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 conferences. Her impactful work has been recognized through awards, publications in top-tier conferences 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 conference. 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

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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.