Hamna Baig | Artificial Intelligence | Young Researcher Award

Ms. Hamna Baig | Artificial Intelligence | Young Researcher Award

Research Internee | COMSATS University Islamabad, Attock Campus | Pakistan

Hamna Baig πŸŽ“ is a passionate and award-winning Electrical Engineering graduate from COMSATS University Islamabad, Attock Campus. A gold medalist πŸ₯‡ with a CGPA of 3.66, she blends academic brilliance with innovative research in AI, IoT, and robotics πŸ€–. Hamna’s dynamic work spans smart environments, RF sensing, and machine learning applications πŸ’‘. She has published multiple research papers πŸ“š, led various technical projects, and participated in prestigious conferences πŸ›οΈ. Her leadership roles and technical writing expertise further reflect her versatility 🧠. Hamna aims to revolutionize engineering solutions through creativity, technology, and social impact 🌍.

Professional profile :Β 

Google Scholar

OrcidΒ 

Summary of Suitability :Β 

Hamna Baig exemplifies the essence of a young and emerging researcher through her exceptional academic performance, innovative contributions to AI-driven engineering, and a prolific portfolio of research publications. A gold medalist in Electrical Engineering from COMSATS University Islamabad, she has demonstrated consistent excellence in both theoretical knowledge and practical application. With multiple high-impact publications, advanced project implementations, and recognized conference presentations, she brings outstanding promise to the future of intelligent systems and healthcare engineering. Her dedication to interdisciplinary innovation, backed by hands-on experience and leadership roles, showcases her as a rising star in engineering research.

πŸ”Ή Education & Experience :

πŸ“˜ Education:

  • πŸŽ“ B.Sc. Electrical Engineering, COMSATS University Islamabad, Attock Campus (2020–2024) – CGPA: 3.66/4.00, Gold Medalist πŸ…

  • πŸ“‘ Final Year Project: AI-based Environmental Control Model for Smart Homes πŸ πŸ€–

πŸ§‘β€πŸ’Ό Experience:

  • πŸ§ͺ Internee, Electrical & Computer Engineering Dept., COMSATS, under PEC GIT Program (2024–Present)

  • ⚑ Internee, Ghazi-Barotha Hydro Power Plant (GBHPP), WAPDA (2023)

  • πŸ–‹οΈ Technical Writer (Electrical/Electronics), CDR Professionals (2023–Present)

Professional Development :

Hamna Baig has actively pursued professional growth through certifications, leadership, and community engagement 🌱. She completed the prestigious “Machine Learning Specialization” by DeepLearning.AI πŸ€–, “Generative AI for Everyone” 🧠, and several tech courses from Stanford, Yonsei, and the University of Michigan via Coursera πŸŽ“. As a proactive learner, she enhances her skills in AI, IoT, wireless communication, and public speaking 🎀. Hamna has held key roles such as President of the Sports Society 🏸, Co-Campus Director of AICP πŸ§‘β€πŸ”¬, and VP of COMSATS Science Society. Her drive to uplift communities and advance technology sets her apart 🌟.

Research Focus :Β 

Hamna’s research centers on the integration of Artificial Intelligence and Machine Learning into real-world electrical and biomedical systems πŸ€–πŸ§ . She explores SDR-based gait monitoring for Parkinson’s patients πŸ§“, AI-controlled environmental systems for energy-efficient smart homes 🌑️, and intelligent robotic applications in agriculture πŸ€–πŸŽ. Her work emphasizes non-invasive health monitoring using RF sensing πŸ›οΈ and AI-powered automation solutions. She is deeply invested in translating complex algorithms into practical, user-centric applications that elevate health, comfort, and productivity ⚑. Her interdisciplinary approach bridges electrical engineering with innovative computing solutions πŸ”ŒπŸ“Š.

Awards & Honors :

  • πŸ† Awards & Certificates:

    • πŸ₯‡ Gold Medalist, COMSATS University Islamabad (2024)

    • 🧾 Certificate of Gratitude, ICTIS Conference, UET Peshawar (2024)

    • πŸ“œ Certificate of Gratitude, ICCSI Conference, University of Haripur (2024)

    • 🧠 ML Specialization Certificate, DeepLearning.AI – Stanford (2023)

    • 🧬 Generative AI for Everyone – DeepLearning.AI (2025)

    • πŸ§β€β™€οΈ Public Speaking Specialization – University of Michigan (2024)

    • πŸ“Ά Wireless Communications Course – Yonsei University (2024)

    • πŸŽ“ Prime Minister’s Youth Laptop Scheme Awardee (2023)

    • πŸ₯‡ Winner – IoT Pick and Place Robotic Competition, COMSATS (2024)

    • πŸ§’ Student of the Year – COMSATS University, Attock (2023)

Publication Top Notes :Β 

  • β€’ Title: Intelligent Frozen Gait Monitoring using Software Defined Radio Frequency Sensing
    Citation: Electronics, 14(8), 1603, 2025
    Authors: Khan, M. B., Baig, H., Hayat, R., Tanoli, S. A. K., Rehman, M., Thakor, V. A., & Haider, D.
    Year: 2025

  • β€’ Title: Machine Learning-Based Estimation of End Effector Position in Three-Dimension Robotic Workspace
    Citation: IJIST Journal (Impact Factor: 4.312)
    Authors: Baig, H., Ahmed, E., Jadoon, I., & Pakistan, K. C. A.
    Year: 2024

  • β€’ Title: A Robotic Approach for Fruit Harvesting with Machine Learning-Based Joint Angles Prediction
    Citation: Submitted to ICCSI – International Conference on Computational Sciences and Innovations
    Authors: Baig, H., Baig, A.A, Ahmed, E., Jadoon, I., & Pakistan
    Year: 2024

  • β€’ Title: Artificial Intelligence Based Adaptive Fan Control in Office Settings for Energy Efficiency
    Citation: Submitted to ICCIS – Proceedings to Springer Journal
    Authors: Baig, H.
    Year: 2024

  • β€’ Title: A Robotic Arm Based Intelligent Biopsy System
    Citation: Submitted to ICCIS – Kohat University, Springer Proceedings
    Authors: Baig, H.
    Year: 2024

  • β€’ Title: Design of an Intelligent Wireless Channel State Information Sensing System to Prevent Bedsores
    Citation: IEEE Sensors Journal (Under Review)
    Authors: Baig, H.
    Year: 2024

  • β€’ Title: Enhancing Home Comfort and Energy Consumption with an Artificial Intelligence-Based Environmental Sensing Control Model
    Citation: PeerJ (Computer Science) (Under Review)
    Authors: Baig, H.
    Year: 2024

  • β€’ Title: Breathing Techniques Redefined: The Pros and Cons of Traditional Methods and the Promise of SDRF Sensing
    Citation: Elsevier – Digital Communications and Networks (Under Review)
    Authors: Baig, H.
    Year: 2024

Conclusion :Β 

  • Hamna Baig not only meets but exceeds the expectations of a Young Researcher Award recipient. Her innovative mindset, research productivity, and real-world problem-solving approach make her an ideal candidate. Her work is not just academically sound but socially impactfulβ€”especially in the domains of healthcare and automation. She is a beacon of excellence and innovation, representing the future of engineering research. 🌟

 

Dr. Yingbin Wang | Artificial Intelligence | Best Researcher Award

Dr. Yingbin Wang | Artificial Intelligence | Best Researcher Award

Dr. Yingbin Wang, Xi’an Institute of Space Radio Technolog, China

Dr. Yingbin Wang is a leading researcher in space microwave communication, detection, and AI-driven signal processing. He earned his Ph.D. in Electronic Science and Technology from Xidian University in 2022 and currently serves as a Senior Engineer at the National Key Laboratory of Science and Technology on Space Microwave at the Xi’an Institute of Space Radio Technology. His research spans Integrated Sensing and Communication (ISAC), deep learning, and anti-jamming satellite systems. With over ten high-impact publications and contributions to national-level R&D projects, Dr. Wang is shaping the future of space-based communication and intelligent sensing. πŸš€πŸ“‘

🌍 Professional Profile:

Google Scholar

πŸ† Suitability for the Best Researcher Award

Dr. Yingbin Wang is a highly qualified candidate for the Best Researcher Award, given his significant contributions to space microwave communication and AI-powered signal processing. His expertise in satellite-terrestrial integration, space-based radar target detection, and anti-jamming satellite systems plays a crucial role in advancing global space technology. With publications in top-tier IEEE journals, participation in national R&D projects, and contributions to cutting-edge ISAC applications, Dr. Wang is at the forefront of next-generation communication research. His work in AI-driven remote sensing is revolutionizing the field, making him a distinguished and deserving nominee. πŸ†πŸš€

πŸŽ“ Education

Dr. Yingbin Wang pursued his entire higher education at Xidian University, China, a prestigious institution in electronic engineering and space communication. He obtained his Ph.D. in Electronic Science and Technology in June 2022, focusing on advanced space microwave systems and AI-enhanced signal processing. His doctoral research contributed to improving satellite communication resilience, radar detection, and deep learning applications in space technologies. Throughout his academic journey, he combined hardware engineering with AI-driven software models, enabling breakthroughs in integrated satellite-terrestrial communication. His strong foundation in electromagnetic waves, remote sensing, and computational intelligence defines his research excellence. πŸŽ“πŸ“‘πŸ”¬

πŸ’Ό ExperienceΒ 

Dr. Yingbin Wang is a Senior Engineer at the National Key Laboratory of Science and Technology on Space Microwave, Xi’an Institute of Space Radio Technology. His role involves leading research in space microwave communication, detection, and AI-driven signal optimization. He has contributed to major national R&D projects, including space-based radar target detection, anti-jamming satellite communication, and integrated sensing for satellite-terrestrial networks. His work on AI-based signal processing and deep learning models has significantly enhanced real-time space communication efficiency. His expertise in high-frequency electromagnetic applications and AI-powered satellite technology is instrumental in shaping the future of space communications. πŸš€πŸ“Ά

πŸ… Awards & HonorsΒ 

Dr. Yingbin Wang has received multiple recognitions for his contributions to space communication and AI-driven signal processing. His research in anti-jamming satellite networks has been awarded national-level research funding. He has received Best Paper Awards at leading IEEE conferences on signal processing and remote sensing. Additionally, his contributions to integrated satellite-terrestrial communication have been recognized by the National Science and Technology Innovation Program. As a reviewer for top IEEE journals, he actively contributes to the scientific community. His pioneering work in AI-enhanced space sensing continues to push the boundaries of satellite communication technologies. πŸ†πŸ“‘

πŸ”¬ Research FocusΒ 

Dr. Yingbin Wang’s research spans Artificial Intelligence, communication, deep learning, and signal processing, with a strong emphasis on space microwave communication and detection. His work explores AI-driven radar target detection, anti-jamming satellite communication, and integrated sensing and communication (ISAC) systems. He develops machine learning models for real-time adaptive signal processing, enhancing satellite-terrestrial connectivity. His studies in neural network-driven space communication systems optimize data transmission efficiency in complex space environments. His research is critical for next-generation deep-space exploration, smart communication networks, and high-performance microwave technology, ensuring global connectivity and security in aerospace applications. πŸš€πŸ“‘πŸ›°οΈ

πŸ“–Β Publication Top Notes

  1. SPB-Net: A Deep Network for SAR Imaging and Despeckling with Downsampled Data
    • Journal: IEEE Transactions on Geoscience and Remote Sensing
    • Publication Year: 2020
    • Citations: 27
  2. Lq-SPB-Net: A Real-Time Deep Network for SAR Imaging and Despeckling
    • Journal: IEEE Transactions on Geoscience and Remote Sensing
    • Publication Year: 2021
    • Citations: 8
  1. Multi-Scale and Single-Scale Fully Convolutional Networks for Sound Event Detection
    • Journal: Neurocomputing
    • Publication Year: 2021
    • Citations: 18
  2. MSFF-Net: Multi-Scale Feature Fusing Networks with Dilated Mixed Convolution and Cascaded Parallel Framework for Sound Event Detection
    • Journal: Digital Signal Processing
    • Publication Year: 2022
    • Citations: 12
  1. A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method
    • Journal: Sensors
    • Publication Year: 2019
    • Citations: 13

 

Prof. Dr. Xin Wang | Distributed AI | Best Researcher Award

Prof. Dr. Xin Wang | Distributed AI | Best Researcher Award

Prof. Dr. Xin Wang, Qilu University of Technology, China

Prof. Dr. Xin Wang is a distinguished scholar in Distributed AIΒ and Federated Learning, currently serving as a Professor at Shandong Computer Science Center, Qilu University of Technology. With a Ph.D. in Control Science and Engineering from Zhejiang University, he has contributed significantly to AI Security, Privacy, and LLM Security. Dr. Wang has led multiple national research projects and received prestigious honors, including the Taishan Scholars Award and the Shandong Provincial Science and Technology Progress Award. His work integrates AI with secure computing, enhancing privacy protection and optimization in collaborative learning systems.

🌍 Professional Profile:

Google Scholar

πŸ† Suitability for AwardΒ 

Dr. Xin Wang’s outstanding contributions to Distributed AI, Federated Learning, and AI Security make him a strong candidate for the Best Researcher Award. As a leader in AI-driven security frameworks, he has spearheaded national-level projects focusing on privacy-preserving AI and secure learning models. His research bridges theory with practical applications, enhancing security in multi-agent and industrial IoT systems. Recognized for his high-impact publications and award-winning research, Dr. Wang’s innovations in cryptographic function identification and UAV data collection optimization demonstrate exceptional originality and real-world relevance, solidifying his place as a leader in computational intelligence and AI security.

πŸŽ“ EducationΒ 

  • Ph.D. in Control Science and Engineering (2015-2020) – Zhejiang University, supervised by Prof. Peng Cheng & Prof. Jiming Chen, specializing in AI Security and Distributed Intelligence.
  • Visiting Scholar in Information Security (2018-2019) – Tokyo Institute of Technology, mentored by Prof. Hideaki Ishii, focusing on cryptographic vulnerabilities and federated learning security.

His multidisciplinary training across AI, security, and automation has positioned him at the forefront of cutting-edge computational research.

πŸ’Ό ExperienceΒ 

  • Professor (2024–Present) – Shandong Computer Science Center, Qilu University of Technology.
  • Associate Professor (2020–2024) – Shandong Computer Science Center, leading research on privacy protection in collaborative AI.
  • Project PI in National Natural Science Foundation of China (2025-2027) – Developing privacy-preserving defense mechanisms for federated learning.
  • Project PI in National Key Research and Development Program (2021-2024) – Developing AI-driven meta-services for cloud-based industrial manufacturing.
  • Visiting Scholar (2018-2019) – Tokyo Institute of Technology, conducting security research on cryptographic vulnerabilities in multi-agent IoT systems.

πŸ… Awards and HonorsΒ 

  • Taishan Scholars Award (2024) πŸ… – Recognized for research excellence in AI security and distributed systems.
  • Leader of Youth Innovation Team (2022) πŸš€ – Acknowledged for driving innovation in Shandong Higher Education Institutions.
  • Second Prize, Shandong Provincial Science and Technology Progress Award (2022) πŸ† – Contributions to federated learning and privacy-preserving AI.
  • Best Paper Award, CCSICC’21 πŸ“„ – Vulnerability Analysis for IoT Devices in Multi-Agent Systems.
  • Best Paper Award, ICAUS’24 ✈️ – Optimized Data Collection for UAVs in Industrial IoT Environments.

πŸ”¬ Research FocusΒ 

Dr. Wang specializes in Distributed AI, Federated Learning, and AI Security & Privacy. His research integrates cryptographic techniques, optimization algorithms, and adversarial defenses to improve the security of collaborative learning models. He has pioneered LLM security frameworks to safeguard against data leakage and adversarial attacks. His work extends into privacy-preserving AI for multi-agent IoT systems and UAV data collection efficiency. Through national projects, he has developed secure meta-services for cloud computing, advancing the field of intelligent automation and resilient AI architectures for real-world deployment in cyber-physical systems and industrial environments.

πŸ“Š Publication Top notes:

  • Title: Privacy-Preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation
    • Year: 2020
    • Citations: 61
  • Title: Privacy-Preserving Collaborative Computing: Heterogeneous Privacy Guarantee and Efficient Incentive Mechanism
    • Year: 2018
    • Citations: 49
  • Title: Differentially Private Maximum Consensus: Design, Analysis and Impossibility Result
    • Year: 2018
    • Citations: 26
  • Title: Dynamic Privacy-Aware Collaborative Schemes for Average Computation: A Multi-Time Reporting Case
    • Year: 2021
    • Citations: 18
  • Title: Leveraging UAV-RIS Reflects to Improve the Security Performance of Wireless Network Systems
    • Year: 2023
    • Citations: 17

 

Dr. Mani shekhar Gupta | AI in Network System | Excellence in Research

Dr. Mani shekhar Gupta | AI in Network System | Excellence in Research Award

Dr. Mani shekhar Gupta | Adani University, Ahmedabad | India

πŸ“š Dr. Mani Shekhar Gupta is an Assistant Professor at Adani University, Ahmedabad, with a Ph.D. in Electronics and Communication Engineering from NIT Hamirpur. πŸš€ His research spans cognitive radio networks, vehicular networks, resource allocation, AI, and next-gen wireless technologies. πŸ“‘ With over 11 years of academic and research experience, he has contributed significantly through projects at IIT Delhi and NIT Hamirpur. πŸ‘¨β€πŸ« A passionate educator and innovator, Dr. Gupta excels in machine learning, green networks, and intelligent transportation systems. πŸ’‘ His dynamic approach blends technical expertise with a love for teaching and discovery. 🌟

Professional Profile:

Google Scholar

Suitability for the Excellence in Research Award

Dr. Mani Shekhar Gupta is highly suitable for the Excellence in Research Award due to his extensive academic background, impactful research contributions, and innovative approaches in the fields of cognitive radio networks, vehicular networks, resource allocation, artificial intelligence, and next-generation wireless technologies. His career, spanning over 11 years, reflects a deep commitment to advancing technological frontiers and fostering academic excellence.

Education πŸ“– & Experience πŸ‘¨β€πŸ’ΌΒ 

  • πŸ“œ Ph.D. in Electronics & Communication Engineering, NIT Hamirpur (2017–2021) – CGPI 9.5
  • 🎯 M.Tech. in Electronics & Communication Engineering, NIT Hamirpur (2009–2011) – CGPI 8.49
  • πŸ… B.Tech. in Electronics & Communication, UPTU, Lucknow (2005–2009) – 74.42%
  • πŸ‘¨β€πŸ« Assistant Professor, Adani University (2022–Present)
  • πŸ”¬ Postdoctoral Researcher, IIT Delhi (2021–2022)
  • πŸŽ“ Ph.D. Research Scholar, NIT Hamirpur (2017–2021)
  • πŸ‘¨β€πŸ’Ό Assistant Professor, PSIT Kanpur (2011–2017)

Professional DevelopmentΒ 

🌐 Dr. Gupta actively engages in continuous professional growth through memberships in global organizations like IEEE πŸ“‘, EAI πŸ‡ͺπŸ‡Ί, IACSIT πŸ‡ΈπŸ‡¬, IAENG πŸ‡­πŸ‡°, and IAAM 🌍. His participation spans technical communities focusing on e-Government, IoT 🌐, Smart Cities πŸ™οΈ, and Autonomous Driving πŸš—. He’s also a member of humanitarian groups like IEEE SIGHT 🀝. Through conferences, workshops, and collaborative projects, Dr. Gupta refines his expertise in wireless networks, machine learning πŸ€–, and green technologies 🌱, ensuring he stays at the forefront of innovation and academic excellence. πŸš€

Research FocusΒ 

πŸ” Dr. Gupta’s research focuses on cognitive radio networks πŸ“‘, vehicular networks πŸš—, and resource allocation strategies for next-generation wireless systems πŸ“Ά. His work integrates AI πŸ€– and machine learning to enhance spectrum management, optimize network efficiency, and support intelligent transportation systems 🚦. He explores green network technologies 🌱, aiming to reduce environmental impact while improving connectivity. His contributions to 5G and beyond involve proactive spectrum sharing, game theory applications 🎯, and cooperative uplink-downlink strategies, making his research pivotal for smart cities πŸ™οΈ and sustainable communication infrastructures. 🌍

Awards & Honors πŸ†Β 

(No specific awards or honors mentioned in the provided details. If you have any, please share for accurate updates.)

  • πŸ… IEEE Membership & Active Roles in Multiple Societies
  • 🌟 Recognized Contributor in DST-SERB & BASF Research Projects
  • πŸ“œ International Memberships: IACSIT πŸ‡ΈπŸ‡¬, IAENG πŸ‡­πŸ‡°, IAAM 🌍

Publication Top Notes:

πŸ“‘ Progression on Spectrum Sensing for Cognitive Radio Networks: A Survey, Classification, Challenges, and Future Research Issues Β πŸ“‘ Cited by 164
🌿 Energy Efficient Transmission Trends Towards Future Green Cognitive Radio Networks (5G): Progress, Taxonomy, and Open Challenges πŸ“‘ Cited by 114
πŸš— Application Aware Networks’ Resource Selection Decision Making Technique Using Group Mobility in Vehicular Cognitive Radio Networks Β πŸ“‘ Cited by 32
πŸ“Ά A Survey on NOMA Techniques for 5G Scenario Β πŸ“‘ Cited by 24
πŸ”€ Seamless Vertical Handover for Efficient Mobility Management in Cooperative Heterogeneous Networks πŸ“‘ Cited by 20