Naveed Ahmed | Technology Scientists Innovations | Best Researcher Award

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Assist. Prof. Dr. Naveed Ahmed | Technology Scientists Innovations | Best Researcher Award

Assistant Professor at University of Tabuk in Saudi Arabia.

Dr. Naveed Ahmed is a distinguished scientist in Medical Microbiology whose research seamlessly blends laboratory science with clinical impact. Currently serving as Assistant Professor at the University of Tabuk, Saudi Arabia, he earned his Ph.D. from Universiti Sains Malaysia, where he was recognized for academic excellence and timely graduation. His work spans infectious disease diagnostics, antimicrobial resistance mechanisms, nanomedicine applications, and computational vaccine design. With over 46 Q1/Q2 publications, an H-index of 23, Dr. Ahmed has contributed to global health datasets and collaborative studies published in top-tier journals such as The Lancet. His innovations include patented laboratory protocols for microbial diagnostics and immune profiling. Known for his capacity to integrate molecular methods, bioinformatics, and translational science, Dr. Ahmed’s career reflects both depth of expertise and breadth of interdisciplinary collaboration, making him a prominent figure in the global fight against infectious diseases.

Professional Profile

Scopus | Google Scholar | ORCID

Education 

Dr. Ahmed holds a Doctor of Philosophy in Medical Microbiology from Universiti Sains Malaysia. His doctoral research, supported by competitive scholarships and awards, focused on molecular pathogenesis of Epstein–Barr Virus-associated cancers and immune checkpoint modulation. Prior to his Ph.D., he earned a Master of Science in Microbiology from the University of Central Punjab, Pakistan, where he developed expertise in bacteriology, immunology, and clinical diagnostics. His academic journey began with a BS (Honors) in Medical Laboratory Technology from the University of the Punjab, Pakistan, where he cultivated laboratory proficiency and research skills. Throughout his education, Dr. Ahmed actively engaged in research projects, academic presentations, and interdisciplinary collaborations, laying a foundation for high-impact publications and translational innovations. This diverse and rigorous educational background enables him to tackle complex biomedical challenges through both experimental and computational approaches.

Experience 

Dr. Ahmed’s professional trajectory blends academic teaching, laboratory management, and high-impact research. As Assistant Professor at the University of Tabuk, he teaches undergraduate and diploma-level courses, designs curricula, and fosters research collaborations with international teams. Previously, as a Graduate Research Assistant at Universiti Sains Malaysia, he managed grant-funded projects, secured ethical clearances, coordinated multi-institutional studies, and delivered results published in Q1/Q2 journals. Earlier roles as Laboratory Technologist at the Pakistan Kidney and Liver Institute and as Microbiology Supervisor at Chughtai Lab honed his expertise in clinical diagnostics, antimicrobial stewardship, biosafety, and ISO 15189 implementation. His teaching experience includes visiting lectureships at the University of Central Punjab and Imperial College of Business Studies. Across all roles, Dr. Ahmed has demonstrated leadership in laboratory innovation, research project management, and academic mentorship, ensuring his contributions extend from the bench to the classroom and into public health policy.

Research Focus 

Dr. Ahmed’s research focuses on the intersection of microbial pathogenesis, diagnostics, and therapeutic innovation. His investigations into antimicrobial resistance encompass genetic profiling of multidrug-resistant pathogens, elucidating resistance mechanisms induced by heavy metal exposure, and identifying virulence factors in hospital-acquired infections. In virology, he has advanced understanding of Epstein–Barr Virus latency genes and their role in immune checkpoint regulation, with implications for immunotherapy. He also explores nanomedicine, developing carbon-based nanomaterials and bioactive microbial compounds as diagnostic and therapeutic agents against cancer. His computational vaccine design projects leverage immunoinformatics to engineer multi-epitope vaccines targeting high-burden pathogens. Additionally, Dr. Ahmed contributes to global health surveillance datasets, applying systematic review and meta-analysis methods to epidemiological trends. His integrative approach combines molecular biology, bioinformatics, and translational science, aiming to bridge laboratory research with deployable healthcare solutions that address both infectious diseases and oncology in resource-diverse settings.

Awards & Honors 

Dr. Ahmed’s achievements are recognized through numerous competitive awards. He received the Graduate on Time Award (2024) and was nominated for the Best Ph.D. Thesis Award at Universiti Sains Malaysia. His presentation skills earned him 2nd place and the Young Investigator Award at the 9th Regional Conference on Molecular Medicine (2023). He twice won the prestigious Sanggar Sanjung Award (2021, 2022) for best publication-based research among USM students and was recognized as Best Oral Presenter in the departmental journal club (2022). Early in his career, he won Best Poster Presentation at the Annual Conference of Medical Microbiology and Infectious Diseases Society of Pakistan (2020). His research funding success includes grants from the Malaysian Ministry of Higher Education and industry collaborations with Medical Innovation Ventures. Combined with international fellowships and professional memberships, these honors underscore his sustained excellence in research, innovation, and scholarly dissemination.

Publication Top Notes

Title: The Microbial Sources of Bioactive Compounds: Potential Anticancer Therapeutic Options
Authors: Ahmed, N., Abusalah, M. A. H. A., Absar, M., Nasir, M. H., Farzand, A., Ahmad, I., Sohail, Z., Singh, K. K. B., Baig, A. A., & Yean, C. Y.
Journal: Nano Life, Vol. 15, 2430007.
Summary: Microbial metabolites from bacteria and fungi were isolated, characterized, and screened for anticancer activity. Several showed high selectivity and strong molecular target binding, offering sustainable leads for oncology drug development.

Title: Carbon-based Nanomaterials as Multifunctional Particles for Cancer Diagnosis and Treatment
Authors: Ahmed, N., Abusalah, M. A. H. A., Absar, M., Noor, M. S., Bukhari, B., Anjum, S. A., Singh, K. K. B., & Yean, C. Y.
Journal: Nano Life, Vol. 15, 2430005.
Summary: Graphene oxide, carbon nanotubes, and fullerenes were functionalized for targeted cancer imaging and therapy. They enabled enhanced tumor visualization, sustained drug release, and effective photothermal/photodynamic treatment, advancing nanotheranostic applications.

Title: Immunoinformatic Execution and Design of an Anti–Epstein–Barr Virus Vaccine with Multiple Epitopes Triggering Innate and Adaptive Immune Responses
Authors: Ahmed, N., Rabaan, A. A., Alwashmi, A. S., et al.
Journal: Microorganisms, Vol. 11, 2448.
Summary: A computational pipeline identified epitopes from EBV latent and lytic proteins, modeled their MHC binding, and simulated strong immune responses. Codon optimization suggested efficient bacterial expression, supporting rapid vaccine prototyping.

Title: Heavy Metal (Arsenic) Induced Antibiotic Resistance among Extended-Spectrum β-Lactamase (ESBL) Producing Bacteria of Nosocomial Origin
Authors: Ahmed, N., Tahir, K., Aslam, S., et al.
Journal: Pharmaceuticals, Vol. 15, 1426.
Summary: Arsenic in hospital effluents was linked to co-selection of plasmid-borne ESBL and arsenic resistance genes. This co-resistance highlights environmental drivers of antimicrobial resistance and the need for better wastewater control.

Title: Updates on Epstein–Barr Virus (EBV)-Associated Nasopharyngeal Carcinoma: Emphasis on the Latent Gene Products of EBV
Authors: Ahmed, N., Abusalah, M. A. H. A., Farzand, A., Absar, M., Yusof, N. Y., Rabaan, A. A., et al.
Journal: Medicina, Vol. 59, Issue 2.
Summary: This review outlines how EBV latent proteins like LMP1 and EBNA1 drive oncogenesis, evade immunity, and present therapeutic targets, emphasizing potential immunotherapy approaches for endemic regions.

Title: The Antimicrobial Efficacy against Selective Oral Microbes, Antioxidant Activity and Preliminary Phytochemical Screening of Zingiber officinale
Authors: Ahmed, N., Karobari, M. I., Yousaf, A., et al.
Journal: Infection and Drug Resistance,pp. 2773–2785.
Summary: Methanolic and aqueous ginger extracts inhibited oral pathogens and showed strong antioxidant activity linked to high phenolic and flavonoid content, supporting its use in oral health products.

Title: Antibiotic Resistance Profile in Relation to Virulence Genes fimH, hlyA and usp of Uropathogenic E. coli Isolates in Lahore, Pakistan
Authors: Ahmed, N., Zeshan, B., Naveed, M., et al.
Journal: Tropical Biomedicine, Vol. 36, pp. 559–568.
Summary:In clinical isolates, fimH and hlyA genes correlated with multidrug resistance. The findings stress the dual risk of resistance and virulence in urinary tract infections.

Conclusion

Dr. Naveed Ahmed possesses the academic excellence, research productivity, and global engagement expected of a Best Researcher Award recipient. His combination of high-impact publications, patents, conference recognition, and international collaborations demonstrates a clear commitment to advancing knowledge and innovation in medical microbiology and infectious diseases. With continued emphasis on leadership in large-scale research initiatives and translational impact, he is exceptionally well-suited for this award and has strong potential to contribute even more significantly to the scientific community in the future.

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.