Huawen Liu | Machine Learning | Distinguished Scientist Award

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Prof. Dr. Huawen Liu | Machine Learning | Distinguished Scientist Award

Professor at Shaoxing University, China

Prof. Huawen Liu 👨‍🏫, a distinguished academic at Shaoxing University 🇨🇳 since 2010, holds a Ph.D. and Ms.D. in Computer Science from Jilin University 🧠💻. He expanded his research globally as a postdoc at the University of South Australia 🇦🇺 (2012–2013) and a visiting fellow at the University of Texas at San Antonio 🇺🇸 (2018–2019). His work spans hash learning, AI, big data, and machine learning 🤖📊. With over 50 publications 📚 in top-tier journals, he actively contributes as an editor and conference organizer. He holds an H-index of 17 📈 and continues to shape intelligent computing worldwide 🌐.

Professional Profile:

Google Scholar

Scopus

🎓 Education & Experience 

  • 🎓 Ph.D. & Ms.D. in Computer Science from Jilin University, China 🇨🇳 (Top-10 university)

  • 🧑‍🔬 Postdoctoral Researcher at University of South Australia 🇦🇺 (2012–2013)

  • 🌍 Visiting Fellow at University of Texas at San Antonio, USA 🇺🇸 (2018–2019)

  • 👨‍🏫 Professor at Shaoxing University since July 2010 🏫

  • 📝 Over 50 peer-reviewed publications in high-impact journals and conferences 📚

🌱 Professional Development 

Prof. Liu has actively participated in shaping the research community 🌐. He serves as the Editor-in-Chief (EIC) of the International Journal of Intelligence and Sustainable Computing 🧠💡, Associate Editor for International Journal of Artificial Intelligence and Tools 🛠️, and Editor for Mathematics ➗📘. He has also led special issues as Guest Editor in Neural Computing and Applications 🧮 and Computing and Informatics 💻. His involvement extends to organizing national and international conferences 🎤📅 and acting as a program committee member for IJCAI, AAAI, CVPR, and others 🤝📊, reflecting his strong engagement with the global AI and computing community.

🔍 Research Focus Category 

Prof. Liu’s research lies at the intersection of artificial intelligence 🤖, machine learning 📚, and data science 📊. He specializes in hash learning, outlier detection, feature selection, and multimedia systems 🎥. His focus extends to practical applications in big data analytics 🗃️ and intelligent systems 💡. With a keen interest in mining patterns from complex datasets, his work contributes significantly to pattern recognition 🧠 and cybernetics 🛡️. He aims to bridge theory and real-world implementation through intelligent algorithms that enhance automated decision-making systems 🧮. His interdisciplinary approach empowers robust AI models with scalable and sustainable solutions 🌍.

🏆 Awards & Honors 

  • 📈 H-index of 17 according to Google Scholar 🧠

  • 📝 Over 50 publications in leading journals such as IEEE TKDE, TNNLS, TMM, TSMC, and more 📚

  • 🧑‍💼 Editor-in-Chief, Int. J. of Intelligence and Sustainable Computing

  • 🛠️ Associate Editor, Int. J. of Artificial Intelligence and Tools

  • ➗ Editor, Mathematics

  • 🧮 Lead Guest Editor for Neural Computing and Applications (NCAA)

  • 💻 Lead Guest Editor for Computing and Informatics (CAI)

  • 🎤 Organising Chair for 2015 National Conf. of Theoretical Computer Science

  • 📊 Organising Chair for 2014 China Conference on Data Mining

  • 🎓 Program Committee Member for top AI conferences: IJCAI, AAAI, CVPR, ADMA, ICBK, KSEM

Publication Top Notes

🔍 1. Outlier Detection Using Local Density and Global Structure

  • Authors: H. Liu, Huawen; S. Zhang, Shichao; Z. Wu, Zongda; X. Li, Xuelong

  • Journal: Pattern Recognition, 2025

  • Citations: 7

  • Summary: This article proposes a novel outlier detection method combining local density estimation with global structural features. It’s likely useful for anomaly detection in high-dimensional or graph-structured data.

🧠 2. Select Your Own Counterparts: Self-Supervised Graph Contrastive Learning With Positive Sampling

  • Authors: Z. Wang, Zehong; D. Yu, Donghua; S. Shen, Shigen; S. Yao, Shuang; M. Guo, Maozu

  • Journal: IEEE Transactions on Neural Networks and Learning Systems, 2025

  • Citations: 2

  • Summary: Focuses on self-supervised learning with graph contrastive methods, improving representation learning by selecting reliable positive samples for contrastive training.

🗣️ 3. Amharic Spoken Digits Recognition Using Convolutional Neural Network

  • Authors: T.A. Ayall, Tewodros Alemu; C. Zhou, Chuangjun; H. Liu, Huawen; S.T. Abate, Solomon Teferra; M. Adjeisah, Michael

  • Journal: Journal of Big Data, 2024 (Open Access)

  • Citations: 3

  • Summary: Presents a CNN-based model for recognizing spoken digits in Amharic, an under-resourced African language — showcasing multilingual AI applications.

🧠 4. An Improved Deep Hashing Model for Image Retrieval With Binary Code Similarities

  • Authors: H. Liu, Huawen; Z. Wu, Zongda; M. Yin, Minghao; X. Zhu, Xinzhong; J. Lou, Jungang

  • Access: Open Access

  • Citations: 0

  • Summary: Describes a deep hashing method that optimizes binary similarity in hash code space for more effective image retrieval.

🧠 5. LGAD: Local and Global Attention Distillation for Efficient Semantic Segmentation

  • Authors: C. Wang, Chen; Y. Qi, Yafei; Q. Li, Qi; H. Liu, Huawen

  • Type: Conference Paper (Open Access)

  • Citations: 1

  • Summary: Proposes an attention distillation method combining local and global context for lightweight semantic segmentation, improving performance while keeping models efficient.

Conclusion:

Dr. Huawen Liu’s exceptional research contributions, leadership in academic organizations, and active engagement in the scientific community make him a strong candidate for the Distinguished Scientist Award. His sustained impact on the field of machine learning and AI, along with his contributions to both theoretical and applied research, exemplify the qualities deserving of such an esteemed recognition.

Vaneet | Machine Learning | Best Researcher Award

Published on by Physicist Particle

Prof. Dr. Vaneet | Machine Learning | Best Researcher Award

Professor at PURDUE UNIVERSITY, United States

vaneet aggarwal is a distinguished professor and university faculty scholar at purdue university, specializing in reinforcement learning, generative AI, quantum machine learning, and LLM alignment 🤖⚛️. With a Ph.D. from Princeton University 🎓 and extensive experience in industry and academia, he has made groundbreaking contributions to networking, robotics, healthcare, and computational biology 🌍🩺. He has served as a visiting professor at KAUST, IIIT Delhi, and IISc Bangalore 📚 and has led major research initiatives at AT&T Labs and Purdue CLAN Labs. His work has been recognized globally through high-impact publications and awards 🏅.

Professional Profile 

Education & Experience 🎓💼

📌 Education:

  • Ph.D. in Electrical Engineering – Princeton University, 2010 🎓 (GPA 4.0/4.0)
  • M.A. in Electrical Engineering – Princeton University, 2007 🏅 (GPA 4.0/4.0)
  • B.Tech in Electrical Engineering – IIT Kanpur, 2005 🎓 (GPA 9.6/10)

📌 Experience:

  • Purdue University (2015–Present) 🏫 – Professor & University Faculty Scholar
  • KAUST, Saudi Arabia (2022–2023) 🏝️ – Visiting Professor
  • IIIT Delhi (2022–2023) 🌏 – Adjunct Professor
  • Plaksha University (2022–2023) 📡 – Adjunct Professor
  • IISc Bangalore (2018–2019) 🏆 – VAJRA Adjunct Faculty
  • AT&T Labs Research, NJ (2010–2014) 📡 – Senior Member, Technical Staff
  • Columbia University, NY (2013–2014) 📚 – Adjunct Assistant Professor

Professional Development 🚀📚

vaneet aggarwal has consistently contributed to cutting-edge advancements in AI, machine learning, and quantum computing 🧠⚡. As Editor-in-Chief of the ACM Journal of Transportation Systems 🚗📖, he shapes global research trends. He has been a technical lead in Purdue’s AI and security programs, fostering industry collaborations 🤝💡. His leadership in AI decision-making, intelligent infrastructures, and computational biology has driven groundbreaking innovations 🏗️🔬. He frequently mentors Ph.D. students and collaborates with top institutions worldwide, ensuring continuous academic excellence and technological impact 🌍🎯. His work bridges fundamental research with real-world applications, influencing multiple industries 🚀.

Research Focus Areas 🔍💡

🔬 Artificial Intelligence & Machine Learning: Reinforcement learning, generative AI, LLM alignment 🤖
⚛️ Quantum Computing: Quantum machine learning, hidden Markov models 🧠
📡 Networking & Systems: Cloud computing, 5G/6G networks, network virtualization 🌐
🛠️ Optimization & Control: Combinatorial bandits, linear optimization ⚙️
🚗 Transportation & Robotics: AI for intelligent infrastructure and automation 🏎️
🩺 Healthcare & Biomedical AI: Drug discovery, computational biology, medical AI 🧬💊

His research transforms fundamental theories into real-world applications, influencing technology, healthcare, and sustainable infrastructure 🌍.

Awards & Honors 🏅🎖️

🏆 University Faculty Scholar, Purdue University (2024) 🏫
🎖️ Best Paper Award – NeurIPS Workshop on Cooperative AI (2021) 📝
📚 VAJRA Adjunct Faculty, IISc Bangalore (2018–2019) 🔬
🥇 Editor-in-Chief, ACM Journal of Transportation Systems (2022–Present) 🚗📖
🌍 Senior Member, IEEE & ACM ⚡
🏅 Best Research Contributions in AI & Quantum Computing 🤖⚛️

Publication Top Notes

1. Stochastic Submodular Bandits with Delayed Composite Anonymous Bandit Feedback

  • Authors: Mohammad Pedramfar, Vaneet Aggarwal
  • Published in: IEEE Transactions on Artificial Intelligence, 2025
  • Summary: This paper addresses the combinatorial multi-armed bandit problem with stochastic submodular rewards and delayed, composite anonymous feedback. The authors analyze three delay models—bounded adversarial, stochastic independent, and stochastic conditionally independent—and derive regret bounds for each. Their findings indicate that delays introduce an additive term in the regret, affecting overall performance.
  • Access: The paper is available as open access.

2. FilFL: Client Filtering for Optimized Client Participation in Federated Learning

  • Authors: Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini, Marco Canini
  • Published in: [No source information available]
  • Summary: This conference paper introduces FilFL, a method to enhance federated learning by optimizing client participation through a filtering mechanism. By selecting a subset of clients that maximizes a combinatorial objective function, FilFL aims to improve learning efficiency, accelerate convergence, and boost model accuracy. Empirical evaluations demonstrate benefits such as faster convergence and up to a 10% increase in test accuracy compared to scenarios without client filtering.
  • Access: The paper is available as open access.

3. Prism Blockchain Enabled Internet of Things with Deep Reinforcement Learning

  • Authors: Divija Swetha Gadiraju, Vaneet Aggarwal
  • Published in: Blockchain: Research and Applications, 2024
  • Summary: This article explores the integration of Prism blockchain technology with the Internet of Things (IoT) using deep reinforcement learning techniques. The approach aims to enhance security, scalability, and efficiency in IoT networks by leveraging the unique features of Prism blockchain and the adaptive capabilities of deep reinforcement learning.
  • Access: The paper is available as open access.

4. GLIDE: Multi-Agent Deep Reinforcement Learning for Coordinated UAV Control in Dynamic Military Environments

  • Authors: Divija Swetha Gadiraju, Prasenjit Karmakar, Vijay K. Shah, Vaneet Aggarwal
  • Published in: Information (Switzerland), 2024
  • Summary: GLIDE presents a multi-agent deep reinforcement learning framework designed for the coordinated control of unmanned aerial vehicles (UAVs) in dynamic military settings. The framework focuses on enhancing mission success rates and operational efficiency by enabling UAVs to adapt to changing environments and collaborate effectively.
  • Access: The paper is available as open access.

5. Near-Perfect Coverage Manifold Estimation in Cellular Networks via Conditional GAN

  • Authors: Washim Uddin Mondal, Veni Goyal, Satish V. Ukkusuri, Mohamed-Slim Alouini, Vaneet Aggarwal
  • Published in: IEEE Networking Letters, 2024
  • Summary: This article proposes a method for estimating coverage manifolds in cellular networks using conditional Generative Adversarial Networks (GANs). The approach aims to achieve near-perfect coverage predictions, which are crucial for optimizing network performance and ensuring reliable communication services.
  • Access: The paper is available as open access.

Conclusion

vaneet aggarwal is a highly suitable candidate for the Best Researcher Award, given his strong publication record, leadership, and multidisciplinary impact. If he strengthens his global recognition, large-scale funding acquisition, and public engagement, he could be an even stronger contender for such an award.