Ying Mao

Fordham University, New York, NY.

yingmao-min.jpg

Dr. Ying Mao is a tenured Associate Professor in the Department of Computer and Information Science at Fordham University in New York City. Currently, he serves as the Associate Chair for Undergraduate Studies.

He received his Ph.D. in Computer Science from the University of Massachusetts Boston advised by Dr. Bo Sheng . Before that, he obtained the Master of Science in Electrical Engineering from the The State University of New York at Buffalo .

His research interests mainly focus on computing systems and applications, including quantum-based systems, quantum-classical co-optimizations, quantum hardware-software co-design, quantum learning systems, cloud virtualization, resource management and system visualization.

His research has been supported by National Science Foundation, Google, NVIDIA, Microsoft and etc. A list of publications can be found on his Google Scholar page.

New Icon Our team has Ph.D. openings with full financial supports. We are looking for self-motivated students to work on quantum-classical system designs, optimizations and applications. Please find our Ph.D. in Computer Science program from this link. You are encouraged to contact me through email if interested.

What's New

.....More News
Nov 07, 2024 Student-led team won the VentureWell E-Team Pioneer award (link). We are exploring Sustainable and Energy-efficient Quantum Computing with Circuit Cutting techniques;
Jul 17, 2024 Two papers accepted to IEEE Quantum Week 2024 (QCE’24), (1) Scalable quantum circuit cutting (up to 2000x cost reduction when compared with IBM Qiskit Circuit Knitting Toolbox, Arxiv link); (2) Benchmarking Optimizers for Qumode State Preparation (Arxiv link);
Mar 07, 2024 Dr. Mao has been selected as a 2024 Google Cloud Research Innovator, the fourth cohort.
Feb 28, 2024 Our project on Quantum Data Science and Resilient Quantum Learning System was funded by National Science Foundation (NSF Link).
Feb 14, 2024 Our paper A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity was accepted by IEEE Transactions on Quantum Engineering (TQE).