Assist. Prof. Dr. Mohanned M. H. AL-Khafaji | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Mohanned M. H. AL-Khafaji | Artificial Intelligence | Best Researcher Award

Engineering | University of Technology | Iraq

Dr. Mohanned Mohammed Hussein Al-Khafaji is an accomplished researcher and academic leader in production engineering, specializing in intelligent manufacturing systems, laser material processing, neural network modeling, and fuzzy logic control applications. As Dean of the College of Production Engineering and Metallurgy at the University of Technology, Baghdad, his research integrates computational modeling, automation, and artificial intelligence to enhance production efficiency and precision engineering. He has made significant contributions to the development of computer-controlled manufacturing systems, laser-based material processing, and predictive modeling using advanced algorithms. His work on CO₂ laser processing, neural network-based machining analysis, and hybrid intelligent systems has advanced industrial automation and smart manufacturing processes. Dr. Al-Khafaji’s research also explores mechatronics, robotic systems, and additive manufacturing, emphasizing simulation tools like Abaqus, COMSOL Multiphysics, and MATLAB. His scientific output reflects substantial academic influence, with 15 Scopus-indexed documents, 41 citations from 37 documents, and an h-index of 3. On Google Scholar, he has accumulated 125 citations, an h-index of 6, and an i10-index of 4, underscoring his growing impact in engineering research.

Profile

Scopus | ORCID | Google Scholar

Featured Publications

Al-Khafaji, M. M. H., & Hubeatir, K. A. (2021). CO2 laser micro-engraving of PMMA complemented by Taguchi and ANOVA methods. Journal of Physics: Conference Series, 1795(1), 012062.

Al-Khafaji, M. M. H. (2018). Neural network modeling of cutting force and chip thickness ratio for turning aluminum alloy 7075-T6. Al-Khwarizmi Engineering Journal, 14(1), 67–76.

Khayoon, M. A., Hubeatir, K. A., & Al-Khafaji, M. M. (2021). Laser transmission welding is a promising joining technology technique – A recent review. Journal of Physics: Conference Series, 1973(1), 012023.

Momena, T. F. A., Mohammed, M. M. H., & Al-Khafaji, M. M. H. (2023). Smart robot vision for a pick and place robotic system. Engineering and Technology Journal, 40(6), 1–15.

Shaker, F., Al-Khafaji, M., & Hubeatir, K. (2020). Effect of different laser welding parameters on welding strength in polymer transmission welding using semiconductor. Engineering and Technology Journal, 38(5), 761–768.*

Assist. Prof. Dr. Mustaqeem Khan | Artificial intelligence | Best Researcher Award

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Assist. Prof. Dr. Mustaqeem Khan | Artificial intelligence | Best Researcher Award

Assist. Prof. Dr. Mustaqeem Khan | Assistant Professor | United Arab Emirates University | United Arab Emirates

Academic Background

Dr. Mustaqeem Khan is a distinguished researcher and academic in the field of Artificial Intelligence and Signal Processing. He earned his Doctorate in Software Convergence from Sejong University, South Korea, where his research focused on emotion recognition using deep learning. He also holds a Master’s degree in Computer Science from Islamia College Peshawar, Pakistan, where he was awarded a Gold Medal for academic excellence, and a Bachelor’s degree in Computer Science from the University of Agriculture, Peshawar. Dr. Khan’s scholarly impact is reflected in his remarkable research record, with Scopus indexing 47 documents and over 2,412 citations, resulting in an h-index of 20. On Google Scholar, his work has gained over 2,934 citations, maintaining an h-index of 21 and an i10-index of 31, positioning him among the top two percentage scientists globally.

Research Focus

His research primarily explores Speech and Audio Signal Processing, Emotion Recognition, and Deep Learning. Dr. Khan’s studies integrate multi-modal data analysis through advanced architectures, such as CNNs and Transformers, for applications in speech emotion recognition, computer vision, and energy analytics.

Work Experience

Dr. Khan serves as an Assistant Professor at the United Arab Emirates University, contributing to teaching, research supervision, and curriculum development. Previously, he worked as a Postdoctoral Fellow and Lab Coordinator at the Mohamed Bin Zayed University of Artificial Intelligence, where he collaborated with the Technical Innovation Institute on drone detection systems and managed multidisciplinary AI research teams. Before that, he gained substantial academic and research experience as a Research Assistant at Sejong University and as a Lecturer at Islamia College Peshawar, mentoring students in core computer science and artificial intelligence subjects.

Key Contributions

Dr. Khan has developed several advanced deep learning models, including hybrid attention transformers, multimodal cross-attention networks, and ensemble architectures for audio-visual recognition tasks. His work has contributed to advancements in emotion recognition, drone-based surveillance, and smart city analytics. He has also participated in major funded projects supported by the National Research Foundation of Korea and the Technology Innovation Institute, UAE.

Awards & Recognition

He has been honored with multiple distinctions, including Best Paper Awards, an Outstanding Research Award during his Ph.D., and recognition as a Gold Medalist for academic performance. His inclusion among the Top 2% Scientists (2023–2024) underscores his exceptional research influence and scholarly excellence.

Professional Roles & Memberships

Dr. Khan is an editorial board member and associate editor for several international journals, including the Annals of Applied Sciences and the European Journal of Mathematical Analysis. He serves as a reviewer for over 35 prestigious journals such as IEEE Access, Applied Soft Computing, and Knowledge-Based Systems, actively contributing to academic quality and peer review.

Profile

Scopus | Google Scholar | ORCID

Featured Publications

Khan, M., Ahmad, J., El Saddik, A., & Gueaieb, W. (2025). Joint Multi-Scale Multimodal Transformer for Emotion Using Consumer Devices. IEEE Transactions on Consumer Electronics.

Khan, M., Tran, P. N., Pham, N. T., & Othmani, A. (2025). MemoCMT: Multimodal Emotion Recognition Using Cross-Modal Transformer-Based Feature Fusion. Nature Scientific Reports.

Khan, M., Ahmad, J., El Saddik, A., & Gueaieb, W. (2024). Drone-HAT: Hybrid Attention Transformer for Complex Action Recognition in Drone Surveillance Videos. Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Khan, M., Kwon, S. (2021). Optimal Feature Selection Based Speech Emotion Recognition Using Two-Stream Deep Convolutional Neural Network. International Journal of Intelligent Systems.

Khan, M., Kwon, S. (2021). Att-Net: Enhanced Emotion Recognition System Using Lightweight Self-Attention Module. Applied Soft Computing.

Impact Statement / Vision

Dr. Mustaqeem Khan envisions advancing AI systems capable of understanding human emotions and behaviors with precision and empathy. His goal is to integrate deep learning and multimodal intelligence into real-world applications that enhance human–machine interaction, healthcare, and smart technologies. His ongoing commitment to innovation continues to shape the future of intelligent computing and global research collaboration.

Cha Joowon | Artificial Intelligence | Best Researcher Award

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Mr. Cha Joowon | Artificial Intelligence | Best Researcher Award

Korea Atomic Energy Research Institute | South Korea

Mr. Cha Joowon is a dedicated researcher in the field of artificial intelligence with a particular focus on its application to nuclear energy systems. He is currently part of the Applied Artificial Intelligence Section at the Korea Atomic Energy Research Institute in Daejeon, South Korea, where he contributes to advancing AI-driven solutions for safe and efficient reactor operations. His academic and research journey reflects a strong commitment to combining computer science with nuclear engineering challenges, working on innovative methods to improve decision-making and system reliability within complex technological environments.

Publication Profile

Scopus

Education Background

Mr. Cha Joowon began his academic path in computer engineering at Korea University of Technology and Education, where he developed a strong foundation in computational methods, algorithms, and systems design. After completing his undergraduate studies, he advanced his pursuit of specialized knowledge by enrolling in the integrated M.S.-Ph.D. program at the University of Science and Technology in Daejeon. His focus within this program is artificial intelligence, where he combines theoretical learning with practical applications in the nuclear energy domain, emphasizing innovation in both academic and applied research contexts.

Professional Experience

Building on his academic background, Mr. Cha Joowon joined the Korea Atomic Energy Research Institute in Daejeon, where he works within the Applied Artificial Intelligence Section. His role centers on exploring how artificial intelligence can enhance reactor safety, operational efficiency, and predictive maintenance in nuclear facilities. His current research integrates advanced machine learning and large language models with engineering systems, demonstrating his ability to bridge computational intelligence with real-world industrial applications. This combination of skills reflects both his technical expertise and his ambition to contribute meaningful solutions to complex engineering challenges.

Awards and Honors

While Mr. Cha Joowon is still early in his professional journey, his commitment to excellence and research potential is evident through his academic trajectory and institutional affiliations. His enrollment in a highly competitive integrated doctoral program at the University of Science and Technology highlights his recognition as a promising scholar. Additionally, his affiliation with the Korea Atomic Energy Research Institute places him in an environment of high-level scientific contributions, offering him opportunities to showcase his growing expertise. His ongoing projects signal the potential for future recognition through awards and professional honors.

Research Focus

Mr. Cha Joowon’s research is centered on applying artificial intelligence to nuclear engineering, with particular attention to developing intelligent systems for reactor operation support. His focus includes integrating large language models and advanced computational techniques to enhance operator decision-making, predictive diagnostics, and system optimization. By combining AI innovation with the unique requirements of nuclear technologies, his research aims to provide reliable and practical solutions for the safe and effective operation of reactors. This interdisciplinary approach reflects his dedication to bridging artificial intelligence with one of the most critical areas of energy research.

Publication Notes

  • Large language model agent for nuclear reactor operation assistance
    Published Year: 2025

Conclusion

In summary, Mr. Cha Joowon represents a new generation of researchers working at the intersection of artificial intelligence and nuclear engineering. His academic foundation in computer engineering, advanced studies in AI, and practical contributions at the Korea Atomic Energy Research Institute mark him as an emerging talent with strong potential to shape the future of intelligent nuclear systems. As he continues to publish and contribute to research, his work is expected to influence both academic communities and industrial applications, solidifying his role as a researcher dedicated to innovation and safety in energy technologies.

Zaid Allal | Machine Learning | Best Researcher Award

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Dr. Zaid Allal | Machine Learning | Best Researcher Award

Dr. Zaid Allal | LISTIC (Laboratory of Computer Science, Systems, Information and Knowledge Processing) | Morocco

Zaid Allal is a Moroccan researcher and doctoral candidate in computer science specializing in artificial intelligence applications for energy systems. With a solid foundation in mathematics and computing, he has built his academic and professional journey through a blend of education, research, and teaching. His work integrates machine learning with renewable energy systems, focusing on optimizing hydrogen energy technologies. Currently affiliated with the University of Savoie Mont Blanc and the LISTIC Laboratory in France, his research explores intelligent solutions for predictive maintenance, fault detection, and system stability. His dedication lies in bridging sustainable energy with advanced AI technologies.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Zaid Allal holds a Master’s degree in Advanced Information Technology and Computing Applications from the University of Franche-Comté in France, graduating with distinction and honors. He earned a Bachelor’s degree in Mathematics and IT Systems from Mohammed First University in Oujda. Before his higher education, he received his Baccalaureate in Physical Sciences and Chemistry with honors. Additionally, he completed a certified training in Mathematics Education, coordinated with the Moroccan Ministry of Education. His strong academic background in both theoretical and applied domains provides a firm base for his research in AI and renewable energy integration.

Professional Experience

Zaid has over seven years of experience in mathematics education under the Moroccan Ministry of Education. Transitioning into research, he engaged in machine learning projects focused on renewable energy systems and hydrogen technologies at the University of Franche-Comté. Currently, he is a Ph.D. researcher at the University of Savoie Mont Blanc and contributes to the LISTIC Laboratory. His projects span predictive analytics, power consumption forecasting, and anomaly detection in smart grids. His work integrates theoretical AI models with practical energy sector challenges, contributing to research publications, international conferences, and innovative academic-industrial collaborations.

Awards and Honors

Zaid Allal has consistently demonstrated academic excellence throughout his career, receiving distinction and honors during both his undergraduate and postgraduate studies. His Master’s program recognized his outstanding performance with academic distinction. In addition to his formal qualifications, he has participated in several high-impact training initiatives, including NASA Space Apps competitions and AI ambassador programs. These accolades reflect his commitment to excellence in education, innovation, and technological advancement, highlighting his dedication to exploring and applying cutting-edge artificial intelligence methods within the energy and environmental sectors.

Research Focus

Zaid’s research centers on applying machine learning and deep learning techniques to address challenges in renewable energy systems and the hydrogen value chain. He focuses on areas such as predictive maintenance, fault and anomaly detection, power forecasting, and system optimization. His expertise extends to smart grids, hydrogen storage systems, and photovoltaic energy solutions. He employs explainable AI and reinforcement learning to develop sustainable, efficient, and interpretable models. By combining theoretical AI approaches with real-world energy applications, he aims to contribute to the advancement of intelligent and sustainable energy infrastructures.

Top  Publications

Explainable AI of Tree-Based Algorithms for Fault Detection and Diagnosis in Grid-Connected PV Systems
Published Year: 2025
Citation: 14

Review on ML Applications in Hydrogen Energy Systems
Published Year: 2025
Citation: 11

Power Consumption Prediction in Warehouses Using Variational Autoencoders and Tree-Based Regression Models
Published Year: 2024
Citation: 9

Efficient Health Indicators for RUL Prediction of PEM Fuel Cells
Published Year: 2024
Citation: 7

Machine Learning Algorithms for Solar Irradiance Prediction: A Comparative Study
Published Year: 2024
Citation: 6

Conclusion

Zaid Allal exemplifies the fusion of academic excellence, professional dedication, and research-driven innovation. With a strong foundation in mathematics and computing, he has evolved into a researcher committed to applying artificial intelligence in solving pressing energy challenges. His work across renewable energy, hydrogen systems, and smart grid technologies positions him as a valuable contributor to the evolving energy-tech landscape. Through ongoing research, publication, and collaboration, he continues to push the boundaries of sustainable innovation, striving to create data-driven and explainable solutions for the future of energy management and system optimization.

Dr. Ashkan Tashk | Applied AI | Excellence Award (Any Scientific field)

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Dr. Ashkan Tashk | Applied AI | Excellence Award (Any Scientific field)

postdoc, Technical University of Denmark.

Dr. Ashkan Tashk is a highly accomplished electrical engineer and postdoctoral researcher with deep expertise in telecommunications, machine learning, and biomedical imaging. With a strong academic and teaching background, he has worked across multiple prestigious institutions in Denmark, Germany, and Iran. His career blends theoretical knowledge with applied innovations, particularly in AI-driven healthcare technologies, contributing significantly to interdisciplinary research and development. He is known for his dedication to science communication, teaching, and AI-based applications in medicine.

Publication Profile

Google scholar

🎓 Education Background:

Ashkan Tashk received his Ph.D. in Electrical Engineering with a focus on Telecommunications in 2015, following his M.Sc. (2010) and B.Sc. (2006) in the same field. His undergraduate project involved designing and constructing a prototype sunlight tracking platform—an early indication of his strong interest in applied engineering and innovation. His academic journey provided a solid foundation in electronics, signal processing, and machine learning, which continues to influence his research today.

💼 Professional Experience:

Dr. Tashk currently serves as a Postdoctoral Researcher at Denmark’s leading universities (2019–present). Prior to that, he worked as a telecommunications expert at FREC and completed a research internship at Karlsruhe Institute of Technology (KIT), Germany. His career includes teaching roles at the University of Southern Denmark, University of Copenhagen, and various Iranian academic institutions. He has taught courses in electrical circuits, microprocessors, statistics, numerical analysis, and MATLAB programming, while also publishing Persian-language technical tutorials and conducting workshops in Europe and Iran.

🏆 Awards and Honors:

Dr. Ashkan Tashk became an IEEE Senior Member in 2022, recognizing his professional maturity and significant contributions to electrical engineering. He has served as a session chair at multiple international conferences such as ACSIT2020 in Copenhagen and ICCAIRO2019 in Athens. He has also completed prestigious programs like the “Science Communication” course by the Royal Danish Academy of Sciences and Letters and the RCR workshop at the University of Copenhagen, demonstrating his commitment to ethical and effective scientific practice.

🔬 Research Focus:

Ashkan’s research centers on the application of artificial intelligence and machine learning in biomedical engineering, particularly in image processing, ultrasound tomography, and cancer diagnostics. Notable projects include developing LSTM-RF models for metastatic prostate cancer prediction, CNN-based biomedical segmentation tools, and advanced metabolomics data imputation methods. His work also spans sonar signal processing, image-based fingerprint recognition, and microprocessor-controlled automation systems. These interdisciplinary projects reflect his strong problem-solving abilities and technological foresight.

🧩 Conclusion:

Dr. Ashkan Tashk is a dynamic academic, educator, and innovator whose work bridges electrical engineering and biomedical science using modern AI tools. His technical skill set, coupled with his teaching excellence and global collaborations, position him as a thought leader in the integration of engineering and healthcare. Fluent in Persian, English, and Danish, and proficient in tools like Python, MATLAB, and various PLC programming languages, he continues to impact both academia and industry with his visionary contributions.

📚 Top Publications & Citations:

Semantic Segmentation of Biomedical Images Using Deep Convolutional Neural Networks
Journal: Journal of Medical Imaging and Health Informatics
Cited by: 24 articles

Predicting Metastatic Prostate Cancer via Biochemical Parameters Using LSTM and RF
Journal: Computers in Biology and Medicine
Cited by: 18 articles

Machine Learning Imputation for Large-scale Metabolomics Data
 Journal: Metabolomics
Cited by: 10 articles

Eye-Tracking Data Analysis Using AI for Cognitive Study
 Journal: IEEE Transactions on Affective Computing
Cited by: 7 articles

Dr. Uddalak Mitra | Machine learning | Best Researcher Award

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Dr. Uddalak Mitra | Machine learning | Best Researcher Award

Assistant Professor, JIS College of Engineering, India

Dr. Uddalak Mitra is an esteemed Assistant Professor at JIS College of Engineering, specializing in bioinformatics, machine learning, and deep learning 🧬🤖. With a strong academic foundation and a passion for research, he has significantly contributed to the intersection of computational intelligence and biological sciences. His expertise lies in decoding DNA, RNA, and protein sequences using cutting-edge AI techniques, paving the way for advancements in healthcare and genomics. Dr. Mitra’s work seamlessly blends theoretical knowledge with real-world applications, making impactful strides in both academia and industry.

Publication Profile

Google Scholar

🎓 Education:

Dr. Mitra has built a solid academic background in computational sciences, equipping himself with the expertise required to address complex biological challenges. His educational journey has provided him with the technical prowess to integrate artificial intelligence into biomedical research.

💼 Experience:

As an Assistant Professor at JIS College of Engineering, Dr. Mitra actively engages in research and mentoring, shaping the next generation of scientists. His work focuses on applying machine learning models to analyze biological data, improving early disease detection methodologies. Additionally, he has authored several research papers and contributed to the academic community through his innovative studies.

🏆 Awards and Honors:

Dr. Mitra has made commendable contributions to scientific research, earning recognition for his published works. He holds 9 patents, showcasing his dedication to innovation. His memberships in esteemed organizations like IFERP and ISTE reflect his commitment to professional development and research excellence.

🔬 Research Focus:

His research primarily revolves around bioinformatics, machine learning, and deep learning. He explores information-theoretic tools for biological sequence analysis, integrating artificial intelligence to derive meaningful insights from genomic data. His recent studies focus on clinical applications of AI, particularly in disease diagnosis and personalized medicine.

🔍 Conclusion:

Dr. Uddalak Mitra is a pioneering researcher dedicated to bridging the gap between bioinformatics and artificial intelligence. His contributions to genomic research, coupled with his expertise in AI-driven disease diagnosis, make him a valuable asset to the scientific community. With a strong foundation in computational biology, he continues to push the boundaries of research, striving for innovations that benefit both medicine and technology.

📚 Publications:

Leveraging AI and Machine Learning for Next-Generation Clinical Decision Support Systems (CDSS) – Published in AI-Driven Innovation in Healthcare Data Analytics, 2025.

Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework – Information, 2025

Integrated System for Disease Detection Using Semiconductor-Based Gas Sensors and AI/ML – IN Patent A61B0005080000, 2025

Significance of AI/ML Wearable Technologies for Education and Teaching – Wearable Devices and Smart Technology for Educational Teaching Assistance, 2025

Integrating AI/ML With Wearable Devices for Monitoring Student Mental Health – Wearable Devices and Smart Technology for Educational Teaching Assistance, 2025

The Evolution of Entrepreneurship in the Age of AI – Advanced Intelligence Systems and Innovation in Entrepreneurship, 2024

A Novel Algorithm for Genomic STR Mining: Application to Phylogeny Reconstruction and Taxa Identification – International Journal of Bioinformatics Research and Applications, 2024

Robin Augustine | Artificial Intelligence | Excellence in Research

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Assoc. Prof. Dr. Robin Augustine | Artificial Intelligence | Excellence in Research

Associate Professor, Uppsala University, Sweden

🎓 Associate Professor Robin Augustine is a renowned expert in Medical Engineering and Microwave Technology, leading research at Uppsala University in Sweden. He heads the Microwaves in Medical Engineering Group at the Angstrom Laboratory, Department of Electrical Engineering, and serves as an Associate Editor for IET journals. His interdisciplinary work spans medical sensor development, bioelectromagnetic interactions, and innovative in-body communication technologies. Robin has collaborated globally as a visiting professor and researcher, focusing on advancements in medical engineering through impactful research projects.

Publication Profile

Scopus

Education

📚 Dr. Robin Augustine earned his Ph.D. in Electronics and Optronics Systems from Université de Paris Est Marne La Vallée, specializing in human tissue electromagnetic modeling and its implications for medical sensor design. He holds an MSc in Electronics Science with a focus on Robotics from Cochin University of Science and Technology, and a BSc in Electronics Science from Mahatma Gandhi University. His expertise is further strengthened by advanced training in Diagnostic and Therapeutic Applications of Electromagnetics from Politecnico di Torino, Italy.

Experience

💼 Robin’s career includes extensive experience as a senior lecturer and associate professor at Uppsala University, where he has been leading research in microwave applications for medical technology since 2011. He has held visiting professorships and research roles at institutions such as the Beijing Institute of Nanoenergy and Nanosystems and University Medical Center Maastricht, contributing to medical sensor innovation and orthopedic measurement systems. Robin has also worked internationally, including postdoctoral research in France, with expertise in antenna design, bioelectromagnetics, and microwave characterization.

Research Focus

🔬 Robin’s research focuses on medical engineering, bioelectromagnetics, and intra-body communication, including developing microwave-based sensors for diagnosing conditions like osteoporosis, skin cancer, and muscular atrophy. As a leader in the B-CRATOS and COMFORT projects, he explores body-centric technologies and in-body wireless communication to enhance medical diagnostics. His pioneering work addresses the integration of electromagnetic technology with healthcare, making strides in non-invasive monitoring systems.

Awards and Honours

🏆 Dr. Augustine’s impactful research has attracted numerous grants and awards, including significant EU funding for projects like PERSIMMON and DIAMPS. He has secured research funding from bodies such as the Swedish Research Council, Vinnova, and the Foundation for Strategic Research, supporting his innovative work on body communication systems and medical diagnostics. His research has earned recognition through the Swedish Excellence Grant for Young Researchers and multiple grants for advancing medical engineering solutions.

Publication Top Notes

Biphasic lithium iron oxide nanocomposites for enhancement in electromagnetic interference shielding properties

Rotation insensitive implantable wireless power transfer system for medical devices using metamaterial-polarization converter

Improving burn diagnosis in medical image retrieval from grafting burn samples using B-coefficients and the CLAHE algorithm

 

Gabriella d’Albenzio | Artificial Intelligence | Best Researcher Award

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Dr. Gabriella d’Albenzio | Artificial Intelligence | Best Researcher Award

Postdoc, Perk Lab Perk Lab Laboratory for Percutaneous Surgery, Canada

🎓 Gabriella d’Albenzio is a talented researcher with a focus on biomedical engineering and medical imaging. Currently pursuing a Ph.D. in Informatics at the University of Oslo, she has an impressive background in clinical engineering and biomedical engineering. Gabriella has worked on cutting-edge projects related to image-guided therapies and deep learning for medical applications, contributing significantly to her field through both research and development.

Profile

Scopus

 

Education

📚 Gabriella d’Albenzio holds a Ph.D. in Informatics from the University of Oslo (2021-2024). She completed her M.Sc. in Biomedical Engineering and B.Sc. in Clinical Engineering at Sapienza University of Rome, Italy, reflecting a solid foundation in both engineering and medical sciences.

Experience

💼 Gabriella d’Albenzio has extensive experience as a Scientific Software Developer at The Intervention Centre in Oslo, Norway, and as a Research Assistant at NTNU. She has also interned at the Rehabilitation Bioengineering Lab in Rome, contributing to various research projects involving advanced medical imaging and deep learning technologies.

Research Interests

🧠 Gabriella’s research interests are centered around enhancing surgical planning and medical imaging through deep learning and advanced computational techniques. Her work focuses on developing algorithms for medical image segmentation and predictive models for surgical outcomes, aiming to improve patient-specific treatment strategies.

Awards

🏅 Gabriella d’Albenzio has been recognized with the Globalink Research Internship by Mitacs, Canada, and a Grant Research Stay Abroad by The Research Council of Norway. These awards highlight her outstanding contributions to research and her commitment to advancing biomedical engineering.

Publications

Optimizing Surgical Plans for Parenchyma-Sparing Liver Resections through Contour-Guided Resection and Surface Approximation

Using NURBS for Virtual Resections in Liver Surgery Planning: A Comparative Usability Study

Patient-Specific Functional Liver Segments Based on Centerline Classification of the Hepatic and Portal Veins

ALive: Analytics for Computation and Visualization of Liver Resections

Laparoscopic Parenchyma-Sparing Liver Resection for Large (≥50 mm) Colorectal Metastases