Ali Mehrizi | Machine Learning | Best Paper Award

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Dr. Ali Mehrizi | Machine Learning | Best Paper Award

Lecturer at Ferdowsi University of Mashhad, Iran.

Ali Mehrizi is a distinguished researcher and lecturer in Artificial Intelligence (AI) and Machine Learning at Ferdowsi University of Mashhad (FUM), Iran. With a wealth of experience exceeding a decade, his expertise spans adaptive probabilistic models, distributed learning, multi-target tracking, time series forecasting, and Gaussian Mixture Probability Hypothesis Density (GMPHD) methods. Dr. Mehrizi has published multiple impactful articles in renowned journals such as Expert Systems with Applications and Fuzzy Sets and Systems. He is deeply committed to advancing the understanding and application of AI techniques and has successfully mentored numerous students in areas ranging from Data Mining to Advanced Operating Systems.

Profile

Google Scholar

Education

Dr. Mehrizi educational background is rooted in Artificial Intelligence. He is currently pursuing a Ph.D. in AI at Ferdowsi University of Mashhad (2017–2024), under the supervision of Professor H. Sadoghi Yazdi. His dissertation focuses on financial time series forecasting using experience-based adaptive learning, a project that has already produced several publications in top-tier journals. Previously, he earned an M.Sc. in AI from Azad University of Mashhad (2011–2013), where he worked on adaptive semi-supervised learning, optimizing self-organizing map models. His early academic journey began with a B.Sc. in Computer Engineering from the University of Birjand, later transferring to Azad University of Mashhad.

Experience

Dr. Mehrizi professional career spans various roles, beginning in 2001 when he became the IT & Network Manager at the Faculty of Engineering. In this capacity, he significantly improved the system performance and network management. Since 2011, he has been involved in research in AI and Machine Learning, contributing to the development of machine learning models and publishing his findings in high-impact journals. He has also served as a lecturer since 2013, teaching a variety of undergraduate and graduate courses, including Data Mining, Operating Systems, and Advanced Operating Systems. As a researcher, he has mentored students in their theses, particularly in machine learning and pattern recognition, fostering the next generation of AI experts.

Research Interests

Dr. Mehrizi  research interests are broad, focusing on several key areas within the domain of AI. His work on distributed adaptive learning, particularly through Diffusion LMS and Diffusion RLS, aims to optimize decentralized data processing for dynamic systems. In addition, he has contributed to probabilistic and hypothesis-based learning, exploring the use of Gaussian Mixture Probability Hypothesis Density (GMPHD) models for uncertainty-based learning and tracking. His research also delves into time series analysis and forecasting, with a particular focus on financial markets. Dr. Mehrizi’s interest in multi-target tracking extends to real-time tracking algorithms, emphasizing performance in noisy and incomplete data environments. He is also committed to semi-supervised learning, exploring hybrid methods that bridge supervised and unsupervised learning approaches in scenarios with limited labeled data.

Awards

Dr. Mehrizi contributions to the fields of AI and machine learning have earned him recognition in various academic and professional circles. He has been nominated for multiple awards for his research, particularly in adaptive learning and time series forecasting. His work is highly regarded in the academic community, and he continues to push the boundaries of AI research, especially in the areas of distributed learning and multi-target tracking.

Publications

Dr. Mehrizi has authored several articles in well-respected journals in AI and machine learning. His key publications include:

Mehrizi, A., & Yazdi, H. S. (2019). “Adaptive probabilistic methods for long-term financial time series forecasting.” Expert Systems with Applications.

Mehrizi, A., & Yazdi, H. S. (2020). “Semi-supervised learning using GSOM for adaptive classification.” Fuzzy Sets and Systems.

Mehrizi, A. (2022). “Distributed adaptive learning for dynamic systems using Diffusion LMS and RLS.” Emerging Markets Finance and Trade.

Mehrizi, A., & Yazdi, H. S. (2021). “Gaussian Mixture Probability Hypothesis Density for multi-target tracking.” Journal of Machine Learning Research.

These publications have been cited extensively by various researchers in the fields of machine learning, AI, and financial forecasting, underscoring Dr. Mehrizi’s significant impact on the academic community.

Conclusion

Dr. Ali Mehrizi is a leading researcher and educator in the field of Artificial Intelligence and Machine Learning, with a deep commitment to advancing these fields through his innovative research. His extensive academic background and his practical experience in both teaching and real-world applications have made him an invaluable asset to Ferdowsi University of Mashhad. With a strong focus on adaptive learning, probabilistic models, and time series forecasting, Dr. Mehrizi continues to contribute to the evolution of AI. His work not only shapes academic research but also provides vital insights into practical AI solutions for industries like finance and engineering. As a mentor and educator, he remains dedicated to shaping the future of AI professionals and researchers.

Preethi Iype | Neural Networks | Best Researcher Award

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Mrs. Preethi Iype | Neural Networks | Best Researcher Award

Asst. Professor at St. Thomas Institute for Science and Technology, India

Preethi Elizabeth Iype is an accomplished academician and researcher with over two decades of experience in the field of Electronics and Communication Engineering. She has made significant contributions to the field of microcontrollers, embedded systems, and IoT-based solutions, with a particular emphasis on health monitoring and electric vehicle battery management systems. Her research primarily focuses on the thermal management of semiconductor devices, particularly High Electron Mobility Transistors (HEMT). Throughout her career, she has actively participated in national and international conferences, published in reputed Scopus and Web of Science indexed journals, and contributed to various academic and professional initiatives. She currently serves as an Assistant Professor at St. Thomas Institute for Science and Technology, where she continues to inspire and mentor students in cutting-edge technological domains.

Profile

Scopus

Education

Preethi Elizabeth Iype has pursued a strong academic foundation in Electronics and Communication Engineering. She completed her Bachelor of Engineering degree from the University of Madras in 2000. Furthering her expertise, she earned her Master of Engineering from Anna University in 2011. Currently, she has submitted her doctoral thesis and is awaiting her open defense for her Ph.D. in Electronics and Communication Engineering from the College of Engineering, Trivandrum, under the University of Kerala. Her academic journey has been marked by a keen interest in semiconductor device performance, particularly focusing on AlGaN/GaN HEMT technology, and its applications in high-power and high-frequency electronics.

Professional Experience

Preethi Elizabeth Iype has a diverse professional background that spans academia and industry. She started her career as a Software Engineer at Amstor Softech, Technopark, where she worked from June 2001 to June 2004 on software development projects related to hotel management systems and industrial applications. Transitioning into academia, she joined Mar Baselios College of Engineering and later St. Thomas Institute for Science and Technology, where she has been serving as an Assistant Professor since 2005. Her teaching portfolio includes core subjects such as Embedded Systems, Real-Time Systems, Wireless Communication, Solid State Devices, and Microcontrollers. In addition to teaching, she has played a crucial role in guiding student research projects, particularly in IoT and embedded systems applications.

Research Interests

Her primary research interests lie in semiconductor device physics, embedded systems, and IoT-based smart solutions. Specifically, her work focuses on the thermal management of High Electron Mobility Transistors (HEMT) using innovative materials and device architectures. She has conducted extensive research on optimizing the electrical and thermal performance of AlGaN/GaN and AlGaAs/GaAs-based HEMT devices. Additionally, her work extends to the application of artificial intelligence and neural networks in thermal efficiency enhancement. Her research has significant implications for high-power applications, radar systems, and next-generation wireless communication technologies.

Awards and Recognitions

Preethi Elizabeth Iype has been an active contributor to academic and research communities, earning recognition for her contributions. She has received accolades for her research presentations at national and international conferences. As a coordinator and SPOC for the NPTEL Local Chapter and Club President of the National Digital Library, India, she has played a pivotal role in promoting digital learning initiatives among students. Her active participation in workshops and seminars at premier institutes such as IISc Bengaluru and VIT Vellore reflects her commitment to continuous learning and knowledge dissemination.

Selected Publications

Preethi Elizabeth Iype, Dr. Anju S, Dr. V Suresh Babu (2021). “Temperature Dependent DC and AC Performance of AlGaN/GaN HEMT on 4H-SiC.” IEEE Conference Series (ICECCT 2021), DOI: 10.1109/ICECCT52121.2021.961668. Cited by: Multiple IEEE articles.

Preethi Elizabeth Iype, Dr. Geenu Paul, Dr. V Suresh Babu (2021). “Thermal and Electrical Performance of AlGaAs/GaAs based HEMT device on SiC substrate.” Journal of Physics: Conference Series, IOP Publishing, DOI: 10.1088/1742-6596/2070/1/012057. Cited by: Various research papers in semiconductor physics.

Preethi Elizabeth Iype, Dr. Geenu Paul, Dr. V Suresh Babu (2024). “Optimizing electrical and thermal performance in AlGaN/GaN HEMT devices using dual metal gate technology.” Heat Transfer, WILEY, DOI: 10.1002/htj.23099. Cited by: Emerging studies in heat transfer and semiconductor devices.

Preethi Elizabeth Iype, Dr. Geenu Paul, Dr. V Suresh Babu (2024). “Investigation of Thermal Efficiency of Recessed Γ gate over Γ gate, T gate and Rectangular gate AlGaN/GaN HEMT on BGO substrate.” Microelectronics Reliability, Elsevier, DOI: 10.1016/j.microrel.2024.115522. Cited by: Recent works on HEMT technology and reliability.

Preethi Elizabeth Iype, Dr. Geenu Paul, Dr. V Suresh Babu (2024). “Sheaf Attention-Based Osprey Spiking Neural Network for Effective Thermal Management and Self Heating Mitigation in GaAs and GaN HEMTs.” Heat Transfer, WILEY, DOI: 10.1002/htj.23099. Cited by: Studies on AI-based thermal efficiency improvements.

Conclusion

Preethi Elizabeth Iype has demonstrated a remarkable blend of teaching, research, and industry experience over the years. Her expertise in embedded systems, IoT, and semiconductor device physics has been instrumental in shaping young minds and contributing to technological advancements. With her research in thermal management of HEMTs and AI-driven solutions, she continues to pave the way for innovations in high-power electronics and wireless communication. Through her dedication to academia and active participation in professional organizations, she remains a key figure in the field of Electronics and Communication Engineering.