISTINCTIONS
National entrance-examination distinctions.
Admitted to Iran’s 3rd-ranked university for M.Sc. study through the National Entrance Examination, with government-funded, tuition-free admission, after ranking in the top 0.07% among 145,564 candidates (2021).
Admitted to Iran’s 9th-ranked university for B.Sc. study through the National Entrance Examination, with government-funded, tuition-free admission, after ranking in the top 0.12% among 761,273 candidates (2016).
ACADEMIC REFERENCES
Academic advisors and referee.
Prof. Mohammad B. Menhaj
M.Sc. Thesis Advisor
Professor, Electrical Engineering, AUT
Prof. Amir Abolfazl Suratgar
M.Sc. Thesis Advisor, Senior Member, IEEE
Professor, Electrical Engineering, AUT
Prof. Seyed Hossein Hosseinian
M.Sc. Thesis Referee & Course Professor
Professor, Electrical Engineering, AUT
CONTACT
Research, PhD opportunities, and academic collaboration.
For research discussions or collaboration related to optimization, control, intelligent systems, power & energy, industrial automation, or digital health, feel free to get in touch.