Mohamadali Tofigh

Contact

Postdoctoral Scholars
Faculty of Science - Computing Science

Email: tofigh@ualberta.ca

Overview

Area of Study / Keywords

Reinforcement Learning Multimodal Large Language Models Embodied AI Multi-Agent Systems Robot Learning Deep Learning for Time Series

Teaching

Teaching Assistant

Machine Learning for Engineering Applications - MecE 610: Fall 2022, Fall 2023, Fall 2024

Feedback Control Design of Dynamic Systems - MecE 420: Summer/Spring 2022, Winter 2023, Summer/Spring 2023, Winter 2024, Summer/Spring 2024, Winter 2025

Featured Publications

RedoxFormer: A two-stage deep neural diagnosis framework for early-stage degradation in fuel cells via in-situ sensor measurements

Mohamadali Tofigh, Masood Fakouri Hasanabadi, Nafiseh Sang Nourpour, Amir Reza Hanifi, Daniel J. Smith, Mahdi Shahbakhti

Applied Energy. 2026 June; 10.1016/j.apenergy.2026.127782

A parallel Seq2Seq neural architecture for long-horizon performance forecasting and online condition monitoring of fuel cells

Mohamadali Tofigh, Daniel J. Smith, Amir R. Hanifi, Mahdi Shahbakhti

Applied Soft Computing. 2026 March; 10.1016/j.asoc.2025.114489

Dynamic Kolmogorov–Arnold networks for time-varying degradation modeling in solid oxide fuel cells

Mohamadali Tofigh, Daniel J. Smith, Amir R. Hanifi, Charles R. Koch, Mahdi Shahbakhti

Advanced Engineering Informatics. 2026 March; 10.1016/j.aei.2025.104233

A generative physics-informed reinforcement learning-based approach for construction of representative drive cycle

Amirreza Yasami, Mohamadali Tofigh, Mahdi Shahbakhti, Charles Robert Koch

Transportation Research Part D: Transport and Environment. 2026 February; 10.1016/j.trd.2025.105150

Intelligent ensemble architecture for capturing transient nitrogen oxides emission spikes in real-world driving conditions

Amirreza Yasami, Mohamadali Tofigh, Bahram Bahri, Charles Robert Koch, Mahdi Shahbakhti

Engineering Applications of Artificial Intelligence. 2025 October; 10.1016/j.engappai.2025.111359

Control-Oriented Modeling of a Solid Oxide Fuel Cell Affected by Redox Cycling Using a Novel Deep Learning Approach

Mohamadali Tofigh, Masood Fakouri Hasanabadi, Daniel Smith, Ali Kharazmi, Amir Reza Hanifi, Charles R. Koch, Mahdi Shahbakhti

Journal of Dynamic Systems, Measurement, and Control. 2025 March; 10.1115/1.4066268

Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells

Zeynab Salehi, Mohamadali Tofigh, Ali Kharazmi, Daniel J. Smith, Amir Reza Hanifi, Charles Robert Koch, Mahdi Shahbakhti

International Journal of Hydrogen Energy. 2025 January; 10.1016/j.ijhydene.2024.12.092

Temporal dilated convolution and nonlinear autoregressive network for predicting solid oxide fuel cell performance

Mohamadali Tofigh, Ali Kharazmi, Daniel J. Smith, Charles Robert Koch, Mahdi Shahbakhti

Engineering Applications of Artificial Intelligence. 2024 October; 10.1016/j.engappai.2024.108994

Transient modeling of a solid oxide fuel cell using an efficient deep learning HY-CNN-NARX paradigm

Mohamadali Tofigh, Zeynab Salehi, Ali Kharazmi, Daniel J. Smith, Amir Reza Hanifi, Charles Robert Koch, Mahdi Shahbakhti

Journal of Power Sources. 2024 June; 10.1016/j.jpowsour.2024.234555

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