Shantao Ping | Computer Vision | Best Researcher Award

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Mr. Shantao Ping | Computer Vision | Best Researcher Award

Associate Senior Engineer, Qiyuan Lab, China

Shantao Ping is an Associate Senior Engineer at Qiyuan Lab, specializing in computer vision, artificial intelligence, and large-scale model algorithms. With a Master’s degree in Computer Science, Shantao has a proven track record of driving innovation through cutting-edge research and development. He has contributed to over 28 research and industry projects and holds 14 national invention patents. His collaborative project with Baidu, an AI-powered medical question-answering system, significantly enhanced user engagement and earned him the prestigious Baidu Best Engineer Award. Shantao is also an active member of the Chinese Institute of Command and Control, where he continuously advances the frontiers of intelligent simulation, image processing, and natural language processing. His work focuses on solving complex engineering problems and has made substantial contributions to simulation scene construction and few-shot object recognition. Passionate about applied research, Shantao Ping is committed to shaping the future of intelligent computing through practical and scalable solutions.

Publication Profile

Education

Shantao Ping holds a Master’s degree in Computer Science from an esteemed institution, equipping him with solid expertise in artificial intelligence, computer vision, and advanced computational algorithms. He also holds the professional qualification of Associate Senior Engineer, recognized by the Ministry of Human Resources and Social Security (MOHRSS), Beijing, China. This designation reflects his deep technical proficiency and leadership in engineering research and development. Throughout his academic and professional training, Shantao focused on bridging theoretical foundations with real-world applications, emphasizing innovation in structured light calibration, simulation modeling, and machine learning-based image processing. His educational journey laid the groundwork for his current role as a highly effective engineer, capable of contributing to both research excellence and industrial breakthroughs. Shantao’s education emphasizes interdisciplinary collaboration, practical application, and a research-driven approach that aligns perfectly with his long-standing commitment to technological advancement and cutting-edge innovation in the rapidly evolving fields of AI and computer vision.

Experience

Shantao Ping is currently an Associate Senior Engineer at Qiyuan Lab, where he has spearheaded numerous high-impact projects in computer vision, AI, and simulation technologies. Over his career, he has successfully completed 28 research and consultancy projects, including a notable collaboration with Baidu to develop an AI-powered medical Q&A system that significantly improved user engagement metrics. His career highlights include leading teams in the development of large-scale model algorithms, simulation scene construction, and few-shot object recognition frameworks. Shantao’s practical experience is reinforced by 14 published or in-process patents and multiple software development achievements, including tools for multi-type algorithm execution and sonar simulation imaging. His work has consistently demonstrated high relevance to industry needs and national innovation strategies. Recognized with the Baidu Best Engineer Award, Shantao continues to push the boundaries of applied AI and intelligent systems. He is also actively involved in the Chinese Institute of Command and Control, enhancing his contributions to the field.

Research Focus

Shantao Ping’s research is primarily centered on computer vision, image processing, natural language processing (NLP), and foundation models. His work addresses critical challenges in simulation scene reconstruction, few-shot object recognition, structured light calibration, and human-computer interaction assisted by large models. He focuses on developing algorithms that integrate simulation with AI to achieve realistic scene modeling and real-time data processing. Shantao is particularly interested in the intersection of AI and simulation, leveraging intelligent algorithms to enhance perception, decision-making, and scene understanding in complex environments. His innovative research in multi-object tracking and global graph matching is paving the way for advanced applications in autonomous systems and smart interaction platforms. Through national patents and practical deployments, he has made significant strides in developing intelligent, scalable solutions that are not only theoretically sound but also practically impactful, contributing directly to the fields of healthcare, simulation technology, and large-scale data interaction.

Publication Top Notes

  1. Multi-view Multi-object Tracking Based on Global Graph Matching Structure (Conference Paper)

    • Authors: Shantao Ping, Chao Li, Hao Sheng, Jiahui Chen, Zhang Xiong

    • Summary: This work proposes a novel global graph matching framework for tracking multiple objects across multiple viewpoints, significantly improving tracking accuracy in complex scenes.

  2. A Method and Apparatus for Specific Target Reconnaissance by Unmanned Aerial Vehicle (Patent)

    • Authors: Shantao Ping, Ying He

    • Summary: Introduces a UAV-based reconnaissance system with enhanced precision for specific target detection in dynamic environments.

  3. A Method, Apparatus, and Device for 3D Scene Construction (Patent)

    • Authors: Shantao Ping, Xulong Ma, Ying He

    • Summary: Details a system for efficient 3D scene modeling using intelligent algorithms, optimizing both speed and accuracy.

  4. Method for Human-Computer Interaction Assisted by Large Models (Patent)

    • Authors: Shantao Ping, Xulong Ma, Ying He, Xiaoqiang Jin, Pinjie Li, Qianchuan Zhao

    • Summary: Presents a human-computer interaction framework enhanced by large foundational models for improved user experience and system adaptability.

  5. Method, Apparatus, Device, and Storage Medium for Generating Sonar Simulated Images (Patent)

    • Authors: Shantao Ping, Xulong Ma, Ying He, Jiacheng Li

    • Summary: Describes a sonar image simulation method that increases the fidelity and reliability of underwater detection simulations.

Conclusion

Shantao Ping is a highly capable, application-driven researcher with an impressive track record of industry-relevant projects, innovative patents, and impactful collaborations, particularly in AI and computer vision. The strong applied research portfolio and demonstrated ability to solve real-world problems make him a solid candidate for the Best Researcher Award. However, to fully align with the traditional benchmarks of this award (which often emphasize academic citations and international recognition), increasing the number of SCI/Scopus journal publications, improving citation metrics, and pursuing more visible academic leadership roles would be beneficial.

Umar Islam | Computer Science | Best Researcher Award

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Mr. Umar Islam | Computer Science | Best Researcher Award

Senior Lecturer, IQRA National University Swat Campus, Pakistan

Mr. Umar Islam is a passionate and accomplished educator and researcher in the field of Computer Science, currently serving as a Lecturer at Iqra National University (INU) Swat Campus, Pakistan. With an impressive academic background spanning 18 years in Computer Science, Mr. Islam has become a recognized expert in AI, machine learning, blockchain security, IoT, bioinformatics, and financial analytics. His work has been published in over 15 research articles, including several in top-tier journals. A dedicated researcher, he focuses on real-time AI solutions, particularly in healthcare and cybersecurity. Mr. Islam is also a committed mentor, providing supervision and guidance to students in advanced topics such as Python programming, machine learning, and AI applications. His contributions to the academic community and his research endeavors demonstrate his commitment to pushing the boundaries of knowledge and solving real-world problems.

Profile

Education

Mr. Umar Islam has an extensive academic journey, earning 18 years of education in Computer Science. His academic path began with a Bachelor’s degree in Computer Science, followed by a Master’s degree, where he built the foundation of his knowledge in various aspects of computing. Mr. Islam’s thirst for knowledge and his passion for research led him to pursue advanced studies in areas like AI, machine learning, IoT, and cybersecurity, with a strong focus on applying these technologies to solve real-world challenges. His educational journey has equipped him with the skills to lead cutting-edge research projects and to innovate in fields like bioinformatics and financial analytics. Currently, he is working toward a PhD, which will further deepen his understanding and expertise in these areas. Through his education, Mr. Islam has gained a comprehensive understanding of theoretical and applied Computer Science, which he integrates into both his teaching and research.

Experience

With six years of teaching experience at the higher education level, Mr. Umar Islam has played a pivotal role in shaping the future of numerous students at Iqra National University (INU) Swat Campus. As a lecturer, he has delivered comprehensive lessons in Computer Science topics such as AI, machine learning, and cybersecurity. His commitment to academic excellence is reflected in his success as a supervisor, guiding students through complex topics like Python programming, e-learning analytics, and AI-driven applications. In addition to teaching, Mr. Islam has gained four years of extensive research experience, with a focus on AI applications in healthcare, cybersecurity, and blockchain security. He has led multiple research projects, producing groundbreaking results, and has contributed significantly to the academic community with over 15 published research articles. His academic experience extends beyond teaching, positioning him as a thought leader in his field.

Research Focus

Mr. Umar Islam’s research is deeply focused on the intersection of artificial intelligence (AI), cybersecurity, healthcare, and financial analytics. One of his key research areas includes AI-driven solutions in healthcare, particularly the development of federated learning-based intrusion detection systems and epileptic seizure prediction models. He is also actively exploring AI in cybersecurity, specifically in blockchain security, to mitigate data tampering risks. His work in financial analytics uses AI and machine learning to predict market trends, including cryptocurrency values, demonstrating his interdisciplinary approach to solving real-world problems. In addition to these topics, Mr. Islam is involved in pioneering research in IoT security and bioinformatics. His research aims to address key global challenges such as healthcare delivery, data security, and economic stability through cutting-edge AI applications. His innovative contributions to various fields have resulted in multiple published articles in prestigious journals, demonstrating the far-reaching impact of his work.

Publication Top Notes

  • Detection of distributed denial of service (DDoS) attacks in IoT-based monitoring system of banking sector using machine learning models 🌐🔐📊
  • IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms 🤖📱💡
  • Real-time detection schemes for memory DoS (M-DoS) attacks on cloud computing applications ☁️💻🛡️
  • Detection of renal cell hydronephrosis in ultrasound kidney images: a study on the efficacy of deep convolutional neural networks 🏥🧠📸
  • A novel anomaly detection system on the internet of railways using extended neural networks 🚆🔍⚙️
  • NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics 🧠💓📈
  • An intelligent approach for preserving the privacy and security of a smart home based on IoT using LogitBoost techniques 🏠🔐💡
  • Enhancing Economic Stability with Innovative Crude Oil Price Prediction and Policy Uncertainty Mitigation in USD Energy Stock Markets 💰📊📉
  • Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data 💾🔎👨‍💻
  • Empowering global ethereum price prediction with EtherVoyant: a state-of-the-art time series forecasting model ⛓️💹🔮

 

 

 

Tuhin Subhra De | Artificial Intelligence Awards | Young Scientist Award

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Mr Tuhin Subhra De | Artificial Intelligence Awards | Young Scientist Award

Mr Tuhin Subhra De, Indian Institute of Technology Kharagpur, India

Tuhin Subhra De is a dual-degree student at the Indian Institute of Technology Kharagpur, specializing in Civil and Environmental Engineering with minors in Mathematics and Computing, and Artificial Intelligence Applications. With a CGPA of 8.7/10.0, Tuhin has demonstrated his prowess in both academics and research. He has gained significant experience as a Deep Learning Research Intern at IIT KGP’s Center for Excellence in AI, where he worked on improving text generation quality using advanced models. Tuhin has also completed internships in Data Science and Machine Learning at prominent organizations like Udaan.com and Deakin University, focusing on predictive modeling and knowledge distillation. His research work has led to multiple publications in esteemed journals and conferences. Outside academics, Tuhin is active in mentoring and social work, contributing to the welfare of underprivileged communities and participating in various sports events.

Publication Profile

Google scholar

Strengths for the Award

  • Academic Excellence: Tuhin Subhra De has demonstrated strong academic performance with a CGPA of 8.7/10.0 from a prestigious institution like IIT Kharagpur, where he completed a dual degree in Civil and Environmental Engineering with minors in Mathematics, Computing, and AI.
  • Research Experience: He has extensive research experience, notably working as a Deep Learning Research Intern at the Center for Excellence in AI, IIT KGP. His work includes complex topics like Hierarchical Variational Autoencoders and diffusion-based models, showcasing his deep understanding and innovation in AI and machine learning.
  • Publications: Tuhin has multiple publications, including articles in respected journals and conferences. These publications reflect his ability to contribute valuable research to the field of AI and machine learning, which aligns well with the award’s focus on young researchers.
  • Technical Skills: His proficiency in various programming languages, tools, and libraries, combined with his experience in implementing complex models, indicates his strong technical foundation, crucial for cutting-edge research.

Areas for Improvement

  • Broader Impact: While Tuhin’s research is technically strong, he could focus on expanding the practical applications and societal impact of his work. Demonstrating how his research can solve real-world problems or benefit specific industries could strengthen his candidacy.
  • Leadership and Collaboration: Although Tuhin has mentored students and participated in team projects, further leadership roles or collaborative research endeavors could enhance his profile. Engaging in interdisciplinary projects or leading a research team could be beneficial.
  • Diversity of Experience: Tuhin’s experience is heavily concentrated in AI and machine learning. Diversifying his research portfolio by exploring related fields or integrating his AI expertise into other disciplines might provide a more well-rounded profile.

Education 

Tuhin Subhra De is currently pursuing a dual degree (B.Tech. and M.Tech.) in Civil and Environmental Engineering from the prestigious Indian Institute of Technology Kharagpur (IIT KGP), where he has maintained an impressive CGPA of 8.7/10.0. His academic journey at IIT KGP includes minors in Mathematics and Computing, and Artificial Intelligence Applications, reflecting his diverse interests and technical proficiency. Tuhin’s coursework encompasses a wide array of subjects, including Artificial Intelligence, Machine Learning, Deep Learning, Big Data Processing, and Secure AI/ML, providing him with a solid foundation in cutting-edge technologies. His strong background in Statistics, Probability, Linear Algebra, and Time Series Modeling further complements his technical expertise. Tuhin’s educational achievements are underscored by his active participation in prestigious competitive exams and Olympiads, where he ranked among the top percentile. His education at IIT KGP has equipped him with the skills and knowledge to excel in both academic and professional pursuits.

Experience  

Tuhin Subhra De has accumulated a wealth of experience through various internships and research roles. At the Center for Excellence in AI at IIT KGP, he is currently a Deep Learning Research Intern, focusing on improving text generation quality using diffusion and energy-based models. During his internship at Udaan.com, Tuhin successfully increased the customer order conversion rate by implementing predictive buying methods and advanced machine learning techniques, which led to a significant improvement in business outcomes. His role as a Machine Learning Research Intern at Deakin University involved applying knowledge distillation to enhance the performance of heterogeneous ML models, demonstrating his ability to innovate in complex environments. Tuhin’s early experiences also include an internship at the Indian Institute of Management Mumbai, where he developed methods to optimize profit in cargo acceptance, combining operations research with machine learning. His hands-on experience across various domains underscores his versatility and technical acumen.

Research Focus  

Tuhin Subhra De’s research focus lies at the intersection of artificial intelligence, machine learning, and data science, with a particular emphasis on deep learning models and their applications. His current research at the Center for Excellence in AI, IIT KGP, involves enhancing the quality of text generation using advanced models like Hierarchical Variational Autoencoders, diffusion models, and energy-based frameworks. Tuhin is also exploring the contrast in learning processes between different AI models, aiming to address challenges such as decoder collapse in generative tasks. His past research at Deakin University centered on knowledge distillation, where he worked on improving the accuracy of machine learning models by transferring knowledge between heterogeneous networks. Additionally, Tuhin’s work at Udaan.com involved developing predictive models that significantly boosted customer order conversion rates. His research is driven by a passion for solving real-world problems through innovative AI and ML techniques, making significant contributions to the field.

Publication Top Notes

A non-linear Lasso and explainable LSTM approach for estimating tail risk interconnectedness

A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce

Prediction of Turn Around Time using Neural Networks – A Case Study of Shipping Port 

Conclusion

Tuhin Subhra De is a strong candidate for the Research for Young Scientist Award, given his academic excellence, significant research contributions, and technical expertise. To further enhance his application, he could focus on demonstrating the broader impact of his research, seeking leadership opportunities, and diversifying his experience. Overall, his profile is highly competitive for the award, reflecting his potential as a promising young scientist in the field of AI and machine learning.