Yueyang Zheng | Artificial Intelligence | Best Researcher Award

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Mr. Yueyang Zheng | Artificial Intelligence | Best Researcher Award

Student at Qingdao University of Science and Technology, China

yueyang zheng is a student at qingdao university of science and technology, specializing in sound event detection (SED). Their research focuses on recognizing sound events in audio, including detecting overlapping occurrences, known as polyphonic event detection. Passionate about audio signal processing, they are dedicated to advancing machine learning techniques for improved accuracy in real-world acoustic environments. With a strong technical foundation and keen interest in artificial intelligence, yueyang aims to contribute innovative solutions to SED challenges. Their academic journey is driven by curiosity and a commitment to enhancing audio analysis through cutting-edge computational methods. 🚀🎶🔍

Professional Profile

Education & Experience 🎓📚

  • 🎓 Qingdao University of Science and Technology – Pursuing studies in sound event detection
  • 🎧 Research Focus – Specialized in polyphonic sound event detection
  • 🖥️ Machine Learning & AI – Implementing computational techniques for audio processing
  • 🔍 Signal Processing – Enhancing SED accuracy using advanced methodologies

Professional Development 🚀📖

yueyang zheng is actively engaged in research and development in sound event detection (SED), focusing on polyphonic event detection where multiple sounds overlap. Their work involves applying deep learning techniques, neural networks, and signal processing strategies to improve recognition accuracy. Constantly learning, they participate in academic conferences, workshops, and online courses to stay updated on the latest advancements in audio AI. With hands-on experience in machine learning frameworks and sound classification models, yueyang is committed to pushing the boundaries of SED. Their goal is to contribute to real-world applications such as environmental monitoring, smart devices, and audio surveillance. 🎶📊🖥️

Research Focus 🔬🎵

yueyang zheng’s research is centered on Sound Event Detection (SED), particularly in recognizing overlapping sound events (polyphonic event detection). Their interests lie in:

  • 🤖 Deep Learning in Audio Processing – Leveraging AI models for improved sound recognition
  • 🔊 Acoustic Scene Analysis – Understanding complex sound environments
  • 🛠️ Neural Network Architectures – Developing models for real-time event detection
  • 📡 Real-world Applications – Implementing SED in smart devices, security, and healthcare
  • 🎤 Speech & Environmental Sound Processing – Enhancing automated sound analysis in noisy environments

Awards & Honors 🏆🎖️

  • 🏅 Best Student Research Award – Recognized for outstanding contributions to sound event detection
  • 🏆 Academic Excellence Scholarship – Honored for top performance in AI and machine learning studies
  • 🎤 Best Paper Presentation – Awarded at a conference for innovative approaches in audio recognition
  • 📚 Research Grant Recipient – Received funding support for SED-related research
  • 🥇 Hackathon Winner – Secured first place in an AI-based audio processing competition

Publication Top Notes

  • Title: ASiT-CRNN: A method for sound event detection with fine-tuning of self-supervised pre-trained ASiT-based model
  • Authors: Yueyang Zheng, Rui Zhang, Shukui Atito, Shiqing Yang, Wei Wang, Yuning Mei
  • Journal: Digital Signal Processing
  • Year: 2025
  • DOI: 10.1016/j.dsp.2025.105055
  • ISSN: 1051-2004
  • Abstract: This paper discusses the ASiT-CRNN model, designed for sound event detection. It leverages fine-tuning of a self-supervised pre-trained ASiT-based model, which enhances the performance of sound event recognition tasks. The authors explore advanced methods for sound event detection, improving both accuracy and computational efficiency in real-world applications.

Vaneet | Machine Learning | Best Researcher Award

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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.

Eman Aldakheel | Artificial Intelligence | Best Researcher Award

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Assist Prof Dr. Eman Aldakheel | Artificial Intelligence | Best Researcher Award

Scopus Profile

Orcid Profile

Educational Details:

Dr. Eman Aldakheel holds a Doctor of Philosophy in Computer Science from the University of Illinois at Chicago, where her dissertation, titled “Deadlock Detector and Solver (DDS),” focused on developing solutions for deadlock issues in computer systems. She earned her Master of Science in Computer Science from Bowling Green State University in Ohio, with a thesis titled “A Cloud Computing Framework for Computer Science Education,” highlighting her interest in leveraging technology for educational advancement. Dr. Aldakheel completed her Bachelor of Science in Computer Science with Honors from Imam Abdulrahman bin Faisal University in Dammam, Saudi Arabia, laying the foundation for her career in academia and research.

Academic Experience:

Dr. Eman Aldakheel has an extensive teaching and training background, starting as an instructor at the New Horizons Institute in Khobar, Saudi Arabia, in 2007, where she trained students on ICDL and IC3 certifications and taught various computer-related courses. She later joined Imam Abdulrahman bin Faisal University (formerly Dammam University) as an instructor, teaching basic computer skills and Microsoft Office applications to students in the Geography department. She also taught computer basics to girls at Riyadh Al-Islam Schools, working with students from elementary to high school levels. From 2012 to 2020, Dr. Aldakheel served as a lecturer at Princess Nourah Bint Abdulrahman University, where she contributed as a research assistant on software engineering projects and taught object-oriented programming. Since Fall 2020, she has been an Assistant Professor at the same institution, teaching various computer science courses ranging from programming to natural language processing. Dr. Aldakheel effectively adapted to remote teaching tools like Blackboard, Teams, and Zoom during the COVID-19 pandemic, ensuring uninterrupted learning for her students.

Honors and Awards:

Dr. Eman Aldakheel has actively participated in prestigious academic workshops and conferences, including the CRA-Women Grad Cohort Workshop, which supports the professional development of women in computing. Her academic achievements have earned her multiple travel awards, including ACM’s SRC (Student Research Competition) Travel Award and the HPDC (High Performance Distributed Computing) Travel Award, both of which provided her with opportunities to present her research and engage with global experts in the field. These accolades reflect her commitment to advancing her knowledge and contributing to the broader academic community.

Service Activities:

Dr. Eman Aldakheel has played a pivotal role in fostering the growth and development of talented students at Princess Nourah Bint Abdulrahman University. She has been actively involved in planning programs and activities aimed at nurturing high-achieving students, ensuring they receive the support and opportunities needed to excel. Dr. Aldakheel designed and built the foundation for the “Foundations of Programming” (GN 044) course, recording its lectures to enhance learning accessibility. Additionally, she supervises the College of Computer and Information student magazine, encouraging student participation in scholarly activities. Her involvement extends to various committees, where she serves as a judge or supervisor for hackathons, contributing her expertise to inspire innovation and creativity among students.

Granted Projects:

Dr. Eman Aldakheel is actively involved in several significant research projects. In 2023, she contributed to the “Researchers Supporting Project” at Princess Nourah Bint Abdulrahman University, under project number PNURSP2023R409. She is also leading two research initiatives funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia. The first, under project number RI-44-0618, focuses on the “Detection and Identification of Plant Leaf Diseases using YOLOv4,” running from November 2022 to May 2024. The second project, WE-44-0279, explores the “Use of Modern Machine Learning Techniques to Combat Extremism and the Role of Women,” also from November 2022 to May 2024. These projects highlight Dr. Aldakheel’s expertise in machine learning and its application to various fields, from agriculture to social issues.

Top Notable Publications

“Performance of Rime-Ice Algorithm for Estimating the PEM Fuel Cell Parameters”

Authors: Ismaeel, A.A.K., Houssein, E.H., Khafaga, D.S., Aldakheel, E.A., Said, M.

Year: 2024

Journal: Energy Reports

Citations: 3

“Outlier Detection for Keystroke Biometric User Authentication”

Authors: Ismail, M.G., Salem, M.A.-M., El Ghany, M.A.A., Aldakheel, E.A., Abbas, S.

Year: 2024

Journal: PeerJ Computer Science

Citations: 0

“Mobile-UI-Repair: A Deep Learning-Based UI Smell Detection Technique for Mobile User Interface”

Authors: Ali, A., Xia, Y., Navid, Q., Aldakheel, E.A., Khafaga, D.

Year: 2024

Journal: PeerJ Computer Science

Citations: 2

“Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies”

Authors: Ghani, M.A.N.U., She, K., Rauf, M.A., Aldakheel, E.A., Khafaga, D.S.

Year: 2024

Journal: Computers, Materials and Continua

Citations: 0

“Detection and Identification of Plant Leaf Diseases using YOLOv4”

Authors: Aldakheel, E.A., Zakariah, M., Alabdalall, A.H.

Year: 2024

Journal: Frontiers in Plant Science

Citations: 1

“Performance of the Walrus Optimizer for Solving an Economic Load Dispatch Problem”

Authors: Said, M., Houssein, E.H., Aldakheel, E.A., Khafaga, D.S., Ismaeel, A.A.K.

Year: 2024

Journal: AIMS Mathematics

Citations: 2

“Efficient Analysis of Large-Size Bio-Signals Based on Orthogonal Generalized Laguerre Moments of Fractional Orders and Schwarz–Rutishauser Algorithm”

Authors: Aldakheel, E.A., Khafaga, D.S., Fathi, I.S., Hosny, K.M., Hassan, G.

Year: 2023

Journal: Fractal and Fractional

Citations: 1

“Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem”

Authors: Ismaeel, A.A.K., Houssein, E.H., Khafaga, D.S., AbdElrazek, A.S., Said, M.

Year: 2023

Journal: Mathematics

Citations: 17

“CyberHero: An Adaptive Serious Game to Promote Cybersecurity Awareness”

Authors: Hodhod, R., Hardage, H., Abbas, S., Aldakheel, E.A.

Year: 2023

Journal: Electronics (Switzerland)

Citations: 3

Carrasquilla, M.D.L., Sun, M., Long, T., Huang, L., & Zheng, Y. (2024). Seismic anisotropy of granitic rocks from a fracture stimulation well at Utah FORGE using ultrasonic measurements. Geothermics, 123, 103129.

Carrasquilla, M.D.L., Parsons, J., Long, T., Zheng, Y., & Han, D.-H. (2023). Ultrasonic measurements of elastic anisotropy of granitic rocks for enhanced geothermal reservoirs. SEG Technical Program Expanded Abstracts, 2023-August, 79–83.

Carrasquilla, M.D.L., Costa, M.D.F.B., Souza, I.J.S., Amanajás, C.E., & Nunes, L.R.A. (2022). Geological, geophysical and mathematical analysis of synthetic bulk density logs around the world – Part II – The use of non-linear regression on empirical parameters estimation. Journal of Applied Geophysics, 206, 104838.

Carrasquilla, M.D.L., Carvalho, C.P., Costa, M.D.F.B., Amanajás, C.E., & Rautino, L. (2022). Geological, geophysical and mathematical analysis of synthetic bulk density logs around the world – Part I – The use of linear regression on empirical parameters estimation. Journal of Applied Geophysics, 204, 104733.