Kin Fai Tong | Engineering | Best Researcher Award

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Prof. Dr. Kin Fai Tong | Engineering | Best Researcher Award

Chair Professor of Antennas and Applied Electromagnetics at Hong Kong Metropolitan University, Hong Kong

Professor Kin Fai (Kenneth) Tong is a highly accomplished researcher in antennas and applied electromagnetics, with a prolific academic and professional career spanning over two decades. He holds a Ph.D. in Electronic Engineering and currently serves as Chair Professor at Hong Kong Metropolitan University, with prior leadership roles at University College London. A Fellow of IEEE and several other prestigious academies, he has received numerous international awards including best paper and innovation accolades. His research is backed by substantial funding from top agencies such as EPSRC, DFID, Innovate UK, and MoD UK, with over 30 funded projects in wireless communication, smart agriculture, IoT, and fluid antenna systems. His work has led to groundbreaking advancements in 6G technologies and hybrid microwave-optical systems. While already a leading expert, future efforts could further focus on commercializing innovations and expanding interdisciplinary collaborations. Overall, Professor Tong is exceptionally well-suited for the Best Researcher Award.

Professional Profile

Education🎓

Professor Kin Fai (Kenneth) Tong possesses an impressive educational background that laid the foundation for his distinguished career in electronic engineering and applied electromagnetics. He earned his Bachelor’s and Master’s degrees in Engineering, followed by a Ph.D. in Electronic Engineering, all from reputable institutions known for their strong emphasis on innovation and technological advancement. His academic journey reflects a commitment to excellence and continuous learning, equipping him with in-depth theoretical knowledge and practical expertise in areas such as antennas, wireless communication, and electromagnetic theory. Throughout his educational career, he demonstrated exceptional aptitude for research and problem-solving, which later translated into pioneering contributions to 5G and 6G wireless systems, microwave photonics, and IoT technologies. Professor Tong’s robust academic training not only shaped his scientific mindset but also prepared him to mentor future engineers and researchers, making him a valuable asset in both educational and research-focused institutions around the world.

Professional Experience📝

Professor Kin Fai (Kenneth) Tong has amassed extensive professional experience in the field of electronic engineering, particularly in applied electromagnetics, wireless communications, and antenna design. He currently serves as a Professor of Microwave and Communication Systems at University College London (UCL), where he leads research initiatives and mentors students in cutting-edge technological domains. Over the years, Professor Tong has held various academic and research positions, contributing significantly to the development of 5G and emerging 6G technologies, microwave photonics, and wearable electronics. His work bridges theoretical research with real-world applications, earning him international recognition. He has collaborated with leading industry partners and academic institutions on numerous high-impact projects, and his research has resulted in over 300 scholarly publications. Beyond his technical achievements, he is an influential educator and speaker, often invited to present his work at global conferences. His professional journey reflects a deep commitment to innovation, leadership, and knowledge dissemination.

Research Interest🔎

Professor Kin Fai (Kenneth) Tong’s research interests lie at the intersection of applied electromagnetics and next-generation wireless communication systems. He focuses on the design and development of advanced antennas, microwave and millimeter-wave systems, and their integration into emerging technologies such as 5G, 6G, and the Internet of Things (IoT). His work also explores microwave photonics, body-centric wireless communications, and wearable electronics—aiming to create high-performance, compact, and energy-efficient communication systems. Professor Tong is particularly interested in reconfigurable intelligent surfaces (RIS), terahertz communications, and electromagnetic compatibility in complex environments. His interdisciplinary approach combines theoretical modeling, simulation, and practical prototyping to address real-world engineering challenges. By collaborating with international partners from academia and industry, he drives innovation in areas such as medical diagnostics, wireless sensing, and smart cities. His research continues to shape the future of wireless connectivity, contributing to transformative solutions that enhance communication efficiency, reliability, and sustainability.

Award and Honor🏆

Professor Kin Fai (Kenneth) Tong has received numerous awards and honors in recognition of his outstanding contributions to the field of electromagnetics and wireless communication. He is a Fellow of the Institution of Engineering and Technology (IET) and a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), reflecting his esteemed professional standing. Over the years, he has been honored with prestigious research grants and awards for excellence in innovation and academic leadership. His pioneering work in body-centric wireless communications and millimeter-wave antenna design has earned accolades from international conferences and professional societies. Professor Tong has also served on editorial boards of reputed journals and has been invited as a keynote speaker at global conferences, further validating his impact on the scientific community. These recognitions highlight his commitment to advancing technology, fostering interdisciplinary collaboration, and mentoring the next generation of engineers and researchers in the field.

Research Skill🔬

Professor Kin Fai (Kenneth) Tong possesses a diverse and robust set of research skills that have significantly advanced the fields of electromagnetics, antenna design, and wireless communication. He excels in the development and analysis of millimeter-wave and terahertz antennas, with a strong command of computational electromagnetic simulation tools and experimental prototyping. His expertise includes designing body-centric wireless systems and wearable antennas, demonstrating a deep understanding of human body interaction with radio frequency signals. He is also proficient in system integration, signal processing, and electromagnetic compatibility. Professor Tong’s interdisciplinary approach allows him to collaborate effectively across engineering, healthcare, and biomedical fields, applying his skills to real-world applications such as remote sensing and wireless body area networks. His ability to lead complex research projects, publish extensively in top-tier journals, and secure competitive funding showcases his strategic thinking and innovative problem-solving abilities, making him a highly skilled and impactful researcher in his domain.

Conclusion💡

Professor Kin Fai (Kenneth) Tong is highly suitable for the Best Researcher Award.
His decades-long contributions to antennas, wireless communications, and applied electromagnetics—combined with high-level funding, awards, publications, and global recognition—make him an ideal candidate. His research has not only advanced scientific knowledge but also shaped industrial applications in 6G, smart cities, and IoT.

Publications Top Noted✍️

  • Title: Advances in Microstrip and Printed Antennas
    Authors: KF Lee, W Chen
    Year: 1997
    Citations: 888

  • Title: Experimental and Simulation Studies of the Coaxially Fed U-slot Rectangular Patch Antenna
    Authors: KF Lee, KM Luk, KF Tong, SM Shum, T Huynh, RQ Lee
    Year: 1997
    Citations: 586

  • Title: A Broad-band U-slot Rectangular Patch Antenna on a Microwave Substrate
    Authors: KF Tong, KM Luk, KF Lee, RQ Lee
    Year: 2000
    Citations: 400

  • Title: Circularly Polarized U-slot Antenna
    Authors: KF Tong, TP Wong
    Year: 2007
    Citations: 330

  • Title: Microstrip Patch Antennas—Basic Characteristics and Some Recent Advances
    Authors: KF Lee, KF Tong
    Year: 2012
    Citations: 312

  • Title: Fluid Antenna Systems
    Authors: KK Wong, A Shojaeifard, KF Tong, Y Zhang
    Year: 2020
    Citations: 296

  • Title: A Survey of Emerging Interconnects for On-Chip Efficient Multicast and Broadcast in Many-Cores
    Authors: A Karkar, T Mak, KF Tong, A Yakovlev
    Year: 2016
    Citations: 183

  • Title: Fluid Antenna Multiple Access
    Authors: KK Wong, KF Tong
    Year: 2021
    Citations: 173

  • Title: Frequency Diverse Array with Beam Scanning Feature
    Authors: J Huang, KF Tong, CJ Baker
    Year: 2008
    Citations: 140

  • Title: Frequency Diverse Array: Simulation and Design
    Authors: J Huang, KF Tong, K Woodbridge, C Baker
    Year: 2009
    Citations: 136

Domenico Di Grazia | Engineering | Industry Innovation Recognition Award

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Dr. Domenico Di Grazia | Engineering | Industry Innovation Recognition Award

Principal Engineer at STMicroelectronics, Italy

Domenico Di Grazia is a seasoned GNSS Signal Senior Engineer and team leader at STMicroelectronics, recognized for his outstanding contributions to satellite navigation technology. With over two decades of experience, he has led the design and implementation of innovative algorithms for signal acquisition, tracking, and precise positioning across global constellations including GPS, Galileo, and Beidou. He holds several U.S. patents in anti-jamming, multipath mitigation, and signal reacquisition, reflecting his pioneering role in advancing GNSS solutions, particularly for autonomous driving applications. His work bridges industrial innovation and academic collaboration, as he actively mentors students and contributes to international projects and publications. While his impact in applied research and embedded system design is significant, further academic publications could enhance his scholarly visibility. Nonetheless, his leadership, technical depth, and real-world impact position him as an ideal candidate for the Research for Innovation Recognition Award, celebrating excellence in applied engineering innovation.

Professional Profile 

Education🎓

Domenico Di Grazia holds a Master’s degree in Telecommunications Engineering from the University of Naples Federico II, one of Italy’s leading technical universities. He graduated summa cum laude in July 2001, demonstrating exceptional academic performance. His thesis focused on MPEG-4 technology, developed in collaboration with Uni.Com (Telit Group), where he gained early exposure to real-world digital signal processing and multimedia systems. His foundational education provided strong expertise in digital communications, signal processing, and embedded systems—core areas that later shaped his professional focus in GNSS technology. Prior to his university studies, he completed his secondary education at Liceo Scientifico in Lagonegro, graduating with a perfect score of 60/60. Throughout his academic journey, Domenico showed a strong inclination toward innovation and research, which has seamlessly translated into his professional achievements. His education laid the groundwork for a successful career in developing cutting-edge satellite navigation technologies and collaborating on international research initiatives.

Professional Experience📝

Domenico Di Grazia brings over 20 years of professional experience in GNSS and digital signal processing, primarily at STMicroelectronics. Since joining the company in 2003, he has advanced from a software designer to the GNSS DSP Team Leader, overseeing algorithm development, chip design specifications, and cross-site team management. His work focuses on the modeling and implementation of advanced signal processing techniques for GPS, Galileo, Beidou, and other global navigation systems, with applications in high-precision positioning and autonomous driving. He has led several innovative projects, authored patents in anti-jamming, signal reacquisition, and tracking, and contributed to international collaborations and conferences. Prior to STMicroelectronics, he worked as a hardware and firmware designer at Uni.Com (Telit Group), gaining hands-on experience in DVB standards and SMART TV systems. Domenico’s career reflects a blend of deep technical expertise, leadership, and real-world impact, making him a driving force in GNSS innovation and embedded system design.

Research Interest🔎

Domenico Di Grazia’s research interests lie at the intersection of advanced signal processing, satellite navigation systems, and embedded system innovation. He specializes in the development of algorithms for GNSS signal acquisition, reacquisition, and tracking across multiple constellations, including GPS, Galileo, Beidou, and IRNSS. His focus extends to precise positioning technologies through carrier phase and pseudorange measurements, multipath mitigation, and cycle slip detection. Domenico is particularly passionate about enhancing GNSS performance in challenging environments, contributing to the evolution of anti-jamming and anti-spoofing techniques for reliable navigation. He is actively involved in designing GNSS-enabled systems for autonomous driving, integrating functional safety standards. His work emphasizes real-time implementation on embedded platforms, bridging theoretical models with practical applications. Additionally, his interest in fostering industry-academia collaboration fuels his contributions to training, mentoring, and joint research initiatives with universities, reinforcing his commitment to technological innovation and next-generation navigation systems.

Award and Honor🏆

Domenico Di Grazia has earned widespread recognition for his contributions to GNSS signal processing and satellite navigation technologies. He holds several prestigious U.S. patents, reflecting his innovative work in areas such as anti-jamming, signal reacquisition, digital demodulation, and multi-constellation satellite tracking. These patented technologies have been instrumental in advancing precise positioning and enhancing signal reliability in complex environments. In addition to his intellectual property achievements, Domenico has co-authored several influential articles published in international journals and conference proceedings, including contributions to ION and GPS World. His role as a team leader at STMicroelectronics and as a key contributor to international collaborative projects has further solidified his reputation as a global expert in GNSS technologies. Recognized within the industry for driving advancements in automotive GNSS applications, particularly for autonomous driving, Domenico’s innovations continue to impact the field. His consistent excellence and commitment make him a strong candidate for technical and research-oriented honors.

Research Skill🔬

Domenico Di Grazia possesses advanced research skills in digital signal processing, algorithm development, and satellite navigation technologies. His expertise spans modeling and real-time implementation of innovative acquisition, reacquisition, and tracking algorithms for multi-constellation GNSS systems, including GPS, Galileo, Beidou, and IRNSS. He is highly skilled in programming languages such as C, MATLAB, and Python, which he uses to develop and test complex signal processing solutions on embedded platforms. Domenico excels in applying carrier phase and pseudorange measurement techniques, multipath mitigation, and cycle slip detection to enhance GNSS accuracy and reliability. His deep understanding of anti-jamming and anti-spoofing strategies supports robust navigation systems for critical applications like autonomous driving. He also demonstrates strong collaboration and mentoring skills, contributing to research initiatives with universities and guiding young engineers. His ability to integrate theoretical research with industrial application showcases his strength as a well-rounded innovator in the field of GNSS technology.

Conclusion💡

Domenico Di Grazia is highly suitable for the Research for Innovation Recognition Award. His career exemplifies cutting-edge technological innovation, deep domain expertise, and meaningful contributions to global industries such as autonomous systems and telecommunications.

His leadership in patent-worthy research, direct real-world impact, and sustained commitment to advancing GNSS technologies make him an excellent candidate. Strengthening academic visibility and broadening interdisciplinary reach could further elevate his innovation profile.

Publications Top Noted✍️

1. Title: Putting the Synthetic Global Navigation Satellite System Meta-Signal Paradigm into Practice: Application to Automotive Market Devices
Authors: Domenico Di Grazia, Fabio Pisoni, Giovanni Gogliettino, Ciro Gioia, Daniele Borio
Year: 2025
DOI: 10.3390/engproc2025088030
Citation:
Di Grazia, D., Pisoni, F., Gogliettino, G., Gioia, C., & Borio, D. (2025). Putting the Synthetic Global Navigation Satellite System Meta-Signal Paradigm into Practice: Application to Automotive Market Devices. Engineering Proceedings, MDPI. https://doi.org/10.3390/engproc2025088030

2. Title: Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites
Authors: Fabio Pisoni, Domenico Di Grazia, Giovanni Gogliettino, Thyagaraja Marathe, Paul Tarantino, Tyler Reid, Mathieu Favreau
Year: 2025
DOI: 10.3390/engproc2025088022
Citation:
Pisoni, F., Di Grazia, D., Gogliettino, G., Marathe, T., Tarantino, P., Reid, T., & Favreau, M. (2025). Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites. Engineering Proceedings, MDPI. https://doi.org/10.3390/engproc2025088022

Amr Shafik | Engineering | Best Researcher Award

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Mr. Amr Shafik | Engineering | Best Researcher Award

Civil Engineering Department at Virginia Tech, United States

Amr Shafik is a dedicated researcher specializing in transportation systems engineering, with over seven years of academic and industry experience in transportation planning, traffic engineering, and intelligent mobility solutions. Currently a Ph.D. candidate in Civil and Environmental Engineering at Virginia Tech, his research focuses on optimizing eco-driving systems for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He has published extensively in IEEE Transactions on Intelligent Transportation Systems and presented at prestigious conferences such as the IEEE Smart Mobility Conference and the Transportation Research Board Annual Meetings. Amr has collaborated with global organizations like the World Bank and EBRD on large-scale mobility projects. With expertise in simulation modeling, data science, and machine learning, he contributes to sustainable transportation innovations. Additionally, he has extensive teaching experience, mentoring students in traffic engineering and transportation planning. His technical skills include Python, R, AutoCAD, GIS, and advanced traffic simulation tools.

Professional Profile

Education

Amr Shafik holds a strong academic background in transportation engineering and data-driven mobility solutions. He is currently pursuing a Ph.D. in Civil and Environmental Engineering at Virginia Tech, where his research focuses on eco-driving optimization for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He earned his Master’s degree in Transportation Engineering from Cairo University, where he specialized in traffic flow theory, simulation modeling, and intelligent transportation systems. His thesis explored data-driven approaches to optimizing urban traffic networks. Prior to that, he completed his Bachelor’s degree in Civil Engineering from Cairo University with distinction, laying the foundation for his expertise in infrastructure design, traffic analysis, and sustainable mobility. Throughout his academic journey, he has engaged in interdisciplinary research, collaborated with global institutions, and honed advanced technical skills in Python, GIS, and transportation simulation tools. His education equips him to tackle real-world transportation challenges with innovative solutions.

Professional Experience

Amr Shafik has extensive professional experience in transportation engineering, data-driven mobility solutions, and intelligent transportation systems. He has worked as a Research Assistant at Virginia Tech, contributing to projects on eco-driving optimization, stochastic traffic signal control, and predictive modeling for connected and automated vehicles. Prior to this, he served as a Transportation Engineer at a leading consultancy, where he specialized in traffic flow analysis, microsimulation modeling, and urban mobility planning. His expertise extends to working with big data analytics, GIS applications, and machine learning for transportation systems. He has collaborated with government agencies and research institutions to develop sustainable and efficient mobility solutions. Additionally, he has experience in teaching and mentoring students in transportation engineering concepts. His strong analytical skills, combined with his hands-on experience in software tools like Python, MATLAB, and traffic simulation platforms, position him as a key contributor to the advancement of smart and sustainable transportation networks.

Research Interest

Amr Shafik’s research interests lie at the intersection of transportation engineering, intelligent mobility, and data-driven traffic management. He focuses on optimizing traffic flow and enhancing transportation efficiency through connected and automated vehicle technologies, eco-driving strategies, and stochastic traffic signal control. His work integrates machine learning, big data analytics, and artificial intelligence to develop predictive models for traffic behavior and mobility patterns. He is particularly interested in sustainable urban transportation, leveraging smart mobility solutions to reduce congestion, emissions, and energy consumption. His research also explores the application of Geographic Information Systems (GIS) and simulation modeling in transportation planning. By collaborating with industry partners and academic institutions, he aims to contribute to the development of next-generation intelligent transportation systems that improve safety, efficiency, and environmental sustainability. His passion for innovation and interdisciplinary research drives him to address real-world transportation challenges through advanced computational and analytical techniques.

Awards and honor

Amr Shafik has received numerous awards and honors in recognition of his contributions to transportation engineering and intelligent mobility research. He has been honored with prestigious research grants and fellowships for his work on data-driven traffic management and sustainable transportation solutions. His innovative research has earned him accolades at international conferences, where he has received Best Paper and Outstanding Research awards. He has also been recognized by professional engineering societies for his significant advancements in traffic optimization and eco-driving strategies. Additionally, he has been awarded competitive scholarships for academic excellence and leadership in the field of intelligent transportation systems. His contributions to collaborative projects with industry and government agencies have further solidified his reputation as a leading researcher in the field. Through his dedication to advancing transportation science, Amr Shafik continues to receive recognition for his impactful work in shaping the future of smart and sustainable mobility solutions.

Research skill

Amr Shafik possesses a diverse set of research skills that contribute to his expertise in transportation engineering and intelligent mobility solutions. He excels in data analysis, statistical modeling, and machine learning applications for traffic flow optimization and predictive analytics. His proficiency in programming languages such as Python, MATLAB, and R enables him to develop advanced algorithms for real-time traffic monitoring and control. He is skilled in using Geographic Information Systems (GIS) and simulation software like VISSIM and SUMO to model transportation networks and assess the impact of smart mobility solutions. Additionally, he has a strong background in sensor data processing and Internet of Things (IoT) applications for connected and autonomous vehicles. His ability to conduct interdisciplinary research, collaborate with industry stakeholders, and publish high-impact studies demonstrates his analytical thinking, problem-solving abilities, and dedication to innovation in the field of intelligent transportation systems and sustainable urban mobility.

Conclusion

Amr Shafik is a strong candidate for the Best Researcher Award due to his extensive contributions to transportation engineering, expertise in traffic optimization, and impactful research in connected and automated vehicles. His impressive academic and industry experience, along with publications in top-tier conferences and journals, showcases his research excellence. To further strengthen his profile, expanding interdisciplinary collaborations, securing independent research funding, and pursuing patents or industry partnerships would be beneficial.

Publications Top Noted

  • Optimization of vehicle trajectories considering uncertainty in actuated traffic signal timings

    • Authors: AK Shafik, S Eteifa, HA Rakha
    • Year: 2023
    • Citations: 19

  • Queue Length Estimation and Optimal Vehicle Trajectory Planning Considering Queue Effects at Actuated Traffic Signal Controlled Intersections

    • Authors: A Shafik, H Rakha
    • Year: 2024
    • Citations: 5

  • Environmental Impacts of MSW Collection Route Optimization Using GIS: A Case Study of 10th of Ramadan City, Egypt

    • Authors: A Shafik, M Elkhedr, D Said, A Hassan
    • Year: 2022
    • Citations: 4

  • Integrated Back of Queue Estimation and Vehicle Trajectory Optimization Considering Uncertainty in Traffic Signal Timings

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 3

  • Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles Towards Uncertainty of Actuated Traffic Signals

    • Authors: A Shafik, S Eteifa, HA Rakha, E Center
    • Year: 2023
    • Citations: 3

  • Development of Online VISSIM Traffic Microscopic Calibration Framework Using Artificial Intelligence for Cairo CBD Area

    • Authors: AK Shafik, A Hassan, AM Saied, AE & Abdelmegeed
    • Year: 2022
    • Citations: 2

  • Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Traffic Signal Switch Times

    • Authors: S Eteifa, AK Shafik, H Eldardiry, HA Rakha
    • Year: 2024
    • Citations: 1

  • Kalman Filter-based Real-Time Traffic State Estimation and Prediction using Vehicle Probe Data

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 1

  • Enhancing and Evaluating a Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Controller on an Isolated Signalized Intersection

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: 1

  • Real-Time Turning Movement, Queue Length, and Traffic Density Estimation and Prediction Using Vehicle Trajectory and Stationary Sensor Data

    • Authors: AK Shafik, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals

    • Authors: S Eteifa, A Shafik, H Eldardiry, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Real-Time Turning Movement, Queue Length and Traffic Density Estimation and Prediction from Probe Vehicle Data: A Kalman Filter Approach

    • Authors: A Shafik, HA Rakha
    • Year: 2025
    • Citations: N/A

  • Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated Signalized Intersections

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: N/A

  • Real-Time Traffic State Estimation and Short-Term Prediction Using Probe Vehicle Data: A Kalman Filter Approach

    • Authors: A Shafik, H Rakha
    • Year: 2024
    • Citations: N/A

  • Queue Estimation and Consideration in Vehicle Trajectory Optimization at Actuated Signalized Intersections

    • Authors: AK Shafik, HA Rakha
    • Year: 2024
    • Citations: N/A

Najeeb ur rehman Malik | Engineering | Best Researcher Award

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Dr. Najeeb ur rehman Malik | Engineering | Best Researcher Award

Assistant Professor at DHA Suffa University, Pakistan

Dr. Najeeb Ur Rehman Malik is a dedicated researcher and electronics engineer specializing in computer vision, deep learning, and image processing. He holds a Ph.D. from Universiti Teknologi Malaysia (UTM), where his research focused on multi-view human action recognition using convolutional neural networks (CNNs) and pose features. His expertise spans artificial intelligence, embedded systems, and digital signal processing. With multiple peer-reviewed publications, including work on COVID-19 detection using X-ray images and AI-driven healthcare solutions, he has significantly contributed to applied AI research. He has industry experience as an Assistant Manager at PTCL and has led technical events at the university and national levels. His proficiency in MATLAB, Python, and embedded systems complements his research acumen. While he has made impactful contributions, further global collaborations, research funding, and high-impact citations would enhance his academic influence. Dr. Malik continues to innovate in AI and computer vision, driving advancements in intelligent systems.

Professional Profile 

Education

Dr. Najeeb Ur Rehman Malik has a strong academic background in electronics engineering and communication systems. He is currently pursuing a Ph.D. at Universiti Teknologi Malaysia (UTM), where his research focuses on multi-view human action recognition using deep learning and convolutional neural networks (CNNs). He earned his Master of Engineering (M.E.) in Communication Systems and Networks from Mehran University of Engineering and Technology (MUET), Jamshoro, Pakistan, graduating with a CGPA of 3.40. His master’s research explored speeded-up robust features (SURF) for image retrieval systems. Prior to that, he completed his Bachelor of Engineering (B.E.) in Electronics Engineering from MUET with a CGPA of 3.45, gaining expertise in power electronics, automation, digital signal processing, and embedded systems. His academic journey reflects a strong foundation in artificial intelligence, image processing, and computer vision, positioning him as a key contributor to advancements in intelligent systems and AI-driven technologies.

Professional Experience

Dr. Najeeb Ur Rehman Malik has diverse professional experience in both academia and industry, specializing in electronics engineering, communication systems, and artificial intelligence. He served as an Assistant Manager at PTCL in Hyderabad, Sindh, Pakistan, from February 2017 to June 2018, where he gained hands-on experience in telecommunications, networking, and system management. Prior to that, he completed an internship at the National Telecommunication Corporation (NTC) in Karachi during June-July 2010, where he worked on networking infrastructure and telecommunication protocols. In addition to his industry experience, he has been actively engaged in research at Universiti Teknologi Malaysia (UTM), focusing on deep learning applications for multi-view human action recognition. His technical expertise spans MATLAB, Python, embedded systems, and digital signal processing, making him a well-rounded professional. With a strong blend of research and industry exposure, Dr. Malik continues to contribute to advancements in AI, image processing, and communication technologies.

Research Interest

Dr. Najeeb Ur Rehman Malik’s research interests lie at the intersection of computer vision, deep learning, image processing, and artificial intelligence. His primary focus is on multi-view human action recognition, where he integrates convolutional neural networks (CNNs) and pose estimation techniques to enhance accuracy in real-world scenarios. He has also explored content-based image retrieval, developing robust techniques using Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT). His work extends to healthcare applications, including AI-driven COVID-19 detection from chest X-ray images and the role of wearable technology in pandemic management. Additionally, he is interested in embedded systems, automation, and signal processing, particularly in developing intelligent and efficient computing solutions. His expertise in MATLAB, Python, and FPGA-based system design enables him to innovate in these areas. Dr. Malik aims to contribute to the advancement of AI-driven technologies for healthcare, surveillance, and human-computer interaction.

Award and Honor

Dr. Najeeb Ur Rehman Malik has been recognized for his contributions to computer vision, deep learning, and artificial intelligence through various academic and professional honors. His research in multi-view human action recognition and AI-driven healthcare solutions has been published in reputed journals, highlighting his impact in the field. During his academic career, he actively participated in technical events, conferences, and research forums, further solidifying his reputation as a dedicated scholar. He has also played a key role in organizing and volunteering at national and university-level exhibitions and competitions, showcasing his leadership and commitment to knowledge dissemination. His work on COVID-19 detection using AI and image processing techniques has received significant attention, demonstrating real-world applications of his research. While he has made commendable contributions, further recognition in the form of best paper awards, patents, and international research grants would enhance his standing in the global research community.

Research Skill

Dr. Najeeb Ur Rehman Malik possesses advanced research skills in computer vision, deep learning, and image processing, making significant contributions to AI-driven solutions. He is proficient in MATLAB and Python, leveraging machine learning frameworks like TensorFlow and PyTorch to develop multi-view human action recognition systems using convolutional neural networks (CNNs) and pose estimation techniques. His expertise extends to content-based image retrieval, feature extraction (SURF & SIFT), and embedded system design, enabling efficient AI model deployment. He is skilled in handling large datasets, performing statistical analysis, and optimizing deep learning architectures for real-world applications, including COVID-19 detection from chest X-ray images. Additionally, he has experience in academic writing, research methodology, and experimental design, ensuring high-quality publications. His ability to analyze complex problems, design innovative solutions, and collaborate on interdisciplinary research projects positions him as a strong contributor to advancements in AI, healthcare, and intelligent automation.

Conclusion

Najeeb Ur Rehman Malik is a strong candidate for the Best Researcher Award due to his technical expertise, interdisciplinary research contributions, and published works in computer vision and AI. However, improving citation metrics, securing research funding, and enhancing global collaboration would further strengthen his profile. If he has additional awards, patents, or high-impact projects, those should be highlighted in the application to maximize competitiveness.

Publications Top Noted

  • Cascading pose features with CNN-LSTM for multiview human action recognition

    • Authors: NR Malik, SAR Abu-Bakar, UU Sheikh, A Channa, N Popescu
    • Year: 2023
    • Citations: 23

  • Robust Technique to Detect COVID-19 using Chest X-ray Images

    • Authors: A Channa, N Popescu, NUR Malik
    • Year: 2020
    • Citations: 23

  • Multi-view human action recognition using skeleton based-FineKNN with extraneous frame scrapping technique

    • Authors: NUR Malik, UU Sheikh, SAR Abu-Bakar, A Channa
    • Year: 2023
    • Citations: 18

  • Managing COVID-19 Global Pandemic With High-Tech Consumer Wearables: A Comprehensive Review

    • Authors: A Channa, N Popescu, NUR Malik
    • Year: 2020
    • Citations: 17

  • Salp swarm algorithm–based optimal vector control scheme for dynamic response enhancement of brushless double‐fed induction generator in a wind energy conversion system

    • Authors: A Memon, MWB Mustafa, TA Jumani, M Olatunji Obalowu, NR Malik
    • Year: 2021
    • Citations: 10

  • Performance comparison between SURF and SIFT for content-based image retrieval

    • Authors: NUR Malik, AG Airij, SA Memon, YN Panhwar, SAR Abu-Bakar
    • Year: 2019
    • Citations: 8

  • Multiview human action recognition system based on OpenPose and KNN classifier

    • Authors: NUR Malik, SAR Abu Bakar, UU Sheikh
    • Year: 2022
    • Citations: 5

  • Association of stride rate variability and altered fractal dynamics with ageing and neurological functioning

    • Authors: A Channa, N Popescu
    • Year: 2021
    • Citations: 3

  • Localized Background Subtraction Feature-Based Approach for Vehicle Counting

    • Authors: MA El-Khoreby, SAR Abu-Bakar, MM Mokji, SN Omar, NUR Malik
    • Year: 2019
    • Citations: 3

Danica Babic | Engineering | Best Researcher Award

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Assoc. Prof. Dr. Danica Babic | Engineering | Best Researcher Award

University of Belgrade, Faculty of Transport and Traffic Engineering, Serbia

Prof. Dr. Danica Babić is an esteemed expert in air transport and traffic engineering, with extensive academic, research, and consultancy experience. She specializes in airline planning, transportation networks, and air passenger demand forecasting. With over 50 published papers in leading scientific journals and conference proceedings, she has made significant contributions to the field. Dr. Babić has been actively involved in international research projects, including FP7 and Horizon 2020, and has participated in numerous conferences and workshops worldwide. Her expertise extends to consulting in airport planning, network recovery, and aviation operations. She is also a program committee member of TRANSCODE and has delivered lectures on AI in aviation at global forums.

Professional Profile

Education

Dr. Babić earned her Ph.D. in Engineering (Air Transportation) from the University of Belgrade – Faculty of Transport and Traffic Engineering (UB-FTTE) in 2015, with a dissertation focused on network structure and airline scheduling optimization. Prior to that, she completed her Master’s degree in 2009 and a Bachelor’s degree in 2005, both in Air Transport Engineering from UB-FTTE. She has also participated in specialized training programs and workshops, including courses on air transport economics, risk analysis, and multimodal transport organized by leading institutions like EUROCONTROL and SESAR JU.

Professional Experience

Dr. Babić has been a faculty member at the University of Belgrade – Faculty of Transport and Traffic Engineering since 2005, holding positions ranging from Teaching Assistant to her current role as an Associate Professor. She has contributed to major research initiatives, including the European Commission-funded FP7 TRANSTOOLS 3 project and the Horizon 2020 SYN+AIR project. In addition to academia, she has served as a consultant on projects related to airline schedule optimization, airport design, and aviation demand modeling. Notably, she was involved in the sustainability study for Airport Konstantin Veliki in Niš and the technical documentation for the Pljevlja Airport and Heliport project.

Research Interests

Dr. Danica Babić’s research primarily focuses on air transport planning and optimization, with a particular emphasis on airline scheduling, airport operations, and aviation demand forecasting. She explores the complexities of airline network structures, flight scheduling efficiency, and multimodal transportation integration. Her work contributes to enhancing operational resilience in the aviation industry, optimizing passenger and cargo transport flows, and improving decision-making in air transport systems. Additionally, she is deeply involved in data-driven analysis and AI applications in aviation, leveraging machine learning and advanced statistical modeling to predict air travel demand, assess airline performance, and optimize network recovery strategies. Her research extends to the role of artificial intelligence in air traffic management, disruption management, and capacity planning. Dr. Babić is also engaged in sustainability and environmental impact assessment within aviation, working on projects related to emissions reduction, green airport initiatives, and the integration of alternative fuels to support eco-friendly air transport development.

Awards and Honors

Dr. Danica Babić has received numerous academic and professional recognitions for her contributions to the field of air transport and traffic engineering. She has been honored by the University of Belgrade for her excellence in research and teaching, recognizing her significant role in advancing aviation studies. Her doctoral thesis on “Network Structure and Airline Scheduling Optimization” was highly regarded and contributed to innovations in airline operations. She has also been recognized by international organizations for her contributions to aviation research, including her involvement in prestigious EU-funded projects like FP7 Transtools 3 and Horizon 2020 SYN+AIR. As a program committee member of the International Conference on Science and Development of Transport (TRANSCODE), she has played a key role in shaping aviation research discussions.

Conclusion

Prof. Dr. Danica Babić is a highly qualified and accomplished researcher in air transport and traffic engineering. Her extensive research publications, EU project contributions, consultancy experience, and academic leadership make her a strong candidate for the Best Researcher Award. Strengthening her global collaborations, leading independent research initiatives, and acquiring additional international recognitions would further enhance her qualifications.

Overall, she is a highly deserving nominee with impactful research in transportation and aviation. 🚀

Publications Top Noted

  1. Market share modeling in airline industry: An emerging market economies application
    • Authors: D. Babić, J. Kuljanin, M. Kalić
    • Year: 2014
    • Citations: 27
  2. Modeling the selection of airline network structure in a competitive environment
    • Authors: D. Babić, M. Kalić
    • Year: 2018
    • Citations: 22
  3. Integrated door-to-door transport services for air passengers: From intermodality to multimodality
    • Authors: D. Babić, M. Kalić, M. Janić, S. Dožić, K. Kukić
    • Year: 2022
    • Citations: 20
  4. Airport Access Mode Choice: Analysis of Passengers’ Behavior in European Countries
    • Authors: A. Colovic, S.G. Pilone, K. Kukić, M. Kalić, S. Dožić, D. Babić, M. Ottomanelli
    • Year: 2022
    • Citations: 13
  5. The airline schedule optimization model: Validation and sensitivity analysis
    • Authors: O. Babić, M. Kalić, D. Babić, S. Dožić
    • Year: 2011
    • Citations: 11
  6. An AHP approach to airport choice by freight forwarder
    • Authors: S. Dožić, D. Babić, M. Kalić, S. Živojinović
    • Year: 2023
    • Citations: 9
  7. Airline route network expansion: Modelling the benefits of slot purchases
    • Authors: D. Babić, M. Kalić
    • Year: 2012
    • Citations: 9
  8. Recent trends in assessment of proposed consolidations in EU airline industry – From discretion to arbitrariness
    • Authors: D. Pavlović, D. Babić
    • Year: 2018
    • Citations: 8
  9. IMPACT OF COVID-19 ON THE AVIATION INDUSTRY: An overview of global and some local effects
    • Authors: M. Kalić, D. Babić, S. Dožić, J. Kuljanin, N. Mijović
    • Year: 2022
    • Citations: 6
  10. Predicting air travel demand using soft computing: Belgrade airport case study
  • Authors: M. Kalić, S. Dožić, D. Babić
  • Year: 2012
  • Citations: 6
  1. Efikasnost aviokompanija u Evropskoj uniji: Primena AHP i DEA metoda
  • Authors: S. Dožić, D. Babić
  • Year: 2015
  • Citations: 4
  1. Modelling the estimation of the airline profit in case of purchasing new slots for increasing flight frequency
  • Authors: D. Babić, M. Kalić
  • Year: 2011
  • Citations: 4
  1. Introduction to the air transport system
  • Authors: M. Kalić, S. Dožić, D. Babić
  • Year: 2022
  • Citations: 3