Mr. Zihao Mo - Neural network Neural network compression verification - Best Researcher Award 

Augusta university - United States

Author Profile

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Early Academic Pursuits

Mr. Zihao Mo embarked on his academic journey with a strong foundation in Electrical Engineering, earning a Bachelor's degree from the University of Missouri - Columbia and a Bachelor of Engineering degree from Southern China University of Technology in 2019. During his undergraduate studies, Mo demonstrated a keen interest in Information Engineering, laying the groundwork for his future endeavors in the realm of technology and computer science.

Driven by a passion for innovation, Mo pursued further academic excellence, obtaining a Master of Science in Electrical and Computer Engineering from the prestigious University of California, San Diego in 2021. Here, he delved deeper into machine learning and data analytics, expanding his skill set and theoretical understanding of these burgeoning fields.

Professional Endeavors

Mr. Mo's professional journey reflects his commitment to the advancement of technology, particularly in the domains of machine learning and data analysis. As a Test Specialist at Digitbridge, Inc., he played a pivotal role in the development and testing of software systems, utilizing his expertise in C# programming and SQL database management. His contributions were instrumental in enhancing the functionality and reliability of WMS, ERP, and Commerce Central systems, showcasing his proficiency in software engineering and quality assurance.

Moreover, Mo's involvement as a Program Committee Member for prestigious conferences such as AAAI and DATA underscores his dedication to academic and research-oriented pursuits. His role in reviewing papers and contributing to the organization of workshops demonstrates his engagement with the scholarly community and his commitment to staying abreast of the latest developments in his field.

Contributions and Research Focus On Neural network 

Mr. Mo's research endeavors have primarily revolved around machine learning, with a focus on neural network compression and optimization techniques. His publications in esteemed journals and conferences elucidate his innovative approaches to addressing challenges in model compression, quantization error computation, and hybrid automaton frameworks. By developing novel methodologies for enhancing the efficiency and effectiveness of convolutional and feedforward neural networks, Mo has made significant contributions to the advancement of machine learning algorithms and techniques.

Accolades and Recognition

Throughout his academic and professional journey, Mo has garnered recognition for his outstanding contributions to the field of computer science and engineering. His publications have been well-received within the academic community, earning him accolades and citations for his pioneering research in neural network compression and optimization. Additionally, his role as a Program Committee Member for prestigious conferences speaks to his expertise and reputation within the research community.

Impact and Influence

Mr. Mo's work has had a tangible impact on both academia and industry, with implications for the development of more efficient and scalable machine learning models. By devising innovative approaches to model compression and quantization, he has paved the way for advancements in edge computing, IoT devices, and other resource-constrained environments. His research has the potential to revolutionize the deployment and implementation of machine learning algorithms across various domains, ultimately benefiting society at large.

Legacy and Future Contributions

As Mo continues to pursue his PhD in Computer and Cyber Science at Augusta University, his research agenda remains focused on pushing the boundaries of machine learning and artificial intelligence. With a steadfast commitment to innovation and excellence, he aims to further explore the intersections of machine learning, cyber-physical systems, and software engineering. Through his ongoing contributions to research, academia, and industry, Mo is poised to leave a lasting legacy in the field of computer science, shaping the future of technology and driving impactful change in the years to come.

Notable Publication

• Maximum Output Discrepancy Computation for Convolutional Neural Network Compression.

DOI: 10.1016/j.ins.2024.120367

• Compression Repair for Feedforward Neural Networks Based on Model Equivalence Evaluation.

DOI: 10.48550/arXiv.2402.11737

• Guaranteed Quantization Error Computation for Neural Network Model Compression.

DOI: 10.1109/ICIT58465.2023.10143141

• A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems.

DOI: 10.1109/ICIT58465.2023.10143031