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Mohammad Sadeghi, Ph.D. has an extensive work experience in the field of Machine Learning. Mohammad started their career in 2012 as a Teaching Assistant at the University of Isfahan. In 2015, they joined Özyeğin University as a Research Assistant and Teaching Assistant. During this time, they conducted fundamental and applied research on the physical layer aspects of communication systems. In the same year, they also joined SUASIS Underwater Systems as a Research and Development Scientist, where they developed and implemented USRP-based adaptive MIMO-OFDM underwater acoustic communication modem. In 2017, they joined the University of Ottawa as a Research Assistant, where they implemented state-of-the-art Bayesian Reinforcement Learning methods in MATLAB to address energy trading problems in multi-agent microgrid systems. In 2021, they became a Mitacs Machine Learning Intern at NetExperience, where they developed deep reinforcement learning algorithms for contention window tuning to optimize performance in enterprise WiFi (IEEE 802. 11ax) Networks. Currently, they are working as a Machine Learning Engineer at both FigBytes Inc. and Immigratic. At Immigratic, they are developing a deep learning model in Python to evaluate the chance of Canada students visa for a particular applicant. Mohammad is also implementing data preprocessing, data augmentation, exploratory data analysis and, visualization and deployment of ML model on cloud services (AWS and GCP).

Mohammad Sadeghi, Ph.D. has a comprehensive educational background. Mohammad obtained their Bachelor of Science (B.S.) in Electrical Engineering from the University of Isfahan in 2014. Mohammad then went on to earn their Master's Degree in Electrical and Electronics Engineering from Özyeğin University in 2017. In 2022, they completed their Doctor of Philosophy - PhD in Electrical, Electronics and Communications Engineering from the University of Ottawa. Additionally, they have obtained several certifications from Coursera in various fields such as Deep Learning Specialization, Sequence Models, Convolutional Neural Networks, Structuring Machine Learning Projects, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Neural Networks and Deep Learning, and Machine Learning with Python.