Stephani Argyrou | Neuromuscular Rehabilitation | Best Researcher Award

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Ms. Stephani Argyrou | Neuromuscular Rehabilitation | Best Researcher Award

PhD candidate at University of Thessaly, Cyprus

Stephani Argyrou is a physiotherapist and researcher whose work bridges advanced physiotherapy, clinical teaching, and women’s health rehabilitation. Currently a PhD candidate at the Technological University of Cyprus, she combines her academic pursuits with clinical expertise in neuromuscular physiotherapy and orthopedic rehabilitation. Her contributions span research, teaching, clinical excellence, and international collaboration, making her a distinguished candidate for this award.

Professional Profile

Google Scholar

Education

Her academic journey began with a bachelor’s degree in Physiotherapy from the TEI of Sterea Ellada, where she developed foundational knowledge in musculoskeletal, neurological, and cardiopulmonary rehabilitation. She then pursued a master’s degree in Advanced Physiotherapy at the same institution, excelling as the top graduate with the highest academic distinction. Building upon her expertise, she is now a PhD candidate at the Department of Nursing at the Technological University of Cyprus. Her doctoral research integrates clinical physiotherapy with scientific inquiry, focusing on innovative therapeutic approaches and evidence-based practice.

Experience

Stephani has demonstrated an outstanding commitment to both academia and clinical practice. She has served as a scientific collaborator at several universities, including the University of Frederic and the University of Nicosia, where she taught biomechanics, neurology, and clinical training in neurological rehabilitation. At the University of Thessaly and the European University of Cyprus, she supervised clinical practice in cardio-respiratory, musculoskeletal, and neurological physiotherapy, contributing to the professional development of future physiotherapists. Her teaching career reflects her dedication to bridging theory with practice and inspiring students to pursue excellence. In addition to her academic roles, she has worked extensively as a clinical physiotherapist in Cyprus and Germany, specializing in orthopedic, sports, and women’s health physiotherapy. She currently practices at a leading orthopedic and sports physiotherapy center, where she applies advanced therapeutic methods to improve patient outcomes.

Research Focus

Her primary research interests revolve around motor learning, postural control, women’s health, and neuromuscular rehabilitation. She has particularly focused on forward head posture, a prevalent musculoskeletal issue, examining the effectiveness of exercise interventions based on motor learning principles. She is also deeply engaged in advancing women’s health physiotherapy, particularly pelvic floor rehabilitation, integrating international training in specialized clinical approaches. Her work highlights the importance of innovative rehabilitation strategies that combine evidence-based practice with personalized patient care, contributing significantly to the development of modern physiotherapy.

Awards and Honors

Stephani’s dedication and excellence have been recognized through prestigious awards and scholarships. She was honored with the Young Researcher Award for best oral presentation at the 8th Conference of ELEMVI for her study on motor learning principles in correcting forward head posture. She also achieved first place in her master’s program in Advanced Physiotherapy, receiving scholarships for her outstanding performance throughout her postgraduate studies. These honors underscore her academic brilliance, research excellence, and commitment to advancing the physiotherapy profession.

Publication Top Notes

Title: Reliability of a Two-Dimensional Video Analysis Protocol to Assess Forward Head Posture during Walking
Authors: Z. Dimitriadis, S. Argyrou, A. Diamantis, K. Kostakis, A. Kanellopoulos
Summary: This study validated a two-dimensional video protocol for assessing forward head posture during walking, demonstrating strong reliability and practical applicability for clinical physiotherapy evaluation and research.

Title: The Effectiveness of an Exercise Program Based on Motor Learning Principles for the Correction of the Forward Head Posture: A Randomized Controlled Trial
Authors: S. Argyrou, P. Kitixis, Z. Dimitriadis, A. Christakou, N. Strimpakos, G. Paras
Summary: This randomized controlled trial showed that exercise interventions incorporating motor learning principles significantly improved forward head posture correction, supporting their integration into physiotherapy rehabilitation programs.

Conclusion

Stephani Argyrou exemplifies the integration of research, teaching, clinical practice, and community engagement in the field of physiotherapy. Her academic excellence, recognized through awards and publications, is matched by her dedication to improving patient outcomes and advancing physiotherapy education. Through her research on motor learning, postural correction, and women’s health rehabilitation, she contributes innovative and impactful knowledge to her discipline. With her combination of academic brilliance, clinical expertise, and commitment to service, she stands out as a highly deserving candidate for recognition in this award nomination.

Lilyana Khatib | Rehabilitation | Best Researcher Award

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Ms. Lilyana Khatib | Rehabilitation | Best Researcher Award

Researcher, University of Haifa, Israel.

Lilyana Khatib is a passionate and skilled Machine Learning (ML) Algorithm Engineer with a focus on machine and deep learning algorithms. Holding a Master’s degree in Computer Science, she specializes in applying advanced machine learning techniques to real-world challenges, particularly in the healthcare and medical fields. She is known for her quick learning ability and disciplined approach to problem-solving. In her current role at Biosense Webster, Lilyana is involved in developing algorithms for electrophysiology and cardiac rhythm, contributing to the advancement of medical technology. Beyond her professional work, she is actively engaged in volunteering efforts, including mentoring and empowering Arab women in STEM and exposing high-school students to the world of technology. Her research interests also extend to adaptive testing systems and computer vision applications.

Profile

Scopus

Education 

Lilyana Khatib completed her M.Sc. in Computer Science at the University of Haifa, graduating with a GPA of 95, cum laude. During her studies, she focused on machine learning and its applications in various domains, including healthcare. She pursued a Deep Learning course at the Technion, where she achieved an impressive grade of 97, demonstrating her mastery in the field. Her academic career was marked by a commitment to excellence, combining theoretical knowledge with practical research. Her B.Sc. in Computer Science from the University of Haifa, with a GPA of 83, laid the foundation for her deep interest in artificial intelligence and machine learning. Lilyana’s academic training enabled her to conduct high-impact research, such as her thesis on adaptive testing for fall risk assessments. She continues to build on this foundation through her professional work and volunteer initiatives.

Experience 

Lilyana Khatib’s professional experience spans across various aspects of machine learning and algorithm development. Currently working as a Machine Learning Algorithm Engineer at Biosense Webster since 2022, Lilyana designs and implements ML algorithms, addressing challenges in electrophysiology and cardiac rhythm. She manages end-to-end ML pipelines, including data collection, feature engineering, model development, and evaluation. In this role, she collaborates with multidisciplinary teams, including hardware, software, and clinical experts, to integrate algorithms seamlessly. Prior to this, she worked as a Research Assistant at Bar Ilan University, contributing to ML research on sign language recognition and motion capture analysis. Additionally, she served as a Teaching Assistant at the University of Haifa, tutoring computer science students and creating AI lab exercises. Lilyana’s diverse experiences allow her to approach problems with both academic rigor and practical insight, making her a versatile contributor to machine learning projects.

Awards and Honors 

Lilyana Khatib has received several academic and professional accolades throughout her career. She graduated with distinction (cum laude) from the University of Haifa with a Master’s degree in Computer Science, reflecting her strong academic performance and dedication to excellence. Her research contributions have also earned recognition in the field of machine learning, with her work on adaptive testing algorithms for older adults being published in Applied Sciences. In addition, her participation in international conferences such as the Language Resources and Evaluation Conference (LREC) in Marseilles, France, where she co-authored a paper on sentiment analysis, further highlights her standing in the research community. Beyond academic awards, Lilyana is also celebrated for her leadership roles, especially in mentoring and empowering underrepresented groups in STEM, such as her involvement as a team lead at AWSc and a mentor at Tsofen, showcasing her commitment to fostering diversity and inclusion in technology.

Research Focus 

Lilyana Khatib’s research focuses on the intersection of machine learning, healthcare, and computer vision. Her primary area of interest lies in developing advanced algorithms for healthcare applications, specifically in cardiac rhythm analysis, electrophysiology, and fall risk assessments. Through her work at Biosense Webster, she applies both classic machine learning and deep learning techniques to real-world challenges in the medical field, improving patient outcomes through more accurate diagnostics and assessments. Lilyana’s M.Sc. thesis, which explored a machine learning-based computerized adaptive testing algorithm for fall risk, demonstrates her dedication to healthcare technology. Additionally, she has worked on projects such as the Crying Detection Application, which uses computer vision to detect emotional states without relying on audio. Her research is highly interdisciplinary, integrating computer science, healthcare, and AI, and aims to make a meaningful impact on medical practices and patient care, particularly for vulnerable populations such as older adults.

Publications

  • Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults 🧠📊 (Applied Sciences, 2025)
  • Capturing Distalization 📖 (Workshop on Sentiment Analysis and Linguistic, LREC, Marseilles, France, 2022)