Elyas Rostami | Mechanical Engineering | Best Researcher Award

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Dr. Elyas Rostami | Mechanical Engineering | Best Researcher Award

Dr. Elyas Rostami is a distinguished faculty member at Buein Zahra Technical and Engineering University, specializing in Aerospace Engineering πŸš€. With a solid academic background, he earned his B.Sc. in Mechanical Engineering πŸ› οΈ from Mazandaran University, followed by an M.Sc. in Aerospace Engineering ✈️ from Tarbiat Modares University, and completed his PhD πŸŽ“ at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles πŸ“„, books πŸ“š, and research projects πŸ” to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization πŸ’§, fluid mechanics 🌊, and sustainable energy applications ⚑. As a dedicated educator πŸ‘¨β€πŸ« and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations 🀝, publishing, and mentoring.

Profile

Education πŸŽ“

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering πŸ› οΈ from Mazandaran University, laying the foundation for his technical expertise. He pursued his M.Sc. in Aerospace Engineering ✈️ at Tarbiat Modares University, where he refined his knowledge in aerodynamics, propulsion, and fluid mechanics 🌬️. Driven by academic excellence, he earned his Ph.D. in Aerospace Engineering πŸš€ from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems πŸ”₯, and energy-efficient solutions πŸ’‘. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems πŸ”§. Through rigorous coursework, experimental research, and publications πŸ“, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠πŸ’ͺ, enabling him to address modern aerospace challenges with innovative thinking πŸš€.

Experience πŸ‘¨β€πŸ«

Dr. Elyas Rostami serves as a faculty member πŸ‘¨β€πŸ« at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering πŸš€. His career spans academic teaching πŸ“–, research leadership πŸ”¬, article peer reviewing 🧐, and supervising research projects πŸ”. He has authored multiple scientific papers πŸ“ and books πŸ“š, advancing the field of propulsion systems, spray technology πŸ’§, and fluid mechanics 🌊. Beyond academia, Dr. Rostami actively engages with industry via research collaborations 🀝 and applied projects that explore new energies 🌱 and aerospace applications ✈️. His professional journey demonstrates versatility and dedication, contributing both as an educator and innovator πŸŽ“βš™οΈ. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions πŸ…

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction πŸŽ–οΈ. His scholarly contributions earned him recognition through research publications in reputable journals πŸ…, invitations to review articles 🧐, and participation in collaborative research projects 🀝. He has authored a scientific book πŸ“˜ and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion πŸ”₯ and fluid dynamics 🌊 has positioned him as a valued academic and research professional. The depth of his work reflects both national and international acknowledgment, solidifying his reputation as a committed and impactful researcher 🌍. Dr. Rostami’s work embodies passion for engineering, teaching excellence, and scientific advancement πŸ’‘πŸ”¬.

Research Interests πŸ”¬

Dr. Elyas Rostami’s research focuses on fluid mechanics 🌊, aerospace propulsion systems πŸš€, spray and atomization processes πŸ’§, and the use of renewable energies in industrial applications 🌱. His investigations address the thermodynamics of propulsion systems πŸ”₯, advancing efficiency and performance in aerospace technology ✈️. He integrates experimental and computational approaches πŸ–₯️ to explore new energy solutions and optimize spray behavior under different conditions βš™οΈ. Through research projects πŸ”¬, journal articles πŸ“„, and book authorship πŸ“š, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation πŸ’‘, making significant strides in the development of sustainable and high-performance propulsion technologies πŸŒπŸš€.

PublicationsΒ 

Lichen Shi | Mechanical Engineering | Best Researcher Award

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Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

 

Profile

Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

  • Developing an AI-based method for reading pointer meters using human-like reading sequences.
  • Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
  • Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
  • Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
  • Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

  • Intelligent Measurement with Deep Learning
  • Equipment Status Monitoring and Fault Diagnosis
  • Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

  • [1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
  • [2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) ：25186-25196. doi:
    10.1109/JSEN.2024.3417231
  • [3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
    Diagnosis of Rotating Machinery[J]. IEEE Sensors Journal, 2024,12:34785-34799. DOI:
    10.1109/access.2024.3368755.
  • 4] Haitao Wang, Mingjun Li, Zelin Liu, Xiyang Dai, Ruihua Wang and Lichen Shi*. RotaryMachinery Fault Diagnosis Based on Split Attention MechanismandGraphConvolutional Domain Adaptive Adversarial Network[J]. IEEE Sensors Journal, 2024,
    24(4) :5399-5413. doi: 10.1109/JSEN.2023.3348597.
  • [5] Haitao Wang, Xiyang Dai, Lichen Shi*. Gearbox Fault Diagnosis Based onMixedData-Assisted Multi-Source Domain Transfer Learning under Unbalanced Data[J]. IEEESensors Journal. doi: 10.1109/JSEN.2024.3477929

 

Kaveri Hatti | Engineering| Women Researcher Award

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Mrs. Kaveri Hatti | Engineering| Women Researcher Award

 

 

Profile

Education

averi Hatti is a dedicated researcher and educator in the field of VLSI Design, Embedded Systems, and Hardware Security. She is currently pursuing a Ph.D. at Amrita School of Engineering, Bangalore, focusing on FPGA-based security architectures. With a strong academic background, she holds an M.Tech in VLSI Design and Embedded Systems from VTU Regional Office, Gulbarga, and a B.Tech in Electronics and Communication Engineering from SLN College of Engineering, Raichur.

Kaveri has extensive teaching experience, having served as a Lecturer at Tagore Memorial Polytechnic College and Government Polytechnic College in Raichur before joining Amrita School of Engineering, Bangalore, as a Teaching Assistant in 2022. Her expertise lies in FPGA design, Verilog, RTL design, and hardware security implementations, utilizing tools like Xilinx ISE, VIVADO, ModelSim, and Cadence.

 

Work experience

Kaveri Hatti has a strong background in academia, with extensive teaching experience spanning several years. She began her career as a Lecturer at Tagore Memorial Polytechnic College, Raichur, from August 2009 to December 2012, where she played a key role in instructing and mentoring students in electronics and communication engineering. Simultaneously, she also served as a Lecturer at Government Polytechnic College, Raichur, from June 2009 to June 2011. Currently, she is working as a Teaching Assistant at Amrita School of Engineering, Bangalore, since February 2022, contributing to research and assisting in the academic development of students in the field of VLSI Design and Embedded Systems.

Publication