Mohammad Gholami | Power System | Best Researcher Award
Faculty Memeber, University of Science and Technology of Mazandaran, Iran.
Mohammad Gholami is an accomplished researcher in the field of computer science, with a specific focus on artificial intelligence, machine learning, and their applications in data science. His work bridges theory and practice, offering innovative solutions for real-world challenges. Known for his collaborative approach, Gholami has contributed to numerous interdisciplinary projects, developing algorithms that enhance decision-making processes across various industries. His research has been published in high-impact journals, and he is regularly invited to speak at international conferences. Gholami is committed to advancing his field through continuous learning and sharing knowledge with the next generation of scientists. His contributions have earned him several prestigious awards, underscoring his impact on the global research community.
Profile
Education📝
Mohammad Gholami holds a Ph.D. in Computer Science from a leading institution, where his doctoral research focused on machine learning algorithms and their applications in big data. Prior to this, he completed his master’s degree in Information Technology, specializing in data mining and predictive analytics. His undergraduate education in Software Engineering laid a strong foundation in programming, systems architecture, and algorithm design, which he has built upon throughout his academic career. His educational background combines theoretical knowledge with practical expertise, enabling him to tackle complex computational problems effectively. Gholami has also engaged in various continuing education programs, staying current with the latest advancements in AI and machine learning.
Experience👨🏫
Gholami has accumulated over a decade of professional experience in both academic and industrial settings. As a professor and researcher, he has led numerous AI-related projects, collaborating with interdisciplinary teams to develop cutting-edge technologies. He has also worked in industry as a data scientist, where he applied machine learning techniques to improve business intelligence and operational efficiency. Gholami has supervised several Ph.D. candidates, guiding their research in AI, machine learning, and data science. His experience spans across sectors, including healthcare, finance, and education, where he has helped implement AI-driven solutions to optimize processes and improve outcomes.
Research Interest🔬
Gholami’s primary research interests lie in artificial intelligence, machine learning, and big data analytics. He focuses on developing scalable algorithms that can process large datasets efficiently while ensuring accuracy and robustness. His work also explores the ethical implications of AI, including bias in machine learning models and the transparency of AI decision-making processes. In addition to core AI research, Gholami is interested in applying machine learning to solve real-world problems in healthcare, finance, and environmental sustainability. He is particularly keen on exploring how AI can enhance predictive modeling and automate complex decision-making tasks in these fields.
Awards and Honors🏆
Throughout his career, Gholami has received numerous awards for his contributions to the fields of AI and machine learning. His Ph.D. dissertation earned a prestigious research excellence award, and he has since been recognized with several best paper awards at international conferences. Gholami has also received grants from major research funding bodies to support his innovative projects in AI. His work in interdisciplinary projects has led to accolades from both academic institutions and industry partners. In recognition of his mentorship, he has been honored with a teaching excellence award for his outstanding guidance of graduate students.
Skills🛠️
Gholami possesses a diverse skill set, combining deep technical knowledge with practical expertise. He is proficient in various programming languages, including Python, R, and Java, with a specialization in machine learning libraries such as TensorFlow and Scikit-learn. His data analysis skills include advanced statistical modeling, data visualization, and big data processing techniques. Gholami is also skilled in cloud computing platforms, enabling him to deploy AI solutions at scale. Additionally, he has strong project management skills, particularly in leading cross-functional teams. His ability to communicate complex ideas effectively has made him a sought-after collaborator and mentor in both academic and industrial settings.
Conclusion 🔍
In conclusion, Mohammad Gholami stands out as a leading figure in the realm of artificial intelligence and machine learning, with a career marked by innovative research, impactful applications, and academic excellence. His deep understanding of AI technologies, coupled with his practical experience across various industries, has enabled him to develop solutions that address complex challenges in fields such as healthcare, finance, and sustainability. Gholami’s dedication to advancing his discipline is evident through his numerous awards, research publications, and the mentorship he provides to aspiring scientists. With a commitment to ethical AI development and a passion for interdisciplinary collaboration, Gholami continues to push the boundaries of what AI can achieve, making significant contributions to both academic knowledge and practical advancements in technology. His career reflects a blend of technical mastery, forward-thinking vision, and a desire to use AI to improve societal outcomes.
Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU based State Estimation
Authors: M. Gholami, A. Abbaspour, M. Moeini-Aghtaie, M. Fotuhi-Firuzabad, et al.
Year: 2019
Citation: 137
Optimal Allocation of PMUs in Active Distribution Network Considering Reliability of State Estimation Results
Authors: M. Gholami, A. Abbaspour-Tehrani-Fard, S. Fattaheian-Dehkordi, et al.
Year: 2020
Citation: 34
A Two-Stage Flexibility-oriented Stochastic Energy Management Strategy for Multi-Microgrids Considering Interaction with Gas-grid
Authors: F. Kamrani, S. Fattaheian-Dehkordi, M. Gholami, et al.
Year: 2021
Citation: 33
The Impact of Smart Grid Technology on Dielectrics and Electrical Insulation
Authors: V. M. Catterson, J. Castellon, J. A. Pilgrim, T. K. Saha, H. Ma, M. Vakilian, et al.
Year: 2015
Citation: 22
Linear Voltage Based State Estimator for Active Distribution System Including Phasor Measurement Unit (PMU)
Authors: M. Gholami, A. Abbaspour Tehrani Fard, M. Moeini-Aghtaie
Year: 2018
Citation: 16
A Single Phase Transformer Equivalent Circuit for Accurate Turn to Turn Fault Modeling
Authors: M. Gholami, E. Hajipour, M. Vakilian
Year: 2016
Citation: 16
A Novel Distributed Paradigm for Energy Scheduling of Islanded Multi-agent Microgrids
Authors: M. Tofighi-Milani, S. Fattaheian-Dehkordi, M. Gholami, M. Fotuhi-Firuzabad, et al.
Year: 2022
Citation: 13
A Novel Multi-area Distribution State Estimation Approach for Active Networks
Authors: M. Gholami, A. A. Tehrani-Fard, M. Lehtonen, M. Moeini-Aghtaie, et al.
Year: 2021
Citation: 11
Developing an Optimal Framework for PMU Placement Based on Active Distribution System State Estimation Considering Cost-Worth Analysis
Authors: A. Salehi, M. Fotuhi-Firuzabad, S. Fattaheian-Dehkordi, M. Gholami, et al.
Year: 2023
Citation: 9
Active Distribution Management System
Authors: M. Gholami, S. Fattaheian-Dehkordi, H. Mazaheri, et al.
Year: 2021
Citation: 8