Ejaz Ahmad Khera | Materials Simulation | Best Researcher Award

Dr. Ejaz Ahmad Khera | Materials Simulation | Best Researcher Award


Dr. Ejaz Ahmad Khera is an Assistant Professor of Physics at The Islamia University of Bahawalpur, Pakistan, and an HEC-approved supervisor with a PhD in the characterization of materials for memory storage and neuromorphic applications using DFT. He has published over 30 high-impact international research articles and currently leads research at the Materials Modeling and Simulation Lab, where he supervises MPhil and PhD students. His group is actively involved in developing efficient double perovskites for solar cell applications. In addition to his academic and research roles, Dr. Khera contributes to departmental administrative functions and has participated in multiple international conferences in physics and materials science. He is proficient in VASP, Wien2k, CASTEP, and other computational tools, reflecting his strong background in computational materials physics.

Profile

Education 🎓

Dr. Khera earned his PhD in Physics from The Islamia University of Bahawalpur (2016–2021), focusing on DFT-based characterization of materials for neuromorphic and memory applications. He holds an MPhil in Physics from Allama Iqbal Open University, Islamabad (2012–2014), and a Master of Science in Physics from Bahauddin Zakariya University, Multan (2007–2010). His academic foundation combines theoretical physics, computational modeling, and practical understanding of solid-state and condensed matter systems. He is trained in multiple simulation platforms including VASP, Wein2k, and CASTEP, and has built expertise in data analysis software like Origin and visualization tools like VESTA and Crystal Maker. His solid academic training has enabled him to mentor graduate students in computational material science and renewable energy research domains. Dr. Khera has also enhanced his academic exposure through participation in pedagogical and research methodology training programs.

Experience 👨‍🏫

Dr. Khera is currently serving as Assistant Professor of Physics at The Islamia University of Bahawalpur since March 2022, where he teaches undergraduate and postgraduate courses, leads research, and manages academic administration. From 2019 to 2021, he was a visiting lecturer at University of Education, Multan Campus, and previously taught physics at Admire Group of Colleges (2017–2021), Muslim Group of Colleges (2013–2017), and Educators Group of Colleges (2011–2013), focusing on undergraduate and higher secondary education. Over a decade of teaching experience has honed his expertise in delivering core physics concepts and mentoring students. In his academic tenure, Dr. Khera has also built research collaborations and guided MPhil and PhD students in materials modeling. His responsibilities have included curriculum design, academic planning, and seminar coordination. He is deeply engaged in academic development through active participation in physics conferences and workshops.

Awards & Recognitions 🏅

Dr. Khera received the Prime Minister Youth Laptop Scheme award from the Federal Government of Pakistan in 2018, recognizing his academic excellence and contribution to higher education. He was also granted the Punjab Government Scholarship under the Fee Reimbursement Scheme for Higher Education in 2017, awarded by the Government of Punjab. These honors reflect his academic merit, commitment to research, and service in the field of physics education. He has participated in multiple international and national-level conferences, including events hosted by The Islamia University of Bahawalpur and The Women University Multan. His consistent performance in academics and his role in teaching and research supervision have earned him institutional recognition. Additionally, his HEC-approved supervisor status further signifies his qualification and leadership in guiding advanced research projects in Pakistan. These accolades support his active involvement in national education development and scientific advancement in materials science.

Research Interests 🔬

Dr. Khera’s research is centered on computational materials science with a focus on density functional theory (DFT)-based investigations of electronic, optical, thermoelectric, and structural properties of novel materials. His primary interest lies in characterizing double perovskites (DPs), Heusler alloys, and vacancy-ordered compounds for use in solar cells, memory storage, brain-inspired neuromorphic systems, and energy harvesting applications. Using simulation tools like VASP, CASTEP, and Wien2k, he studies materials such as Ga₂PtX₆, Li₂ATlCl₆, and Na₂PtX₆ for their optoelectronic and thermoelectric performance. His recent publications explore halogen modification, spin polarization, and hybrid perovskites for sustainable technologies. His research group actively contributes to discovering lead-free, high-efficiency materials that align with global goals for clean and renewable energy. Dr. Khera also collaborates internationally, co-authoring papers with scientists across Asia and Europe, thereby advancing the field of computational physics and sustainable material development.

Publications

 

  • Probing the opto-electronic, thermoelectric, thermodynamic and elastic responses
    of lead-free double perovskite Li2ATlCl6 (A= Na and K) for potential photovoltaic
    and high-energy applications: A DFT study
    Reference: Qiguo Xiao, Abrar Nazir, Ejaz Ahmad Khera, Mumtaz Manzoor, Ramesh
    Sharma, Javed Rahman, Sabah Ansar, Farooq Ali
    Link: https://www.sciencedirect.com/science/article/pii/S0921510724006895

 

  • First principles investigation of structural, electronic, optical, transport properties
    of double perovskites X2TaTbO6 (X= Ca, Sr, Ba) for optoelectronic and energy
    harvesting applications
    Reference: Mudassir Ishfaq, Muniba Urooj, Muhammad Sajid, Khawar Ismail, Rimsha
    Baqeel, Ejaz Ahmad Khera, Rajwali Khan, Sattam Al Otaibi, Khaled Althubeiti, Hassan Ali,
    Ghulam Murtaza, Muhammad Jamil
    Link: https://www.sciencedirect.com/science/article/pii/S0022369724005675

 

 

Elyas Rostami | Mechanical Engineering | Best Researcher Award

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 🚀. With a solid academic background, he earned his B.Sc. in Mechanical Engineering 🛠️ from Mazandaran University, followed by an M.Sc. in Aerospace Engineering ✈️ from Tarbiat Modares University, and completed his PhD 🎓 at K. N. Toosi University of Technology. Dr. Rostami is an active researcher with numerous journal articles 📄, books 📚, and research projects 🔍 to his credit. His professional interests lie in aerospace propulsion systems, spray and atomization 💧, fluid mechanics 🌊, and sustainable energy applications ⚡. As a dedicated educator 👨‍🏫 and reviewer, he contributes to shaping future engineers while advancing scientific knowledge through research collaborations 🤝, publishing, and mentoring.

Profile

Education 🎓

Dr. Elyas Rostami holds a B.Sc. in Mechanical Engineering 🛠️ 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 🚀 from K. N. Toosi University of Technology, specializing in thermodynamics, propulsion systems 🔥, and energy-efficient solutions 💡. His academic path reflects deep commitment to understanding advanced mechanical and aerospace systems 🔧. Through rigorous coursework, experimental research, and publications 📝, Dr. Rostami has cultivated profound expertise that bridges theory and practice 🧠💪, enabling him to address modern aerospace challenges with innovative thinking 🚀.

Experience 👨‍🏫

Dr. Elyas Rostami serves as a faculty member 👨‍🏫 at Buein Zahra Technical and Engineering University, where he imparts knowledge in Aerospace Engineering 🚀. His career spans academic teaching 📖, research leadership 🔬, article peer reviewing 🧐, and supervising research projects 🔍. He has authored multiple scientific papers 📝 and books 📚, advancing the field of propulsion systems, spray technology 💧, 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 🎓⚙️. His research passion and practical approach shape students into future-ready engineers, while his publications impact global scientific discourse 🌐.

Awards & Recognitions 🏅

Dr. Elyas Rostami’s career is marked by academic excellence and research distinction 🎖️. His scholarly contributions earned him recognition through research publications in reputable journals 🏅, invitations to review articles 🧐, and participation in collaborative research projects 🤝. He has authored a scientific book 📘 and continuously advances his field through active publishing and innovation 🌟. His expertise in aerospace propulsion 🔥 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 💡🔬.

Research Interests 🔬

Dr. Elyas Rostami’s research focuses on fluid mechanics 🌊, aerospace propulsion systems 🚀, spray and atomization processes 💧, and the use of renewable energies in industrial applications 🌱. His investigations address the thermodynamics of propulsion systems 🔥, advancing efficiency and performance in aerospace technology ✈️. He integrates experimental and computational approaches 🖥️ to explore new energy solutions and optimize spray behavior under different conditions ⚙️. Through research projects 🔬, journal articles 📄, and book authorship 📚, he contributes to the understanding of modern aerospace challenges. Dr. Rostami’s work supports both academic progress and industrial innovation 💡, making significant strides in the development of sustainable and high-performance propulsion technologies 🌍🚀.

Publications 

Lichen Shi | Mechanical Engineering | Best Researcher Award

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