Hamna Baig | Artificial Intelligence | Young Researcher Award

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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. 🌟

 

Prof. Dr. Dongxing Song | Machine Learning | Best Researcher Award-3904

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Prof. Dr. Dongxing Song | Machine Learning | Best Researcher Award

Prof. Dr. Dongxing Song, Zhengzhou University, China

Prof. Dr. Dongxing Song is an innovative researcher in power engineering and thermophysics, currently serving as a Research Fellow at Zhengzhou University’s School of Mechanics and Safety Engineering. He earned his doctoral degree from Tsinghua University and previously studied at Xi’an Jiaotong University and Central South University. His expertise lies in nanofluid dynamics, ionic thermoelectric conversion, and energy system optimization. Dr. Song’s research integrates machine learning with thermodynamics, pushing boundaries in sustainable energy technologies. His work has been published in top-tier journals such as Joule and Cell Reports Physical Science, gaining recognition for both originality and technical depth. Driven by scientific rigor and curiosity, Dr. Song continues to shape future solutions for clean energy and advanced material systems. βš›οΈπŸ”¬πŸŒ±

🌍 Professional Profile 

Orcid

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πŸ† Suitability for Best Researcher AwardΒ 

Prof. Dr. Dongxing Song is a standout candidate for the Best Researcher Award due to his cutting-edge work in ionic thermoelectric energy conversion and nanoscale heat transfer. His publications in high-impact journals, including Joule and Cell Reports Physical Science, demonstrate his role in shaping the future of clean and efficient energy generation. Dr. Song has independently led national-level research projects supported by the NSFC and China Postdoctoral Science Foundation, focusing on ion-electron coupling mechanisms and dynamic heat-mass transport. His interdisciplinary approachβ€”blending thermophysics, machine learning, and materials scienceβ€”makes him a trailblazer in green energy innovation. His research not only advances scientific understanding but also offers scalable solutions for low-grade waste heat recovery. πŸ”‹πŸ…πŸŒ

πŸŽ“ Education

Prof. Dr. Dongxing Song holds a robust academic background in power engineering and thermophysics. He completed his Ph.D. at Tsinghua University (2018–2022) under Prof. Weigang Ma, following his Master’s studies at Xi’an Jiaotong University (2015–2018) under Prof. Dengwei Jing. His foundational education in Thermal Energy and Power Engineering was completed at Central South University (2011–2015), where he was mentored by Dengwei Jing and Jianzhi Zhang. Throughout his academic journey, Dr. Song developed deep expertise in energy conversion, ionic transport, and thermodynamic modeling. His cross-institutional training at China’s most prestigious engineering schools laid the groundwork for his innovative and interdisciplinary research in the clean energy domain. πŸŽ“πŸ“˜βš™οΈ

πŸ’Ό Experience

Since February 2022, Dr. Dongxing Song has served as a Research Fellow at the School of Mechanics and Safety Engineering, Zhengzhou University, contributing significantly to ionic thermoelectric research. He previously pursued advanced research at Tsinghua University, one of China’s top engineering institutions, from 2018 to 2022. His earlier academic appointments include graduate research at Xi’an Jiaotong University and Central South University, where he gained hands-on experience in power engineering, energy optimization, and thermophysical modeling. In every role, Dr. Song has demonstrated scientific leadership, managing national-level projects and publishing influential research. His experience reflects a well-rounded career rooted in high-impact research and technological innovation in sustainable energy. πŸ§‘β€πŸ”¬πŸ”‹πŸ“ˆ

πŸ… Awards and Honors

Prof. Dr. Dongxing Song has received prestigious grants and recognition from leading national institutions. He is the Principal Investigator of a National Natural Science Foundation of China (NSFC) Original Exploration Program Project, as well as multiple China Postdoctoral Science Foundation awards, including the Innovative Talents Grant (BX20220275). His work on ion thermoelectric conversion received a high recommendation from Joule Preview, marking him as a rising star in energy systems innovation. Dr. Song’s publications in top-impact journals and his ability to secure competitive funding reflect his academic excellence and research potential. These accolades highlight his position as a thought leader in the next generation of thermophysical science and energy innovation. πŸ₯‡πŸ›οΈπŸ“š

πŸ”¬ Research Focus

Dr. Dongxing Song’s research centers on the optimization of power generation systems for low-grade waste heat recovery, specifically using ion thermoelectric conversion and salt gradient power. He investigates the fundamental coupling between heat and ion transport and has derived a new expression for the ionic Seebeck coefficient, setting the stage for thermoelectric optimization. His studies also integrate nanofluidic heat transfer, solid-state ion battery transport, and machine learning to enhance the performance of sustainable energy devices. His broader focus includes nanoscale heat and mass transfer, where he explores transport mechanisms across interfaces using simulation and experimental validation. Dr. Song’s pioneering models are helping redefine energy recovery systems with enhanced efficiency and low environmental impact. πŸ”¬β™»οΈπŸ§ͺ

πŸ“ŠΒ Publication Top Notes

  • Design of Microchannel Heat Sink with Wavy Channel and Its Time-Efficient Optimization with Combined RSM and FVM Methods

    • Citations: 209

    • Year: 2016

  • Optimization of a Circular-Wavy Cavity Filled by Nanofluid under Natural Convection Heat Transfer

    • Citations: 194

    • Year: 2016

  • Optimization of a Lid-Driven T-Shaped Porous Cavity to Improve the Nanofluids Mixed Convection Heat Transfer

    • Citations: 138

    • Year: 2017

  • Prediction of Hydrodynamic and Optical Properties of TiOβ‚‚/Water Suspension Considering Particle Size Distribution

    • Citations: 87

    • Year: 2016

  • A Nitrogenous Pre-Intercalation Strategy for the Synthesis of Nitrogen-Doped Ti₃Cβ‚‚Tβ‚“ MXene with Enhanced Electrochemical Capacitance

    • Citations: 71

    • Year: 2021

 

Dr. Ryszard Δ†wiertniak | Artificial Intelligence | Best Researcher Award

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Dr. Ryszard Δ†wiertniak | Artificial Intelligence | Best Researcher Award

Dr. Ryszard Δ†wiertniak, Krakow University of Economics, Poland

Dr. Ryszard Δ†wiertniak is an accomplished expert in project management, specializing in agile methodologies, Design Thinking, and AI-driven innovation. He holds a PhD in Management and Quality Sciences from the University of Economics in Krakow and has a strong academic and professional background in administration, management, and electrical engineering. With extensive experience in research and teaching, he has contributed to the fields of digital transformation, e-learning, and Industry 4.0. As an IBM Design Thinking mentor and Early Warning Europe ambassador, he helps businesses implement cutting-edge solutions. His work spans academia, consulting, and applied research in AI and business process optimization.

🌍 Professional Profile:

Orcid

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πŸ† Suitability for Best Researcher AwardΒ 

Dr. Ryszard Δ†wiertniak’s pioneering research in AI-driven project management, digital transformation, and innovation management makes him an outstanding candidate for the Best Researcher Award. His involvement in Erasmus+ projects, contributions to Industry 4.0, and mentorship in agile methodologies showcase his impact on academia and industry. His expertise in AI-based decision-making, personalized education, and digital business models has transformed organizational processes. With numerous peer-reviewed publications, a book, and a grant-winning project, his research advances the future of smart business ecosystems. His leadership in AI-powered business solutions and educational innovations solidifies his reputation as a top researcher in the field.

πŸŽ“ EducationΒ 

Dr. Ryszard Δ†wiertniak earned his PhD in Management and Quality Sciences from the University of Economics in Krakow (2019), focusing on innovation management. He also holds a Master’s degree in Administration and Management from the University of Warsaw (1994). In addition, he has a background in electrical engineering, equipping him with a multidisciplinary approach to research. His academic journey reflects a deep commitment to combining management principles with technology, particularly in AI applications, e-learning, and agile business strategies. His education has laid the foundation for his expertise in digital transformation, business innovation, and advanced project management methodologies.

πŸ’Ό Professional ExperienceΒ 

Dr. Δ†wiertniak currently serves as an academic teacher at Krakow University of Economics, specializing in technology and product ecology. Previously, he was the Rector’s Representative for Quality of Education and E-learning at the College of Economics and Computer Science (2020–2024). His role in the Early Warning Europe initiative highlights his expertise in digital business transformation. He also contributes to the Erasmus+ program, working on AI-powered educational solutions. As an IBM Design Thinking mentor, he facilitates agile project implementation. His professional engagements bridge academia and industry, driving innovation, AI adoption, and digital business strategies in various sectors.

πŸ… Awards and HonorsΒ 

πŸ”Ή Early Warning Europe Ambassador (2021–Present) – Recognized for supporting digital business transformation.
πŸ”Ή Erasmus+ Research Grant Recipient – Contributed to AI-driven education models.
πŸ”Ή Ministerial Research Grant Winner (2021) – Awarded funding for advancing e-learning and digital education techniques.
πŸ”Ή IBM Design Thinking Mentor – Certified expert in guiding agile and innovative project execution.
πŸ”Ή Industry 4.0 & AI Innovation Contributor – Acknowledged for pioneering work in integrating AI with project management and digital marketing.
πŸ”Ή Invited Researcher at THWS Business School (2024) – Recognized for leadership in AI-based digital transformation.

His contributions to AI, project management, and education technology have earned him national and international acclaim.

πŸ”¬ Research Focus

Dr. Δ†wiertniak’s research spans AI-driven project management, innovation strategies, digital transformation, and e-learning technologies. He explores Industry 4.0 applications, AI-based decision-making, and agile methodologies to optimize business processes. His focus on digital business models, social media analytics, and e-commerce strategies has redefined marketing and management practices. Through Design Thinking and AI integration, he enhances project execution efficiency. His research also covers personalized education using AI, ensuring smarter, data-driven learning environments. As an expert in AI-powered business solutions, he contributes to making organizations more adaptable and efficient in an era of rapid technological advancements.

πŸ“ŠΒ Publication Top Notes:

  1. Rola potencjaΕ‚u innowacyjnego w modelach biznesowych nowoczesnych organizacji – prΓ³ba oceny

    • Citations: 11
    • Year: 2015

  2. ZarzΔ…dzanie portfelem projektΓ³w w organizacji: Koncepcje i kierunki badaΕ„

    • Citations: 4
    • Year: 2018

  1. Addressing students’ perceived value with the virtual university concept

    • Citations: 3
    • Year: 2022

  2. KsztaΕ‚towanie portfela projektΓ³w w zarzΔ…dzaniu innowacjami

    • Citations: 2
    • Year: 2018

  1. The concept of project evaluation in the implementation of innovation

    • Citations: 1
    • Year: 2020

 

 

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

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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

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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

 

 

 

Satish Mahadevan Srinivasan | Machine Learning | Best Researcher Award

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Satish Mahadevan Srinivasan | Machine Learning | Best Researcher Award

Dr. Satish Mahadevan Srinivasan, Penn State Great Valley , United States.

Dr. Satish Mahadevan Srinivasan is a Tenured Associate Professor of Information Science at Penn State Great Valley, with expertise spanning data mining, machine learning, cybersecurity, and bioinformatics. With a Ph.D. in Information Technology from the University of Nebraska, his research contributions include class-specific motif discovery in protein classification and tools for metagenomic analysis. Dr. Srinivasan’s work merges cutting-edge technologies with practical applications, contributing to bioinformatics, distributed computing, and artificial intelligence. He has a rich academic and professional journey, publishing impactful research and developing transformative software tools.Β πŸŒπŸ“ŠπŸ”¬

Publication Profiles

Googlescholar

Education and Experience

Education

  • πŸŽ“Β Ph.D. in Information Technology, University of Nebraska, 2010
  • πŸŽ“Β M.S. in Industrial Engineering & Management, IIT Kharagpur, 2005
  • πŸŽ“Β B.E. in Information Technology, Bharathidasan University, 2001

Experience

  • πŸ“šΒ Tenured Associate Professor, Penn State Great Valley (2019–Present)
  • πŸ“šΒ Assistant Professor, Penn State Great Valley (2013–2019)
  • πŸ”¬Β Postdoctoral Researcher, Computational Bioinformatics, UNMC (2011–2013)
  • πŸ’»Β Postdoctoral Research Assistant, Computer Science, University of Nebraska (2010–2011)
  • πŸ› οΈΒ Project Assistant, IIT Kharagpur (2001–2005)

Suitability For The Award

Dr. Satish Mahadevan Srinivasan, a Tenured Associate Professor at Penn State, excels in interdisciplinary research spanning data mining, bioinformatics, machine learning, and cybersecurity. His groundbreaking tools like MetaID and Monarch have advanced microbial analysis and software engineering. With impactful publications, innovative solutions, and practical applications, Dr. Srinivasan exemplifies research excellence, making him highly deserving of the Best Researcher Award.

Professional Development

Dr. Srinivasan has developed innovative tools and frameworks, including MetaID for metagenomic studies and Monarch for transforming Java programs for embedded systems. His interdisciplinary research bridges machine learning, predictive analytics, and cybersecurity with bioinformatics, aiding microbial classification and software optimization. By integrating artificial intelligence and distributed computing, he has addressed complex challenges in data science, genomics, and engineering. His professional journey reflects a commitment to cutting-edge technology, impactful research, and knowledge dissemination through teaching and mentorship.Β πŸŒŸπŸ”

Research Focus

Dr. Satish Mahadevan Srinivasan’s research focuses on leveraging advanced technologies to address complex problems in data science, bioinformatics, and cybersecurity. His work inΒ data miningΒ andΒ machine learningΒ aims to uncover patterns and develop predictive models for diverse applications. InΒ bioinformatics, he has designed tools like MetaID for microbial classification and motif discovery in protein sequences, contributing to genomics and medical advancements. His expertise extends toΒ cybersecurity, where he explores cryptographic techniques to enhance internet security, andΒ distributed computing, optimizing system performance. Dr. Srinivasan’s interdisciplinary approach bridgesΒ artificial intelligence,Β predictive analytics, andΒ software engineeringΒ to create impactful solutions.Β πŸŒπŸ”¬πŸ“Š

Awards and Honors

  • πŸ†Β Awarded research grants for innovative bioinformatics tools.
  • πŸ“œΒ Recognized for contributions to cybersecurity and internet authentication.
  • 🌟 Acknowledged as a leading researcher in predictive analytics and machine learning.
  • πŸ“ŠΒ Published in high-impact journals like BMC Bioinformatics and BMC Genomics.

Publication Top Notes

  • Effect of negation in sentences on sentiment analysis and polarity detectionΒ  – Cited by 93, 2021Β πŸ“ŠπŸ“š
  • LocSigDB: A database of protein localization signalsΒ  – Cited by 49, 2015Β πŸ§¬πŸ“–
  • K-means clustering and principal components analysis of microarray data of L1000 landmark genes– Cited by 46, 2020Β πŸ§ͺπŸ“Š
  • Mining for class-specific motifs in protein sequence classification – Cited by 29, 2013Β πŸ”¬πŸ“œ
  • Web app security: A comparison and categorization of testing frameworks– Cited by 27, 2017Β πŸ”’πŸ–₯️
  • MetaID: A novel method for identification and quantification of metagenomic samples – Cited by 23, 2013Β πŸŒπŸ”
  • Sensation seeking and impulsivity as predictors of high-risk sexual behaviours among international travellers – Cited by 21, 2019 ✈️🧠
  • Cybersecurity for AI systems: A survey – Cited by 20, 2023Β πŸ€–πŸ”