PhD Researcher in Electrical Engineering | Scientific Machine Learning | Reliable AI | Predictive Modeling

I am an electrical engineering researcher working at the intersection of Artificial Intelligence, Scientific Machine Learning, and complex engineering systems. My research focuses on developing reliable, data-driven, and scientifically grounded computational methods for predictive modeling, time-series analysis, and engineering prognostics.

I am particularly interested in combining machine learning, mathematical modeling, and physical knowledge to improve predictive performance, reliability, and generalization in complex engineering applications.

• Scientific Machine Learning and Reliable AI
• Machine Learning and Deep Learning
• Time-Series Modeling and Predictive Analytics
• Remaining Useful Life (RUL) Prediction
• Battery Degradation and Health Prognostics
• Physics-Informed Machine Learning
• Statistical Learning and Uncertainty Quantification
• Feature Engineering and Model Evaluation
• Mathematical Modeling and System Identification
• Nanoelectronics and Semiconductor Devices
• Bioelectronics and Intelligent Sensing Systems

My research explores computational methods for modeling, analyzing, and predicting the behavior of complex physical and engineering systems.

My current research directions include:

• Battery Prognostics: Investigating battery degradation, State of Health (SOH) estimation, and Remaining Useful Life (RUL) prediction.
• Time-Series Modeling: Studying temporal representations and their effects on predictive performance.
• Scientific Machine Learning: Exploring data-driven and physics-informed approaches to engineering problems.
• Reliable AI: Emphasizing appropriate validation, leakage prevention, transparent experimentation, and careful interpretation of model performance.
• Computational Engineering: Applying machine learning, statistical methods, and mathematical modeling to complex engineering systems.

My goal is to develop predictive methodologies that are not only accurate but also reproducible, scientifically defensible, and relevant to practical engineering problems.

Repository: time-series-rul-benchmark

I am developing a reproducible benchmark for Remaining Useful Life prediction using multivariate time-series data from the NASA C-MAPSS FD001 dataset.

The project investigates the role of temporal representation in RUL prediction and emphasizes transparent experimental protocols and reliable model evaluation.

Key objectives:

• Establish reproducible machine learning baselines.
• Evaluate the influence of temporal window representations.
• Apply appropriate data splitting and preprocessing procedures to prevent data leakage.
• Compare predictive models using established regression metrics.
• Maintain transparent configurations and reproducible experiments.

Research areas: Time-Series Analysis, Predictive Maintenance, Machine Learning, and Prognostics.

I am interested in developing data-driven and physics-informed computational approaches for analyzing complex engineering systems. My broader research direction includes temporal learning, feature engineering, mathematical modeling, system identification, and rigorous model validation.

My research interests include battery degradation analysis, impedance-related features, State of Health estimation, and Remaining Useful Life prediction. I am interested in investigating reliable predictive methodologies and scientifically defensible evaluation strategies for electrochemical energy systems.

Private datasets, unpublished findings, and confidential experimental results are kept separate from public research repositories unless explicitly prepared for release.

• Python
• MATLAB
• C/C++
• SQL
• Bash

• Machine Learning and Deep Learning
• Statistical Learning
• Predictive Modeling
• Time-Series Analysis
• Feature Engineering
• Uncertainty Quantification
• Physics-Informed Machine Learning
• Model Evaluation and Validation

• NumPy
• Pandas
• SciPy
• Scikit-learn
• PyTorch
• TensorFlow
• JupyterLab
• Matplotlib
• Git
• LaTeX

• Mathematical Modeling
• Signal Processing
• System Identification
• Optimization
• Statistical Analysis
• Computational Analysis of Physical Systems
• Reproducible Experimental Design

My interdisciplinary interests span the following application domains:

• Electrochemical energy systems and battery prognostics
• Intelligent sensing and bioelectronics
• Nanoelectronics and semiconductor technology
• Smart grids and renewable energy systems
• Industrial Internet of Things (IIoT)
• Predictive analytics for complex engineering systems

My publications cover artificial intelligence, optimization, electric load prediction, renewable energy integration, and intelligent decision-making in electrical power systems.

1. A Hybrid Artificial-Based Model for Accurate Short Term Electric Load Prediction. Journal of Intelligent & Fuzzy Systems, 27(6), 3103–3110, 2014. Google Scholar

2. A New Modified Bat Algorithm to Solve Optimal Management of Multi-Objective Reconfiguration Problem. Journal of Intelligent & Fuzzy Systems, 27(3), 1567–1573, 2014. Google Scholar

3. Optimal Unit Commitment of Renewable Energy Sources in the Micro-Grids with Storage Devices. Journal of Intelligent & Fuzzy Systems, 28(2), 537–546, 2015. Google Scholar

4. Stochastic Reactive Power Planning in Distribution Systems Considering Wind Turbines Electric Power Variations. Journal of Intelligent & Fuzzy Systems, 28(3), 1081–1087, 2015. Google Scholar

5. A New Intelligent Method for Optimal Coordination of Vehicle-to-Grid Plug-in Electric Vehicles in Power Systems. Journal of Intelligent & Fuzzy Systems, 28(3), 1291–1298, 2015. Google Scholar

6. Energy, Economic and Environmental Impacts of Cryptocurrency Mining: A Review of Sustainable Integration Pathways in Power Systems. Energy Conversion and Management: X, article 101413, 2026. Google Scholar

For the complete publication record, see my Google Scholar profile.

I maintain an academic research profile documenting my publications, citation record, and research collaborations.

• Google Scholar: Sadreddin Saleh
• GitHub: sadra-github
• Reproducible Research: Time-Series RUL Benchmark

According to the Google Scholar information provided, my profile lists 36 citations, an h-index of 4, and an i10-index of 0. These metrics may change over time.

My background combines electrical engineering, computational research, scientific machine learning, and applied engineering. My interests also encompass engineering education, programming, mathematical methods, and research-oriented problem solving.

I am interested in interdisciplinary collaboration connecting artificial intelligence, physical sciences, and engineering applications.

I founded and direct iotnanoai, an initiative focused on applied research and engineering consulting involving artificial intelligence, the Internet of Things, nanotechnology, smart grids, renewable energy, and industrial systems.

Website: iotnanoai.com

My approach to research is based on scientific rigor, reproducibility, methodological transparency, and evidence-based evaluation.

I consider reliable model evaluation as important as predictive performance. My research approach emphasizes appropriate data splitting, leakage prevention, transparent preprocessing, systematic baseline comparison, and suitable evaluation metrics.

I am interested in understanding not only whether a model performs well, but also why it performs well, under which conditions its performance remains reliable, and how its limitations can be identified. I value careful interpretation of experimental results and avoid making claims that extend beyond the available evidence.

Where appropriate, I explore the integration of mathematical modeling and physical knowledge with data-driven learning. My goal is to develop machine learning methodologies that are scientifically defensible and relevant to real-world engineering systems.

I also value open science and responsible research communication. I aim to share public code, documented experiments, and reproducible benchmarks while protecting private datasets, unpublished findings, and confidential research materials.

I welcome opportunities for academic collaboration, interdisciplinary research, and scientific exchange in:

• Scientific Machine Learning and Reliable AI
• Time-Series Modeling and Predictive Analytics
• Battery Prognostics and Remaining Useful Life Prediction
• Physics-Informed Machine Learning
• Computational Engineering and Intelligent Energy Systems

• Email: sadreddin.saleh@ut.ac.ir
• Email: iotnanoai@gmail.com
• Professional Inquiries: info@iotnanoai.com
• Website: iotnanoai.com

Research philosophy: reproducibility, methodological transparency, rigorous evaluation, and scientifically grounded machine learning.