Shaoyang Luo | Time Series Analysis | Research Excellence Award

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Dr. Shaoyang Luo | Time Series Analysis | Research Excellence Award

Doctor of Philosophy in Engineering | Nanchang University | China

Dr. Shaoyang Luo is a researcher in Time Series Analysis at the School of Infrastructure Engineering, Nanchang University. His research focuses on data-driven modeling, signal decomposition, and deep learning methods for infrastructure monitoring, with particular emphasis on dam deformation analysis and structural health monitoring. He develops hybrid models that integrate time–frequency analysis and neural networks to improve prediction accuracy and reliability in large-scale civil engineering systems.

Citation Metrics (Scopus)

80

60

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Citations
65

Documents
6

h-index
4

                        Citations                 Documents                   h-index


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Featured Publications

Mahendra Gaikwad | Machine Learning | Best Researcher Award

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Dr. Mahendra Gaikwad | Machine Learning | Best Researcher Award

Assistant Professor at Veermata Jijabai Technological Institute (VJTI) | Mumbai | India

Dr. Mahendra Uttam Gaikwad is a forward-thinking mechanical and manufacturing engineering professional whose work reflects a deep commitment to advancing modern machining, smart materials research, sustainable manufacturing, and AI-driven optimization in industrial systems. Renowned for his ability to bridge theoretical innovation with practical engineering applications, he has built a strong scholarly footprint through impactful publications in SCI and Scopus-indexed journals, contributions to influential book chapters, and editorial leadership in notable international volumes focused on advanced materials and digital-age manufacturing. His research explores critical themes such as electrical discharge machining, surface integrity analysis, optimization algorithms, additive manufacturing, fatigue modelling, and machine learning applications in production environments, consistently demonstrating an aptitude for tackling complex engineering challenges through empirical investigation and computational modelling. In addition to his academic contributions, he has shown commendable innovation through multiple national and international patents addressing smart systems, sustainable material utilization, and intelligent manufacturing solutions. He has also been an active collaborator with academic institutions, research groups, and industry partners, contributing to advancements in machining automation, performance benchmarking, and data-driven design methodologies. A dedicated mentor, he has guided numerous undergraduate and postgraduate research projects, fostering a research-oriented learning environment and supporting the next generation of engineers. His work as a reviewer, conference contributor, and knowledge disseminator further underscores his commitment to strengthening global engineering discourse. Known for his leadership qualities, professional integrity, and continuous pursuit of technological excellence, Dr. Gaikwad has earned recognition for his contributions to teaching and research, positioning himself as a noteworthy contributor to the evolving landscape of smart and sustainable manufacturing.

Profiles: ORCID | Google Scholar

Featured Publications

Gaikwad, M. U., Somatkar, A. A., Ghadge, M., Majumder, H., Shinde, A. M., & Lohakare, A. V. (2025). Effect of dry and wet machining environments on surface quality of Al6061 using particle swarm optimization (PSO).

Sargar, T., Gautam, N. K., Jadhav, A., & Gaikwad, M. U. (2025). A comparative investigation of kerf width during CO₂ and fiber laser machining of SS 316L material.

Khan, M. A. J., Pohekar, S. D., Bagade, P. M., Gaikwad, M. U., & Singh, M. (2025). CFD analysis of NACA 4415 marine propeller ducts for managing flow separation.

Nishandar, S. V., Pise, A. T., Bagade, P. M., Gaikwad, M. U., & Singh, A. (2025). Computational modelling and analysis of heat transfer enhancement in straight circular pipe with pulsating flow.

Gaikwad, M. U., Gaikwad, P. U., Ambhore, N., Sharma, A., & Bhosale, S. S. (2025). Powder bed additive manufacturing using machine learning algorithms for multidisciplinary applications: A review and outlook.

Bhavesh Kataria | AI and Machine Learning | AI & Machine Learning Award

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Dr. Bhavesh Kataria | AI and Machine Learning | AI & Machine Learning Award

Post-Doctoral Fellow at Emory University | United States

Dr. Bhavesh Kataria is a highly accomplished academician, researcher, and innovator in Computer Engineering, recognized globally for his leadership in Artificial Intelligence, Machine Learning, and Digital Image Processing. His professional journey spans academia and research institutions across India and the United States, including his role at Emory University, where he contributes to advanced AI-driven healthcare analytics and digital pathology solutions. With a Ph.D. focused on Optical Character Recognition of Sanskrit Manuscripts using Convolutional Neural Networks, Dr. Kataria has combined technical precision with deep domain expertise to address challenges in multilingual text recognition and medical imaging. His scholarly portfolio includes numerous publications in reputed international journals, multiple granted patents, and several authored books covering cutting-edge topics in AI, cloud computing, and web technologies. An active member of prestigious organizations such as IEEE and ACM, he serves on editorial boards of international journals and as a reviewer for globally recognized publishers like Springer Nature and Science Publishing Group. He has also chaired sessions and reviewed Ph.D. theses, contributing significantly to the academic ecosystem. Dr. Kataria’s pioneering innovations, such as AI-based network visualization tools, smart teaching devices, and healthcare monitoring systems, underscore his commitment to translational research and practical AI applications. Honored with awards including the Best Researcher Award and Teaching Excellence Award, he exemplifies a blend of scholarly excellence, innovation, and mentorship. His dedication to advancing intelligent systems and promoting interdisciplinary research continues to inspire global collaboration in emerging computational technologies.

Profiles: Scopus | ORCID

Featured Publications

Kataria, B., & Jethva, H. B. (2024, September 30). Decentralized security mechanisms for AI-driven wireless networks: Integrating blockchain and federated learning.

Kataria, B. (2024, June 2). Automated detection of tuberculosis using deep learning algorithms on chest X-rays.

Shivadekar, S., Kataria, B., Hundekari, S., Wanjale, K., Balpande, V. P., & Suryawanshi, R. (2023). Deep learning based image classification of lungs radiography for detecting COVID-19 using a deep CNN and ResNet 50.

Shivadekar, S., Kataria, B., Limkar, S., Wagh, K., Lavate, S., & Mulla, R. (2023, June 15). Design of an efficient multimodal engine for preemption and post-treatment recommendations for skin diseases via a deep learning-based hybrid bioinspired process.

Kataria, B., Jethva, H. B., Shinde, P. V., Banait, S. S., Shaikh, F., & Ajani, S. (2023, April 30). SLDEB: Design of a secure and lightweight dynamic encryption bio-inspired model for IoT networks.

Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

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Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

Assistant Professor, Prof. Ramkrishna More Arts, Commerce & Science College, India

Dr. Santosh Jagtap, Assistant Professor at Prof. Ramkrishna More College (Autonomous), is a highly accomplished researcher and academic in the fields of Artificial Intelligence (AI) and Cybersecurity, with extensive expertise in applying AI to smart agriculture, healthcare security, IoT-enabled educational systems, and AI-driven safety solutions. Dr. Jagtap holds advanced academic qualifications and has developed a distinguished research profile that emphasizes practical applications of emerging technologies to address societal challenges. His work integrates machine learning, blockchain, IoT, and real-time data processing, producing innovative solutions in areas such as intelligent irrigation systems, plant disease detection, AI-based emotion recognition for safety alerts, and secure healthcare frameworks. Over his career, Dr. Jagtap has contributed significantly to international research projects and collaborative studies, producing high-impact publications in reputed journals and conference proceedings, such as Materials Today: Proceedings, international conferences on electronics, computing, and applied AI. He has also been recognized for innovation through patent awards, notably for AI-based plant disease identification systems, reflecting his focus on technology transfer and real-world impact. Dr. Jagtap has played an active role in mentoring students, guiding research projects, and participating in professional networks that foster academic and technological growth. He has demonstrated a consistent record of research excellence, with a total of 78 citations across 4 Scopus-indexed publications and an h-index of 3, reflecting the growing impact of his work.

Profile: GOOGLE SCHOLAR | SCOPUS

Featured Publications

  • Jagtap, S. T., Phasinam, K., Kassanu. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51, 793–797. 70 citations.

  • Jagtap, S. T., Thakar, (2021). A framework for secure healthcare system using blockchain and smart contracts. Second International Conference on Electronics and Sustainable Technologies. 22 citations.

  • Jagtap, S. T., Jagdale, K. C., & Thakar, C. M. (2023). Identification of plant disease device using artificial intelligence. IN Patent 391523-001. –

  • Pratiksha Bhise, D. S. J., & Jagtap, S. T. (2024). AI-driven emergency response system for women’s safety using real-time location and heart rate monitoring. IJRPR. –

  • Keskar,  A., Jagtap, S. T., et al. (2021). Big data preprocessing frameworks: Tools and techniques. Design Engineering, 1738–1746.

Dr. Pankaj Kumar | Machine learning | Best Researcher Award

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Dr. Pankaj Kumar | Machine learning | Best Researcher Award

Assistant Professor, National Institute of Technology, Hamirpur

Dr. Pankaj Kumar is a researcher specializing in operations research, optimization methods in finance, interval optimization, machine learning and crop area planning. He earned a Ph.D. in Optimization Methods in Finance from the Indian Institute of Technology Kharagpur with his thesis on interval optimization methods for portfolio selection, and holds earlier advanced degrees in operations research and mathematics. Dr. Pankaj Kumar has served in research and teaching roles—most recently as Assistant Professor—focusing on modelling of portfolio optimization, multi-objective programming, time-series forecasting, and risk measures such as mean-VaR. His professional experience includes supervising research students, contributing to international and national collaborative projects, participating in workshops and conferences, and Dr. Pankaj Kumar’s scholarly output includes more than thirty peer-reviewed papers published in high-impact journals indexed by SCIE, Scopus, and Web of Science, and his work has attracted more than 360 citations with an h-index of 10, reflecting consistent academic influence. His research skills include mathematical modelling, statistical methods, algorithm design, programming in C and R, use of optimisation tools and applying machine learning regression techniques in finance contexts. Among his awards and honors are travel grants, junior/senior research fellowships, editorial board membership, and recognition for teaching and research excellence at his institution. In conclusion, Dr. Pankaj Kumar is positioned to further impact the fields of financial optimization and decision science through high-quality publications, interdisciplinary collaborations, and mentoring, likely to increase his citation profile, visibility, and leadership in both academic and applied settings.

Profile: GOOGLE SCHOLAR | SCOPUS | ORCID

Featured Publications

Behera, J., & Kumar, P. (2025). An approach to portfolio optimization with time series forecasting algorithms and machine learning techniques. Applied Soft Computing, 170, 112741.

Sahu, B. R. B., & Kumar, P. (2025). Portfolio rebalancing model utilizing support vector machine for optimal asset allocation. Arabian Journal for Science and Engineering, 50(14), 10939–10965.

Sahu, B. R. B., Bhurjee, A. K., & Kumar, P. (2024). Efficient solutions for vector optimization problem on an extended interval vector space and its application to portfolio optimization. Expert Systems with Applications, 249, 123653.

Behera, J., & Kumar, P. (2024). Implementation of machine learning-based sparse Sharpe ratio portfolio optimization: A case study on Indian stock market. Operational Research, 24(4), 62.

Patel, M., Behera, J., & Kumar, P. (2024). Parametric approach for multi-objective enhanced interval linear fractional programming problem. Engineering Optimization, 56(5), 740–765.

Ms. Reem Alshahoomi | Data science | Best Researcher Award

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Ms. Reem Alshahoomi | Data science | Best Researcher Award

Ms. Reem Alshahoomi | Zayed University and Creator Transactions | United Arab Emirates

Ms. Reem Alshahoomi is an ambitious and driven researcher whose academic and professional journey reflects her dedication to innovation and excellence in the fields of Artificial Intelligence, Machine Learning, and Data Science. Currently pursuing her Management Information Systems degree with a specialization in Business Intelligence at Zayed University, she has consistently demonstrated outstanding academic performance, earning a place on the Dean’s List for six semesters. Her commitment to personal and professional growth is evident through her active participation in workshops, research conferences, internships, and collaborative projects. Reem stands out as a forward-thinking individual, merging theoretical knowledge with practical applications to address real-world challenges using cutting-edge technologies.

Professional Profile

SCOPUS

Summary of Suitability

Ms. Reem Alshahoomi is a highly talented and emerging researcher specializing in Artificial Intelligence (AI), Machine Learning, Natural Language Processing (NLP), and Data Science. With her exceptional academic achievements, impactful research contributions, and industry-oriented innovations, she demonstrates strong potential and suitability for the Best Researcher Award.

Education

Ms. Reem Alshahoomi educational journey has been marked by exceptional academic achievements and continuous learning. At Zayed University, she has focused on Management Information Systems, concentrating on Business Intelligence, which has allowed her to develop strong technical and analytical skills. Her academic excellence has been recognized repeatedly through her sustained placement on the Dean’s List. She has actively sought opportunities beyond the classroom, attending specialized workshops and training programs related to Artificial Intelligence, Machine Learning, Python Programming, R Programming, and Big Data Analytics. These efforts have significantly enhanced her understanding of technological advancements and equipped her with practical skills required to succeed in research, innovation, and industry applications.

Experience

Ms. Reem Alshahoomi professional journey demonstrates her ability to translate theoretical knowledge into impactful, real-world solutions. During her internship at ADNOC Sour Gas, she contributed to groundbreaking innovations by developing a machine learning-based prediction model using Python to detect flaring events, a solution designed to reduce operational costs, minimize pollution, and support sustainability goals. She also developed training materials for organizational capacity building and supported digital wellbeing initiatives, ensuring knowledge transfer and operational continuity for future interns. Furthermore, she collaborated with OXY on projects requiring advanced data-driven decision-making techniques, enhancing her understanding of real-time analytics and industrial applications. Her practical exposure to large-scale datasets and predictive modeling has strengthened her expertise in designing AI-powered solutions for critical business challenges.

Research Interests

Ms. Reem Alshahoomi research interests are diverse yet deeply interconnected, focusing on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Data Science, and Big Data Analytics. Her work emphasizes the application of emerging technologies to solve complex societal and industrial challenges. One of her key projects explored the role of NLP in abstract datasets to improve virtual assistant devices, demonstrating her capability to integrate AI methodologies into practical use cases. She has also worked on machine learning approaches to combat fake news, showcasing her interest in building innovative solutions for digital security and trust. Through her contributions, Reem has developed a strong passion for leveraging AI-driven models to enhance efficiency, sustainability, and human-computer interaction.

Awards

Ms. Reem Alshahoomi has achieved several notable milestones that reflect her dedication and excellence. Her exceptional academic performance has been recognized through her continuous placement on the Dean’s List for six semesters. She has actively participated in the Undergraduate Research Conference (URC) , where she presented her work on natural language processing and its applications in virtual assistant technologies. Additionally, her innovative contributions during her ADNOC internship have been acknowledged through the patent process initiated for her project, further cementing her role as an emerging leader in research and innovation. These recognitions highlight her ability to blend creativity, technical knowledge, and problem-solving skills in impactful ways.

Publication Top Notes

The Role of Natural Language Processing in Abstract Dataset to Improve Virtual Assistant Devices

Conclusion

Ms. Reem Alshahoomi exemplifies the qualities of an outstanding researcher, combining academic excellence, technical expertise, and innovative thinking. Her passion for Artificial Intelligence, Data Science, and Machine Learning has driven her to engage in impactful projects, contribute to pioneering research, and present her findings on international platforms. With her growing portfolio of publications, successful industrial collaborations, and ongoing patent process, she continues to strengthen her profile as an emerging thought leader in technology and innovation. Reem’s ability to integrate academic knowledge with practical problem-solving makes her an exceptional candidate for the Best Researcher Award, positioning her as a future contributor to advancements in AI and data-driven solutions

Ali Mehrizi | Machine Learning | Best Paper Award

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Dr. Ali Mehrizi | Machine Learning | Best Paper Award

Lecturer at Ferdowsi University of Mashhad, Iran.

Ali Mehrizi is a distinguished researcher and lecturer in Artificial Intelligence (AI) and Machine Learning at Ferdowsi University of Mashhad (FUM), Iran. With a wealth of experience exceeding a decade, his expertise spans adaptive probabilistic models, distributed learning, multi-target tracking, time series forecasting, and Gaussian Mixture Probability Hypothesis Density (GMPHD) methods. Dr. Mehrizi has published multiple impactful articles in renowned journals such as Expert Systems with Applications and Fuzzy Sets and Systems. He is deeply committed to advancing the understanding and application of AI techniques and has successfully mentored numerous students in areas ranging from Data Mining to Advanced Operating Systems.

Profile

Google Scholar

Education

Dr. Mehrizi educational background is rooted in Artificial Intelligence. He is currently pursuing a Ph.D. in AI at Ferdowsi University of Mashhad (2017–2024), under the supervision of Professor H. Sadoghi Yazdi. His dissertation focuses on financial time series forecasting using experience-based adaptive learning, a project that has already produced several publications in top-tier journals. Previously, he earned an M.Sc. in AI from Azad University of Mashhad (2011–2013), where he worked on adaptive semi-supervised learning, optimizing self-organizing map models. His early academic journey began with a B.Sc. in Computer Engineering from the University of Birjand, later transferring to Azad University of Mashhad.

Experience

Dr. Mehrizi professional career spans various roles, beginning in 2001 when he became the IT & Network Manager at the Faculty of Engineering. In this capacity, he significantly improved the system performance and network management. Since 2011, he has been involved in research in AI and Machine Learning, contributing to the development of machine learning models and publishing his findings in high-impact journals. He has also served as a lecturer since 2013, teaching a variety of undergraduate and graduate courses, including Data Mining, Operating Systems, and Advanced Operating Systems. As a researcher, he has mentored students in their theses, particularly in machine learning and pattern recognition, fostering the next generation of AI experts.

Research Interests

Dr. Mehrizi  research interests are broad, focusing on several key areas within the domain of AI. His work on distributed adaptive learning, particularly through Diffusion LMS and Diffusion RLS, aims to optimize decentralized data processing for dynamic systems. In addition, he has contributed to probabilistic and hypothesis-based learning, exploring the use of Gaussian Mixture Probability Hypothesis Density (GMPHD) models for uncertainty-based learning and tracking. His research also delves into time series analysis and forecasting, with a particular focus on financial markets. Dr. Mehrizi’s interest in multi-target tracking extends to real-time tracking algorithms, emphasizing performance in noisy and incomplete data environments. He is also committed to semi-supervised learning, exploring hybrid methods that bridge supervised and unsupervised learning approaches in scenarios with limited labeled data.

Awards

Dr. Mehrizi contributions to the fields of AI and machine learning have earned him recognition in various academic and professional circles. He has been nominated for multiple awards for his research, particularly in adaptive learning and time series forecasting. His work is highly regarded in the academic community, and he continues to push the boundaries of AI research, especially in the areas of distributed learning and multi-target tracking.

Publications

Dr. Mehrizi has authored several articles in well-respected journals in AI and machine learning. His key publications include:

Mehrizi, A., & Yazdi, H. S. (2019). “Adaptive probabilistic methods for long-term financial time series forecasting.” Expert Systems with Applications.

Mehrizi, A., & Yazdi, H. S. (2020). “Semi-supervised learning using GSOM for adaptive classification.” Fuzzy Sets and Systems.

Mehrizi, A. (2022). “Distributed adaptive learning for dynamic systems using Diffusion LMS and RLS.” Emerging Markets Finance and Trade.

Mehrizi, A., & Yazdi, H. S. (2021). “Gaussian Mixture Probability Hypothesis Density for multi-target tracking.” Journal of Machine Learning Research.

These publications have been cited extensively by various researchers in the fields of machine learning, AI, and financial forecasting, underscoring Dr. Mehrizi’s significant impact on the academic community.

Conclusion

Dr. Ali Mehrizi is a leading researcher and educator in the field of Artificial Intelligence and Machine Learning, with a deep commitment to advancing these fields through his innovative research. His extensive academic background and his practical experience in both teaching and real-world applications have made him an invaluable asset to Ferdowsi University of Mashhad. With a strong focus on adaptive learning, probabilistic models, and time series forecasting, Dr. Mehrizi continues to contribute to the evolution of AI. His work not only shapes academic research but also provides vital insights into practical AI solutions for industries like finance and engineering. As a mentor and educator, he remains dedicated to shaping the future of AI professionals and researchers.

Diana Morales | Deep Learning | Best Researcher Award

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Dr. Diana Morales | Deep Learning | Best Researcher Award

Critical Care Fellow | University of Toronto | Canada

Dr. Diana Morales Castro, MD, MSc, is a renowned Costa Rican physician specializing in critical care medicine, echocardiography, and perioperative medicine. Currently serving as an Adult Critical Care Senior International Fellow at Toronto General Hospital, University Health Network, and University of Toronto, Dr. Morales Castro has an extensive academic and clinical background. With advanced training in critical care, anesthesiology, and echocardiography, her expertise has been shaped by prestigious fellowships and master’s programs in various global institutions, including the University of Toronto and University College London. She has contributed significantly to research in pharmacokinetics, critical care, and echocardiography, publishing in esteemed medical journals. Her dedication to education is evidenced by her role as a mentor for the European Diploma in Advanced Critical Care Echocardiography.

Profile

Scholar

Education

Dr. Morales Castro’s educational background is rooted in excellence and dedication to advancing medical knowledge. She graduated with a Licentiate in Medicine and Surgery from the University of Costa Rica in 2011, followed by a Specialty in Anesthesiology and Recovery in 2015 from the same institution. Seeking to deepen her knowledge in critical care, she completed a Master in Perioperative Medicine at University College London in 2018. Her journey continued with a series of fellowships, including the Adult Critical Care Medicine Fellowship and Adult Critical Care Echocardiography Fellowship at the University of Toronto in 2018 and 2020, respectively. Dr. Morales Castro further expanded her expertise by pursuing a Master in Pharmaceutical Sciences at the University of Toronto, which she is expected to complete in 2024.

Experience

Dr. Morales Castro’s clinical experience spans across several high-profile institutions in Costa Rica and Canada. She began her career as a General Physician at the El Caoba EBAIS in Costa Rica, where she served in mandatory social service. She then advanced to become an Attending Anesthesiologist at Trauma Hospital and Hospital Calderón Guardia, before further specializing in adult critical care at the University of Toronto. Her role as an Attending Intensivist at the National Transplant and ECMO Center in Costa Rica was a significant milestone, where she provided critical care to patients undergoing complex treatments like ECMO. Currently, she balances her work as an attending physician with her position as a mentor for advanced critical care echocardiography at the European Society of Intensive Care Medicine.

Research Interests

Dr. Morales Castro’s research primarily focuses on pharmacokinetics and pharmacodynamics in critically ill patients, particularly those undergoing extracorporeal membrane oxygenation (ECMO). Her work delves into optimizing sedative and anesthetic pharmacokinetics during critical illness and exploring the role of therapeutic drug monitoring for drugs like propofol and fentanyl in patients on ECMO. She also investigates the impact of echocardiography and ultrasound techniques in the management of critically ill patients, with a special interest in COVID-19-related complications. Her work not only contributes to improving clinical outcomes but also advances the education of healthcare providers through innovative teaching methods like self-learning videos in transthoracic echocardiography.

Awards

Dr. Morales Castro has received numerous accolades throughout her career, recognizing her excellence in research, education, and clinical care. She was awarded the 2023 Allan Spanier Award for the best education study on simulator-based echocardiography training. In 2022, she received the MD Program Teaching Award of Excellence from the Temerty Faculty of Medicine at the University of Toronto. Her dedication during the COVID-19 pandemic was recognized with a certificate from the Costa Rican Social Security. Further demonstrating her academic prowess, she received honors for her master’s degree in perioperative medicine from University College London in 2019 and honors for her specialty in anesthesiology from the University of Costa Rica in 2015.

Publications

Dr. Morales Castro has authored several impactful publications in leading medical journals, reflecting her research contributions in critical care and pharmacokinetics. Key publications include:

Morales Castro D, Wong I, Panisko D, Najeeb U, Douflé G. Self-Learning Videos in Focused Transthoracic Echocardiography Training. Clin Teach. 2025 Feb;22(1):e70014.

Morales Castro D, Balzani E, Abdul-Aziz MH, et al. Propofol and Fentanyl Pharmacokinetics and Pharmacodynamics in Extracorporeal Membrane Oxygenation. Annals of the American Thoracic Society. 2025;22(1):121-9.

Morales Castro D, Granton J, Fan E. Ceftobiprole and Cefiderocol for Patients on Extracorporeal Membrane Oxygenation: The Role of Therapeutic Drug Monitoring. Current Drug Metabolism. 2024;25:1-5.

Morales Castro D, Ferreyro B.L., McAlpine D, et al. Echocardiographic Findings in Critically Ill COVID-19 Patients Treated with and Without ECMO. J Cardiothorac Vasc Anesth. 2024.

Douflé G, Dragoi L, Morales Castro D, et al. Head-to-Toe Bedside Ultrasound for ECMO Patients. Intensive Care Med. 2024.

Morales Castro D, Dresser L, Granton J, Fan E. Pharmacokinetic Alterations in Critical Illness. Clin Pharmacokinet. 2023; 62(2):209-220.

Morales Castro D, Abdelnour-Berchtold E, Urner M, et al. Transesophageal Echocardiography-Guided ECMO Cannulation in COVID-19. J Cardiothorac Vasc Anesth. 2022;36(12):4296-4304.

Conclusion

Dr. Diana Morales Castro stands out as a dedicated physician, educator, and researcher with a profound impact on the fields of critical care medicine and pharmacokinetics. Through her academic achievements, clinical experience, and innovative research, she has contributed to improving the quality of care in critical settings, especially for patients undergoing complex treatments like ECMO. Her commitment to education and mentorship further elevates the standards of healthcare. As she continues to explore the intersections of critical care, pharmacokinetics, and echocardiography, Dr. Morales Castro’s work promises to shape the future of intensive care and pharmacological management in critically ill patients.