Assist. Prof. Dr Saeid Afshari | Artificial intelligence | Best Researcher Award |

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Assist. Prof. Dr Saeid Afshari | Artificial intelligence | Best Researcher Award | 

Faculty , at University of Isfahan , Iran.

Dr. Saeid Afshari is an Assistant Professor of Computer Engineering at the University of Isfahan, Iran. With extensive expertise in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and Human-Computer Interaction (HCI), he has significantly contributed to both academia and industry. His research spans AI-driven solutions for healthcare, security, organizational management, and multimedia applications. An experienced IT project manager, he has successfully led the development of various smart software systems, AI-based analytics platforms, and e-Government services. Dr. Afshari is also a dedicated educator, mentoring numerous students in AI, data science, and software engineering.

Professional Profile

Scopus

Google Scholar

Orcid

Education 🎓

Ph.D. in Computer Engineering – Artificial Intelligence (2015) | University of Isfahan, Iran

Thesis: Online Quality of Experience Assessment System for Interactive Applications in Computer Networks

M.Sc. in Computer Engineering – Artificial Intelligence (2005) | University of Isfahan, Iran

Thesis: Using Agents in Workflow Management System of Distributed Organizations

B.Sc. in Hardware Engineering (2000) | University of Isfahan, Iran

Professional Experience 💼

Dr. Afshari has extensive experience in managing and developing AI-powered IT projects. He has led multiple organizational management software, AI-based sports and athlete management platforms, and e-Government solutions. As an educator, he has taught a range of subjects, including AI, data science, statistical pattern recognition, and programming in Python, C++, and MATLAB. His supervision of numerous master’s theses on AI applications highlights his mentorship and contribution to academic research.

Research Interests 🌍

Dr. Afshari’s research explores AI-driven problem-solving, deep learning applications, and human-computer interaction. His work includes machine learning-based image processing for medical diagnostics (cancer detection, skin tissue analysis), AI-powered workflow automation, smart organizational management, and multimedia quality assessment. He has also worked on neural network-based human activity recognition, deep learning models for license plate recognition, and machine translation systems. His interdisciplinary approach integrates AI with healthcare, business management, and security applications.

Awards & Honors 🏆

Recognized for significant contributions in AI-driven software development and project leadership

Successfully implemented over 150 AI-based software applications in academia and industry

Leading researcher in QoE (Quality of Experience) assessment for interactive applications

Top Noted Publications 📚

Load Balancing of Servers in Software-Defined Internet of Multimedia Things Using the Long Short-Term Memory Prediction Algorithm

Authors: S. Imanpour, A. Montazerolghaem, S. Afshari

Conference: 10th International Conference on Web Research (ICWR)

Citations: 7

Year: 2024

QoE Assessment of Interactive Applications in Computer Networks

Authors: S. Afshari, N. Movahhedinia

Journal: Multimedia Tools and Applications, Volume 75, Pages 903-918

Citations: 7

Year: 2016

Non-Intrusive Online Quality of Experience Assessment for Voice Communications

Authors: S. Afshari, N. Movahhedinia

Journal: Wireless Personal Communications, Volume 79, Pages 2155-2170

Citations: 3

Year: 2014

Optimizing Server Load Distribution in Multimedia IoT Environments Through LSTM-Based Predictive Algorithms

Authors: A. R. Montazerolghaem, S. Imanpour, S. Afshari

Journal: International Journal of Web Research

Year: 2025

Projecting Road Traffic Fatalities in Australia: Insights for Targeted Safety Interventions

Authors: A. Soltani, S. Afshari, M. A. Amiri

Journal: Injury

Year: 2025

Conclusion

Dr. Saeid Afshari is a strong candidate for the Best Researcher Award, particularly for his contributions to AI, machine learning, and deep learning applications. To further enhance his competitiveness, he could focus on increasing high-impact publications, international collaborations, patents, and research funding. If the award prioritizes real-world AI applications, software development, and mentorship, he would be an excellent nominee.

Dr Han Gao | Artificial Intellegence | Best Researcher Award |

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Dr. Han Gao | Artificial Intellegence | Best Researcher Award

postdoctoral fellow, at Harvard University, United States.

Dr. Han Gao is a dedicated researcher specializing in scientific deep learning, computational mechanics, and generative models for spatiotemporal physics. With a strong background in machine learning-driven physics simulations, he has contributed significantly to advancing numerical modeling and data-driven solutions for complex physical systems. His work bridges the gap between deep learning and traditional computational fluid dynamics, with applications in turbulence modeling, inverse problems, and reduced-order modeling.

Professional Profile

Scopus

Google Scholar

Education 🎓

Dr. Gao earned his Ph.D. in Aerospace and Mechanical Engineering from the University of Notre Dame (2018–2023), where he focused on scientific deep learning for forward and inverse modeling of spatiotemporal physics. He also holds a Master’s degree in Mechanical Engineering & Materials Science from Washington University in St. Louis (2016–2018), with research on numerical simulations of jet impingement and rotor blade effects. His academic journey began with a Bachelor’s degree in Mechanical Engineering from Shanghai University of Electric Power (2012–2016).

Professional Experience 💼

Dr. Gao is currently a Postdoctoral & Teaching Fellow at Harvard University (2023–present), where he continues his research in deep learning-driven physics simulations while mentoring students. Previously, he served as a Research & Teaching Assistant at the University of Notre Dame (2018–2023) and Washington University in St. Louis (2016–2018). Additionally, he gained industry experience as a Research Intern at Google Research (2022), where he worked on advanced AI-driven physics simulations.

Research Interests 🌍

Dr. Gao’s research revolves around the integration of deep learning techniques with physics-based modeling, particularly in solving partial differential equations (PDEs), turbulence modeling, generative models, and reduced-order modeling. He has developed novel physics-informed neural networks (PINNs), Bayesian generative models, and machine-learning frameworks for high-dimensional complex systems. His work is widely applicable in computational fluid dynamics (CFD), climate modeling, aerodynamics, and engineering simulations.

Awards & Honors 🏆

Dr. Gao has been recognized for his outstanding contributions to computational mechanics and machine learning applications in physics. His publications in top-tier journals, including Nature Communications, and prestigious machine learning conferences such as NeurIPS, ICML, and ICLR, reflect his impact in the field. He has also received competitive research opportunities, including a Google Research internship, showcasing his industry relevance.

Top Noted Publications 📚

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
L. Sun, H. Gao, S. Pan, J.-X. Wang – 916 citations – Computer Methods in Applied Mechanics and Engineering, 2020

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
H. Gao, L. Sun, J.-X. Wang – 567 citations – Journal of Computational Physics, 2021

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
H. Gao, M. J. Zahr, J.-X. Wang – 240 citations – Computer Methods in Applied Mechanics and Engineering, 2022

Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
H. Gao, L. Sun, J.-X. Wang – 214 citations – Physics of Fluids, 2021

Predicting physics in mesh-reduced space with temporal attention
X. Han, H. Gao, T. Pfaff, J.-X. Wang, L.-P. Liu – 106 citations – ICLR, 2022

Conclusion

Dr. Han Gao is a highly promising researcher with a strong publication record, interdisciplinary expertise, and experience at prestigious institutions. His contributions to scientific deep learning and computational mechanics make him a strong contender for the Best Researcher Award. To further solidify his case, he could focus on gaining more individual recognitions, expanding his leadership roles, and demonstrating the real-world impact of his research.

Mr Siraj Khan | Computer Vision | Best Researcher Award |

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Mr. Siraj Khan | Computer Vision | Best Researcher Award

PhD Scholar, at Islamia College Peshawar,Pakistan

Mr. Siraj Khan is a dedicated researcher and educator specializing in digital image processing and medical image analysis. He is currently affiliated with the Digital Image Processing Lab at Islamia College, Peshawar, Pakistan. Mr. Khan holds a Ph.D. in Computer Science, focusing on the detection and classification of cells in blood smear images using Convolutional Neural Networks (CNNs). His research interests span deep learning applications in medical imaging, IoT, bioinformatics, and smart healthcare services. With expertise in Python, MATLAB, and various deep learning frameworks like TensorFlow and PyTorch, Mr. Khan is passionate about deploying research to improve healthcare technologies in smart cities. His work has been published in several high-impact journals, contributing significantly to the fields of medical diagnostics and artificial intelligence.

Professional Profile

Scopus

Google scholar

Education 🎓

Mr. Siraj Khan is a highly skilled researcher and educator, currently affiliated with the Digital Image Processing Lab at Islamia College, Peshawar, Pakistan. He holds a Ph.D. in Computer Science, specializing in medical image processing, from Islamia College, Peshawar (2018–2021). His research for his Ph.D. focused on “Detection and Classification of Cells in Blood Smear Images using Convolutional Neural Networks.” He also holds a Master’s degree in Computer Vision (2014–2016) from the same institution, where he developed a resource-aware framework for leucocyte segmentation in blood smear images. Earlier, Mr. Khan completed his MCS (Computer Science) at the Federal Urdu University of Arts Science & Technology, Karachi (2008–2010), focusing on computer vision.

Experience 💼

Mr. Khan is currently a full-time researcher at the Digital Image Processing Laboratory (DIP Lab) at Islamia College, Peshawar. He has led numerous research projects related to medical image segmentation, classification, and detection, applying deep learning tools like TensorFlow, Keras, and PyTorch. In addition, Mr. Khan has collaborated with various research groups internationally, enhancing the scope and impact of his work. His work on healthcare services for smart cities reflects his ability to bridge the gap between academia and practical, deployable technology.

Research Interest 🔬

Mr. Khan’s research primarily revolves around deep learning applications in medical image analysis, focusing on the detection, segmentation, and classification of cells in blood smear images. His work employs advanced techniques such as Convolutional Neural Networks (CNNs) and dual attention networks for efficient leukocyte detection. His research also extends to smart healthcare services in smart cities, leveraging IoT technologies and bioinformatics to enhance healthcare systems. Additionally, Mr. Khan is passionate about deploying his research in real-world applications, using frameworks like TensorFlow, PyTorch, and MATLAB.

Award 🏅

Mr. Khan’s research excellence is evident in his contributions to the fields of medical image processing and smart healthcare. Although specific honors are not listed, his work has received significant academic recognition through publications in high-impact journals and conference proceedings. His contributions to the development of healthcare frameworks, including the Raspberry Pi-assisted face recognition system for law enforcement in smart cities, have been acknowledged by peers in the academic community.

Top Noted Publication 📑

Raspberry Pi Assisted Face Recognition Framework for Enhanced Law-Enforcement Services in Smart Cities

Authors: M Sajjad, M Nasir, K Muhammad, S Khan, Z Jan, AK Sangaiah, …

Citations: 241

Index: Future Generation Computer Systems

Year of Publication: 2020

 

Brain Tumor Segmentation Using K-means Clustering and Deep Learning with Synthetic Data Augmentation for Classification

Authors: AR Khan, S Khan, M Harouni, R Abbasi, S Iqbal, Z Mehmood

Citations: 218

Index: Microscopy Research and Technique

Year of Publication: 2021

 

Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Authors: M Sajjad, S Khan, Z Jan, K Muhammad, H Moon, JT Kwak, S Rho, …

Citations: 131

Index: IEEE Access

Year of Publication: 2016

 

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Authors: S Khan, M Sajjad, T Hussain, A Ullah, AS Imran

Citations: 107

Index: IEEE Access

Year of Publication: 2020

 

Computer Aided System for Leukocytes Classification and Segmentation in Blood Smear Images

Authors: M Sajjad, S Khan, M Shoaib, H Ali, Z Jan, K Muhammad, I Mehmood

Citations: 24

Index: 2016 International Conference on Frontiers of Information Technology (FIT)

Year of Publication: 2016

Conclusion

Mr. Siraj Khan exhibits the qualities of a strong contender for the Best Researcher Award. His exceptional contributions to medical image processing, particularly in the use of deep learning for blood smear analysis, are groundbreaking and highly relevant in the healthcare sector. His technical abilities, combined with a proven track record of high-impact publications, make him an ideal candidate for this award. Expanding his research into other cutting-edge areas of AI and increasing his research visibility through international collaborations and media could elevate his career further.