Weihua Liu| AI | Best Researcher Award

 Dr. Weihua Liu| AI | Best Researcher Award

 Dr at AthenaEyesCO., LTD. China

With extensive contributions to academia and industry, I’ve authored over 20 influential papers and filed more than 80 patents, underscoring my commitment to innovation at the intersection of AI and healthcare. My career spans leadership in national science foundation projects and pioneering advancements in medical imaging and diagnostic technologies.

Profile

  1. Orcid

🎓Education

Post-Doctoral Research Beijing Institute of Technology, School of Medical Technology (Nov 2021 – Jun 2024) Co-supervisor: Chen Duanduan, Focus: Construction and Application of Medical Multi-modal Large Models. PhD in Computer Science Beijing Institute of Technology, School of Computer Science (Sep 2014 – Jun 2021) Supervisor: Liu Xiabi Dissertation: “Deep Network Structure and Its Learning Method Based on Pulmonary Nodule Detection and Lung Parenchyma Segmentation”. Bachelor’s and Master’s Degrees Changsha University of Science and Technology, School of Computer and Communication Engineering Bachelor’s Degree in Computer Science and Technology (Sep 2002 – Jun 2006), Master’s Degree in Software Engineering and Theory (Graduated Jun 2009), Research Focus: Image Processing and Pattern Recognition

🔬Research Projects

National Natural Science Foundation of China Project: Research on Intelligent Assessment Method for Stroke Risk Based on High-Risk Carotid Plaque-Complex Blood Flow Image Feature Analysis (2023-2026). Beijing Natural Science Foundation Project: Research on the Model of Acute Respiratory Distress Syndrome Assisted Diagnosis and Treatment Based on AI and Data Mining (2023-2026). Changsha Major Science and Technology Special Project Research and Application of Trustworthy Intelligent Vision Key Technologies in 5G Environment (2020-2023)

🚀 Professional Experience:

As an AI Algorithm Scientist at 3M’s Beijing Research and Development Center, I spearheaded the development of the BAX framework, a unified cross-platform AI deployment system widely adopted in biometric intelligence systems globally.

💡 Patents:

I’ve filed over 80 patents, showcasing my innovations in areas like multimodality-based medical models, facial recognition, and medical identity authentication.

🌟 Research Expertise:

With a profound focus on AI and healthcare intersections, I bring extensive theoretical knowledge in biometric technology, physiological and psychological computing, and medical assistant diagnosis.

Publications Top Notes 📝

  • Shape-margin knowledge augmented network for thyroid nodule segmentation and diagnosis
    • Year: 2024
    • Authors: Liu, Weihua; Lin, Chaochao; Chen, Duanduan; Niu, Lijuan; Zhang, Rui; Pi, Zhaoqiong
    • Source: Computer Methods and Programs in Biomedicine

 

  • A pyramid input augmented multi-scale CNN for GGO detection in 3D lung CT images
    • Year: 2023
    • Authors: Liu, Weihua; Liu, Xiabi; Luo, Xiongbiao; Wang, Murong; Han, Guanghui; Zhao, Xinming; Zhu, Zheng
    • Source: Pattern Recognition

 

  • Stone needle: A general multimodal large-scale model framework towards healthcare
    • Year: 2023
    • Authors: Liu, Weihua; Zuo, Yong
    • Source: arXiv preprint arXiv:2306.16034

 

  • Contraction Mapping of Feature Norms for Data Quality Imbalance Learning
    • Year: 2022
    • Authors: Liu, Weihua; Liu, Xiabi; Li, Huiyu; Lin, Chaochao
    • Source: Available at SSRN 4250246

 

  • Integrating lung parenchyma segmentation and nodule detection with deep multi-task learning
    • Year: 2021
    • Authors: Liu, Weihua; Liu, Xiabi; Li, Huiyu; Li, Mincan; Zhao, Xinming; Zhu, Zheng
    • Source: IEEE Journal of Biomedical and Health Informatics

 

  • A new three-stage curriculum learning approach for deep network based liver tumor segmentation
    • Year: 2020
    • Authors: Li, Huiyu; Liu, Xiabi; Boumaraf, Said; Liu, Weihua; Gong, Xiaopeng; Ma, Xiaohong
    • Source: 2020 International Joint Conference on Neural Networks (IJCNN)

 

  • URDNet: a unified regression network for GGO detection in lung CT images
    • Year: 2020
    • Authors: Liu, Weihua; Ren, Yuchen; Li, Huiyu
    • Source: Wireless Communications and Mobile Computing

 

  • Content-sensitive superpixel segmentation via self-organization-map neural network
    • Year: 2019
    • Authors: Wang, Murong; Liu, Xiabi; Soomro, Nouman Q; Han, Guanhui; Liu, Weihua
    • Source: Journal of Visual Communication and Image Representation

 

  • Hybrid resampling and multi-feature fusion for automatic recognition of cavity imaging sign in lung CT
    • Year: 2019
    • Authors: Han, Guanghui; Liu, Xiabi; Zhang, Heye; Zheng, Guangyuan; Soomro, Nouman Qadeer; Wang, Murong; Liu, Weihua
    • Source: Future Generation Computer Systems

 

sofia aftab | Neural network | Best Researcher Award

 Ms. sofia aftab | Neural network | Best Researcher Award

Ms. Norges Teknisk-Naturvitenskapelige Universitet, Norway

👩‍🔬 Highly experienced Data Scientist with expertise in research, data science, machine learning, advanced analytics, and Generative AI. Proficient in Python, SQL, SAS, Teradata, R, and QlikView. Possesses strong skills in business analysis, model building, and communication. Excels in statistical techniques, machine learning algorithms, deep learning frameworks, NLP, data engineering, and Generative AI. Experienced in agile project management and collaborative team environments.🌟

Profile

Scopus

 

 

Scholar

 

🎓📚Academics

  • MS (IT) specialization in Data Mining/Analytics from NUST
  • Ph.D (Machine Learning/Data Science) from NTNU

 

💼📊Career Skills/Knowledge

Over 12 years of experience as an enthusiastic Data Scientist Strong business analysis and data mining skills Expertise in model building and execution Proficient in segmentation and customer value management analytics Skilled in statistical techniques including regression, A/B testing, and statistical significance of ML models Experienced in machine learning algorithms such as NN, SVM, Naive Bayes, ensemble modeling, and deep learning (DNN)Knowledgeable in NLP techniques, data engineering, MLOps, and cloud deployments Experienced in Generative AI techniques including prompt engineering, Lang chain, and Semantic search Research-focused with experience in improving evaluation metrics and developing recommendation algorithms Proficient in agile project management methodologies

🏢💼Experience

Accenture-Norway (Aug 2022 – Present)Data Science Consultant/Team Lead Built and maintained data and ML pipelines Developed ML models and CI/CD workflows Collaborated with cross-functional teams Led agile projects and worked on Generative AI Telenor-Norway (Dec 2020 – Aug 2022)Data Scientist Evaluated and improved ML projects Transformed business questions into analysis Led agile projects and presented to management NTNU (Apr 2018 – 2020)Research Scientist Worked on Recommendation systems using deep learning Improved evaluation metrics for recommender systems HPE (May 2016 – 2018)Data Scientist (Consultant)Identified cross-sell and upsell opportunities Developed and maintained next best action engine Telenor (Aug 2015 – May 2016)Specialist Advance Analytics and Consumer Insight Conducted subscriber analysis and developed churn/retention framework Telenor (Aug 2013 – Aug 2015)Executive Advance Analytics and Consumer Insight Developed behavioral segmentation and churn prediction models Protégé Global (Aug 2012 – 2013)Team Lead Data Miner/Data Scientist Managed a team for data mining projects and conducted exploratory data analysis Muhammad Ali Jinnah University (MAJU) (2012 – 2013)Lecturer, Trainer National University of Science and Technology (NUST) (2011 – 2012)Research Assistant

Publications Top Notes 📝

  1. Title: Data Mining in Insurance Claims (DMICS) Two-way mining for extreme values
    • Authors: S Aftab, W Abbas, MM Bilal, T Hussain, M Shoaib, SH Mehmood
    • Conference: Eighth International Conference on Digital Information Management (ICDIM)
    • Year: 2013
    • Citations: 6

 

  1. Title: Evaluating top-n recommendations using ranked error approach: An empirical analysis
    • Authors: S Aftab, H Ramampiaro
    • Journal: IEEE Access
    • Volume: 10
    • Pages: 30832-30845
    • Year: 2022
    • Citations: 4

 

  1. Title: Improving top-N recommendations using batch approximation for weighted pair-wise loss
    • Authors: S Aftab, H Ramampiaro
    • Journal: Machine Learning with Applications
    • Volume: 15
    • Pages: 100520
    • Year: 2024

 

  1. Title: Deep Contextual Grid Triplet Network for Context-Aware Recommendation
    • Authors: S Aftab, H Ramampiaro, H Langseth, M Ruocco
    • Journal: IEEE Access
    • Year: 2023

 

  1. Title: Data Mining in Insurance Claims (DMICS)
    • Authors: S Aftab, W Abbass, MM Bilal, T Hussain, M Shoaib, SH Mehmood