Muhammad Usman Arif
Education
- Ph.D. in Computer Science (AI), IBA Karachi
- MS in Computer Science & I.T., NED University
- M.Sc. in Applied Physics & B.Sc. in Mathematics, University of Karachi
Teaching Experience
- Assistant Professor (Computer Science), Iqra University
- Assistant Professor, Dow University of Health Sciences (Data Science for Healthcare Informatics)
- Adjunct Faculty & Researcher, IBA Karachi (Artificial Intelligence Lab)
- Senior Lecturer (Computer Science), UIT University
- Lecturer (Applied Physics), University of Karachi
Industry Experience
- Technical Trainer (AI), Habib Bank Limited (Corporate training in AI & Data Science)
- Senior Consultant, Business Grid Pvt. Ltd. (AI-based solutions)
Courses Taught
- Artificial Intelligence
- Data Structures and Algorithms
- Programming
- Robotics
- Deep Learning
- Healthcare Informatics
Research Interests
- Cognitive Robotics
- Computational Intelligence
- AI & Machine Learning
- Data Science & Optimization
- Robotics
Selected Awards and Accomplishments
- Proven Professional Data Science Associate, DELL EMC Certification
Biography
Dr. Muhammad Usman Arif is a computer scientist and researcher with a diverse academic journey, beginning with undergraduate studies in Mathematics and Applied Physics, and evolving into a career in Artificial Intelligence and Robotics. He holds a Ph.D. in Computer Science with a focus on AI-driven planning, task optimization, and scheduling. During his Ph.D. at IBA Karachi, he explored task allocation in swarm-based robotics. He also ventured into effective gait design of humanoid robots using different AI Optimization techniques.
His research contributions are recognized in top-tier journals and international conferences such as AAAI, ACM-TAAS, and IEEE ROBIO. Alongside academia, he has been actively involved in providing corporate training in AI and Data Science to banking professionals and consulting on AI-based automation solutions. Usman has taught undergraduate and graduate courses in Computer Science, Data Science, Deep Learning, AI, and Health Informatics.
Key Publications
- On-line Task Allocation for Multi-Robot Teams Under Dynamic Scenarios, Intelligent Decision Technologies (2024)
- A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking, ACM-TAAS (2022)
- Robot Coalition Formation Against Time-Extended Multi-Robot Tasks, International Journal of Intelligent Unmanned Systems (2022)
- A Generic Evolutionary Algorithm for Multi-Robot Task Allocation, CAAI (2019)
- A Flexible Evolutionary Algorithm for Task Allocation in Multi-Robot Teams, ICCCI (2018)
- An Evolutionary Algorithm Based Framework for Task Allocation in Multi-Robot Teams, AAAI (2017)
- On Developing a Hybrid Approach For Kick Optimization in Humanoid Robots, IEEE ROBIO (2014)
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