Zihao Mo – Neural network Neural network compression verification – Best Researcher Award 

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

Augusta university - United States

Author Profile

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

DOI: 10.1016/j.ins.2024.120367

DOI: 10.48550/arXiv.2402.11737

DOI: 10.1109/ICIT58465.2023.10143141

DOI: 10.1109/ICIT58465.2023.10143031

 

Zheyi Chen | Machine Learning Applications | Best Researcher Award

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Zheyi Chen | Best Researcher Award - Biography

Prof Dr Zheyi Chen : Machine Learning Applications

Professional profile:

Early Academic Pursuits:

Prof Dr. Zheyi Chen embarked on his academic journey with a Bachelor's degree in Computer Science and Technology from Shanxi University, China, where he laid the foundation for his future endeavors. He continued his education with a Master's degree in Computer Science and Technology from Tsinghua University, China, under the guidance of Prof. Yongwei Wu.

For his doctoral studies, Prof. Chen pursued a Ph.D. in Computer Science at the University of Exeter, U.K., from September 2017 to October 2021. During this period, he worked under the supervision of Prof. Geyong Min and Dr. Jia Hu, contributing to the field of computer science and further honing his research skills.

Professional Endeavors:

Following his academic pursuits, Prof Dr. Zheyi Chen transitioned into the role of an Associate Professor and Ph.D. Supervisor at the College of Computer and Data Science, Fuzhou University, China, from December 2021 to January 2023. Subsequently, he assumed the position of a Professor and Ph.D. Supervisor in the same department from February 2023 onwards, signifying a progression in his academic career.

Contributions and Research Focus:

Prof Dr. Chen's research interests encompass Cloud/Edge Computing, Resource Optimization, Deep Learning, and Deep Reinforcement Learning. His notable contributions include leading research projects such as "Research on Efficient and Adaptive Resource Optimization in Cloud-Edge Environments" and "Research and Application of Key Technologies for Resource Optimization and Data Processing in Cloud-Edge Collaborative Environment."

He has significantly advanced the understanding of resource allocation in mobile edge computing and the development of key technologies for big data intelligence. His expertise extends to the exploration and practice of training postgraduate students aligned with the "Digital Fujian" strategy.

Accolades and Recognition:

Prof Dr. Zheyi Chen's contributions to the field have been acknowledged through prestigious awards, including the "World’s Top 2% Scientists" in 2023, the "First Prize of Fujian Science and Technology Progress" in 2021, and multiple "First Prizes for Outstanding Papers" at the Annual Academic Conference of Fujian Computer Society in 2021 and 2023.

Impact and Influence:

As an active member of IEEE, ACM, and CCF, Prof. Chen has played a pivotal role in the academic community. His involvement in conference organization, as seen in his roles as Web and System Management Chair for IEEE SustainCom-2020 and IEEE SmartCity-2018, highlights his commitment to fostering scholarly exchange.

He has also served as a reviewer for reputable journals and conferences, including IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, and IEEE/CAA Journal of Automatica Sinica, contributing to the rigorous evaluation and dissemination of research in his field.

Legacy and Future Contributions:

Prof Dr. Zheyi Chen's legacy is marked by his leadership in influential research projects, academic recognition, and service to the community. His commitment to advancing knowledge in Cloud/Edge Computing and related fields positions him as a key figure in shaping the future of computer science. As he continues to lead research projects, mentor students, and contribute to scholarly activities, Prof. Chen is poised to leave a lasting impact on the field and inspire future generations of researchers.

Notable Publications:

Citations:

A total of  921 citations for his publications, demonstrating the impact and recognition of his research within the academic community.

  • Citations      921
  • h-index         13
  • i10-index      15

Zhi Liu | Best Researcher Award | Machine Learning Applications

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 Zhi Liu | Best Researcher Award  Winner 2023  🏆

Dr Zhi Liu : Machine Learning Applications

🏆 Congratulations to Dr. Zhi Liu!

 International Research Data Analysis Excellence Awards Best Researcher Award Winner Recognizing Dr. Liu's outstanding contributions in Machine Learning Applications. Your dedication to research excellence has set a benchmark for the global scientific community. Well-deserved honor!

Professional profile:

Early Academic Pursuits

Liu Z started his academic journey with a focus on computer software and theory at Northeast Normal University, where he pursued a master's degree in Computer Software and Theory from September 2014 to January 2017. During this period, his research interests included recommendation systems, artificial intelligence, machine learning, and natural language processing.

Professional Endeavors

Subsequently, he advanced his academic pursuits by obtaining a Ph.D. in Mechanical Manufacturing and Automation from the University of the Chinese Academy of Sciences (2017-2022). His doctoral research concentrated on artificial intelligence, machine teaching, knowledge graphs, natural language processing (NLP), natural language understanding (NLU), machine reading comprehension (MRC), and knowledge modeling.

Research Focus

The core of Liu Z's doctoral research was the development of machine teaching methods for human-machine collaboration. His dissertation focused on leveraging natural language processing (NLP) techniques in the medical domain to explore how AI models could be taught through interactive human-machine methods. The goal was to enhance learning efficiency by incorporating existing knowledge systems and incorporating correction mechanisms into the learning process.

Contributions and Research Output

Liu Z has made significant contributions to the field, as evidenced by his research outcomes and publications. Notably, he co-authored a paper titled "AttentiveHerb: A Novel Method for Traditional Medicine Prescription Generation" and secured a patent for a method analyzing symptom-drug relationships using new artificial intelligence technology.

His work extended to Chinese traditional medicine, with a paper on "TCMNER and PubMed: A Novel Chinese Character-Level-Based Model and a Dataset for TCM Named Entity Recognition." In addition, Liu Z contributed to the field of event extraction with a paper on structural dependency self-attention-based hierarchical event models.

Recognition and Impact

Liu Z's contributions have not gone unnoticed, as he received funding from the 71st batch of the China Postdoctoral Science Foundation. His work in the medical domain, specifically on the generation of traditional herbal medicine prescriptions, was acknowledged in a transfer learning model published in the journal "Artificial Intelligence in Medicine."

Future Contributions and Legacy

Transitioning to a postdoctoral role at Zhejiang Lab, Liu Z continued his research in natural language processing, understanding, and knowledge graphs. His accomplishments include the development of large models tailored to specific vertical domains, such as finance and traditional Chinese medicine. His work in numerical reasoning and a transfer learning-based dual-augmentation strategy for rare disease syndrome differentiation in traditional Chinese medicine showcases his commitment to advancing the field.

Liu Z's legacy extends beyond academic achievements, with patents, conference papers, and impactful research contributing to the broader landscape of artificial intelligence, natural language processing, and medical applications. His efforts are likely to leave a lasting mark on the integration of AI into medical practices and knowledge systems.

Notable publication :

International Research Data Analysis Excellence Awards | Award Winners 2023

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Raj kumar Singh

Congratulations, Raj kuma Singh |  Best Researcher  Award  Winner 2023 🏆

Raj kumar Singh : Satellite remote sensing, Machine learning

Raj Kumar Singh's groundbreaking work in the realms of satellite remote sensing and machine learning has earned him the esteemed Young Scientist Award at the International Research Data Analysis Excellence Awards. His innovative approach to leveraging satellite data and cutting-edge machine learning algorithms has not only advanced scientific understanding but has also showcased his commitment to pushing the boundaries of research.This accolade serves as a testament to Singh's dedication, talent, and exceptional capabilities in utilizing technology to unravel complex insights from vast datasets.

Professional profile:

Early Academic Pursuits:

Raj Kumar Singh began his academic journey with a Master of Science (M.Sc.) in Remote Sensing and GIS from Jiwaji University, Gwalior, India, in 2009. His thesis focused on hyperspectral remote sensing-based studies for the extraction of vegetation quality parameters using Hyperion data. This laid the foundation for his future specialization in geoinformatics.

He continued his education with a Master of Technology (M.Tech.) in Remote Sensing and GIS with a specialization in Atmospheric Science from the Indian Institute of Remote Sensing (IIRS-ISRO), Dehradun, India, in 2015. His thesis, titled "Spatial-temporal variability of Aerosol Optical properties and their impact on Aerosol Radiative forcing over Indo-Gangetic plain," showcased his commitment to understanding atmospheric dynamics through remote sensing.

Raj Kumar Singh completed his academic journey with a Doctor of Philosophy (Ph.D.) in Geoinformatics and Agroforestry from the Indian Institute of Technology Kharagpur (IIT-KGP) in 2023. His doctoral thesis, "Agroforestry Interventions for Improved Crop Management Using Geospatial Technology," demonstrated his expertise in applying geospatial techniques to agriculture.

Professional Endeavors:

Raj Kumar Singh has accumulated 14 years of professional experience, primarily focusing on satellite remote sensing and its applications in agriculture and agroforestry. He has worked with various national and international organizations, including ISRO, JNU, ICARDA, and currently leads geospatial activities at World Agroforestry (CIFOR-ICRAF) in New Delhi.

His roles include being a Geoinformatics Scientist at World Agroforestry, ICARF, where he leads and plans geoinformatics activities, contributes to technical exchanges, project reports, and proposals, and develops advanced remote sensing and geoinformatics applications for agroforestry. He has also led the development of a mobile app for site-specific agroforestry suitability.

Raj Kumar Singh served as a Remote Sensing Specialist at the International Centre for Agricultural Research in the Dry Areas (ICARDA) in New Delhi, where he characterized rice fallows, assessed long-term trends of crop fallows, and developed pulse crop suitability maps using advanced decision-making techniques.

He has also worked as a Research Fellow at Jawaharlal Nehru University (JNU) in New Delhi, focusing on geomorphological and geological mapping, and as a Junior Research Fellow at the Regional Remote Sensing Centre (RRSC)-East, Indian Space Research Organization (ISRO), Kolkata, where he mapped tea growing areas and conducted detailed site suitability analysis for tea plantations.

Additionally, Raj Kumar Singh served as a GIS Engineer at Magus Infocom Pvt. Ltd. in Noida, Delhi-NCR, contributing to spatial database creation for urban infrastructure using satellite and ground-based data.

Contributions and Research Focus:

Raj Kumar Singh's research encompasses biomass estimation, species distribution, and ecological modeling using geospatial data analytics, machine learning, and image processing software. He has hands-on experience with hyperspectral and SAR data, leading teams in the development of tools and applications for agroforestry suitability and carbon estimation.

His expertise extends to cloud-based platforms such as Google Earth Engine (GEE) and programming languages like Python and R. He has actively contributed to the development of geospatial decision layers, mobile applications, and dashboards for real-time monitoring and analysis.

Raj Kumar Singh has been actively involved in projects with organizations like USAID, NRSC, ISRO, ICAR, Tea Board, and Survey of India, showcasing his versatility in addressing diverse geospatial challenges.

Accolades and Recognition:

Raj Kumar Singh has received recognition for his work through memberships in professional bodies such as the Indian Society of Remote Sensing (ISRS) and the Indian Society of Agroforestry. He serves as a reviewer for reputable journals in the field, including the Journal of Environment and Management, Environmental Monitoring and Assessment, Remote Sensing, and Tropical Ecology.

His commitment to advancing knowledge is evident through his participation in numerous professional training programs, including those focused on Google Earth Engine, Web GIS Technology, Earth Observation for Carbon Cycle Studies, and Machine Learning.

Impact and Influence:

Raj Kumar Singh's work has had a significant impact on the field of geoinformatics, particularly in the application of remote sensing to agriculture and agroforestry. His research outputs, including over 15 peer-reviewed publications and presentations at national and international conferences, contribute to the body of knowledge in the domain.

As a leader at World Agroforestry, he has influenced the development of innovative tools, platforms, and applications for site-specific agroforestry interventions and biomass estimation. His work on mobile apps and decision support tools has facilitated practical applications of geospatial technology in the field.

Legacy and Future Contributions:

Raj Kumar Singh's legacy lies in his comprehensive contributions to the integration of geoinformatics into agricultural and agroforestry practices. His focus on sustainable solutions, capacity building through training, and active participation in collaborative projects positions him as a thought leader in the geospatial community.

Looking forward, Raj Kumar Singh is likely to continue shaping the future of geoinformatics by leveraging emerging technologies, such as machine learning and cloud-based platforms. His commitment to research, training, and the practical application of geospatial solutions ensures a lasting impact on the field.

Notable publication:

Decentralized proximal gradient algorithms with linear convergence rates

A linearly convergent proximal gradient algorithm for decentralized optimization

Linear convergence of primal–dual gradient methods and their performance in distributed optimization

On the influence of bias-correction on distributed stochastic optimization

Distributed coupled multiagent stochastic optimization

A proximal diffusion strategy for multiagent optimization with sparse affine constraints

A unified and refined convergence analysis for non-convex decentralized learning

Removing data heterogeneity influence enhances network topology dependence of decentralized sgd

Citation:

A total of 447  citations for his publications, demonstrating the impact and recognition of his research within the academic community.

Citations              447

h-index  10           10

i10-index             10