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Administrative data

Ali Pilehvar Meibody

ali.pilehvarmeibody@epfl.ch

Nationality: Iranian

EPFL › ETU › EL-S › EL-E

Expertise

  • Algorithm–Hardware Co-Design for Spiking Neural Networks (SNNs)
  • Neuromorphic Computing & Hardware-Efficient AI
  • Semiconductor Devices & Edge-AI Systems
  • Machine Learning & Deep Learning for Materials Science
  • Computational Materials Modeling & Simulation
  • Nanomaterials & Additive Manufacturing (LPBF/EBM)
  • Python-based Scientific Software Development (PyGAMLab)
Ali Pilehvar Meibody is a graduate student in Materials Engineering for Industry 4.0 at Politecnico di Torino, specializing in the convergence of artificial intelligence, semiconductor technologies, and computational materials science. His work focuses on developing advanced machine learning methodologies and intelligent modeling frameworks to accelerate materials design, optimize manufacturing processes, and enable next-generation hardware-efficient AI.

He previously served as the Lead of the Artificial Intelligence Group at the Graphene and Advanced Materials Laboratory (GAMLab), where he worked on data-driven methods for nanomaterials, additive manufacturing, and functional material systems. In parallel, he has contributed to PyGAMLab, an open-source Python ecosystem designed to integrate nanomaterial simulation, AI-based analysis, and automated scientific workflows, supporting reproducible and scalable computational research.

He is currently completing his thesis at EPFL Microcity under the supervision of Prof. Sandro Carrara in the Bio/CMOS Interfaces (BCI) Laboratory, focusing on “Algorithm-Hardware Co-Design for Supervised Learning in Fully Hardware-Implemented Spiking Neural Networks.” His research aims to co-optimize learning rules, neuronal architectures, and physical hardware constraints to realize energy-efficient neuromorphic processors with direct relevance to future semiconductor and edge-AI technologies.

In addition to his research activities, he is engaged in AI education, scientific training, and open-source development, contributing to the broader adoption of computational intelligence across materials science and engineering.

Professionals experiences

Head Leader of Artificial Intelligence group

Dedicated to acquiring and harnessing engineering data across diverse fields, I specialize in deploying deep learning methodologies to construct predictive and optimization models. In addition to my research, I actively contribute to the education landscape by teaching Python and machine learning to students. My commitment extends to advancing scientific libraries in both Python and C programming, contributing to the intersection of theory and practical applications.

2023–2023

GAM Laboratory (Graphene and Advanced Material)

Education

Ms.C

| Material Engineering for Industry 4

2023 – 2025 Politecnico Di Torino

B.Sc.

| Polymer Science and Engineering

2017 – 2022 Amirkabir University of Technology (AUT)

Selected publications

A Neuromorphic Front-End based on Memristive Biosensors for Risk Pre-Screening by Pulse-Encoded Signals

Junrui Chen, Ali Pilehvar Meibody, Sandro Carrara
Published in IEEE Sensors Journal in 2026

Challenges and opportunities in additive manufacturing of high entropy alloys

Mohammad Taghian, Ali Pilehvar Meibody, Abdollah Saboori, Luca Iuliano
Published in Journal of Alloys and Compounds in 2025

Enabling Ultrastable Microbubbles with Graphene Aerogel Enrichment and Machine Learning for Highly Efficient Carbon Storage

Mohammad Hossein Akhlaghi, Malek Naderi, Ali Pilehvar Meibody, Shurui Yang, Mojtaba Abdi-Jalebi
Published in Available at SSRN 5393066 in 2025

PyGamlab: A Python framework for advanced modeling, simulation, and AI in nanotechnology and materials science

Ali Pilehvar Meibody, Danial Nekoonam, Malek Naderi
Published in Nanoscale and Advanced Materials in 2025

Unraveling the molecular magic: AI explains the formation of the most stretchable hydrogel

Shahriar Hojjati Emami, Ali Pilehvar Meibody, Lobat Tayebi, Mohammadamin Tavakoli, Pierre Baldi
Published in Reaction Chemistry & Engineering in 2025

Metamaterials in ultra-strain hydrogels

Shahriar Hojjati Emami, Ali Pilehvar Meibody
Published in Results in Physics in 2025

Toward closed-loop quality assurance in powder bed fusion additive manufacturing: Defect detection, machine learning, and computational modeling

Ali Pilehvar Meibody , M Taghian, A Saboori, L Iuliano
Published in Journal of Manufacturing Processes in 2026

Synthesis of Red Iron Oxide Pigments from a Mill Scale: Process Optimization and Machine Learning-Based Color Prediction

A Ghazitabar, Z Moradi, AP Meibody, J Asghari, M Naderi, A Kordbacheh
Published in Materials Today Communications in 2026