Khurshid Hussain | Artificial Intelligence in Statistics | Research Excellence Award

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Mr. Khurshid Hussain | Artificial Intelligence in Statistics | Research Excellence Award

Kiost | South Korea

Mr. Khurshid Hussain is a dynamic researcher whose work spans advanced automotive engineering, semiconductor design, integrated sensing and communications, and AI-driven signal processing, establishing him as a multidisciplinary contributor across next-generation wireless, cybersecurity, and intelligent vehicular systems. He holds a Master’s degree in Advanced Automotive Engineering from Sun Moon University, South Korea, where he specialized in high-performance millimeter-wave circuit design with emphasis on 60 GHz digital variable-gain amplifiers, beamforming architectures, low-power attenuators, and chip-level ISAC systems for secure and intelligent communication. His research extends into geomatics and remote sensing, focusing on multimodal mapping using optical, SAR, and LiDAR streams, change detection, 3D reconstruction, and uncertainty-aware geospatial pipelines, alongside self-supervised and weak-supervised learning approaches for large-scale spatial data modeling. He is the inventor of a patented transistor-array-based variable attenuator and has authored an expanding collection of peer-reviewed publications in leading journals such as Electronics, IEEE Access, Applied Sciences, and IEEE Transactions, addressing topics ranging from radar–communication co-design and ultrasonic 3D beamforming sensors to predictive maintenance of aerospace components, OTFS-based V2X ISAC architectures, and AI-enhanced signal intelligence. His scholarly profile includes 9 documents, 77 citations, and an h-index of 4, reflecting his growing influence in mmWave IC design, wireless sensing, and AI-integrated communication. Khurshid has delivered technical presentations at major international conferences covering maritime IT convergence, high-frequency amplifier design, battery analytics, advanced beamforming, and power-efficient RF front-end systems. His expertise spans Cadence, HFSS, Python, MATLAB, OrCAD, cybersecurity tools, and vector network analyzers, reinforced by experience in transceiver integration, AI-chip convergence, intrusion detection systems, battery research, and embedded engineering. Earlier, he completed his B.Sc. in Electrical Engineering with a focus on IoT-based renewable-energy automation, where he developed sensor-driven, cloud-connected, and energy-efficient systems. Fluent in English and active in multicultural environments, Khurshid is known for his creativity, leadership, communication skills, and passion for innovation, continually advancing secure, intelligent, and energy-efficient technologies for the automotive, wireless, and sensing industries.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Hussain, K., & Yoo, J. (2025). Low-latency marine-based OTFS echo parameter estimation enabled by AI. Sensors, 25(23), Article 7104. DOI: 10.3390/s25237104

Hussain, K., Ali, E. M., Hussain, W., Raza, A., & Elkamchouchi, D. H. (2025). Robust OTFS-ISAC for vehicular-to-base station end-to-end sensing and communication. Electronics, 14(21), Article 4340. DOI: 10.3390/electronics14214340

Hussain, K., Jeon, W., Lee, Y., Song, I., & Oh, I. (2025). CMOS-compatible ultrasonic 3D beamforming sensor system for automotive applications. Applied Sciences, 15(16), Article 9201. DOI: 10.3390/app15169201

Hussain, K., & Oh, I. (2024). Joint radar, communication, and integration of beamforming technology. Electronics, 13(8), Article 1531. DOI: 10.3390/electronics13081531

Hussain, K., & Oh, I. (2024). Review of joint radar, communication, and integration of beam-forming technology. Preprint. DOI: 10.20944/preprints202404.0208.v1

Ki Ryong Kwon | Artificial Intelligence in Statistics | Best Faculty Award

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Prof. Ki Ryong Kwon | Artificial Intelligence in Statistics | Best Faculty Award

Pukyong National University | South Korea

Professor Ki-Ryong Kwon is an eminent and highly respected scholar in electronics engineering, artificial intelligence, and computer science, serving as a leading Professor in the Division of Computer Engineering and AI at Pukyong National University, South Korea. he pursued his academic journey at Kyungpook National University, where he completed his bachelor’s, master’s, and doctoral degrees in Electronics Engineering, later advancing his research expertise through a prestigious postdoctoral fellowship at the University of Minnesota under the mentorship of Prof. Ahmed H. Tewfik, followed by a visiting scholar appointment at Colorado State University that further broadened his international academic exposure. Throughout his long-standing academic career, Professor Kwon has demonstrated exceptional leadership by serving as Dean of the College of Engineering, Vice-Dean of Engineering, Director of the Artificial Intelligence Lab, Director of the Center for Start-up Foundation, Director of the Center of IP Transfer, and Vice-Director of Industry-University Foundation Cooperation, contributing extensively to academic development, research infrastructure advancement, and student innovation ecosystems. Beyond the university environment, he has played influential strategic roles as Chairman of the Global Fintech Industry Promotion Center, Vice-Chairman of the Korea Cloud Association, Vice-Chairman of the Busan Federation of Service Industry, and Chair of the 4th Industrial Revolution Leadership Promotion Team for Busan City, where he has been instrumental in guiding regional and national initiatives in AI-driven transformation, digital economy growth, and emerging technology integration. His contributions to the global research community include major leadership roles in IEEE, the Korea Multimedia Society, the Korea Information Processing Society, and multiple international conferences where he has served as General Chair, Industrial Chair, Program Chair, and Organizing Chair. His research encompasses deep learning, digital watermarking, image forensics, signal processing, marine AI applications, smart digital-twin systems, cybersecurity, and intelligent multi-agent architectures. With approximately 118 published research documents, around 1,309 citations, and an estimated h-index in the mid-20s, he maintains a strong and influential academic footprint. Over his career, he has been honored with numerous Best Paper Awards, national recognitions, institutional leadership awards, and international distinctions that reflect his outstanding dedication to research excellence, innovation leadership, and the advancement of technology-driven societal development.

Profile: Scopus

Xu Ge | Statistical Applications in Engineering | Industrial Statistics Award

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Mr. Xu Ge | Statistical Applications in Engineering | Industrial Statistics Award

Shanghai Jiao Tong University | China

Mr. Xu Ge is a promising researcher specializing in control science, soft sensing, and intelligent systems, currently pursuing his Ph.D. at the UM-SJTU Joint Institute, Shanghai Jiao Tong University. He earned his Bachelor’s degree in Automation from the School of Mechanical Engineering and Automation at Harbin Institute of Technology (Shenzhen), where he consistently demonstrated academic excellence and technical innovation. Throughout his academic journey, Xu Ge has been recognized with numerous honors, including the prestigious National Scholarship, Topband Enterprise Scholarship, and First-Class Academic Scholarship. His university distinctions-Outstanding Student, Outstanding CYL Member, Outstanding Graduate, and Outstanding Thesis-further highlight his commitment to excellence. Xu has achieved remarkable success in national competitions, winning the ROBOCOM National First Prize, ROBOCON National Third Prize, the National Undergraduate Smart Car Competition (Outdoor Track) National Third Prize, and the National Undergraduate Mathematics Competition Provincial First Prize. His research experience reflects a strong interdisciplinary background bridging control engineering, machine learning, and system modeling. Notably, in the NSFC project “Online Estimation of Loads and Fatigue Life Prediction of Key Chassis Components under Random Driving Conditions,” he designed a soft-sensing framework that enables high-accuracy signal estimation through data-driven models and developed an embedded system for real-world vehicle testing. His collaboration with BYD on the “New Energy Vehicles Technology Program” showcased his expertise in robotics and deep learning, where he constructed datasets from BYD blade-battery modules and integrated neural networks with classical algorithms for precise robotic welding detection and operation. Xu Ge has contributed several impactful publications, including works in Mechanical Systems and Signal Processing and IEEE Transactions on Vehicular Technology, and papers accepted for presentation at IECON 2025. His accepted and submitted research covers a wide range of topics, such as vehicle sensor optimization, kernelized modeling for wheel load estimation, and battery electrochemical parameter identification through hybrid optimization methods. With his strong foundation in algorithm design, system integration, and data-driven control, Xu Ge continues to push the frontiers of intelligent mechanical systems and vehicular sensing technologies, aspiring to develop innovative, high-performance solutions that bridge theoretical advancements with industrial applications.

Profile: Google Scholar

Featured Publications

Ge, X., Li, M., Zhou, J., Qiu, Y., & Zhang, M. (2026). MMSE noncausal FIR based wheel force soft-sensing under Bernoulli-uniform prior. Mechanical Systems and Signal Processing, 242, 113601.

Ge, X., Zhang, M., Zhou, J., Chen, W., Li, X., & Li, M. (2025). Vehicle sensor configuration optimization for tire force estimation based on Min-Max SDP. In IECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society.