Prateek Chandrakar | Structural Mechanics | Best Researcher Award

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Mr. Prateek Chandrakar | Structural Mechanics | Best Researcher Award

Research Scholar, Indian Institute of Technology Kharagpur, India.

Dr. Prateek Chandrakar is an emerging expert in structural mechanics, known for his work on stochastic modeling of composite laminates. He holds a Ph.D. in Aerospace Engineering from IIT Kharagpur, with a thesis on machine learning-assisted uncertainty analysis in thermally loaded and damaged composite structures. A recipient of multiple international Award grants and reviewer for reputed journals, his innovative integration of artificial intelligence with classical mechanics has contributed to designing robust aerospace and mechanical structures. His research has led to high-impact publications in Q1 journals like Composite Structures and European Journal of Mechanics A/Solids. His dedication to computational mechanics and his skillful use of tools like ABAQUS, MATLAB, and RBF networks make him a key contributor to the next generation of structural mechanics solutions.

👤Author’s Profile

🎓 Education 

Prateek Chandrakar completed his Ph.D. in Structures from IIT Kharagpur (2020–2025), achieving a stellar CGPA of 9.36. His research focused on variable fiber spacing and curvilinear fiber composites under uncertain environments. He earned his M.Tech. in Machine Design from IIT Guwahati (2017–2019) and B.E. in Mechanical Engineering from CSVTU Bhilai (2012–2016). Prior to this, he secured 90% in his matriculation and 80.8% in his intermediate studies from the Chhattisgarh Board. He holds additional certifications in machine learning, MATLAB, ABAQUS, and HyperMesh. Throughout his academic journey, Prateek has consistently demonstrated academic excellence and an aptitude for both foundational and advanced subjects in solid mechanics and composite structures.

💼 Experience 

Prateek has served as a Senior Research Fellow and Teaching Assistant at IIT Kharagpur (2022–2025), contributing to courses such as Finite Element Methods and Engineering Mechanics. Previously, he was a Junior Research Fellow (2020–2022) in Aerospace Structures. During his M.Tech. at IIT Guwahati, he worked as a Teaching Assistant for courses like Computational Continuum Mechanics. His industrial exposure includes a short training stint at NTPC-SAIL Power Company. Beyond teaching, he has completed hands-on projects in hydraulic mechanisms, nonlinear FEM, and RVE modeling. Prateek’s blend of academic and applied experience highlights his ability to bridge theory with practical research problems.

🏅 Awards & Honors

  • Best Conference Grant – IIT Kharagpur, ECCM 2024 (France)

  • SERB ITS Grant – For ICTAM 2024 (Korea)

  • ICTAM Support Grant Award – Full fee waiver for outstanding research

  • MHRD Fellowships – Junior & Senior Research Fellow at IIT Kharagpur (2020–2025), PG Fellowship at IIT Guwahati (2017–2019)

  • GATE 2017 – 98.98 percentile in Mechanical Engineering

  • Peer Reviewer – Journals: Acta Mechanica, Awards: ISTAM, AIAA SciTech

  • Vaidik Maths Contest – Regional 2nd Position

  • Volunteer and Mentor – DISHA (Free education initiative at IIT Guwahati)

🔬 Research Focus 

Prateek’s research is centered around stochastic structural mechanics—particularly the buckling and dynamic responses of variable stiffness composite laminates under thermal loads and manufacturing defects. He has advanced the application of machine learning (RBFN, SVR, ABC) to create surrogate models for high-fidelity simulations in nonlinear environments. His studies tackle the challenges of material variability, geometric imperfection, and delamination in aerospace-grade composite materials. His Ph.D. thesis innovatively integrates third-order shear deformation theory and SPR techniques to improve stress prediction accuracy. His goal is to bridge uncertainty quantification with practical design optimizations, particularly for thermal buckling, nonlinear flexure, and damage mechanics.

📚 Publication Top Notes

1. Characterizing Flexural Randomness in Delaminated Curvilinear Fiber Composites

Journal: Composite Structures (2025) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This study develops a machine learning-assisted surrogate modeling framework to predict the nonlinear flexural behavior of delaminated curvilinear fiber-reinforced composites. By combining Support Vector Regression (SVR) and Radial Basis Function Networks (RBFN), the research significantly reduces computational effort while accurately estimating the impact of tow angle randomness and damage. The paper proposes optimal tow angle layups for enhancing stiffness while accommodating uncertainties, demonstrating a reliable and fast alternative to traditional finite element simulations. The research is particularly valuable for lightweight structural applications in aerospace engineering where manufacturing defects like delamination are unavoidable.

2. Damage-Induced Buckling in Thermally Loaded VAT Laminates under Uncertainty

Journal: European Journal of Mechanics – A/Solids (2024) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This paper investigates the influence of thermal loads and damage mechanisms (e.g., delamination) on the buckling performance of Variable Angle Tow (VAT) composite laminates under uncertainty. Using a probabilistic modeling approach, the authors incorporate variability in material properties and geometrical imperfections. Third-order shear deformation theory is employed along with stochastic finite element analysis to evaluate thermal buckling loads. This research provides essential insight into designing safer composite structures subjected to thermal and mechanical instability, relevant for aerospace panels and automotive body structures.

3. Stochastic Buckling of Variable Fiber Spacing Composite Plates

Journal: Journal of Composite Materials (2023) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
Focusing on the uncertainty quantification of buckling behavior in variable fiber spacing composites (VFSCs), this work employs Monte Carlo simulations to assess the reliability of such structures under thermal loading. The results reveal the probabilistic distribution of critical buckling loads under realistic imperfections and loading scenarios. The novelty lies in accounting for both deterministic and stochastic fiber layout variations, leading to design guidelines that improve robustness and structural integrity for high-performance composite components.

4. Buckling Variability in Damaged Composite Laminates in Thermal Fields

Journal: Journal of Thermal Stresses (2024) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This study explores how delamination and temperature variations affect the buckling characteristics of composite laminates. A combination of the improved first-order shear deformation theory and a continuum damage model is applied to simulate thermomechanical behavior. Uncertainty in input parameters is tackled through a stochastic analysis, revealing critical design zones where damage-induced degradation is more severe. The findings can help in the predictive maintenance and thermal stability assessment of structural composite components in sectors such as defense and aviation.

5. Stochastic RBFN-Based Reliability Estimation in Thermal Loading

Journal: International Journal of Advances in Engineering Sciences and Applied Mathematics (2024)
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
In this work, Radial Basis Function Networks (RBFN) are used to build fast and efficient surrogate models for reliability estimation of VFSC laminates under thermal stresses. The study shows that RBFN can effectively capture nonlinearities caused by fiber angle variation, material uncertainties, and temperature-induced stresses. This method significantly reduces computational time compared to traditional Monte Carlo techniques, providing a viable tool for real-time reliability assessments in design optimization of composite structures.

6. Delamination Effects via Polynomial Neural Network on Dynamic Characteristics

Journal: Acta Mechanica (2024) 
Authors: N. Sharma, P. Chandrakar, D.K. Maiti
Summary:
This paper introduces a Polynomial Neural Network (PNN) model to quantify the influence of delamination on the uncertain dynamic response of Variable Angle Tow composites. The work includes both free and forced vibration analyses, capturing variability in material damping, boundary conditions, and geometric configurations. The model is validated against high-fidelity simulations, showing high accuracy with minimal computational resources. Applications include condition monitoring and damage detection in aerospace-grade structural panels.

7. Uncertain Buckling with Internal Defects in VFSC Laminates

Journal: Journal of Composite Materials (2024) 
Authors: P. Chandrakar, N. Sharma, D.K. Maiti
Summary:
This research evaluates the buckling behavior of variable fiber spacing composites with embedded internal flaws such as voids, microcracks, and fiber waviness. Through stochastic modeling and sensitivity analysis, the study reveals how defect parameters affect critical buckling loads. The study’s contribution is twofold: it quantifies uncertainty due to imperfections and recommends defect-tolerant configurations for high-reliability designs. This work is vital for industrial applications where manufacturing variability cannot be completely avoided.

Conclusion

Prateek Chandrakar is a deserving and promising candidate for a Best Researcher Award, especially within the domains of aerospace structures, uncertainty quantification, and composite mechanics. His combination of deep domain expertise, strong publications, machine learning integration, and international exposure demonstrates a research trajectory that is both impactful and forward-looking.

Sampath Dakshina Murthy Achanta | Machine Learning | Best Researcher Award

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Dr. Sampath Dakshina Murthy Achanta | Machine Learning | Best Researcher Award

Associate Professor, Vignan’s Institute of Information Technology, India

Dr. Achanta Sampath Dakshina Murthy is a dynamic academician and innovator currently serving as a Senior Associate Professor in the Department of ECE and Head of Vignan’s Centre for Innovations & Startups at Vignan’s Institute of Information Technology (A), Visakhapatnam 📍. With a strong foundation in electronics and communication engineering , he has carved a niche in interdisciplinary research focusing on biomedical image processing, IoT, and human motion analysis. His visionary leadership in innovation ecosystems is evident through his roles in startup incubation, curriculum development, and R&D strategy 💡. Dr. Murthy is also a prolific researcher, boasting 70+ publications, multiple patents 🧠, and editorial roles across reputed journals. His work on AI-powered gait analysis systems demonstrates his dedication to inclusive technological solutions for the physically challenged ♿. A mentor, reviewer, and award-winning educator, he exemplifies excellence in blending academic rigor with practical impact .

Professional Profile

Scopus

ORCID

Google Scholar

📘 Education 

Dr. Murthy holds a Ph.D. in Image Processing from KL University, Andhra Pradesh (2023) . He earned his M.Tech in Digital Electronics and Communication Systems in 2015 and B.Tech in Electronics and Communication Engineering in 2013, both from JNTU Kakinada . His academic journey began with an Intermediate (M.P.C) in 2009 from the Board of Intermediate, A.P. and Class X (CBSE) in 2007 🏫. His continuous pursuit of excellence in education forms the foundation of his pioneering work in research and teaching .

💼 Experience 

Dr. Murthy brings over a decade of experience, including 9 years in academia and 1 year in industry 🏭. He has served as Assistant, Associate, and now Senior Associate Professor at Vignan’s Institute of Information Technology (A), Visakhapatnam since 2016 👨‍🏫. Beyond teaching, he leads as the Head of the Vignan Centre for Innovations & Startups, and has held leadership roles like Associate Dean of R&D and IQAC Coordinator . His experience in Nirmala Industries and involvement in institutional development highlight his blend of academic, administrative, and industrial perspectives .

🔍 Research Focus

Dr. Murthy’s research centers on cutting-edge domains like Human Motion Analysis, Biomedical Image & Video Processing, and the Internet of Things (IoT) 🧬. His notable Ph.D. work developed AI-based gait analysis systems to prevent falls in physically challenged individuals, integrating wearable IoT sensors with machine learning algorithms like HMDTW, ANFIS, and PTBO . With 70+ research papers, 20+ awards, and several grants—including STPI and seed funds—he has built a robust research portfolio . His innovations, such as “Smart Shoe–Better Walking for Future,” exemplify translational research that bridges healthcare and technology . His work contributes to smart assistive technology, healthcare diagnostics, and predictive analytics, positioning him as a trailblazer in applied AI and IoT-driven solutions for human well-being .

🏅 Awards & Honors 

Dr. Murthy is the recipient of numerous national and international honors, including the “Innovative Researcher of the Year 2024” 🥇, “STEM Best Research Award 2024” , and the prestigious Dr. A.P.J. Abdul Kalam National Academician Award 🧑‍🚀. He’s been celebrated with multiple Sastra Awards and Young Scientist recognitions across years . Institutions like Vignan’s and international bodies in the UK, Germany, and the US have acknowledged his exceptional contributions to research, innovation, and teaching . With over 20 awards and 100+ certificates of appreciation, Dr. Murthy exemplifies excellence, impact, and commitment in every dimension of academic life .

Publications Top Notes

An Automated Detection of Heart Arrhythmias Using Machine Learning Technique: SVM
Authors: CU Kumari, ASD Murthy, BL Prasanna, MPP Reddy, AK Panigrahy
Journal: Materials Today: Proceedings, Volume 45, Pages 1393–1398
Year: 2021
Citations: 228
📘 Summary:
This paper presents a machine learning-based system for the automated detection of heart arrhythmias using Support Vector Machine (SVM) algorithms. The study leverages ECG datasets to train and validate the model, achieving high classification accuracy in detecting irregular heartbeat patterns. The proposed system contributes significantly to early diagnosis and risk mitigation in cardiac care through non-invasive, real-time monitoring techniques.

AI-Oriented Competency Framework for Talent Management in the Digital Economy: Models, Technologies, Applications, and Implementation
Author: A. Khang
Publisher: CRC Press
Year: 2024
Citations: 63
📘 Summary:
Although Dr. Murthy is not listed as an author in this title, it appears in the list. This book outlines how Artificial Intelligence can transform talent management strategies in organizations operating within the digital economy. It discusses competency modeling, AI tools, and frameworks for workforce development.  (Note: Please confirm Dr. Murthy’s involvement, as his name does not appear in the author list here.)

An IoT-Based Agriculture Maintenance Using Pervasive Computing with Machine Learning Technique
Authors: S Kailasam, SDM Achanta, P Rama Koteswara Rao, R Vatambeti, et al.
Journal: International Journal of Intelligent Computing and Cybernetics, Volume 15, Issue 2, Pages 184–197
Year: 2022
Citations: 61
📘 Summary:
This research introduces an IoT-driven smart agriculture model integrated with pervasive computing and machine learning algorithms. It monitors and manages agricultural parameters such as soil moisture, temperature, and irrigation efficiency. The system utilizes real-time data collection and predictive analytics to optimize farming practices, thereby enhancing productivity and sustainability in agriculture .

Implementation of Online and Offline Product Selection System Using FCNN Deep Learning: Product Analysis
Authors: MN Mohammad, CU Kumari, ASD Murthy, BOL Jagan, K Saikumar
Journal: Materials Today: Proceedings, Volume 45, Pages 2171–2178
Year: 2021
Citations: 53
📘 Summary:
This paper presents a product recommendation system using Fully Connected Neural Networks (FCNN) to support both online and offline retail environments. The model analyzes user behavior and product features to provide intelligent suggestions. The deep learning approach improves product visibility, customer satisfaction, and market analysis in e-commerce platforms .

A Wireless IoT System Towards Gait Detection Technique Using FSR Sensor and Wearable IoT Devices
Author: SDM Achanta
Journal: International Journal of Intelligent Unmanned Systems, Volume 8, Issue 1, Pages 43–54
Year: 2020
Citations: 50
📘 Summary:
This solo-authored paper by Dr. Murthy explores a wearable IoT-based solution for gait analysis using Force Sensitive Resistor (FSR) sensors. The system helps detect mobility patterns and abnormalities, primarily aimed at elderly and physically challenged individuals. It highlights the role of smart health devices in preventive diagnostics and rehabilitation .

📌 Conclusion 

Dr. Achanta Sampath Dakshina Murthy is a distinguished academic, visionary innovator, and dedicated mentor who continues to push the boundaries of research and education . His commitment to societal advancement through intelligent healthcare systems and academic excellence makes him a role model in the scientific community . Through his leadership in startup incubation, research development, and student mentorship, he shapes the future of innovation in India and beyond . With a passion for continuous learning and contribution, Dr. Murthy stands as a beacon of interdisciplinary impact and scholarly inspiration .

Kishwar Ali | Nanophotonics | Best Paper Award

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Mr. Kishwar Ali | Nanophotonics | Best Paper Award

PhD Student, University of L’Aquila, Italy.

Kishwar Ali is a doctoral researcher at the University of L’Aquila, Italy, specializing in nanophotonics and advanced electromagnetic modeling. His core expertise lies in investigating the Goos–Hänchen shift (GHS) in novel metamaterial configurations using fractional calculus and time-space modulated media. Through strong collaborations with international experts and rigorous theoretical contributions, he has developed new paradigms for controlling light reflection and propagation in hyperbolic and zero-index materials. His vision blends deep physics with real-world applications, such as hyperlensing and photonic sensors. Kishwar is an active member of the IEEE Antennas and Propagation Society and is committed to pushing the boundaries of optical theory toward practical innovation.

📌Author Profile

🎓 Education 

Kishwar Ali is currently pursuing his PhD at the University of L’Aquila in Italy. His doctoral training emphasizes advanced computational modeling and electromagnetic field theory applied to metamaterials and nanophotonics. His research bridges theoretical optics, mathematical modeling in fractional dimensions, and practical application in layered photonic structures. His academic background integrates foundational knowledge in applied physics with specialized training in electromagnetic theory and materials science. During his doctoral journey, Kishwar has been mentored by leading scientists and has contributed to multiple high-impact publications, enhancing his research rigor and interdisciplinary insights.

💼 Experience

As a PhD student, Kishwar Ali has co-authored four peer-reviewed publications, tackling complex optical phenomena such as the Goos–Hänchen shift and its manipulation in fractional and anisotropic media. He is actively engaged in a major project on spatiotemporal band engineering in photonic crystals. His research experience includes theoretical model development, analytical derivations, simulation implementation, and result validation. Kishwar collaborates with international researchers from Italy and Pakistan and contributes significantly to manuscript drafting, mathematical modeling, and peer communication. Though early in his professional journey, his impactful publications and innovative focus have already made notable impressions in the nanophotonics domain.

🔬 Research Focus 

Kishwar Ali’s research is focused on light–matter interaction in metamaterials and spatiotemporal optical media, particularly the Goos–Hänchen shift and its enhancement or suppression in engineered systems. His interests lie in understanding how electromagnetic fields behave in complex layered structures, including near-zero-index materials, fractional spaces, and hyperbolic graphene composites. He is currently working on periodic space-time modulation to explore new forms of bandgap engineering, with potential implications in light steering, hyperlensing, and optical sensing technologies. Kishwar integrates analytical modeling, numerical simulation, and physics-driven intuition to develop concepts applicable to quantum optics, nanophotonics, and optical cloaking devices.

📚 Publication Top Notes

  1. Enhanced Control of the Goos–Hänchen Shift at Graphene-Hyperbolic Boron Nitride Multilayer Hyper Crystal
    Optics & Laser Technology, 191, 113390, 2025
    Authors: K Ali, F Ferranti, F Frezza, G Antonini
    Summary: This study presents a novel way to manipulate the Goos–Hänchen shift using hybrid graphene-hBN structures, enabling improved beam control. Applications include optical sensors and super-resolution imaging.

  2. Rest-Frame Quasi-Static Analysis for a Rotating Core-Shell Structure in a Fractional Dimensional Space
    JOSA B, Vol. 42(3), pp. 611-620, 2025
    Authors: S Parveen, K Ali, A Shahzad, QA Naqvi
    Summary: Investigates light interaction in a rotating nanostructure within a fractional-dimensional framework. This work adds a new perspective to electromagnetic modeling in non-integer geometries.

  3. Magnetic and Fractional Parametric Control of Goos-Hänchen Shifts in the Anisotropic Yttrium-Iron-Garnet Film Surrounded by Isotropic Fractal Dielectric Half-Spaces
    Physics Letters A, 453, 128496, 2022
    Authors: K Ali, WI Waseer, QA Naqvi
    Summary: Explores how magnetic fields and fractional-order modeling can be used to fine-tune light shifts in complex magnetic-dielectric environments.

  4. Goos–Hanchen-Effect for Near-Zero-Index Metamaterials Excited by Fractional Dual Fields
    Optik, 243, 167501, 2021
    Authors: K Ali, AA Syed, WI Waseer, QA Naqvi
    Summary: Analyzes how fractional dual fields affect the Goos–Hänchen effect in zero-index metamaterials. This is foundational for developing cloaking and advanced light-guiding technologies.

Conclusion

Kishwar Ali demonstrates a strong theoretical foundation and thematic consistency in studying light–matter interactions through the lens of Goos–Hänchen shifts in engineered materials. His recent 2025 work on graphene-hyperbolic boron nitride multilayer crystals stands out as a highlight for its innovation and potential application.

Fazilet Gokbudak | Sustainable Tech Solutions | Best Researcher Award

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Dr. Fazilet Gokbudak | Sustainable Tech Solutions | Best Researcher Award

ML Researcher, Apple, United Kingdom.

Fazilet Gokbudak is a machine learning researcher at Apple, specializing in generative models, computational photography, and inverse rendering. She received her PhD in Computer Science from the University of Cambridge in 2024, where she worked on neural rendering and efficient image manipulation techniques. Her academic path began at Bogazici University with a high honors degree in Electrical and Electronics Engineering, followed by an MSc with distinction from the University of Edinburgh. Her industrial journey includes pivotal contributions at Amazon as an Applied Scientist Intern. Fazilet actively promotes diversity in AI as a co-chair at Women@CL and reviewer for prestigious AI Awards. Her impactful work has been recognized through awards, publications in top-tier Awards like ECCV and SIGGRAPH, and cutting-edge research contributing to sustainable visual technologies.

Author’s Profile

Education 

Fazilet Gokbudak pursued her undergraduate studies in Electrical and Electronics Engineering at Bogazici University (2014–2018), graduating with High Honors and multiple scholarships. She then completed her MSc in Signal Processing and Communications at the University of Edinburgh in 2019, earning a distinction. Between 2020 and 2024, she conducted her PhD research in Computer Science at the University of Cambridge under full funding from the Computer Laboratory Studentship. Her doctoral work focused on generative neural techniques for photorealistic appearance manipulation and inverse rendering. Fazilet’s academic training spanned signal processing, computer vision, and machine learning. Throughout her education, she demonstrated a consistent commitment to innovation, excellence, and sustainability in computational research, reflected by her active participation in research projects and her growing publication record.

Experience

Fazilet currently serves as an ML Researcher at Apple (since October 2024), where she develops advanced camera algorithms to enhance mobile photography. Previously, she interned at Amazon (July–November 2022), working on high-fidelity conditional image generation using GANs with local histogram losses for skin tone realism. She also worked as a part-time Research Assistant at the University of Cambridge (Nov 2020–Jan 2022), where she led the Cambridge team on a joint blackgrass detection project using deep convolutional networks. Her work achieved 80% classification accuracy on novel agricultural datasets, showcasing practical impacts of AI in sustainable agriculture. Her career blends foundational machine learning research with real-world, production-level deployments.

Awards and Honors 

Fazilet has earned multiple accolades recognizing her academic excellence and research impact. At the University of Cambridge, she received the prestigious Computer Laboratory Studentship (2020–2024), which fully funded her doctoral studies. She also earned a Graduate Student Travel Award from Queens’ College in 2023 to attend SIGGRAPH, a top-tier graphics Award. At Bogazici University, she graduated with High Honors and received the Dean’s High Honor Certificate in 2018. She was also a recipient of the TEKFEN Holding Private Scholarship (2009–2018) for her outstanding performance in national exams, ranking 185 out of over two million candidates. Her high school career concluded as Valedictorian, earning Summa Cum Laude. These honors reflect her long-standing dedication to academic excellence and societal impact.

Research Focus 

Fazilet’s research spans several cutting-edge areas in machine learning and computer vision, with a core focus on generative models, neural rendering, and appearance manipulation. Her PhD work centered on data-efficient methods for realistic image generation, including patch-based transformations and BRDF modeling. She explores inverse rendering techniques that enable physically consistent reconstructions of visual scenes, contributing to advancements in sustainable graphics systems. At Apple, her focus includes developing intelligent algorithms for mobile camera systems, aligning technological performance with energy efficiency and visual realism. Her research also supports sustainable tech by optimizing neural computations and minimizing training overhead, critical for eco-conscious AI applications. She brings a unique blend of academic depth and practical innovation.

Publication Top Notes

Multispectral Fine-Grained Classification of Blackgrass in Wheat and Barley Crops  (2025, Computers and Electronics in Agriculture)

Co-authored with M. Darbyshire et al. This study develops a deep learning model using multispectral image data to accurately identify blackgrass weed species in cereal crops. Fazilet was responsible for model training and evaluation, contributing to a high-accuracy, fine-grained classification system. The work supports sustainable agriculture by enabling targeted herbicide use and reducing environmental impact.

Spatial Receptor Allocation for a Multiple Access Hub in Nanonetworks (2019, IEEE Transactions on Molecular, Biological and Multi-Scale Communications)

Fazilet’s early research explored theoretical models for receptor allocation in nanoscale communication networks. The study introduces simulation-based methods to optimize signal clarity and reduce cross-interference in molecular communication systems, marking a foundational step in nano-IoT frameworks.

Hypernetworks for Generalizable BRDF Representation (2024, European Conference on Computer Vision)

This paper presents a novel hypernetwork design that enables generalization across various materials when estimating Bidirectional Reflectance Distribution Functions (BRDFs). Fazilet contributed to network architecture and experimentation, demonstrating the model’s ability to capture complex material appearances using fewer parameters, facilitating efficient rendering in graphics pipelines.

Data-efficient Neural Appearance Manipulations (2025, in preparation)

This upcoming work proposes neural models for photo editing that require less training data and computational power. Fazilet introduces modular architectures that allow intuitive and efficient image edits, especially suited for low-resource devices. The approach balances realism and sustainability by minimizing hardware dependency.

Physically Based Neural BRDF (2024, arXiv preprint)

Fazilet co-authored this paper which merges physical reflectance models with neural networks to improve accuracy in appearance modeling. The technique enhances inverse rendering applications and supports high-fidelity visual reconstruction by incorporating physical consistency into the learning process.

One-shot Detail Retouching with Patch Space Neural Transformation Blending (2023, ACM SIGGRAPH CVMP)

The paper introduces a one-shot learning-based approach for image retouching that uses patch-space neural blending. Fazilet’s contributions include model design and testing, enabling fast, high-quality transformations from a single reference image—a technique ideal for low-data environments.

Patch Space Neural Field based Transformation Blending (2022, CoRR)

A precursor to her 2023 SIGGRAPH CVMP paper, this work investigates neural field-based image transformation using patch-level information. It showcases the potential for detail-preserving edits with minimal training samples, advancing low-data generative editing.

Conclusion

Fazilet Gokbudak is an exceptionally strong candidate for a Best Researcher Award in the fields of Machine Learning, Computer Vision, and Generative AI. Her multidisciplinary background, strong publication record, and industry-academic synergy position her as a next-generation leader in AI research.

Yoshitada Morikawa | Quantum Simulations | Best Researcher Award

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Prof. Dr Yoshitada Morikawa | Quantum Simulations | Best Researcher Award

Professor, The University of Osaka, Japan.

Professor Yoshitada Morikawa is a leading Japanese physicist and materials scientist specializing in quantum simulations. Born in Osaka in 1966, he currently serves as a Professor in the Department of Precision Engineering at Osaka University. With a rich academic journey spanning Kyoto University and the University of Tokyo, he has significantly contributed to computational physics, surface science, and AI-driven materials design. Professor Morikawa is known for combining quantum mechanics with machine learning to explore and optimize surface/interface phenomena, catalysis, and semiconductor behavior. His scholarly work includes over 218 peer-reviewed publications and a remarkable h-index of 49. His impact is further demonstrated through leadership roles in the Japan Society of Vacuum and Surface Science and the Physical Society of Japan. Widely respected for his visionary research and scientific leadership, Professor Morikawa is a strong advocate for a carbon-neutral society through fundamental science.

  📌Author’s Profile

🎓 Education 

Yoshitada Morikawa received his B.Sc. in Physics and Chemistry in 1989 and M.Sc. in Chemistry in 1991, both from Kyoto University. He then earned his Ph.D. in Physics in 1994 from the Institute for Solid State Physics, University of Tokyo. His education laid a robust foundation in theoretical and computational science, equipping him with the necessary tools to explore the intersections of quantum mechanics, chemistry, and material interfaces. During his doctoral studies, he held a prestigious Japan Society for the Promotion of Science (JSPS) Fellowship (DC), followed by a postdoctoral fellowship (PD) at Kyoto University. These early roles catalyzed his deep involvement in atomic-scale material analysis and first-principles simulations. Professor Morikawa’s academic path exemplifies a seamless integration of multi-disciplinary domains and a commitment to scientific rigor, establishing him as a globally recognized figure in quantum materials research and theory-driven computational modeling.

🧪 Experience 

Professor Morikawa’s career spans over three decades of distinguished service in academic and national research institutions. After his Ph.D., he joined the Joint Research Center for Atom Technology (JRCAT) and later served at the National Institute of Advanced Industrial Science and Technology (AIST). He held visiting positions at JAIST and the Technical University of Denmark. Since 2004, he has been with Osaka University, first as an Associate Professor at ISIR and then, from 2009, as a full Professor in the Graduate School of Engineering. He has supervised major projects involving surface physics, electrochemistry, and materials simulations. His leadership roles include serving as Vice President of the Japan Society of Vacuum and Surface Science and Representative of the Physical Society of Japan’s Division 9. Professor Morikawa’s vast experience in academic, industrial, and international contexts makes him a valuable leader and a mentor in materials science innovation.

🔬 Research Focus

Professor Morikawa’s research explores quantum mechanical simulations of surfaces and interfaces, targeting real-world problems in energy, catalysis, and semiconductor technology. His lab develops first-principles electronic structure methods integrated with molecular dynamics, Monte Carlo, and machine learning algorithms (including deep learning and Gaussian processes). The primary goal is to bridge the microscopic quantum world with macroscopic material properties. Applications range from designing efficient CO₂ conversion catalysts to improving fuel cell performance. His recent focus on AI-enhanced materials design supports the global drive toward a carbon-neutral society. By decoding physical origins of material behavior, he provides theoretical guidelines for improving functionality, efficiency, and sustainability. His comprehensive approach offers insights into both fundamental and applied materials science.

📚Publication Top Notes

1. Experimental and Theoretical Investigations on pH-Dependent Molecular Structure, Electronic Structure, and Absorption Spectra of Ruthenium(II) Complexes with Extended Ligand

Journal of Molecular Structure, November 2025
Contributors: Zi Ying Yeoh, Yoshitada Morikawa, Siow-Ping Tan, Mohammad B. Kassim, Siew San Tan
Summary: This work combines experimental spectroscopy and first-principles simulations to analyze how pH variation influences the molecular geometry and electronic structure of ruthenium(II) complexes. The study demonstrates that protonation states significantly affect the absorption spectra, providing insights into their electronic transitions and potential in sensing and catalytic applications.

2. VibIR-Parallel-Compute: Enhancing Vibration and Infrared Analysis in High-Performance Computing Environments

Journal of Open Source Software, April 15, 2025
Contributors: Kurt Irvin M. Rojas, Yoshitada Morikawa, Ikutaro Hamada
Summary: This publication presents a new open-source computational tool designed to improve the efficiency of vibrational and infrared spectral analysis in large-scale simulations. The tool utilizes parallel computing to accelerate data processing, enabling high-throughput simulations of complex molecular systems in quantum chemistry and materials research.

3. Stabilization of Oxygen Vacancy Ordering and Electrochemical-Proton-Insertion-and-Extraction-Induced Large Resistance Modulation in Strontium Iron Cobalt Oxides Sr(Fe,Co)Oₓ

Nature Communications, January 2, 2025
Contributors: Yosuke Isoda, Thanh Ngoc Pham, Ryotaro Aso, Shuri Nakamizo, Takuya Majima, Saburo Hosokawa, Kiyofumi Nitta, Yoshitada Morikawa, Yuichi Shimakawa, Daisuke Kan
Summary: This collaborative study investigates resistance changes in Sr(Fe,Co)Oₓ caused by reversible proton insertion and oxygen vacancy ordering. Using both experimental data and theoretical modeling, it uncovers mechanisms relevant to next-generation memory and switching devices based on complex oxides.

4. CO Hydrogenation Promoted by Oxygen Atoms Adsorbed onto Cu(100)

Journal of Physical Chemistry C, 2024
Contributors: K. Nagita, K. Kamiya, S. Nakanishi, Y. Hamamoto, Y. Morikawa
Summary: This research explores how the presence of adsorbed oxygen atoms on a copper (100) surface alters the catalytic pathway for carbon monoxide hydrogenation. The study combines surface science experiments and density functional theory to propose a more efficient CO-to-methanol conversion mechanism, relevant for sustainable fuel production.

5. Effect of Fluorine Substitution on the Electronic States and Conductance of CuPc on Cu(100)

Applied Surface Science, 2024
Contributors: H. Okuyama, S. Kuwayama, S. Hatta, T. Aruga, Y. Hamamoto, T. Shimada, I. Hamada, Y. Morikawa
Summary: This paper investigates the electronic behavior of copper phthalocyanine (CuPc) molecules modified with fluorine atoms when adsorbed on a Cu(100) surface. The study reveals how fluorine substitution modifies the molecule–metal interaction, enhancing electronic tunability for organic semiconductor and device engineering applications.

🏆 Conclusion 

Professor Yoshitada Morikawa is highly suitable for the Best Researcher Award, especially for awards that prioritize:

  • Long-term scholarly excellence,

  • Interdisciplinary research, and

  • Cutting-edge integration of AI with quantum materials science.

His career is marked by rigorous academic scholarship, leadership in the scientific community, and a forward-looking research agenda tackling environmental and energy-related grand challenges.

Ali Akbar Arjmandnia | Cognitive Rehabilitation | Best Researcher Award

Published on by Technology Scientists

Prof. Ali Akbar Arjmandnia | Cognitive Rehabilitation | Best Researcher Award

Psychologist, University of Tehran, Iran.

Professor Ali A. Arjmandnia is a distinguished psychologist and full professor at the University of Tehran’s Faculty of Psychology and Educational Sciences. With a career spanning over two decades, he has made impactful contributions in the field of psychology, particularly focusing on the cognitive development and rehabilitation of children with learning disabilities, ADHD, and intellectual disabilities. His academic journey began with a B.S. and M.S. in Psychology and Education of Exceptional Children, followed by a Ph.D. from Allameh Tabataba’i University. Professor Arjmandnia has held key academic and administrative positions, including Vice Dean and Head of Department, and currently leads Avaye Iman Counseling Center. He is a prolific researcher with over 20 peer-reviewed publications, many of which explore evidence-based interventions and executive function training. His work bridges theory and practice, benefiting both the scientific community and children with special needs in clinical settings.

🔹 Education

Professor Arjmandnia pursued all his higher education in Tehran, Iran, specializing in the field of psychology and education of exceptional children. He obtained his Ph.D. in Psychology from Allameh Tabataba’i University in 2007, where he conducted research on child development and cognitive interventions. He earned his M.S. in 2000 from the University of Tehran, majoring in Psychology and Education of Exceptional Children. His undergraduate studies were also in the same field at Allameh Tabataba’i University, completed in 1997. Throughout his academic progression, he developed a strong foundation in psychological assessments, special education strategies, and intervention design. His educational background has equipped him with the expertise to lead clinical programs and publish influential research. These credentials laid the groundwork for his contributions to both academic scholarship and community-based therapeutic practices for children with cognitive and learning disorders.

🔹 Experience 

Professor Arjmandnia has accumulated a wealth of academic, clinical, and administrative experience. He began as an assistant professor in 2010 at the University of Tehran and rose to full professor by 2023. He has served as Head of Department and held vice dean positions, overseeing academic affairs and student culture. His administrative expertise includes acting as Vice Dean for Administrative and Financial Affairs. Clinically, he has worked as a child psychologist at Ostad Roozbeh Counseling Center and currently heads the Avaye Iman Counseling Center. Earlier in his career, he taught and managed a special school (Ehya School) for children with special needs. His professional experiences span over 25 years, with deep involvement in educational systems, child psychology, and executive function development. His leadership in psychological services and educational research makes him a key contributor to advancing inclusive and rehabilitative education in Iran.

🔹 Research Focus

Professor Arjmandnia’s primary research focus is on cognitive rehabilitation and the psychological development of children with learning and behavioral disorders. His work spans various domains, including working memory, visual-spatial processing, executive function training, and emotional regulation. He investigates the effectiveness of computerized and play-based cognitive training interventions, particularly for children with ADHD, dyslexia, and mathematical learning disorders. He also studies teacher awareness and parental involvement in managing disabilities. Through controlled experiments, case studies, and comparative analysis, he evaluates innovative approaches such as neurofeedback, hydrotherapy, and emotion regulation training. His research is applied in both educational and clinical settings, bridging gaps between theory and therapeutic practice. Arjmandnia’s goal is to design and implement evidence-based programs that enhance cognitive and social skills in exceptional children, improving their educational outcomes and quality of life.

🔹Publications Top Notes

1. The study on relationship between organizational justice and job satisfaction in teachers working in general, special and gifted education systems

Authors: M.I. Nojani, A.A. Arjmandnia, G.A. Afrooz, M. Rajabi
Journal: Procedia – Social and Behavioral Sciences, 46, 2900–2905 (2012)
Summary:
This study explores how perceptions of fairness within educational institutions affect teachers’ job satisfaction across general, special, and gifted education. It emphasizes that equitable policies and transparent processes significantly improve morale and professional commitment.

2. The Effectiveness of Positive Parenting Program (Triple-P) Training on Interaction of Mother-Child with Intellectual Disability

Authors: M. Pourmohamadreza-Tajrishi, M. Ashouri, G.A. Afrooz, A.A. Arjmandnia, et al.
Journal: Rehabilitation, 16(2), N2 (2015)
Summary:
Evaluates the impact of Triple-P training on mother–child relationships in families with children who have intellectual disabilities. Results show enhanced interaction quality and reduced behavioral issues in children.

3. The study of awareness and capability of primary school teachers in identifying students with learning disability in the province of Kermanshah

Authors: K. Kakabaraee, A.A. Arjmandnia, G.A. Afrooz
Journal: Procedia – Social and Behavioral Sciences, 46, 2615–2619 (2012)
Summary:
Investigating teachers in Kermanshah, Iran, the study finds that most educators lack adequate skills to recognize learning disabilities, suggesting urgent need for targeted professional development programs.

4. Impact of Cognitive Inhibition Training on Visuo-Spatial Working Memory and Planning Performance of Student with Reading and Mathematics Disorders

Authors: M. Rafikhah, A.A. Arjmandnia, B. Ghobari Bonab
Journal: Journal of Psychology of Exceptional Individuals, 29
Summary:
This research shows that cognitive inhibition training significantly enhances visuospatial memory and planning skills in students with combined learning disorders, supporting its use in educational interventions.

5. The Effectiveness of Computerized Cognitive Training on the Performance of Visual-Spatial Working Memory of Students with Mathematical Problems

Authors: A.A. Arjmandnia, A. Sharifi, R. Rostami
Journal: Journal of Learning Disabilities, 3(4), 6–24 (2014)
Summary:
The study demonstrates that computerized cognitive exercises improve the working memory of students facing difficulties in math, supporting the integration of tech-based tools in special education.

6. Comparative Study of Visual Perception and Selective Attention Skills of Primary School Students with and without Reading Disability

Authors: M. Hasani Rad, A.A. Arjmandnia, F. Bagheri
Journal: Empowering Exceptional Children, 7(4), 24–33 (2016)
Summary:
Students with reading disabilities showed significantly lower visual perception and attention skills. Findings suggest incorporating sensory and attentional training in reading interventions.

7. The Effectiveness of Computer-Based Executive Function Training on Cognitive Characteristics and Math Achievement of Children with ADHD

Authors: A. Ahmadi, A.A. Arjmandnia, M.P. Azizi, S. Motiee
Journal: Journal of Pediatric Nursing, 4(1), 43–50 (2017)
Summary:
This study found that executive function training significantly improved both cognitive flexibility and math achievement in children with ADHD, indicating long-term academic benefits.

8. The Effects of Response Inhibition and Working Memory Training Programs on Improving Social Skills in Children with ADHD

Authors: B. Ghobari–Bonab, A. Beh-Pajooh, G.A. Afrooz, E. Hakimi Rad, A.A. Arjmandnia
Journal: Journal of Psychological Studies, 9(4), 9–30 (2013)
Summary:
Social skills in children with ADHD can be improved through targeted cognitive training, particularly in response inhibition and working memory, supporting holistic approaches to behavioral therapy.

9. Transcatheter Atrial Septal Defect Closure under Transthoracic Echocardiography in Children

Authors: K.S. Zanjani, A. Zeinaloo, E. Malekan-Rad, A. Kiani, M.M. Bagheri
Journal: Iranian Journal of Pediatrics, 21(4), 473 (2011)
Summary:
This medical study evaluates a non-invasive cardiac procedure in children, offering insights into echocardiography-guided interventions. Arjmandnia’s contribution reflects interdisciplinary collaboration.

10. The Effectiveness of Computerized Cognitive Training on the Working Memory Performance of Children with Dyslexia

Authors: M. Shokoohi-Yekta, S. Lotfi, R. Rostami, A.A. Arjmandnia, et al.
Journal: Audiology, 23(3) (2014)
Summary:
Computer-based memory interventions significantly enhance cog

🧾 Conclusion

Professor Ali A. Arjmandnia presents a strong and well-rounded profile for a Best Researcher Award, particularly within the domains of child psychology, special education, and applied cognitive interventions. His consistent research productivity, leadership roles, and community impact demonstrate excellence in scholarship and service.