Ruochen Li | Artificial Intelligence | Best Researcher Award

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Ruochen Li | Artificial Intelligence | Best Researcher Award

Dr. Ruochen Li, BOHUA UHD Co., Ltd. , China.

Ruochen Li, PhD candidate at Macau University of Science and Technology, specializes in Artificial Intelligence with a focus on no-reference video quality assessment, cross-modal audio-visual retrieval, and image-based sound source localization. With expertise in cutting-edge AI technologies like PyTorch, TensorFlow, and MindSpore, Li has achieved groundbreaking research in video quality evaluation and audio-visual content correlation, earning recognition in top-tier journals. He has also received a prize in the National Artificial Intelligence Competition for his contributions to ultra-high-definition video processing. 📊📹🔍

Publication Profile

Scopus

Education and Experience

  • 🎓 PhD in Artificial Intelligence (2021-2024), Macau University of Science and Technology.
  • 🎓 Master’s in Control Engineering (2016-2019), Jiangsu University of Science and Technology.
    • Supervisor: Associate Prof. Shuxia Ye.
  • 🎓 Bachelor’s in Control Engineering (2012-2016), Jiangsu University of Science and Technology.
  • 📑 Research Participant: National Ultra-High Definition Video Innovation Center.
  • 📑 Research Contributor: China Science and Technology Information Research Institute.

Suitability For The Award

Dr. Ruochen Li is an accomplished researcher specializing in artificial intelligence, video quality assessment, and audio-visual event retrieval. With a Ph.D. in Artificial Intelligence from Mauca University of Science and Technology and extensive expertise in PyTorch, TensorFlow, and MindSpore, Li has contributed significantly to advancing multimedia technologies. Their innovations include state-of-the-art datasets, algorithms like Reformer, and multimodal fusion techniques with applications in accessibility, entertainment, and surveillance. Recognized through high-impact publications and awards, including third prize in the National Artificial Intelligence Competition, Ruochen Li exemplifies excellence in research and innovation, making them a strong candidate for prestigious honors such as the Best Researcher Award.

Professional Development

Ruochen Li’s professional journey is defined by innovations in AI and deep learning. He developed the UHD-VQ5k dataset and proposed novel algorithms for ultra-high-definition video quality assessment, utilizing advanced models like Resformer. His work in audio-visual content analysis, featured in his doctoral dissertation, emphasizes the integration of audio-visual features using deep neural networks. As a key participant in national projects, he has contributed to cloud-based UHD video platforms and AI policy analysis. His collaborations and publications underscore his commitment to advancing AI research and applications. 📊🤖📈

Research Focus

Ruochen Li’s research revolves around Artificial Intelligence applications in multimedia. His expertise spans no-reference video quality assessment, where he develops datasets and benchmarks for UHD video, to cross-modal audio-visual retrieval, enhancing machine understanding of multimodal content. His work also extends to image-based sound source localization, integrating audio-visual data for precise event detection. Through pioneering algorithms, Li bridges gaps between modalities, advancing the interplay of audio and video content in deep learning applications. His contributions drive progress in multimedia AI. 🎥🔊🧠

Awards and Honors

  • 🏆 Prize Winner: National Artificial Intelligence Competition.
  • 🏅 CET-6 Certificate: Scored 490.
  • 🏅 CET-4 Certificate: Scored 552.

Publication Top Notes

  • 📜 SgLFT: Semantic-guided Late Fusion Transformer for Video Corpus Moment Retrieval – Neurocomputing, 2024. 📚
  • 📜 Ultrahigh-definition Video Quality Assessment: A New Dataset and Benchmark – Neurocomputing, 2024, 📊
  • 📜 TA2V: Text-Audio Guided Video Generation – IEEE Transactions on Multimedia, 2024, 🎥🎶
  • 📜 Cross-Modality Knowledge Calibration Network for Video Corpus Moment Retrieval – IEEE Transactions on Multimedia, 2024,  🌐📑
  • 📜 Maximizing Mutual Information Inside Intra- and Inter-Modality for Audio-Visual Event Retrieval – International Journal of Multimedia Information Retrieval, 2023, 🔗🎧

Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

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Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

Mr. Seyed matin malakouti, University of Rijeka, Croatia

Seyed Matin Malakouti is an accomplished electrical engineer and researcher specializing in control systems engineering and machine learning. He completed his Master of Science in Electrical Engineering from the University of Tabriz, Iran, after earning his Bachelor’s degree from Isfahan University of Technology. His research spans various applications of machine learning, including wind power generation prediction, heart disease classification using ECG data, and solar farm power generation forecasting. Seyed’s work has resulted in several high-impact publications in prestigious journals, with his research on wind energy and machine learning techniques receiving significant citations. He has also been involved in cutting-edge projects such as predicting global temperature change and advancing renewable energy solutions. In recognition of his contributions, Seyed has received multiple awards, including the Best Researcher Award at the International Conference on Cardiology and Cardiovascular Medicine in 2023, and nominations for Best Paper and Best Researcher Awards in other international conferences. Additionally, he actively contributes to the scientific community as a peer reviewer for numerous journals in the fields of artificial intelligence, environmental sciences, and electrical engineering.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Seyed Matin Malakouti is a highly qualified and accomplished researcher in the field of Electrical Engineering, specializing in Control Systems, Machine Learning, and Data Science. His impressive academic background includes a Master’s degree in Electrical Engineering from the University of Tabriz and a Bachelor’s degree from Isfahan University of Technology.

Education & Training 🎓

  • 2020 – 2022: M.Sc. in Electrical Engineering – Control System Engineering, University of Tabriz, Iran
  • 2014 – 2019: B.Sc. in Electrical Engineering, Isfahan University of Technology, Iran

Awards & Honors 🏆

  • 2023: Best Researcher, International Conference on Cardiology and Cardiovascular Medicine
  • 2023: Nominated for Best Paper Award, International Research Awards on Mathematics and Optimization Methods
  • 2024: International Young Scientist Awards, Best Researcher Category

Technical Skills 🛠️

  • Machine Learning 🤖
  • Data Science 📊
  • Programming Languages: MATLAB, Python 💻

Peer Review Activities 🧐

Seyed has reviewed articles for prestigious journals, such as:

  • IEEE Access
  • Artificial Intelligence Review
  • BMC Public Health
  • Environmental Monitoring and Assessment 🌱

Publication top Notes:

Machine learning and transfer learning techniques for accurate brain tumor classification

ML: Early Breast Cancer Diagnosis

Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron + Bayesian optimization, ensemble learning, and CNN-LSTM models

Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in predicting wind speed and energy generation

Discriminate primary gammas (signal) from the images of hadronic showers by cosmic rays in the upper atmosphere (background) with machine learning

Estimating the output power and wind speed with ML methods: A case study in Texas

Assoc Prof Dr. Wenlong Hang | Artificial Intelligence Award | Best Researcher Award

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Assoc Prof Dr. Wenlong Hang | Artificial Intelligence Award | Best Researcher Award

Assoc Prof Dr. Wenlong Hang, Nanjing Tech University, China

Wenlong Hang holds a Doctor of Engineering degree from Jiangnan University, where he graduated in June 2017, specializing in Light Industry Information Technology. During his doctoral studies, he visited both Hong Kong Polytechnic University and the Shenzhen Institutes of Advanced Technology. Since September 2017, Dr. Hang has been a faculty member at the School of Computer Science and Technology at Nanjing Tech University. His research interests primarily focus on artificial intelligence and machine learning, with a particular emphasis on medical image analysis and EEG signal processing. He has published more than 30 papers in reputable journals and conferences, contributing significantly to semi-supervised learning, federated learning, and EEG classification techniques. His representative works include research on medical image segmentation, reliability-aware semi-supervised frameworks, and domain-generalized EEG classification.

Professional Profile:

Summary of Suitability for Best Researcher Award :

Wenlong Hang is highly suitable for the Best Researcher Award based on his extensive research and contributions in the fields of artificial intelligence, machine learning, and medical image processing. His academic background, with a Doctor of Engineering degree from Jiangnan University, and professional experiences at institutions like Hong Kong Polytechnic University and Shenzhen Institutes of Advanced Technology, demonstrates his deep involvement in advanced technological research.

Education:

  • Doctor of Engineering (Graduated in June 2017)
    • Major: Light Industry Information Technology
    • Institution: Jiangnan University
    • Doctoral Visits: Hong Kong Polytechnic University, Shenzhen Institutes of Advanced Technology

Work Experience:

  • Since September 2017: Faculty Member
    • Position: Professor at the School of Computer Science and Technology
    • Institution: Nanjing Tech University

Research Areas:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Medical Image Segmentation
  • EEG Classification

Publication top Notes:

CITED: 109
CITED: 109
CITED: 73
CITED: 67
CITED: 34
CITED: 33

 

Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award

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Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award 

Mr. Zhongwen Hao, Cranfield University, China

Zhongwen Hao is a Master’s candidate in Aerospace Manufacturing at Cranfield University, UK, and concurrently pursuing a Master of Mechanical Engineering at Nanjing University of Aeronautics and Astronautics, China. He completed his Bachelor’s degree in Electronic Information with a focus on Image Processing from China University of Mining and Technology. His research interests include robot control, visual servoing, image processing, and deep learning. Zhongwen has led notable projects such as visual servoing of robotic arms using deep learning techniques and galaxy image classification. His proficiency in programming with C++, Python, and MATLAB, coupled with his skills in deep learning and image processing, underscores his technical expertise. He has published research on motion prediction and object detection in visual servoing systems. Zhongwen is known for his strong project execution abilities, team spirit, and resilience.

Professional Profile:

Summary of Suitability:

Hao’s research direction aligns well with cutting-edge fields such as robot control, visual servoing, image processing, and deep learning. These areas are highly relevant and significant in contemporary technological advancements. Hao has a solid educational foundation with advanced studies in Aerospace Manufacturing and Mechanical Engineering, complemented by a bachelor’s degree in Electronic Information with a focus on Image Processing. This diverse yet interconnected educational background enhances his research capabilities.

Education

  1. Cranfield University, Bedford, UK
    Master’s Candidate of Aerospace Manufacturing
    Major: Deep Learning and Image Processing
    September 2023 – September 2024
  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China
    Master of Mechanical Engineering
    Major: Mechanical
    September 2022 – June 2025 (Expected)
  3. China University of Mining and Technology, Xuzhou, China
    Bachelor of Electronic Information
    Major: Image Processing
    September 2017 – June 2021

Work Experience

  1. Project Leader
    Research on Visual Servoing of Robotic Arms Based on Deep Learning
    June 2024 – September 2024

    • Led research on target detection using the DETR model, trajectory planning with the PSO algorithm, and motion prediction using BiLSTM and KAN neural networks.
    • Integrated and simulated algorithms in ROS using Gazebo to validate their effectiveness.
  2. Participator
    Galaxy Image Classification Based on Deep Learning
    February 2024 – March 2024

    • Handled image preprocessing and reconstruction, and implemented galaxy image classification using the VIT model, achieving a classification accuracy of 90%.

Publication top Notes:

Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning

 

Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award

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Mr. Omer Tariq | Artificial Intelligence Award | Best Researcher Award 

Mr. Omer Tariq, Korea Advanced Institute of Science and Technology, KAIST, South Korea

Omer Tariq is a Ph.D. candidate at the Korea Advanced Institute of Science and Technology (KAIST), specializing in efficient and privacy-preserving deep learning for AIoT and autonomous systems. With a strong foundation in digital ASIC design, embedded systems, and hardware design, Omer has over seven years of experience in developing and deploying innovative machine learning solutions using TensorFlow, TensorRT, and PyTorch. His research includes advanced robotics software systems, autonomous navigation, and state-of-the-art motion planning algorithms. He has led teams in high-performance SoC/RTL design and verification at the National Electronics Complex, Pakistan, and contributed to satellite imaging systems at SUPARCO. Omer holds a BSc in Electrical Engineering from the University of Engineering and Technology, Taxila, and has published several papers in prominent journals. His technical skills are complemented by a range of certifications in machine learning, data science, and digital signal processing.

Professional Profile:

Summary of Suitability for Best Researcher Award

Omer Tariq is a Ph.D. candidate specializing in efficient and privacy-preserving deep learning for AIoT and Autonomous Systems. His work is highly relevant to current technological advancements and addresses significant challenges in machine learning, robotics, and autonomous systems. His research includes:

Education

Korea Advanced Institute of Science and Technology (KAIST)
Doctor of Philosophy (Ph.D.) in Computer Science
May 2021 – July 2025

  • Majors: Machine Learning & AI
  • CGPA: 3.74/4.3
  • Coursework: Programming for AI, Introduction to Artificial Intelligence, Design and Analysis of Algorithms, Intelligent Robotics, Human-Computer Interaction, Artificial Intelligence and Machine Learning, Technical Writing for Computer Science, Advanced Machine Learning, IoT Datascience

University of Engineering and Technology (UET), Taxila
Bachelor of Science in Electrical Engineering
Nov 2010 – July 2014

  • CGPA: 3.25/4.0
  • Thesis: Computer Vision-Assisted Object Detection and Control Framework for 3-DoF Robotic Arm
  • Area: Microelectronics, Control Systems, and Advanced Computer Architecture

Work Experience

Department of Industrial & Systems Engineering (ISysE), KAIST
Research Assistant
Nov. 2023 – March 2024

  • Designed and developed the electronics and power management module for the DAIM-Autonomous Mobile Robot, enhancing operational efficiency and reliability.
  • Engineered advanced robotics software systems for autonomous navigation and task execution.
  • Implemented state-of-the-art robot motion planning, mapping, and localization (SLAM) algorithms to improve real-time navigation accuracy.

National Electronics Complex, Pakistan (NECOP)
Engineering Manager & Team Lead
Apr. 2019 – Sep. 2022

  • Led verification and validation of high-performance SoC/RTL designs, ensuring system performance and reliability.
  • Spearheaded RTL development and optimization for high-performance IC designs, including logic synthesis, DFT, scan chain insertion, formal verification, and static timing analysis.
  • Managed the use of Synopsys and Cadence EDA tools for front-end and back-end digital IC design processes.

National Space Agency, Pakistan (SUPARCO)
Assistant Manager
Oct. 2014 – Apr. 2019

  • Designed and developed satellite imaging payload systems for national satellite missions.
  • Engineered high-speed, multi-layer PCB designs and conducted signal/power integrity simulations for satellite systems.
  • Developed embedded systems for the Satellite Ku-Band Positioning Unit, enhancing communication and positioning capabilities.

Publication top Notes:

2D Particle Filter Accelerator for Mobile Robot Indoor Localization and Pose Estimation

TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor Detection

Compact Walsh–Hadamard Transform-Driven S-Box Design for ASIC Implementations

DeepIOD: Towards A Context-Aware Indoor–Outdoor Detection Framework Using Smartphone Sensors

 

 

Mr. Lianfa Li | Artificial Intelligence Award | Top Researcher Award

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Mr. Lianfa Li | Artificial Intelligence Award | Top Researcher Award 

Mr. Lianfa Li, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China 

Dr. Lianfa Li is a distinguished Senior Research Associate and Lead Data Scientist at the University of Southern California’s Department of Population and Public Health Sciences. Since August 2017, he has been at the forefront of innovations in data science and machine learning, with a particular focus on remote sensing and air pollution modeling to study exposure and health effects. Dr. Li’s academic journey began with a Bachelor of Science in Resources, Planning, and Management from Nanjing University in 1998, followed by a Ph.D. in Geographical Information Science from the Institute of Geographical Sciences and Natural Resources Research at the Chinese Academy of Sciences in 2005. His career includes significant roles such as Associate Professor at the Chinese Academy of Sciences, Postdoctoral Scholar and Associate Specialist at the University of California, Irvine, and Research Associate at USC’s Department of Preventive Medicine.

Professional Profile:

 

ORCID

 

Summary of Suitability for the Top Researcher Award

Lianfa Li, PhD, currently a Senior Research Associate and Lead Data Scientist at the University of Southern California’s Department of Population and Public Health Sciences, is an exemplary candidate for the Top Researcher Award. His extensive background in data science and machine learning, particularly in the realm of remote sensing and air pollution exposure, positions him as a leader in his field. Below are the reasons why Dr. Li is suitable for this prestigious award:

EDUCATION 🎓📚

  • PhD in Geographical Information Science (June 2005)
    Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
    Advisor: Prof. Jinfeng Wang
  • Bachelor of Science in Resources, Planning and Management (Aug 1998)
    Nanjing University, Nanjing, Jiangsu Province, China
    Advisor: Prof. Yunliang Shi

ACADEMIC EMPLOYMENT 🏛️💼

  • Senior Research Associate, Lead Data Scientist (Aug 2017-Present)
    Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA
    Leading innovations in data science and machine learning, and the modeling efforts in remote sensing and air pollution (exposure and health effects)
  • Research Associate (Aug 2017-July 2014)
    Department of Preventive Medicine, University of Southern California, Los Angeles, CA
  • Associate Specialist (June 2013-June 2014)
    Program in Public Health, University of California, Irvine, CA

HONORS AND AWARDS 🏆🎖️

  1. 2010.6
    The paper about Bayesian risk modeling (Risk Analysis, 30(7), 1157-1175) selected for a media outreach campaign in 2010 by Society for Risk Analysis
  2. 2007.5
    Chinese Academy of Sciences KC Wong Work Incentive Fund
  3. 2004.3
    The Excellent Presidential Scholarship of Chinese Academy of Sciences, 2004

WORKSHOP AND PRESENTATION 🎤📅

  1. Biweekly workshop: “Air pollution and exposure modeling” (2015-present, University of Southern California, California, USA)
  2. Invited presentation: “GCN-assisted U-Net for segmentation of OCT images” (Bay area data science workshop, Mar. 27, 2021)
  3. Invited presentation: “Enhancing semantic segmentation with contextual information” (Bay area data science workshop, Dec. 07, 2019)

Publication top Notes:

Geocomplexity Statistical Indicator to Enhance Multiclass Semantic Segmentation of Remotely Sensed Data with Less Sampling Bias

Multiscale Entropy-Based Surface Complexity Analysis for Land Cover Image Semantic Segmentation

Generating Fine-Scale Aerosol Data through Downscaling with an Artificial Neural Network Enhanced with Transfer Learning

Encoder–Decoder Full Residual Deep Networks for Robust Regression and Spatiotemporal Estimation

Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation

Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation

Prof. Changgyun Kim | Artificial Intelligence Award | Best Researcher Award

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Prof. Changgyun Kim | Artificial Intelligence Award | Best Researcher Award 

Prof. Changgyun Kim, Department of Artificial Intelligence & Software/Samcheok,South Korea

Changgyun Kim is an esteemed academic and researcher associated with Kangwon National University, Department of Artificial Intelligence & Software, and Dongguk University’s Industrial Engineering department in South Korea. His research expertise spans deep learning, healthcare, and data mining. He has made significant contributions to the field, including developing AI-based systems for detecting betting anomalies in sports, diagnosing tooth-related diseases using panoramic images, and creating models for obesity diagnosis using 3D body information. His work is published in renowned journals such as Scientific Reports, Annals of Applied Sport Science, JMIR Medical Informatics, Sensors, Sustainability, the International Journal of Distributed Sensor Networks, and Applied Sciences. Dr. Kim’s notable projects include establishing IoT-based smart factories for SMEs in Korea and developing web applications for obesity diagnosis using data mining methodologies. His extensive research portfolio underscores his commitment to advancing AI applications in various domains

Professional Profile:

ORCID

 

Education

No specific details about Changgyun Kim’s educational background are provided in the provided information. To give a more comprehensive overview, details such as degrees obtained, institutions attended, and fields of study would be needed.

Work Experience

  1. Dongguk University: Jung-gu, Seoul, KR
    • Department: Industrial Engineering
    • Position: Not specified in the provided information.
  2. Kangwon National University
    • Department: Artificial Intelligence & Software
    • Position: Not specified in the provided information.

Publication top Notes:

 

AI-based betting anomaly detection system to ensure fairness in sports and prevent illegal gambling

Detectability of Sports Betting Anomalies Using Deep Learning-based ResNet: Utilization of K-League Data in South Korea

Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study

Development of an Obesity Information Diagnosis Model Reflecting Body Type Information Using 3D Body Information Values

Development of a Web Application Based on Human Body Obesity Index and Self-Obesity Diagnosis Model Using the Data Mining Methodology

Establishment of an IoT-based smart factory and data analysis model for the quality management of SMEs die-casting companies in Korea