Muhammad Kashif
Postdoctoral Associate
Affiliation: NYU Abu Dhabi
Education: BS Comsats Institute of Information Technology, Pakistan; MS Istanbul Sehir University Turkey; PhD Hamad Bin Khalifa University Qatar
Research Websites: Center for Quantum and Topological Systems eBRAIN Lab
Research Areas: Quantum computing; quantum machine learning; quantum algorithms
Muhammad Kashif began his academic journey with a Bachelor's degree in Electrical (Electronics) Engineering from COMSATS Institute of Information Technology, Pakistan, in 2015. He then completed his MSc in Electronics and Computer Engineering from Istanbul Sehir University, Turkey, in 2020. He pursued his PhD studies at Hamad Bin Khalifa University in Qatar, where he focused on exploring potential quantum advantages in machine learning during the Noisy Intermediate-Scale Quantum (NISQ) era, specifically addressing trainability challenges in Quantum Neural Networks (QNNs). He successfully defended his PhD thesis in May 2023.
Currently, Kashif serves as the Research Team Lead at eBRAIN Lab, , Department of Computer Engineering, Division of Engineering, and a Postdoctoral Research Associate at the Center for Quantum and Topological Systems (CQTS) at New York University (NYU) Abu Dhabi. His research interests lie in Quantum Machine Learning (QML) and classical machine learning, particularly in how these fields intersect and complement each other.
Research Summary
Muhammad Kashif's research interests focus on the design space exploration of quantum-specific parameters in QNNs, benchmarking QML algorithms to assess the benefit of incorporating quantum components into neural networks, and addressing the barren plateaus problem in QNNs. His work also aims to enhance the trainability of QNNs and Quantum Convolutional Neural Networks while exploring how classical machine learning techniques can benefit QML and vice versa. He is actively engaged in applying QML and quantum optimization algorithms to the finance sector, including areas such as portfolio optimization and credit risk analysis, etc. Additionally, Kashif is keen on studying the effects of quantum hardware noise on QML algorithms and developing strategies for noise mitigation.