Saqib Qamar | Artificial Intelligence | Best Researcher Award

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 Dr. Saqib Qamar |Artificial Intelligence
| Best Researcher Award

Assistant Professor at Sohar University, India.

Dr. Saqib Qamar is a dedicated Assistant Professor at Sohar University, Oman, specializing in computer science with expertise in medical image analysis and deep learning. With a Ph.D. from Huazhong University of Science and Technology, China, and postdoctoral research from Sweden’s top institutes—KTH and Umea University—he has demonstrated strong research capabilities through high-quality publications and international collaboration. His academic career spans teaching, curriculum development, and industry experience. Known for his work ethic, innovation, and research productivity, Dr. Qamar has a profound commitment to student success and scientific excellence. His scholarly contributions and interdisciplinary engagement make him a compelling candidate for the Best Researcher Award.

🌍 Professional Profile:

ORCID

Google Scholar

🏆 Suitability for the Best Researcher Award :

Dr. Saqib Qamar’s academic journey reflects consistent excellence and impactful contributions to computer science, particularly in medical image analysis using AI. With experience in global research environments and a record of peer-reviewed publications, he bridges theory with real-world applications. His Ph.D. work on 3D CNNs for brain MRI segmentation was both innovative and practically relevant. As a postdoctoral fellow at KTH and Umea University, he engaged in collaborative, cutting-edge research. He also actively mentors students and contributes to academic discourse. His awards, research leadership, and ongoing projects demonstrate a trajectory of influence, making him highly suitable for the Best Researcher Award.

🎓 Education :

Dr. Saqib Qamar earned his Ph.D. in Computer Science from Huazhong University of Science and Technology, China (2015–2019), with a dissertation focused on 3D CNN models for brain MRI segmentation. His doctoral research integrated deep learning with medical imaging and received the HUST Excellence Award. He completed his Master’s in Computer Science at Aligarh Muslim University, India (2010–2013), where he studied AI, programming, and software engineering, and was awarded a Merit Cum Means scholarship. His academic foundation was laid through a B.Sc. (Hons.) in Statistics from the same university (2007–2010), where he focused on probability, statistics, and linear algebra, graduating with First Division.

🏢 Work Experience :

Dr. Saqib Qamar is currently an Assistant Professor at Sohar University, Oman (2025–present), where he teaches and conducts research in AI and medical image computing. He was previously a postdoctoral fellow at KTH Royal Institute of Technology (2024–2025) and Umea University (2022–2024), Sweden, focusing on machine learning and deep learning applications. At Umea, he also taught deep learning courses. Earlier, he served as Assistant Professor at Madanapalle Institute of Technology and Science, India (2019–2021), where he taught programming and ML subjects. Prior to academia, he worked as a Database Developer at nServices, Delhi (2013–2015), specializing in Oracle-based data systems and programming.

🏅Awards and Honors

Dr. Saqib Qamar has been recognized for his academic performance and research excellence throughout his career. He received the HUST Excellence Award during his Ph.D. at Huazhong University of Science and Technology, acknowledging his exceptional work in medical image segmentation using deep learning. He was also awarded the Merit Cum Means Scholarship by the Government of India during his Master’s studies. His postdoctoral research in Sweden was supported through prestigious international fellowships, and he has contributed to multiple international projects. Additionally, he has received appreciation for teaching excellence and academic service in both India and Oman. His record of honors reflects his dedication to advancing AI and medical informatics research globally.

🔬 Research Focus :

Dr. Saqib Qamar’s research focuses on medical image analysis, deep learning, and 3D convolutional neural networks. His doctoral work centered on brain MRI segmentation, proposing efficient and parallelized CNN architectures. He has expanded his expertise during postdoctoral stints in Sweden, exploring advanced AI techniques in healthcare imaging, neural cell recognition, and explainable AI. His work integrates datasets from MRI and CT scans with machine learning algorithms to enhance diagnostic capabilities. He is also interested in real-time data processing, parallel computing, and interpretable AI models. With an aim to bridge clinical needs with computational innovation, Dr. Qamar’s research contributes significantly to the domains of health informatics and intelligent medical systems.

📊 Publication Top Notes:

📘 Techniques of data mining in healthcare: a review
🗓️ Year: 2015 | ✍️ P Ahmad, S Qamar, SQA Rizvi | 📖 International Journal of Computer Applications | 🔢 Cited by: 174 📊

🧠 A variant form of 3D-UNet for infant brain segmentation
🗓️ Year: 2020 | ✍️ S Qamar, H Jin, R Zheng, P Ahmad, M Usama | 📰 Future Generation Computer Systems | 🔢 Cited by: 132 🧬

🦴 CT-based automatic spine segmentation using patch-based deep learning
🗓️ Year: 2023 | ✍️ SF Qadri, H Lin, L Shen, M Ahmad, S Qadri, S Khan, M Khan, SS Zareen, S Qamar | 🧾 International Journal of Intelligent Systems | 🔢 Cited by: 77 🧠

🧠 Context aware 3D UNet for brain tumor segmentation
🗓️ Year: 2020 | ✍️ P Ahmad, S Qamar, L Shen, A Saeed | 📘 MICCAI Brainlesion Workshop | 🔢 Cited by: 57 🧪

🧬 MH UNet: A multi-scale hierarchical based architecture for medical image segmentation
🗓️ Year: 2021 | ✍️ P Ahmad, H Jin, R Alroobaea, S Qamar, R Zheng, F Alnajjar, F Aboudi | 📰 IEEE Access | 🔢 Cited by: 51 🔬

🧴 Dense encoder-decoder–based architecture for skin lesion segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 🧠 Cognitive Computation | 🔢 Cited by: 50 🧪

🧠 HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation
🗓️ Year: 2021 | ✍️ S Qamar, P Ahmad, L Shen | 📘 Brainlesion: Glioma, MS, Stroke and TBI Workshop | 🔢 Cited by: 46 💡

Hao Zhang | Artificial Intelligence | Best Researcher Award

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Dr. Hao Zhang | Artificial Intelligence
| Best Researcher Award

Associate professor at Carnegie Mellon University, United States.

Hao Zhang is a Research Associate at Carnegie Mellon University (CMU), conducting postdoctoral research at the Safe AI Lab under Prof. Ding Zhao. He also serves as the Associate Director of the ETAIC Research Lab at the University of Texas at Arlington, led by Prof. Eric Tseng (NAE Member). He holds a Ph.D. from Tsinghua University, co-advised by Prof. Zhi Wang and Prof. Shengbo Eben Li. With over 35 SCI/EI publications and 17 patents, his research advances multi-agent reinforcement learning and closed-loop LLMs for real-world AI deployment in autonomous vehicles, robotics, and smart energy systems. He collaborates globally with academic and industrial leaders such as BYD, SAIC, Dongfeng Motor, and UCL, making impactful contributions to intelligent mobility.

🌍 Professional Profile:

ORCID

Google Scholar 

Scopus 

🏆 Suitability for the Best Researcher Award :

Dr. Hao Zhang is an exceptional candidate for the Best Researcher Award due to his groundbreaking work at the intersection of artificial intelligence and real-world applications. His achievements in scalable AI for micro-mobility and autonomous vehicles have led to industrial deployments across leading automotive manufacturers. With a strong publication record, global collaborations, and 17 patents, he exemplifies innovation, impact, and leadership. He bridges theory and practice, pushing the boundaries of safe and trustworthy AI agents. His dual appointments at CMU and UTA and contribution to both academia and industry reflect his versatile excellence. Dr. Zhang’s work not only enhances technological advancement but also fosters a responsible and intelligent future for mobility and energy systems.

🎓 Education :

Hao Zhang received his Ph.D. in Mechanical Engineering from Tsinghua University, one of China’s most prestigious institutions, where he was co-advised by renowned scholars Prof. Zhi Wang and Prof. Shengbo Eben Li. During his Ph.D., he focused on reinforcement learning and its applications to intelligent vehicle systems. Prior to that, he completed his undergraduate and master’s studies with distinction, developing a strong foundation in robotics, automation, and control systems. His education also included collaborative learning experiences with industry, which laid the groundwork for his multidisciplinary approach to research. Currently, he is expanding his expertise through postdoctoral research at Carnegie Mellon University, contributing to the development of safe AI systems under the mentorship of Prof. Ding Zhao.

🏢 Work Experience :

Dr. Zhang has a rich portfolio of academic and industrial experience. As a Research Associate at Carnegie Mellon University, he works at the forefront of AI safety, while simultaneously serving as Associate Director at the ETAIC Lab at UTA. He has led or participated in five major government-funded research projects and four OEM-sponsored industry projects. His efforts have directly supported intelligent system development for companies such as BYD Auto, SAIC Motor, and Dongfeng. His engineering solutions have real-world applications in autonomous driving, energy management, and mobile robotics. His academic roles, coupled with his industrial consultancy, enable him to effectively translate research into practice. Dr. Zhang’s interdisciplinary experience sets him apart as a leader in applied AI and automation.

🏅Awards and Honors

Hao Zhang’s research excellence has earned him notable recognition across academia and industry. He has published over 35 SCI/EI-indexed journal articles, authored a technical book (ISBN: 9780443329845), and holds 17 patents related to intelligent control and autonomous systems. His work has been cited nearly 500 times, demonstrating significant influence. His research contributions have been integrated into industrial platforms at BYD and Dongfeng, marking a rare crossover between lab and large-scale deployment. Although he is still early in his postdoctoral career, his consistent innovation and impact have made him a rising leader in AI-powered mobility. His contributions position him for prestigious honors such as the Best Researcher Award and similar recognitions for scientific leadership.

🔬 Research Focus :

Dr. Zhang’s research focuses on scalable and trustworthy AI for autonomous systems and smart energy applications. His core expertise includes multi-agent reinforcement learning, closed-loop large language models (LLMs), and intelligent motion control. He develops AI algorithms that can be safely deployed in micro-mobility devices (assistive and mobile robots), connected vehicles, and distributed energy platforms. His work contributes to both algorithmic innovation and real-world adoption, ensuring AI agents are reliable, interpretable, and responsive to dynamic environments. He is particularly interested in bridging theory with practice by collaborating with top-tier institutions and OEMs. Dr. Zhang’s interdisciplinary approach merges robotics, automotive systems, control engineering, and deep learning to create adaptive, secure, and energy-efficient intelligent agents.

📊 Publication Top Notes:

📘 Impact of ammonia addition on knock resistance and combustion performance in a gasoline engine with high compression ratio
📅 Year: 2023 | 📊 Cited by: 75 | 🛠️ Energy efficiency, combustion

📘 Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine
📅 Year: 2021 | 📊 Cited by: 42 | ⚡ Hybrid vehicles, control systems

📘 Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles
📅 Year: 2023 | 📊 Cited by: 40 | 📡 Connected vehicles, optimization

📘 Experimental study on combustion and emission characteristics of ethanol-gasoline blends in a high compression ratio SI engine
📅 Year: 2023 | 📊 Cited by: 36 | 🔬 Fuel science, engine performance

📘 Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information
📅 Year: 2022 | 📊 Cited by: 34 | 🤖 AI control, mobility systems

📘 Integrated thermal and energy management of connected hybrid electric vehicles using deep reinforcement learning
📅 Year: 2023 | 📊 Cited by: 30 | 🧠 Deep learning, hybrid energy systems

Hazrat Bilal | Robotics Engineering | Young Scientist Award

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Dr. Hazrat Bilal |Robotics Engineering
| Young Scientist Award

Postdoctoral Research Fellow at Shenzhen University, China .

Dr. Hazrat Bilal is a highly accomplished researcher and Postdoctoral Fellow at Shenzhen University, China. With a Ph.D. in Control Science & Engineering from the University of Science & Technology of China, he brings exceptional expertise in intelligent robotics, deep learning, and fault diagnosis. Dr. Bilal has authored over 30 high-impact publications and has earned 1200+ citations (h-index: 12). His outstanding academic record is complemented by rich industry experience and several prestigious fellowships and awards. A dedicated IEEE member and registered engineer with PEC, he is passionate about pushing the boundaries of intelligent automation. His remarkable early-career accomplishments and leadership in research make him a strong candidate for the Young Scientist Award.

🌍 Professional Profile:

Scopus 

Orcid

Google scholar

🏆 Suitability for the Best Researcher Award


Dr. Hazrat Bilal is a highly deserving candidate for the Young Scientist Award, with an exceptional academic record (Ph.D., USTC, GPA 3.88/4.00; M.S., NUST, GPA 3.85/4.00). As a Postdoctoral Fellow at Shenzhen University, he has made significant contributions to intelligent robotics, fault diagnosis, and AI-driven control systems. With over 15 high-impact Q1/Q2 journal publications—including in the IEEE IoT Journal, Soft Computing, Bioengineering, and Human-centric Computing—his research demonstrates innovation and practical value. His interdisciplinary work in blockchain-enabled IoRT, fuzzy-ADRC control, and AI for medical and UAV systems reflects his leadership in emerging technologies. With over 1200 citations and global collaborations, Dr. Bilal stands out as a promising young researcher committed to advancing intelligent and sustainable technologies.

🎓 Education 

Dr. Hazrat Bilal’s academic journey reflects a commitment to excellence in engineering and research. He earned his Ph.D. in Control Science & Engineering from the University of Science & Technology of China (2018–2024), maintaining an exceptional GPA of 3.88/4.00. Prior to that, he completed his Master’s degree in the same field at Nanjing University of Science & Technology (2015–2018) with a GPA of 3.85/4.00 and received an Outstanding Graduate Certificate. His undergraduate studies in Electrical (Electronics) Engineering were completed at FUUAST Islamabad, Pakistan (2010–2014), earning a GPA of 3.18/4.00. This strong academic foundation underpins his work in robotics, automation, and advanced control systems.

🏢 Work Experience 

Dr. Bilal brings interdisciplinary and global experience from academia and industry. He worked as a Performance Software Engineer at ZF Friedrichshafen, Shanghai, where he developed vision algorithms and optimized embedded system performance. Prior to that, he served as a Telecom Engineer at Real Solution Pvt. Ltd., Pakistan, contributing to 3G system installations and RF optimization. He also worked at Dargai Hydropower Plant as a Control Engineer, where he led power generation and preventive maintenance operations. Additionally, he completed internships and technical training at the National Telecommunication Corporation, University of Tennessee (USA), and CAE Pakistan. These roles have refined his expertise in automation, control systems, robotics, and smart grid technologies.

🏅 Awards and Honors 

Dr. Hazrat Bilal has received numerous awards recognizing his academic excellence and innovation. He is a recipient of the CAS-TWAS Fellowship Award at USTC and the NMG-NUST Joint Scholarship for his master’s studies. His exceptional performance earned him the Outstanding Graduate Certificate from Nanjing University of Science & Technology. As an undergraduate, he was awarded scholarships from Akhuwat Foundation and Pakistan Bait-ul-Mal, along with a Prime Minister’s Laptop under the national scheme. He secured funding for his final-year project through the National Grassroots ICT R&D Program. Dr. Bilal also received a Letter of Appreciation from IEEE FUUAST and continues to excel as an IEEE member and professional contributor to engineering societies.

🔬 Research Focus 

Dr. Hazrat Bilal’s research lies at the intersection of robotic control systems, artificial intelligence, and fault diagnosis. His recent works emphasize hybrid deep learning algorithms for the Internet of Robotic Things (IoRT), adaptive control of flexible manipulators, and trajectory tracking. He has proposed advanced techniques integrating fuzzy logic, ADRC, CNNs, LSTMs, and blockchain for secure, intelligent, and resilient systems. His current research includes generative AI for traffic simulation, nanorobot control, and intelligent medical diagnosis using AI. Dr. Bilal’s work contributes significantly to the future of smart manufacturing, healthcare, and autonomous vehicles. His focus on interdisciplinary, experimental, and data-driven approaches positions him as a leader in intelligent control systems.

📊 Publication Top Notes:

  • Title: A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things
    Authors: H. Xu, Z. Sun, Y. Cao, H. Bilal
    Journal: Soft Computing, 27(19), 14469–14481
    Citations: 172
    Year: 2023

  • Title: Pruning filters with L1-norm and capped L1-norm for CNN compression
    Authors: A. Kumar, A. M. Shaikh, Y. Li, H. Bilal, B. Yin
    Journal: Applied Intelligence, 51(2), 1152–1160
    Citations: 147
    Year: 2021

  • Title: A practical study of active disturbance rejection control for rotary flexible joint robot manipulator
    Authors: H. Bilal, B. Yin, M. S. Aslam, Z. Anjum, A. Rohra, Y. Wang
    Journal: Soft Computing, 27(8), 4987–5001
    Citations: 128
    Year: 2023

  • Title: Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach
    Authors: H. Bilal, B. Yin, A. Kumar, M. Ali, J. Zhang, J. Yao
    Journal: Soft Computing, 27(7), 4029–4039
    Citations: 118
    Year: 2023

  • Title: Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties
    Authors: H. Bilal, W. Yao, Y. Guo, Y. Wu, J. Guo
    Conference: 2017 36th Chinese Control Conference (CCC), 4192–4197
    Citations: 115
    Year: 2017

  • Title: Real-time lane detection and tracking for advanced driver assistance systems
    Authors: H. Bilal, B. Yin, J. Khan, L. Wang, J. Zhang, A. Kumar
    Conference: 2019 Chinese Control Conference (CCC), 6772–6777
    Citations: 101
    Year: 2019

  • Title: Regional feature fusion for on-road detection of objects using camera and 3D-LiDAR in high-speed autonomous vehicles
    Authors: Q. Wu, X. Li, K. Wang, H. Bilal
    Journal: Soft Computing, 27(23), 18195–18213
    Citations: 90
    Year: 2023

  • Title: Reduction of multiplications in convolutional neural networks
    Authors: M. Ali, B. Yin, A. Kunar, A. M. Sheikh, H. Bilal
    Conference: 2020 39th Chinese Control Conference (CCC), 7406–7411
    Citations: 85
    Year: 2020

  • Title: Second-order convolutional network for crowd counting
    Authors: L. Wang, Q. Zhai, B. Yin, H. Bilal
    Conference: Fourth International Workshop on Pattern Recognition, 11198, 158–163
    Citations: 83
    Year: 2019

  • Title: A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways
    Authors: H. Dou, Y. Liu, S. Chen, H. Zhao, H. Bilal
    Journal: Soft Computing, 27(21), 16373–16388
    Citations: 82
    Year: 2023

  • Title: Advanced efficient strategy for detection of dark objects based on spiking network with multi-box detection
    Authors: M. Ali, B. Yin, H. Bilal, A. Kumar, A. M. Shaikh, A. Rohra
    Journal: Multimedia Tools and Applications, 83(12), 36307–36327
    Citations: 50
    Year: 2024