Dr Salem Ameen
School of Science, Engineering & Environment
Current positions
Lecturer
Biography
Lecturer in Artificial Intelligence Robotics and Automation.
Dr. Salem Ameen is a prominent academic and researcher in the dynamic fields of Artificial Intelligence (AI), Robotics, and Automation. His journey into academia culminated in earning his Ph.D. in 2017 from the University of Salford, where he specialized in "Optimizing Deep Learning Using Multi-Armed Bandit," a significant milestone that set the stage for his future contributions in deep learning.
After completing his doctorate, Dr. Ameen began his academic career at the University of Salford. Starting as a Sessional Lecturer in 2018, he demonstrated unwavering commitment and expertise, which led to his recent appointment as a full Lecturer in AI, Robotics, and Automation. This progression reflects his profound knowledge and dedication to his field.
In his research, Dr. Ameen focuses on enhancing the efficiency and effectiveness of AI technologies, with particular interest in optimizing deep learning networks. He has also explored the application of AI in healthcare, striving to improve diagnostic processes and patient outcomes. His research and leadership have significantly contributed to advancements in AI and its practical applications.
As an educator, Dr. Ameen is passionate about bringing real-world challenges into the classroom. He has supervised a variety of Master's projects, encompassing areas such as Computer Vision, Deep Learning, Reinforcement Learning, Robotics, and Machine Learning, thereby playing a key role in developing the next generation of AI experts.
Currently, Dr. Ameen is at the forefront of integrating AI with robotics to create advanced automated systems. His work promises to be revolutionary, particularly in the healthcare sector. As a visionary in his field, Dr. Ameen continues to inspire and lead in the evolving landscape of artificial intelligence and automation technologies.
Areas of Research
Deep Learning Optimization: Advancing the efficiency of deep neural networks using innovative techniques such as multi-armed bandit algorithms.
Neural Network Pruning: Creating methods to streamline deep neural networks, enabling their deployment in computationally limited environments.
Machine Learning in Healthcare: Building and applying machine learning models to improve medical diagnostics and patient care systems.
Artificial Intelligence in Robotics: Integrating AI with robotics to enhance automation in various sectors, with a special interest in healthcare applications.
Educational Technology: Exploring the use of AI and machine learning to improve learning outcomes and pedagogical methods.
Areas of Supervision
Deep Learning
Explainable AI (XAI)
Generative Adversarial Networks (GANs)
Deep Reinforcement Learning
Neural Architecture Search (NAS)
Efficient Deep Learning Training Methods
Machine Learning
Federated Learning
Causal Inference in Machine Learning
Robustness in Machine Learning
Bayesian Machine Learning
Transfer Learning
Robotics
Human-Robot Interaction (HRI)
Swarm Robotics
Bio-inspired Robotics
Robot Perception and Sensing
Soft Robotics
Automation
AI in Smart Manufacturing
Autonomous Vehicle Systems
Robotics in Precision Agriculture
Automation in Healthcare
Machine Learning for Smart Grid Technology
Level 7:
Artificial Intelligence
Mobile Robotics
Interactive Visualization
Automation and Robotics
Level 5:
Numerical Analysis
Computing Laboratory (Numerical Methods and Simulation)
Level 4:
Probability
Mathematics and Computing
Level 3:
Mathematics 1
Mathematics 2
Qualifications
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Machine learning
2013 - 2017 -
Computer Science and Engineering
2007 - 2009