Zuheng Ming | Artificial intelligence | Best Researcher Award

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Dr. Zuheng Ming | Artificial intelligence | Best Researcher Award

Associate professor at Sorbonne Paris North University, France

🧑‍🏫 Dr. Zuheng Ming is an Assistant Professor at L2TI, Sorbonne Paris North University, France. He earned his PhD in 2013 from Grenoble Alpes University 🇫🇷, specializing in speech parameter mapping. His expertise spans multimodal learning, computer vision, and deep learning 🤖. Dr. Ming has 30+ publications 📝 in top-tier journals (JCR Q1/Q2) and conferences (ICIP, ICPR, ICDAR). He has supervised doctoral and master’s theses and collaborated internationally with CVC, RIKEN AIP, and Oulu University 🌍. He has led funded research projects on face anti-spoofing and document analysis 📄. Additionally, he serves as a guest editor and reviewer for prestigious journals. ✨

Publication Profile

Google Scholar

🏅 Professional Experience

Dr. Zuheng Ming is an accomplished researcher and educator in computer vision and deep learning 🤖. Since September 2022, he has been serving as an Assistant Professor at L2TI, Sorbonne Paris North University, France 🇫🇷. Prior to this, he was a Lecture-Researcher at L3i, La Rochelle University (2021-2022) 📚. From 2016 to 2021, he worked as a Postdoctoral Fellow and Assistant Lecturer at L3i, La Rochelle University. Earlier, from 2014 to 2015, he pursued a postdoctoral fellowship at Bordeaux University 🏛️, contributing significantly to cutting-edge research in multimodal learning and artificial intelligence. ✨

🎓 Educational Background

Dr. Zuheng Ming holds a PhD in Computer Science from Grenoble Alpes University, France (2013) 🇫🇷, where he specialized in spectral parameters mapping for cued speech using multi-linear and GMM approaches 🔬. He earned his Master’s degree in Pattern Recognition and Artificial Intelligence from Beijing Institute of Technology (2008) 🎭🤖. His academic journey began with a Bachelor’s degree in Electronic and Automatic Systems Engineering from Hunan University, China (2003) ⚡. His strong educational foundation has driven his research contributions in computer vision, deep learning, and multimodal learning 📚✨.

🔬 Research Activities

Dr. Zuheng Ming has been actively involved in research supervision, mentoring 1 PhD thesis, 2 Master’s theses, and 6 internships 🎓📖. He has established six international collaborations with prestigious institutions, including CVC (Spain) 🇪🇸, RIKEN AIP (Japan) 🇯🇵, Oulu University (Finland) 🇫🇮, Northwestern Polytechnical University (China) 🇨🇳, and Xidian University (China) 🇨🇳. His global academic engagement also includes an academic visit to Kyoto University, Japan, in 2015 🌍🏫. Through his extensive research network, Dr. Ming continues to make significant contributions to computer vision, deep learning, and multimodal learning 📊🤖.

🎓 Teaching Experience

Dr. Zuheng Ming has extensive teaching experience in cutting-edge technologies related to artificial intelligence and computer vision 🧠📸. He has taught courses on Deep Learning, Advanced Image Processing, and Intelligent Systems in Computer Vision 🤖🖼️, equipping students with the latest advancements in AI. Additionally, he has imparted knowledge in Database Management and Object-Oriented Programming 💾💻, fostering strong software development skills. His expertise in both theoretical foundations and practical applications makes him a valuable mentor in the field of AI and computer vision, guiding students toward innovative research and industry-ready solutions 🚀📚.

🔍 Research Focus

Dr. Zuheng Ming’s research primarily focuses on computer vision, deep learning, and document security 🧠📸🔏. His contributions span facial recognition, anti-spoofing techniques, and face liveness detection 🤖😃, enhancing biometric security. He has also worked extensively on document image classification and authentication 📄🔍, improving identity verification systems. His expertise in multi-modal learning, pattern recognition, and deep feature fusion enables advancements in AI-driven document forensics and secure authentication 🚀🔐. Collaborating internationally, he applies machine learning and self-attention networks to solve real-world challenges in face recognition, fraud detection, and intelligent systems 🌍🔬.

Publication Top Notes

📸 A survey on anti-spoofing methods for facial recognition with RGB cameras of generic consumer devices – Z Ming, M Visani, MM Luqman, JC Burie | Journal of Imaging | 88 citations | 2020

📄 Visual and textual deep feature fusion for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE/CVF Conference on Computer Vision | 63 citations | 2020

🔍 Simple triplet loss based on intra/inter-class metric learning for face verification – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | IEEE/CVF International Conference on Computer Vision | 57 citations | 2017

😊 Facial action units intensity estimation by fusion of features with multi-kernel SVM – Z Ming, A Bugeau, JL Rouas, T Shochi | IEEE International Conference on Automatic Face and Gesture Recognition | 54 citations | 2015

🆔 MIDV-2020: A comprehensive benchmark dataset for identity document analysis – BK Bulatovich, EE Vladimirovna, TD Vyacheslavovich, SN Sergeevna, … | Computer Optics | 51 citations | 2022

🙂 Dynamic Multi-Task Learning for Face Recognition with Facial Expression – Z Ming, J Xia, MM Luqman, JC Burie, K Zhao | IEEE/CVF International Conference on Computer Vision Workshop | 40 citations | 2019

📜 VLCDoC: Vision-language contrastive pre-training model for cross-modal document classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol, OR Terrades | Pattern Recognition | 33 citations | 2023

🔐 FaceLiveNet: End-to-end networks combining face verification with interactive facial expression-based liveness detection – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | International Conference on Pattern Recognition | 30 citations | 2018

📑 Cross-modal deep networks for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE International Conference on Image Processing | 23 citations | 2020

📃 Document liveness challenge dataset (DLC-2021) – DV Polevoy, IV Sigareva, DM Ershova, VV Arlazarov, DP Nikolaev, Z Ming, … | Journal of Imaging | 21 citations | 2022

📹 ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection – Z Ming, Z Yu, M Al-Ghadi, M Visani, M Muzzamil Luqman, JC Burie | IEEE International Conference on Image Processing | 21 citations | 2022

🌲 Multiple sources data fusion via deep forest – J Xia, Z Ming, A Iwasaki | IGARSS IEEE International Geoscience and Remote Sensing Symposium | 15 citations | 2018

🆔 Face detection in camera captured images of identity documents under challenging conditions – S Bakkali, MM Luqman, Z Ming, JC Burie | International Conference on Document Analysis and Recognition Workshops | 11 citations | 2019

📑 EAML: Ensemble self-attention-based mutual learning network for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | International Journal on Document Analysis and Recognition | 10 citations | 2021

🧠 Synthetic evidential study as augmented collective thought process – Preliminary report – T Nishida, M Abe, T Ookaki, D Lala, S Thovuttikul, H Song, Y Mohammad, … | ACIIDS Asian Conference | 10 citations | 2015

🆔 Identity documents authentication based on forgery detection of guilloche pattern – M Al-Ghadi, Z Ming, P Gomez-Krämer, JC Burie | arXiv preprint | 8 citations | 2022

 

Mohit Kataria | Machine Learning | Best Researcher Award

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Mr. Mohit Kataria | Machine Learning | Best Researcher Award

Professor at IIT-Delhi

📌  Mohit Kataria is a 4th-year Ph.D. scholar at the School of Artificial Intelligence, IIT Delhi, India, specializing in Graph Machine Learning. His research focuses on scalability of graph algorithms, including graph coarsening, structure learning, federated learning, and large-scale applications. He has published in top venues like NeurIPS, MICAAI, and CBME. Mohit holds a Master’s in Computer Applications (80.1%) and has expertise in Python, PyTorch, TensorFlow, CUDA, and C/C++. His skill set spans deep learning (GNNs, CNNs, RNNs), machine learning (SVM, XGBoost), and mathematical optimization.

Publication Profile

Google Scholar

Academic Background 🎓🔬

📌 Mohit Kataria is a Ph.D. scholar in Graph Machine Learning at the MISN Lab, IIT Delhi, maintaining an 8.0 CGPA since August 2021. He holds a Master’s in Computer Applications (80.1%) from May 2020. His technical expertise spans Python, PyTorch, TensorFlow, CUDA, MPI, C/C++, Java, MySQL, and Erlang. 🖥️ He specializes in Machine Learning (SVM, Random Forest, XGBoost, Decision Trees) and Deep Learning (ANNs, GNNs, CNNs, RNNs, LSTM, VAE, GANs). 📊 His strong foundation in Linear Algebra, Probability, and Optimization fuels his research in scalable graph algorithms and AI applications. 🚀

💼 Professional Experience of Mohit Kataria

📌 Mohit Kataria has been actively involved in AI/ML training at IIT Delhi (2021-Present), where he has helped train 260+ industry experts in a six-month AI/ML program, covering fundamentals to advanced ML models. 🎓 He also conducted 5-day ML training programs for CAG and CRIS, Government of India. As a WebMaster (2022-Present), he manages the Yardi-ScAI and MISN group websites. 🌐 Previously, as a Member of Technical Staff at Octro.Inc (2020-2021), he led a team of four and contributed to the backend architecture of multiplayer games like Poker3D and Soccer Battles. 🎮🚀

🔬 Research Focus of Mohit Kataria

📌 Mohit Kataria specializes in Graph Machine Learning, focusing on graph coarsening, structure learning, and scalable AI applications. His work enhances GNN performance on heterophilic datasets 🧠, improves large-scale single-cell data analysis 🧬, and optimizes histopathological image processing 🔍. His research, published in NeurIPS, MICAAI, and CBME, develops efficient graph-based frameworks for biomedical and computational applications. 🏥 His expertise spans AI-driven healthcare, graph-based AI models, and machine learning scalability, making significant contributions to bioinformatics, medical imaging, and large-scale data processing. 🚀

Publication Top Notes 

 

 

 

Md Erfan | Machine Learning | Best Researcher Award

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Mr. Md Erfan | Machine Learning | Best Researcher Award

Mr. Md Erfan, University of Barishal, Bangladesh

Assistant Professor, Department of Computer Science and Engineering, University of Barishal, Bangladesh. His research focuses on flaky test detection, compilation error resolution, and AI applications in automation, decision-making, and problem-solving. He holds an MSSE and BSSE from the University of Dhaka. Erfan has published in Elsevier, Springer, and IEEE, exploring NLP, machine learning, and software engineering. He serves as Project Coordinator for Bangladesh’s EDGE Project and has mentored in NASA Space Apps Challenge. An athlete, he won medals in national athletic competitions. 

Publication Profile

Google Scholar

Education 🎓📚

Md Erfan holds a Master of Science in Software Engineering (MSSE) 🖥️ from the Institute of Information Technology, University of Dhaka (2016), with an impressive CGPA of 3.81/4.0 (WES Equivalent: 3.97/4.00). His thesis, supervised by Dr. Md Shariful Islam, focused on an Efficient Runtime Code Offloading Mechanism for Mobile Cloud Computing ☁️💻. He also earned a Bachelor of Science in Software Engineering (BSSE) 🏆 from the same institute in 2014, achieving a CGPA of 3.80/4.0 (WES Equivalent: 3.88/4.00). His undergraduate thesis, guided by Dr. Kazi Muhaimin-us-Sakib, explored approximating social ties based on call logs 📞📊.

Research Experience 🔬📊

In Summer 2024, Md Erfan worked as a Research Student in the UIUC+/ASSIP Summer Research Program 🎓. Collaborating with Dr. Wing Lam (George Mason University) 🏛️ and Dr. August Shi (University of Texas at Austin) 🤖, he focused on automating the end-to-end reproduction of flaky test methods 🛠️. His work involved leveraging issue data, compiling code, running tests, analyzing results, and logging dependencies. Additionally, he created Dockerized environments 🐳 to ensure reproducibility, enhancing software testing efficiency and reliability. His contributions aimed at improving software quality assurance and automation in test debugging 🔍✅.

Professional Experience 💼📚

Md Erfan is an Assistant Professor (2020–Present) at the Department of Computer Science and Engineering, University of Barishal 🏛️, where he teaches Software Engineering, Software Quality Assurance, Data Structures, Algorithms, and Mathematical Analysis 📖💻. Since January 2024, he has also served as a Project Coordinator for the EDGE Project 🌐, managing a 5 crore BDT ($384,615 USD) fund 💰 to enhance digital governance and the economy in Bangladesh. Previously, he worked as a Lecturer (2016–2020) 🎓, a Trainer (2015–2016) 🖥️, and a Software Engineer Intern (2014) 🔍, focusing on testing tools and Microsoft SharePoint development.

Awards and Achievements 🏆🎖️

Md Erfan has been a Regional Mentor (2021–2023) 🌍🚀 for the NASA Space Apps Challenge, guiding innovative projects. He received the Pre-graduation Merit Award (2015) 🎓 from the University of Dhaka for outstanding academic performance. Beyond academics, he has excelled in athletics, securing 3rd place 🥉 in the 5000m and 10000m races 🏃‍♂️ at the Bangladesh Inter-University Athletic Competition (2015) and 2nd place 🥈 in multiple track events (2014–2015). Since 2016, he has been the Coach and Manager ⚽🏅 of the University of Barishal Football and Athletics teams, fostering sports excellence.

 

Research Interests 🔍💻

Md Erfan’s research primarily focuses on Software Engineering, specializing in flaky test detection and mitigation as well as compilation error resolution to enhance software reliability and development efficiency. Additionally, he explores the applications of Artificial Intelligence (AI), leveraging Machine Learning (ML) 🤖, Natural Language Processing (NLP) 🗣️, and Computer Vision 👀 to tackle real-world challenges. His work aims to improve automation, decision-making, and problem-solving across various domains, ensuring smarter and more efficient technological advancements. Through his research, Erfan contributes to optimizing software development and AI-driven innovations for practical applications. 🚀

Research Focus Areas 🧑‍💻📡

Md Erfan’s research spans multiple domains in Software Engineering and Artificial Intelligence. His work focuses on Mobile Cloud Computing ☁️📱, including task allocation and code offloading for performance optimization. He explores Machine Learning 🤖 applications, such as flaky test detection, compilation error resolution, and autism spectrum disorder detection 🧠. His contributions in Natural Language Processing (NLP) 🗣️ involve cyberbullying classification and user similarity computation. Additionally, he applies Computer Vision 👁️ techniques for mosquito species identification and assistive robotics. His interdisciplinary approach integrates automation, decision-making, and problem-solving in real-world applications.

Publication Top Notes

  • Mobility aware task allocation for mobile cloud computing
    Cited by: 8
    Year: 2016 📱☁️
  • Task allocation for mobile cloud computing: State-of-the-art and open challenges
    Cited by: 4
    Year: 2016 📊
  • Identification of Vector and Non-vector Mosquito Species Using Deep Convolutional Neural Networks with Ensemble Model
    Cited by: 2
    Year: 2022 🦟🤖
  • Recurrent neural network based multiclass cyber bullying classification
    Cited by: 1
    Year: 2024 💻🗣️
  • User Similarity Computation Strategy for Collaborative Filtering Using Word Sense Disambiguation Technique
    Cited by: 1
    Year: 2023 🔍📚
  • Approximating Social Ties Based on Call Logs: Whom Should We Prioritize?
    Cited by: 1
    Year: 2015 📱📞
  • An exploration of machine learning approaches for early Autism Spectrum Disorder detection
    Year: 2025 🧠🤖
  • Experimental Study of Four Selective Code Smells Declining in Real Life Projects
    Year: 2024 🧑‍💻🔧
  • Autism Spectrum Disorder Detecting Mechanism on Social Communication Skills Using Machine Learning Approaches
    Year: 2023 🧠💡
  • Dynamic Method Level Code Offloading for Performance Improvement and Energy Saving
    Year: 2017 ⚡💻
  • A comparative study of early autism spectrum disorder detection using deep learning based models
    Year: 2017 🧠🔍
  • An Optimal Task Scheduling Mechanism for Mobile Cloud Computing
    Year: 2016 ☁️📊
  • WVGM: Water View Google Map, Introducing Water Paths on Rivers to Reach One’s Destination using Various Types of Vehicles
    Year: 2016 🌍🚗
  • A comprehensive survey of code offloading mechanisms for mobile cloud computing
    Year: 2016 ☁️🔄
  • MICROCONTROLLER BASED ROBOTICS SUPPORT FOR BLIND PEOPLE
    Year: 2016 🤖👨‍🦯

Conclusion 🌟

Mr. Md Erfan is a highly suitable candidate for the Research for Best Researcher Award due to his strong academic background, impactful research in software engineering and AI, extensive publications, leadership in digital governance projects, and active contributions to global research collaborations. His work demonstrates innovation, technical expertise, and a commitment to advancing knowledge in his field.

 

 

Hong Wang | Artificial Intelligence | Best Researcher Award

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Prof. Hong Wang | Artificial Intelligence | Best Researcher Award

Prof. Hong Wang, Shandong Normal University, China

Prof. Wang earned his Ph.D. in Computer Science from the Chinese Academy of Sciences. His research focuses on Artificial Intelligence, Machine Learning, Healthcare Big Data, and Bioinformatics. 🧠 He has extensive teaching experience, with roles from Lecturer to Doctoral Supervisor. He has received multiple honors, including the Outstanding Graduate Tutor award and Shandong Province Science and Technology Progress prizes. 🏆 Prof. Wang has published widely, including papers on molecular property prediction and drug interactions. His current research includes cutting-edge AI applications in health. 💻

 

Publication Profile

Google Scholar

Education Background 🎓

Prof. Hong Wang completed his PhD in Computer Science from the Chinese Academy of Sciences in Beijing, China, from 1999 to 2002. Prior to that, he earned a Master of Science in Computer Science from Tianjin University in Tianjin, China, between 1988 and 1991. His academic journey began at Tianjin University, where he obtained his Bachelor of Science in Computer Science in 1988. His strong educational foundation has supported his exceptional career in AI, machine learning, and bioinformatics. 📚💻

 

Working Experience 👨‍🏫

Prof. Hong Wang has had a distinguished academic career at Shandong Normal University, starting as a Teaching Assistant from 1991 to 1995. He then served as a Lecturer from 1995 to 2000 and quickly advanced to the position of Associate Professor from 2000 to 2006. Since 2006, he has held the prestigious title of Professor, contributing significantly to the university’s academic growth. In 2009, Prof. Wang also became a Doctoral Supervisor, guiding the next generation of scholars and researchers. His career spans over three decades, focusing on teaching, research, and mentorship. 🎓📚👨‍🔬

 

Honors and Awards 🏅

Prof. Hong Wang has received numerous prestigious honors throughout his career, reflecting his dedication and contributions to academia. In March 2021, he was recognized as a March 8th Red Banner Holder. He was named Outstanding Graduate Tutor in September 2021 for his exceptional mentoring. In March 2019, he received the award for Outstanding Contribution to Achievement. His excellence in teaching was acknowledged with the University-Level Distinguished Teacher award in December 2014, followed by the Individual with Excellence in Teacher Ethics award in September 2014. Additionally, he was honored as a Good Teacher and Friend to College Students in January 2003. 🌟🎓👨‍🏫

 

Research Experience and Achievements 🔬

Prof. Hong Wang has led impactful research projects, including funding from the National Natural Science Foundation of China, with programs spanning from 2021 to 2024 (62072290) and 2017 to 2020 (61672329). He is also part of the Jinan City Science and Technology Bureau project from 2023 to 2024 (202228110). His outstanding contributions have earned him several prestigious awards, such as the Shandong Computer Society Science and Technology Progress Second Prize (First Place) in July 2024. Additionally, he received the Shandong Province Science and Technology Progress First Prize (7th place) in December 2022 and the Shandong Province Higher Education Outstanding Research Achievements Second Prize (First Place) in both 2020 and 2018. 🏆📚

 

Publication Top Notes

  • EDDINet: Enhancing drug-drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning – 2024
  • EMPPNet: Enhancing Molecular Property Prediction via Cross-modal Information Flow and Hierarchical Attention – Cited by 3, 2023
  • GCNs–FSMI: EEG recognition of mental illness based on fine-grained signal features and graph mutual information maximization – Cited by 8, 2023
  • Detecting depression tendency with multimodal features – Cited by 9, 2023
  • A Soft-Attention Guidance Stacked Neural Network for neoadjuvant chemotherapy’s pathological response diagnosis using breast dynamic contrast-enhanced MRI – Cited by 1, 2023
  • Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reactions – Cited by 6, 2023
  • Predicting drug-drug adverse reactions via multi-view graph contrastive representation model – Cited by 11, 2023
  • Explainable knowledge integrated sequence model for detecting fake online reviews – Cited by 9, 2023
  • CasANGCL: Pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction – Cited by 19, 2023
  • Dual network contrastive learning for predicting microbe-disease associations – Cited by 2, 2022
  • Knowledge graph construction for computer networking course group in secondary vocational school based on multi-source heterogeneous data – Cited by 2, 2022
  • Test Paper Generation Based on Improved Genetic Simulated Annealing Algorithm – 2022
  • MS-ADR: Predicting drug–drug adverse reactions based on multi-source heterogeneous convolutional signed network – Cited by 6, 2022
  • Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events – Cited by 1, 2022

Yunge Zou | Computer Science | Best Scholar Award

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Dr. Yunge Zou | Computer Science | Best Scholar Award

Dr. Yunge Zou, Chongqing University, China

Dr. Yunge Zou is a Ph.D. scholar at Chongqing University, specializing in hybrid powertrain design and battery degradation in the Department of Automotive Engineering. He is a talent under the Chongqing Excellence Program and a Shapingba Elite Talent (2023–2025). Dr. Zou has led key projects, including the National Key R&D Program, focusing on high-efficiency powertrain technologies. His contributions include innovative methods like Hyper-Rapid Dynamic Programming, which optimizes multi-mode hybrid powertrains. With multiple patents and high-impact publications, he collaborates with leading automotive firms like Chang’an New Energy, advancing sustainable transportation. 🚗🔋📚

 

Publication Profile

Orcid

Google Scholar

Academic and Professional Background 🔋

Dr. Yunge Zou earned his B.E. degree in Automotive Engineering from Chongqing University, China, in 2018. Currently, he is pursuing his Ph.D. in hybrid powertrain design and optimization at the Vehicle Power System Lab, Department of Automotive Engineering, Chongqing University. Recognized for his exceptional talent, Dr. Zou is part of the prestigious Chongqing Excellence Program and was honored as a Shapingba Elite Talent for 2023–2025. His research focuses on hybrid powertrain topology design, battery degradation, energy management systems (EMS), and enhancing battery life, contributing to sustainable transportation innovation. 📚🔧🌱

 

Research and Innovations 🚗

Dr. Yunge Zou is leading several groundbreaking research projects in the field of hybrid powertrain design and optimization. His work includes the National Key Research and Development Program of China on high-efficiency range extender assembly and electric vehicle integration (2022-2024), with a funding of 2.5 million yuan. He is also working on optimizing hybrid electric vehicle design through the National Science Fund for Excellent Young Scholars (2023-2025). Additionally, he contributes to various projects focusing on hybrid vehicle dynamics, energy efficiency, and low-emission technologies, backed by substantial funding from multiple prestigious organizations. 🛠️⚡

 

🛠️ Research Focus

Dr. Yunge Zou’s research primarily focuses on hybrid powertrain design and optimization for electric and range-extended vehicles. His work includes the development of control strategies and topology design for hybrid systems, aiming to improve fuel economy, efficiency, and reduce emissions. Dr. Zou has made significant advancements in aging-aware optimization and mode-switching mechanisms for multi-mode hybrid vehicles. His contributions also extend to battery degradation, energy management, and the computational efficiency of fuel economy assessment using innovative algorithms like Hyper Rapid Dynamic Programming (HR-DP). His work is instrumental in the evolution of transportation electrification. 🚗⚡

 

Publication Top Notes

  • “Design of all-wheel-drive power-split hybrid configuration schemes based on hierarchical topology graph theory” – Energy 242, 122944 (Cited by 14, 2022) 🔋
  • “Aging-aware co-optimization of topology, parameter and control for multi-mode input-and output-split hybrid electric powertrains” – Journal of Power Sources 624, 235564 (Cited by 1, 2024) ⚙️
  • “Design of optimal control strategy for range extended electric vehicles considering additional noise, vibration and harshness constraints” – Energy 310, 133287 (Cited by 1, 2024) 🚗
  • “Computationally efficient assessment of fuel economy of multi-modes and multi-gears hybrid electric vehicles: A Hyper Rapid Dynamic Programming Approach” – Energy, 133811 (Cited by 0, 2024) 🔧

Federico D’ Asaro | Artificial intelligence | Best Researcher Award

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Mr. Federico D’ Asaro | Artificial intelligence | Best Researcher Award

Mr. Federico D’ Asaro, Politecnico di Torino, Italy

Based on Mr. Federico D’Asaro’s background and achievements, he appears to be a strong candidate for the Research for Best Researcher Award. Here’s an evaluation of his suitability:

Publication profile

Education 🎓

  • Polytechnic University of Turin: M.Sc. in Data Science and Engineering with a thesis on algorithm discrimination. Achieved a final grade of 110/110.
  • University of Palermo: B.Sc. in Engineering and Management, graduating cum laude with a final grade of 110/110.
  • Scientific Lyceum “Galileo Galilei”: High School Diploma with a grade of 83/100.

Work Experience 💼

  • Ph.D. Student at Polytechnic University of Turin: Conducting research on Modality-Gap in multimodal feature space and submitting articles to prominent conferences.
  • AI Applied Researcher at LINKS Foundation: Developed advanced applications in business analytics, sentiment analysis, retrieval systems, and speech emotion recognition. Engaged in proposal writing and reviewing conference papers.
  • Intern at Technology Reply: Gained experience in NLP, sentiment analysis, and textual data modelization.

Skills and Competencies 🛠️

  • Proficient in multiple programming languages and tools including Python, Java, TensorFlow, PyTorch, and SQL.
  • Experienced in data visualization, machine learning, and deep learning.
  • Competent in using various IT tools and frameworks like Apache Spark and Hadoop.

Other Information 🌍

  • Languages: Fluent in Italian and proficient in English (B2 level).
  • Interests: Diverse interests including sports, literature, and technology.
  • Certifications: B2 First (FCE) and driving license.

Publication Top Notes

Zero-Shot Content-Based Crossmodal Recommendation System

Sensitive attributes disproportion as a risk indicator of algorithmic unfairness

Conclusion🏆

Mr. Federico D’Asaro demonstrates a solid academic background, relevant work experience, and a diverse skill set, aligning well with the criteria for the Research for Best Researcher Award. His ongoing research and contributions to AI applications show a strong potential for impactful research and innovation.

Dharmapuri Siri | Deep Learning Award | Best Researcher Award

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Dr. Dharmapuri Siri | Deep Learning Award | Best Researcher Award

Dr. Dharmapuri Siri, Gokaraju Rangaraju Institute of Engineering and Technology, India

Based on Dr. Dharmapuri Siri’s resume, here is a conclusion on his suitability for the Research for Best Researcher Award:

Publication profile

Scopus

Career Experience

Dr. Dharmapuri Siri has extensive teaching experience spanning over 11 years across various institutions, including TRR Engineering College, TRR College of Engineering, and Malla Reddy Engineering College for Women. His role as an Assistant Professor in Computer Science and Engineering highlights a solid foundation in academic and practical knowledge.

Educational Background

Dr. Siri’s educational qualifications are robust, with a Ph.D. in Computer Science and Engineering from JJT University, an M.Tech from JNTU Hyderabad, and a B.Tech from JNTU Hyderabad. His academic background demonstrates a strong commitment to his field and a continual pursuit of advanced knowledge.

Research 

Dr. Siri has made significant contributions to research, particularly in the areas of software quality, machine learning, and image analysis. His journal publications and conference presentations reflect a broad range of research interests, from bug prediction models to sentiment analysis and cancer diagnosis. Notable papers include his work in IEEE Access and various webE3S conferences.

Workshops and Training

He has actively participated in multiple workshops and refresher courses, focusing on cloud computing, innovative teaching methods, and problem-solving techniques. This engagement in continuous professional development underscores his dedication to staying current in his field.

Patent and Projects

Dr. Siri holds a patent for a “Vehicle with Smart Biometric Device,” showcasing his ability to apply theoretical knowledge to practical solutions. His Ph.D. thesis on “Bug Prediction Model For Software Quality Using Machine Learning Techniques” further emphasizes his research focus and expertise.

Conclusion

Dr. Dharmapuri Siri is a strong candidate for the Research for Best Researcher Award due to his comprehensive academic background, extensive teaching experience, substantial research contributions, and practical innovations. His work in improving software quality through machine learning and his active involvement in professional development make him a suitable candidate for this accolade.

 

Publications Top Notes

Analyzing Public Sentiment on the Amazon Website: A GSK-Based Double Path Transformer Network Approach for Sentiment Analysis

Automated System for Bird Species Identification Using CNN

Nafis Uddin Khan | Artificial Intelligence | Best Researcher Award

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Dr Nafis Uddin Khan | Artificial Intelligence | Best Researcher Award 

Dr Nafis Uddin Khan, SR University Warangal India, India

Dr. Nafis Uddin Khan is a distinguished academic and researcher at SR University in Warangal, India. His expertise spans a variety of fields, contributing significantly to both the academic community and industry advancements. Dr. Khan’s work is characterized by a strong focus on innovative solutions and sustainable practices, reflecting his commitment to addressing contemporary challenges through research and education. At SR University, he plays a pivotal role in mentoring students and leading research initiatives that aim to drive technological progress and societal impact.

Profile

Orcid

Education

       Bachelor of Engineering (B.E.) in Electronics and Telecommunication Engineering from Amravati University  Maharashtra, in 2003.

       Master of Technology (M.Tech.) in Software Systems from S.A.T.I. Vidisha under Rajiv Gandhi Technological University, Bhopal, in 2008.

        Doctor of Philosophy (Ph.D.) in Signal and Image Processing from the Atal Bihari Vajpayee – Indian Institute of Information Technology & Management, Gwalior

Professional experience

  1. Workshop Coordinator: Coordinated a two-day workshop titled “Synthesis of Wisdom: Crafting AI Tutor Assistants, Navigating Future-Ready Digital Libraries and Elevating Pedagogy in the Age of Skill Enhancement and AI Mastery” on February 02–03, 2024, at the School of CS & AI, SR University, Warangal, India.
  2. Faculty Development Program Coordinator: Coordinated “Utkarsh – Take Flight,” a two-day Faculty Development Program on Leadership and Excellence in Quality Education organized by Jaypee University of Information Technology, Solan (H.P.), on December 22–23, 2022.
  3. Chief Coordinator: Served as Chief Coordinator in a student outreach activity organized by the Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan (H.P.), on December 20, 2022.
  4. Invited Session Chair: Served as an Invited Session Chair in the Congress on Intelligent Systems (CIS 2020), World Conference in virtual format, on September 05–06, 2020.
  5. Organizing Committee Member and Reviewer: Participated as an Organizing Committee Member and Reviewer in the Congress on Intelligent Systems (CIS 2020), World Conference in virtual format, on September 05–06, 2020.
  6. Convener: Organized a one-week online short-term course on “Recent Advances in Computational Intelligence for Signal Processing” (RACISP – 2020) at Jaypee University of Information Technology, Solan (H.P.), from August 10–15, 2020.
  7. Coordinator: Coordinated a five-day short-term course on “Recent Advances in Signal and Image Processing” (RASIP – 2019) at Jaypee University of Information Technology, Solan (H.P.), from June 24–28, 2019.
  8. Organizing Committee Member: Served in the organizing committee for the 5th IEEE International Conference on “Signal Processing, Computing and Control” (ICSPC 2019) organized by the Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan (H.P.), from October 10–12, 2019.
  9. Invited Session Chair: Chaired an invited session in the 2019 IEEE International Conference on “Image Information Processing” (ICIIP 2019) organized by the Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan (H.P.), from November 15–17, 2019.
  10. Organizing Committee Member: Participated as an organizing committee member in the 4th IEEE International Conference on “Signal Processing, Computing and Control” (ICSPC 2017) at Jaypee University of Information Technology, Solan (H.P.), from September 21–23, 2017.
  11. Invited Session Chair: Served as an invited session chair at the 7th IEEE International Conference on “Computational Intelligence and Communication Networks” (CICN 2015) at Gyan Ganga Institute of Technology and Science, Jabalpur (M.P.), on December 12, 2015.
  12. National Advisory Committee Member: Served on the National Advisory Committee for the National Conference on Contemporary Computing organized by the Department of Computer Science and Information Technology, Chameli Devi Group of Institutions, Indore (M.P.), from October 21–22, 2016.

Research Focus

      Fuzzy Logic and Optimization: Utilizing fuzzy logic for applications in signal and image processing, including the development of fuzzy-based diffusion coefficient functions for selective noise smoothing.

      Medical Image Processing: Enhancing medical imaging techniques, such as de-speckling in ultrasound and X-ray images, and improving image de-noising using soft optimization techniques.

      Pattern Analysis in Machine Intelligence: Investigating pattern analysis and its applications within machine intelligence to improve the accuracy and efficiency of image processing algorithms.

Awards and Honors 

Rainer Knauf | Evolutionary Algorithms | Lifetime achievement Award

Published on by Academic Awards

Prof Dr Rainer Knauf |  Evolutionary Algorithms |  Lifetime achievement Award

Fachgebietsleiter für KI at  Technische Universität Ilmenau, Germany

Rainer Knauf is an apl. Prof. Dr.-Ing. habil., currently serving as the Chair of Artificial Intelligence at the Faculty of Computer Science and Automation, Technical University Ilmenau, Germany. He earned his Diploma Engineer (Dipl.-Ing.) in Electrical and Computer Engineering in 1987, followed by a Doctor of Engineering (Dr.-Ing.) in Computer Engineering in 1990, and a Doctor of Engineering habilitatus (Dr.-Ing. habil.) in Computer Science in 2000, all from Technical University Ilmenau. His research focuses on knowledge acquisition, validation, and refinement of intelligent systems, inductive inference, and machine learning.

 

profile

🎓 Education:

  • Dipl.-Ing. in Electrical and Computer Engineering
    Technical University Ilmenau, Germany
    📅 February 5, 1987
  • Dr.-Ing. in Computer Engineering
    Technical University Ilmenau, Germany
    📅 September 25, 1990
    Dissertation: “Applying Logic Programming to Design Knowledge Based Systems for Diagnostic Problems”
  • Dr.-Ing. habil. in Computer Science
    Technical University Ilmenau, Germany
    📅 November 15, 2000
    Habilitation: “Validating Rule Based Systems: A Complete Methodology”

💼 Professional Experience:

  • Full Professor (apl. Prof.)
    Chair of Artificial Intelligence, Technical University Ilmenau
    📅 March 2010 – Present
  • Associate Professor (Privatdozent)
    Chair of Artificial Intelligence, Technical University Ilmenau
    📅 April 2004 – February 2010
  • Assistant Professor (Privatdozent)
    Technical University Ilmenau
    📅 December 2000 – March 2004
  • Scientific Assistant
    Technical University Ilmenau
    📅 September 1991 – November 2000
  • Scientific Associate
    Ilmenau Institute of Technology
    📅 March 1987 – August 1991

🏅 Awards & Recognitions

  • Fellowship Awards from the Japan Society for the Promotion of Science 📜 (2008, 2011, 2015)
  • Graduate Faculty Scholar at the University of Central Florida 🎓 (2010)

Research Focus: Evolutionary Algorithms 🧬💡

Research Interests:

  • Optimization and Search Algorithms: Rainer Knauf’s work in evolutionary algorithms involves developing and improving algorithms for optimization and search problems. These algorithms are inspired by the principles of natural selection and genetics.
  • Artificial Intelligence Applications: He applies evolutionary algorithms to various AI challenges, including machine learning, robotics, and automated reasoning.
  • Knowledge Acquisition and Refinement: His research integrates evolutionary algorithms with knowledge-based systems to enhance the processes of knowledge acquisition, validation, and refinement.
  • Data Mining: Knauf explores the use of evolutionary algorithms in data mining, particularly in extracting meaningful patterns and insights from large datasets.
  • Inductive Inference: His work also includes using evolutionary algorithms for inductive inference, aiming to generalize from specific data to broader rules or patterns.

Citation:

Cited by:

  • All: 1082 citations
  • Since 2019: 253 citations

h-index:

  • Overall: 16
  • Since 2019: 7

i10-index:

  • Overall: 33
  • Since 2019: 4

Publication Top Notes:

  • “Didactic design through storyboarding: Standard concepts for standard tools”
    • Authors: KP Jantke, R Knauf
    • Publication: Proceedings of the 4th International Symposium on Information and Communication Technologies
    • Citations: 122 (2005)
    • Summary: This paper explores the use of storyboarding as a method for didactic design, emphasizing standard concepts to standardize tools for educational purposes.
  • “A framework for validation of rule-based systems”
    • Authors: R Knauf, AJ Gonzalez, T Abel
    • Publication: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
    • Citations: 80 (2002)
    • Summary: This paper presents a comprehensive framework for validating rule-based systems, addressing the need for systematic validation processes in artificial intelligence.
  • “Validation of human behavior representation”
    • Authors: SY Harmon, VB Barr, AJ Gonzalez, DC Hoffmann, R Knauf
    • Publication: University Library
    • Citations: 45 (2006)
    • Summary: The authors discuss methodologies for validating models of human behavior representation, crucial for developing reliable AI systems that simulate human actions.
  • “Modeling didactic knowledge by storyboarding”
    • Authors: R Knauf, Y Sakurai, S Tsuruta, KP Jantke
    • Publication: Journal of Educational Computing Research
    • Citations: 39 (2010)
    • Summary: This research focuses on the use of storyboarding to model didactic knowledge, enhancing the design and delivery of educational content through structured visual methods.
  • “Toward reducing human involvement in validation of knowledge-based systems”
    • Authors: R Knauf, S Tsuruta, AJ Gonzalez
    • Publication: IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans
    • Citations: 25 (2006)
    • Summary: This paper proposes methods to minimize human intervention in the validation process of knowledge-based systems, aiming for more autonomous and efficient validation techniques.
  • “Tweet credibility analysis evaluation by improving sentiment dictionary”
    • Authors: T Kawabe, Y Namihira, K Suzuki, M Nara, Y Sakurai, S Tsuruta, R Knauf
    • Publication: 2015 IEEE Congress on Evolutionary Computation (CEC)
    • Citations: 24 (2015)
    • Summary: This work evaluates the credibility of tweets by enhancing sentiment dictionaries, leveraging evolutionary computation techniques to improve the accuracy of sentiment analysis.
  • “A simple optimization method based on backtrack and GA for delivery schedule”
    • Authors: Y Sakurai, K Takada, N Tsukamoto, T Onoyama, R Knauf, S Tsuruta
    • Publication: 2011 IEEE Congress of Evolutionary Computation (CEC)
    • Citations: 22 (2011)
    • Summary: The authors present an optimization method combining backtracking and genetic algorithms (GA) to improve delivery scheduling, demonstrating the application of evolutionary algorithms in logistics.
  • “Generation of a minimal set of test cases that is functionally equivalent to an exhaustive set, for use in knowledge-based system validation”
    • Authors: T Abel, R Knauf, AJ Gonzalez
    • Publication: Proceedings of the 9th FLAIRS Conference
    • Citations: 22 (1996)
    • Summary: This paper discusses a method for generating a minimal set of test cases that maintains functional equivalence to an exhaustive set, enhancing the efficiency of knowledge-based system validation.
  • “Modeling academic education processes by dynamic storyboarding”
    • Authors: Y Sakurai, S Dohi, S Tsuruta, R Knauf
    • Publication: Journal of Educational Technology & Society
    • Citations: 21 (2009)
    • Summary: The study models academic education processes through dynamic storyboarding, offering a structured approach to designing and implementing educational curricula.
  • “Validating Rule-Based Systems: A Complete Methodology”
    • Author: R Knauf
    • Publication: Shaker
    • Citations: 21 (2000)
    • Summary: This book provides a comprehensive methodology for the validation of rule-based systems, detailing systematic approaches to ensure the reliability and accuracy of these systems.

 

 

Zhidong CAO | Artificial Intelligence Award | Best Researcher Award

Published on by Academic Awards

Mr Zhidong CAO | Artificial Intelligence Award | Best Researcher Award

Mr Zhidong CAO, Institute of Automation, Chinese Academy of Sciences, China

Zhidong Cao 🎓 is a prominent Professor and Principal Investigator at the National Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences. He holds a Doctor of Science degree from the Institute of Geographic Sciences and Natural Resources Research, CAS. With over a decade of experience at CAS, Cao has contributed significantly to AI and automation research, leading over 20 national-level projects. His scholarly impact includes 120+ papers in prestigious journals and international conferences, along with authoring 3 books. Cao has been honored with multiple awards, highlighting his substantial contributions to Chinese scientific advancement.

Publication profile

Scopus

Education

Zhidong Cao 📚 pursued a rigorous academic journey that culminated in a Ph.D. from the Institute of Geographic Sciences and Natural Resources Research at the Chinese Academy of Sciences, completed from September 2005 to July 2008. Prior to his doctoral studies, he earned a Master’s degree from Changsha University of Science and Technology, spanning from September 2002 to July 2005. Cao’s educational foundation began with a Bachelor’s degree, also from Changsha University of Science and Technology, covering the period from September 1997 to July 2001. These academic milestones provided him with a comprehensive background for his subsequent influential research career in artificial intelligence and automation.

Experience