Zaka Ur Rehman | Deep Learning and Medical Imaging | Best Researcher Award

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Dr. Zaka Ur Rehman | Deep Learning and Medical Imaging | Best Researcher Award

Postdoctoral Researcher at Multimedia University, Malaysia

Zaka Ur Rehman is a dedicated AI researcher specializing in digital pathology and biomedical image analysis. Currently based in Cyberjaya, Malaysia, he is pursuing a Ph.D. in Engineering at Multimedia University with a research focus on machine learning, deep learning, and data analysis. His professional journey encompasses teaching, advanced algorithm development, and medical image interpretation. With over five years of academic and industry experience, Zaka has demonstrated a strong commitment to AI research, especially in medical diagnostics. His expertise spans the use of CNNs, Vision Transformers, and self-supervised learning to solve real-world healthcare problems. He is the author of several impactful publications in top-tier journals and has presented his work at esteemed international conferences. Beyond his research contributions, Zaka actively engages in workshops and training sessions to promote scientific communication and technical writing. He is also known for his involvement in various academic collaborations and capacity-building programs. With a cumulative journal impact factor of over 17, his work significantly advances the field of computational pathology. Zaka is fluent in English and Urdu, skilled in programming, and passionate about knowledge dissemination. His dedication and technical acumen make him a valuable contributor to AI-based healthcare innovation.

Profile

Google Scholar

Education

Zaka Ur Rehman’s academic path reflects a progressive journey into specialized domains of engineering and artificial intelligence. He is currently enrolled in a Ph.D. program at Multimedia University, Malaysia (2021–2025), focusing on digital pathology and AI. His CGPA of 3.8/4.0 is a testament to his academic rigor. Prior to this, he completed his M.S. in Electrical Engineering from COMSATS University, Islamabad, Pakistan (2015–2018), with a specialization in biomedical image processing and a CGPA of 3.51/4.0. His master’s thesis centered on brain tumor segmentation using machine learning techniques. Zaka holds a B.Sc. in Computer Systems Engineering from The Islamia University of Bahawalpur, Pakistan (2010–2014), where he explored networks, image processing, and graphics. Earlier academic milestones include his F.Sc. in Pre-Engineering (75%) and Matriculation in Science (78.5%), highlighting consistent excellence in mathematics, physics, and chemistry. His educational foundation is robustly interdisciplinary, bridging computer systems, electrical engineering, and artificial intelligence. With strong theoretical grounding and practical implementation, Zaka’s education has prepared him to tackle complex biomedical challenges through computational means, especially within healthcare imaging. His academic progression aligns seamlessly with his current research on computational histopathology and deep learning, setting a solid stage for his scholarly and professional pursuits.

Professional Experience

Zaka Ur Rehman’s professional background is diverse and rooted in both academia and industry. He currently serves as a Graduate Research Assistant at Multimedia University, Malaysia (2021–2024), where he leads initiatives in AI-driven digital pathology under the Faculty of Engineering. Previously, from 2018 to 2021, he worked as a Lecturer at the University of Lahore, Gujrat Campus. There, he delivered computer science courses, mentored final-year projects, and contributed to curriculum design and quality assurance processes. His earlier roles include working as a PM Youth Internee at Zarai Taraqiati Bank Ltd. (2016–2017), where he was recognized for outstanding performance in IT support, and as an IT Intern at HR Development Secretariat (2015), where he managed web portals and assisted with server administration. Additionally, he has hands-on teaching experience in core courses like machine learning, image processing, and digital logic design. His pedagogical strengths are complemented by practical insights from his industry stints. Throughout his career, Zaka has maintained a balance between instructional responsibilities and applied research. His ability to navigate both technical development and academic instruction positions him uniquely as a researcher-educator with a strong command over emerging technologies in AI and healthcare informatics.

Research Interest

Zaka Ur Rehman’s research interests lie at the intersection of artificial intelligence and biomedical imaging, with a particular emphasis on digital histopathology. His core focus includes the development of AI models for HER2-SISH/IHC analysis and computational biomarker quantification. He is deeply involved in solving complex problems related to nuclei segmentation, stain normalization, and tumor localization. Zaka’s work leverages deep learning architectures such as convolutional neural networks (CNNs), vision transformers, and attention mechanisms. He is also interested in self-supervised learning for applications in computational pathology. Additional focus areas include retinal fundus analysis, optic disk localization, and facial recognition systems. Notably, his research contributions in superpixel-based segmentation and brain tumor detection have been recognized in everal peer-reviewed publications. His passion for merging healthcare with computer vision continues to drive his investigation into AI-based clinical diagnostic tools. Zaka’s innovative research addresses critical gaps in medical image analysis and enhances the potential for AI to assist in disease detection and treatment planning. His scholarly activities reflect a commitment to pushing the boundaries of AI in healthcare, particularly in pathology, where precision and automation are essential for improved patient outcomes.

Research Skills

Zaka Ur Rehman possesses a rich blend of research and technical skills critical for modern AI-driven healthcare innovation. His core competencies include supervised and unsupervised learning, feature extraction, classification algorithms, and biomedical image segmentation. He is proficient in using scientific tools such as TensorFlow, Keras, MIPAV, and LATEX for deep learning model development and documentation. Zaka is well-versed in programming languages like Python and MATLAB, with additional experience in OpenGL for graphical interfaces. His data analysis skills are evidenced by his handling of large-scale datasets—up to 200GB—for histopathological image processing. He has hands-on experience in creating and optimizing CNNs, vision transformers, and attention-based models for medical diagnostics. His research workflow includes data preprocessing, stain normalization, nuclei segmentation, and cancer-region detection from WSIs (Whole Slide Images). Zaka is also adept at technical communication, frequently conducting workshops and training sessions in scientific writing and LaTeX. His ability to link computational tools with clinical problems makes him a versatile researcher. His holistic skill set spans data handling, algorithm development, visualization, and publication—key components for success in AI-based medical research and interdisciplinary collaborations.

Awards and Honors

Zaka Ur Rehman’s scholarly excellence and leadership have been acknowledged through multiple awards and honors. In 2020, he received a prestigious Final Year Project (FYP) Grant Award worth RM 70,000, funded by IGNITE National Technology Fund under Pakistan’s Ministry of IT—a recognition of his innovative research contributions. Earlier in 2013, he was awarded “Best Student of the Semester” for achieving third position in his academic project within the Department of Computer Engineering at Islamia University Bahawalpur. His publication record boasts a cumulative journal impact factor of 17.53 as of 2018, reflecting his commitment to impactful and high-quality research. Zaka has also been invited to present at major international conferences such as NBEC 2023 and ISPACS 2022, underscoring his credibility in academic circles. His role as an instructor in LaTeX workshops, organized by institutions like the University of Lahore and HEC Pakistan, further testifies to his contributions toward community learning. These accolades highlight not only his technical excellence but also his dedication to academic mentorship, innovation, and scientific communication—hallmarks of a rising scholar in the field of AI and biomedical engineering.

Publications

Zaka Ur Rehman has an impressive publication record that underscores his expertise in computational pathology and AI applications in biomedical imaging. His peer-reviewed journal articles have appeared in reputable publications such as Expert Systems with Applications, Medical Hypotheses, Diagnostics, PeerJ Computer Science, and Cancers. His key works include studies on brain tumor segmentation, optic disc analysis, HER2 biomarker quantification, and stain normalization. Notable among them is his 2019 article on superpixel-based brain tumor segmentation and his 2024 work on deep learning-based HER2-SISH histopathology analysis. These publications are methodologically robust and have been widely cited, reflecting the scholarly impact of his research. He also contributed to conference proceedings at major international platforms like NBEC 2023 and ISPACS 2022. His research encompasses both theoretical model development and experimental validation using large histopathological datasets. Zaka’s publication strategy highlights a balanced focus on novelty, clinical relevance, and reproducibility. He collaborates with esteemed academics from Malaysia, Pakistan, and Saudi Arabia, adding to the global relevance of his research. Through consistent publication in high-impact venues, Zaka is steadily advancing the field of medical image computing and AI-driven diagnostics, positioning himself as a promising voice in academic and translational research.

Conclusion

Zaka Ur Rehman exemplifies a new generation of AI researchers dedicated to bridging technology and healthcare. With a strong academic foundation, practical teaching experience, and a focused research agenda, he has built an impactful profile in biomedical image analysis and digital pathology. His contributions to machine learning, particularly in cancer detection and biomarker quantification, stand out in today’s AI-driven medical landscape. He is skilled in cutting-edge tools and methodologies, fluent in technical communication, and actively involved in academic mentorship. The awards and recognitions he has received highlight his innovative thinking and academic excellence. His publications, often tackling clinically relevant problems, demonstrate both technical rigor and practical utility. Zaka’s multidisciplinary expertise and collaborative spirit are key strengths that will continue to fuel his success in academia and beyond. As he advances toward completing his Ph.D., his work holds great promise for transforming clinical diagnostics and healthcare delivery through intelligent systems. Zaka Ur Rehman is not just a researcher, but a visionary contributor whose work contributes meaningfully to the evolving field of computational medicine and AI.

Lahcen Tamym | AI in Healthcare | Best Researcher Award

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Assoc. Prof. Dr. Lahcen Tamym | AI in Healthcare | Best Researcher Award

Associate Professor at Jean Monnet University, France

Lahcen Tamym is a dynamic academic professional and researcher in the field of computer science, currently serving as an Assistant Professor in Industrial Engineering and Healthcare Systems Engineering at Jean Monnet University, Saint-Étienne, within the LASPI Laboratory. His academic and research journey is rooted in Big Data and Data Science, with a particular focus on sustainable, resilient, and intelligent networked enterprises. With a passion for innovation at the intersection of emerging technologies and socio-environmental goals, Dr. Tamym has developed advanced frameworks for optimizing supply chains, improving life cycle sustainability, and enhancing decision-making using Big Data Analytics (BDA), Machine Learning (ML), Internet of Things (IoT), and Blockchain technologies. His multidisciplinary work continues to advance smart industrial systems aligned with the Sustainable Development Goals (SDGs).

Profile

Scopus

Education

Lahcen Tamym began his academic path with a Bachelor’s degree in Mathematical and Computer Sciences from Ibn Zohr University in Morocco, where he developed optimization models using CPLEX and MINOS. He then pursued a Master’s degree in Intelligent and Decision Support Systems at Sidi Mohamed Ben Abdellah University, where his work focused on deep learning for graph representation. He earned his Ph.D. in Computer Science from Aix-Marseille University, France, and Université Moulay Ismail, Morocco. His doctoral research specialized in Big Data Analytics for managing flexible, robust, and sustainable networked enterprises. The study emphasized machine learning, predictive analytics, and optimization in supply chains and sustainable value creation, forming the foundation for his continuing contributions to sustainable industrial development.

Experience

Dr. Tamym’s professional trajectory includes teaching and research across several institutions in France and Morocco. Before his current faculty position at Jean Monnet University, he served as a Temporary Teaching and Research Assistant (ATER) at Aix-Marseille University. During this time, he was involved in both instructional duties and cutting-edge research in computer science and interaction. His earlier involvement in collaborative research at Laboratoire d’Informatique et Systèmes (LIS) in France and Laboratoire d’Informatique et Applications (IA) in Morocco provided him with diverse academic exposure and the opportunity to build multidisciplinary solutions addressing real-world challenges in industrial and healthcare domains.

Research Interest

Dr. Tamym’s research revolves around the application of advanced data-driven methods to enhance sustainability, flexibility, and resilience in networked enterprises. His areas of interest include Big Data Analytics, IoT, Blockchain, Machine Learning, and Decision Support Systems. He is particularly focused on sustainable supply chain management, life-cycle assessment, risk analysis, and social sustainability evaluation. He has also explored blockchain-based security models for IoT, financial fraud detection systems using deep learning, and natural language processing in educational and healthcare systems. By integrating these technologies, he aims to create intelligent, transparent, and adaptive networks capable of responding to dynamic global and industrial demands.

Award

Although specific awards are not listed, Dr. Tamym’s consistent involvement in prestigious conferences and publication in reputable journals underlines his recognition within the academic community. His work has been well-received at international forums such as the International Conference on Ambient Systems, Networks and Technologies, and IFAC conferences, reflecting the impact and value of his contributions. His research aligns with global agendas for sustainable industry and digital transformation, enhancing his profile as a leading researcher in his domain.

Publication

Dr. Tamym has published widely in top-tier journals and conferences. Notable publications include:

  1. Big Data Analytics-based life cycle sustainability assessment for sustainable manufacturing enterprises evaluation, Journal of Big Data (2023), cited for contributions to sustainable manufacturing assessment.

  2. Big data analytics-based approach for robust, flexible and sustainable collaborative networked enterprises, Advanced Engineering Informatics (2023), cited for its novel integration of resilience in supply chains.

  3. A big data based architecture for collaborative networks: Supply chains mixed-network, Computer Communications (2021), contributing to architecture modeling for collaborative systems.

  4. A Big Data Analytics-Based Methodology For Social Sustainability Impacts Evaluation, Procedia Computer Science (2023), a case-based analysis on social sustainability metrics.

  5. How Can Big Data Analytics and Artificial Intelligence Improve Networked Enterprises’s Sustainability?, IEEE ACDSA Conference (2024), exploring AI’s role in sustainable enterprise development.

  6. Distributed Deep Learning-Based Model for Financial Fraud Detection in Supply Chain Networks, ICICT 2024, addressing cybersecurity challenges in digital supply chains.

  7. The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey, Procedia Computer Science (2023), examining AI’s applications in personalized learning.

Conclusion

Lahcen Tamym stands at the forefront of interdisciplinary research, bridging data science and sustainable systems engineering. His academic contributions are rooted in practical application, ensuring that intelligent technologies directly impact the design and operation of industrial and healthcare systems. With a forward-looking vision aligned with Industry 5.0 principles and the SDGs, his research continues to influence the development of smart, ethical, and eco-efficient networks. Dr. Tamym’s commitment to fostering data-driven innovation across domains positions him as a transformative figure in the evolving landscape of sustainable and resilient enterprise systems.

Wisal Zafar | AI in Healthcare | Data Scientist of the Year Award

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Mr. Wisal Zafar | AI in Healthcare | Data Scientist of the Year Award

Lecturer at Cecos University of IT and Emerging Sciences, Pakistan

Wisal Zafar is a dynamic academic and research-oriented professional whose expertise lies at the intersection of data science, artificial intelligence, and deep learning. With a strong foundation in software engineering, he has progressively transitioned into data-centric domains where he now actively contributes as a lecturer, researcher, and data scientist. His work integrates modern machine learning techniques and neural networks to tackle real-world problems ranging from healthcare to education. His career is marked by a drive to foster innovation through technology, an unwavering commitment to academic excellence, and a passion for nurturing student potential in both undergraduate and postgraduate settings.

Profile

Scopus

Education

Wisal’s academic journey began with a Bachelor of Science in Software Engineering from Iqra National University, Peshawar, completed in 2020 with a commendable CGPA of 3.47/4.00. Building on this strong foundation, he pursued a Master of Science in Software Engineering at the same university, expected to be completed by mid-2024, where he currently holds a CGPA of 3.50/4.00. His academic record reflects a consistent pursuit of knowledge and skill advancement in software technologies, deep learning, and data analysis. Prior to his university education, he completed his Intermediate from Capital Degree College and matriculation from The Jamrud Model High School with notable academic performances.

Experience

Professionally, Wisal has held several key positions in academia and data processing. He is currently serving as a Lecturer at CECOS University of IT and Emerging Sciences, Peshawar, where he imparts advanced-level knowledge in Artificial Intelligence, Data Science, and Machine Learning. Before this, he contributed significantly to Iqra National University both as a Lecturer and as an EDP Officer, where he oversaw electronic data processing and optimized data accessibility across research and academic projects. His roles have consistently involved not only teaching but also mentorship, particularly in guiding final-year students through research and development of innovative software solutions. His earlier professional engagements also include roles as a Junior Web Developer and teaching positions, showcasing a diverse skill set in both educational and technical domains.

Research Interests

Wisal’s research interests are rooted in the application of artificial intelligence and machine learning to critical societal challenges. His work spans brain tumor detection, plant disease classification, emotion recognition in educational settings, and mental health analysis using social media data. He is particularly intrigued by hybrid deep learning architectures, transformer-based models, and neural networks. He consistently integrates image processing techniques and NLP tools to build intelligent, data-driven solutions. His recent focus includes real-time decision support systems, content-based image retrieval, and multi-scale classification, which have promising implications for both healthcare and education systems.

Awards

In recognition of his exceptional contribution to the academic and technical environment, Wisal was honored with the “Best Employee of the Year 2023” award at Iqra National University. This accolade acknowledges his consistent performance, innovative approach to teaching and research, and his ability to blend administrative responsibilities with cutting-edge academic delivery. His recognition serves as a testament to his dedication, collaborative spirit, and leadership potential in the academic research community.

Publications

Wisal has made significant scholarly contributions, with several research publications in high-impact international journals. His paper “Enhanced TumorNet: Leveraging YOLOv8s and U-Net for Superior Brain Tumor Detection and Segmentation Utilizing MRI Scans” was published in Results in Engineering (2024) and is cited for its innovative approach to medical imaging using hybrid models. Another influential work, “Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed”, appeared in MDPI-Healthcare (2023) and explores diagnostic modeling using AI techniques. His third publication, “A Survey on Big Data Analytics (BDA) Implementation and Practices in Medical Libraries of Punjab”, published in the Journal of Computing & Biomedical Informatics (2023), provides insights into the integration of BDA in healthcare information systems. These publications highlight his range—from healthcare diagnostics to knowledge systems—and his adaptability in multiple AI-driven domains.

Conclusion

Wisal Zafar stands out as a highly motivated data scientist and academician with a clear vision for the future of AI and its applications. Through his diverse academic background, hands-on teaching experience, impactful research, and recognized contributions to institutional growth, he exemplifies the qualities of an innovative thinker and dedicated professional. His continued exploration of deep learning and intelligent systems is not only enriching the academic field but also paving the way for practical solutions to societal challenges. With a growing portfolio of research and a keen eye for technological advancements, Wisal is well-poised to make long-term contributions to AI-based research and higher education. His career trajectory illustrates a seamless blend of academic rigor, technical skill, and research excellence.