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Alessandro MengarelliView profile

Research Fellow

• Motion Analysis
• Biomechanics
• Biosignal Processing
• +3 more

Alessandro Mengarelli is a Research Fellow at the Department of Information Engineering (DII) of Università Politecnica delle Marche. He holds a Ph.D. in Information Engineering (2017) from the same institution, focusing on balance and motor control in dynamic tasks under Prof. Sandro Fioretti's supervision. His research integrates biomechanics, biosignal processing, and wearable technologies, with a focus on neuromuscular control strategies and assistive systems. Key research areas include motion analysis, muscle co-contraction patterns, gait biomechanics, and the application of machine learning to biomedical data. He is actively involved in IEEE societies (EMBS, CTSoc, RAS) and the Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System (IUC BOHNES). His work spans from wearable sensor development to clinical applications in diabetic neuropathy and neuro-rehabilitation. Mengarelli's recent publications emphasize innovations in gait phase recognition, EMG-based intent detection for prosthetics, and IoT-driven health monitoring. His articles highlight advancements in sensor fusion, deep learning models for biomechanical analysis, and minimal setup solutions for practical clinical use. He has contributed as a reviewer and Guest Editor for peer-reviewed journals, reflecting his engagement in advancing the field. His research bridges theoretical models (e.g., intermittent control theory) with practical applications, aiming to improve human-machine interfaces and assistive technologies.

• Biomedical Engineering
• Human Movement Analysis
• Neuromuscular Control
• +3 more

Federica Verdini is a Researcher and Chief-staff scientist at the Movement Analysis Lab, Università Politecnica delle Marche. She teaches Automatic Control for Biomedical and Electronic Engineering courses. Her research focuses on biological signal processing for human movement analysis and neuromuscular control modeling, with applications in diabetic neuropathy screening and gait analysis. She holds an MS in Electronic Engineering (Ancona University, 1997) and a PhD in Bioengineering (Bologna University, 2001). She has contributed to numerous European and national research projects. Verdini’s work involves developing algorithms for EMG and inertial sensor data analysis, wearable technologies, and clinical applications. Her studies include gait phase recognition, balance maintenance modeling, and minimal sensor setups for activity recognition. She collaborates on projects like the Kimore Dataset for remote rehabilitation monitoring and validation of Nintendo Wii Balance Board for clinical assessments. Key technical contributions include neuromuscular control models, signal fusion techniques, and machine learning approaches for biomechanical analysis. She has published extensively on topics such as EMG-based force estimation, postural stability in diabetic patients, and robotic control systems. Her research bridges engineering and medicine, aiming to improve clinical diagnostics and assistive technologies.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Ethan Goan is a Research Fellow at Queensland University of Technology (QUT) within the Faculty of Engineering, specifically affiliated with the School of Electrical Engineering & Robotics. He is actively involved with the Centre for Data Science and specializes in Signal Processing, Artificial Intelligence, and Vision Technologies research areas. His educational background includes: PhD pending conferral (as of February 27, 2024) from Queensland University of Technology Bachelor of Engineering (Electrical) from Queensland University of Technology Dr. Goan's research focuses on the intersection of artificial intelligence and signal processing, with particular expertise in Bayesian neural networks and their applications. His work spans multiple domains including medical imaging, radar signal processing, computer vision, and digital health. His recent publications demonstrate strong emphasis on uncertainty quantification in deep learning models, particularly for real-time applications on embedded systems. His research combines theoretical advances in probabilistic modeling with practical applications across healthcare, robotics, and signal processing domains. His publication record shows consistent output since 2016, with increasing focus on deep learning approaches to solve complex problems in signal processing and computer vision. Recent work demonstrates expertise in zero-shot learning frameworks, semantic segmentation, and the application of AI to medical diagnostics and digital health monitoring. Dr. Goan collaborates extensively with researchers across multiple institutions, as evidenced by his co-authorship on large-scale collaborative projects such as the Parkinson's Disease Digital Biomarker DREAM Challenge. His work appears in prestigious venues including IEEE conferences, Nature partner journals, and specialized machine learning publications. He is affiliated with the Centre for Data Science at QUT and works within the Signal Processing, Artificial Intelligence and Vision Technologies research group, contributing to QUT's growing reputation in AI and robotics research.

Fredrik GeorgssonView profile

Senior Lecturer

• Medical Image Processing
• Computer Aided Mammography
• Fractal Analysis
• +3 more

Fredrik Georgsson is a Senior Lecturer and Program Director at the Department of Computing Science, Umeå University. His roles include teaching, research leadership, and academic administration. He holds a PhD in Computing Science (2001) and has been at Umeå University since 1996, progressing from PhD student to his current Senior Lecturer position. Education: PhD in Computing Science, Umeå University (2001) Licentiate of Philosophy, Umeå University (1999) MSc in Computing Science and Engineering, Umeå University (1996) Research Interests: Medical Image Processing (e.g., mammography, tumor analysis) Fractal analysis of medical images Autonomous vehicle systems Pattern recognition and computer vision Teaching & Advising: Responsible for courses like Medical Image Processing and Computational Intelligence Supervised over 15 Master's and PhD students in medical imaging and related fields Developed curricula for advanced medical image analysis courses Grants & Projects: Lead developer of autonomous forest machinery path-tracking systems Recipient of multiple research grants for medical imaging projects Labs & Teams: Principal investigator in the Medical Imaging Analysis Lab and Autonomous Systems Group at Umeå University.

• HIV Research
• Sexually Transmitted Infections (STIs)
• Health Economics
• +3 more

Sally Zhang is a Casual Research Assistant at the Melbourne Sexual Health Centre, affiliated with the School of Translational Medicine at Monash University. Her academic rank is Researcher, and she holds a Master of Public Health from the University of Sydney and a Doctorate by Research from Monash University. Her research focuses on HIV/AIDS, sexually transmitted infections (STIs), and health economics, with a particular emphasis on developing AI-driven interventions for sexual health diagnostics and public health policy. Her work contributes to UN Sustainable Development Goals related to good health and well-being. She has collaborated on projects addressing HIV self-testing, syphilis utility values, and improving access to pre-exposure prophylaxis (PrEP) for migrant populations. Her interdisciplinary approach integrates epidemiology, artificial intelligence, and cost-effectiveness analysis to inform healthcare strategies. Led or co-authored research outputs include studies on STI diagnostics, behavioral health interventions, and global health equity. She actively engages in translational research to bridge clinical practices and public health policies, with a focus on marginalized populations.

• Machine Learning
• Artificial Intelligence
• Medical Image Analysis
• +7 more

Francesco Ponzio is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, where he contributes to teaching and research in information processing systems. He is actively involved in multiple engineering programs, including Computer, Aerospace, and Management Engineering. University: Politecnico di Torino Department: Department of Control and Computer Engineering (DAUIN) Role: Fixed-term Assistant Professor Scientific Sector: IINF-05/A - Information Processing Systems Francesco Ponzio's research spans artificial intelligence and machine learning with applications in medical imaging, industrial automation, and smart manufacturing. His work integrates advanced computational models into real-world systems, particularly in healthcare and advanced manufacturing. His recent publications reveal a strong trend toward interdisciplinary research, combining deep learning, radiomics, and physics-informed neural networks to address challenges in biomedical diagnostics and industrial process optimization. Notably, his work in histopathological image analysis, tumor classification, and real-time anomaly detection highlights his expertise in both algorithmic development and practical deployment. Francesco Ponzio has contributed to high-impact journals and top-tier conferences such as IEEE ETFA, DAC, DATE, and Frontiers in Oncology. His recent work includes journal articles in Journal of Intelligent Manufacturing and Scientific Reports , as well as conference papers in ACM/IEEE Design Automation Conference and IEEE ICIT. He has served as a course collaborator for Machine Learning in Applications and as a lecturer and collaborator in foundational computer science courses across various engineering disciplines. He is also an invited member of two degree program councils, reflecting his institutional engagement. francesco.ponzio@polito.it

Juan ShanView profile

Associate Professor

• Machine Learning
• Medical Image Analysis
• Computer-Aided Diagnosis (CAD)
• +2 more

Juan Shan is an Associate Professor in the Department of Computer Science at Pace University's Seidenberg School of CSIS. She joined Pace in 2013 after serving as an Assistant Professor at Benedictine College from 2011-2013. Her work focuses on applying machine learning to medical imaging and computer-aided diagnosis systems. PhD in Computer Science from Utah State University (2011) BSc in Computer Science from Harbin Institute of Technology (2004) Research spans medical image analysis , machine learning , and clinical decision support . Recent publications emphasize deep learning for knee osteoarthritis , hand radiograph analysis , and automated segmentation . Awards include: 2024 Kenan Award for Teaching Excellence 2017 NSF SCH Grant (PI) for 3D knee osteoarthritis modeling 2017 DoD DAISEC Grant (Co-PI) in cybersecurity data analytics Professional memberships include IEEE and IEEE Women in Engineering.

Frank Del FaveroView profile

Professor

• Educational Leadership
• Data Analysis
• Root Cause Analysis
• +5 more

Dr. Frank Del Favero is a Professor and Graduate Coordinator for the Master's Degree Program in Educational Leadership in the Department of Educational Foundations & Leadership at the University of Louisiana at Lafayette, College of Education & Human Development. His work integrates academic theory with practical K–12 leadership experience. His research focuses on educational leadership , data analysis , root cause analysis , and school improvement . He emphasizes the use of data to inform instructional strategies and assessment practices, promoting evidence-based decision-making in schools. Dr. Del Favero has contributed significantly through publications in peer-reviewed journals and as editor of influential books such as Instructional Leadership Knowledge and Skills for K-12 Success (2019) and Organizational Leadership Knowledge and Skills for K-12 Success (2021). His research spans topics including formative assessment, technology integration, educational law, and international accountability systems. Florence Mauboules/BORSF Endowed Professorship in Education He mentors students in research related to educational leadership and school improvement. Though specific grants are not listed, his endowed professorship indicates sustained research support. He teaches courses such as EDLD 501: Using Data to Effect Change and advises on curriculum development and leadership training. Dr. Del Favero is actively involved in academic service and leadership development through his publications, editorial work, and coordination of graduate programs, contributing to both national and international discourse on effective educational leadership.

• Synthetic Biology
• Cancer Diagnosis and Prevention
• Functional Food
• +3 more

Lígia Raquel Marona Rodrigues is an Associate Professor with Habilitation at the University of Minho (UM), Portugal , where she serves as Vice Dean of the School of Engineering and President of the Pedagogical Council . She is also Associate Director of the Centre of Biological Engineering (CEB) and former Head of the Department of Biological Engineering (2018-2022) . Her academic journey includes a Ph.D. in Chemical and Biological Engineering from UM (2005) , a Habilitation from UM (2017) , and postdoctoral work at UM, IPATIMUP (University of Porto), and University of Lund (2006-2007). Research Focus: Synthetic biology, cancer diagnostics, functional food, biosurfactants, and bioprocess development. Leadership: Directed the MSc in Technology and Food Science (2014-2019), co-founded spin-off companies in Life Sciences, and evaluates for Horizon Europe and other funding bodies.

• Antimicrobial Resistance
• Clinical Microbiology
• Molecular Diagnostics
• +3 more

Roxana Filip is a Professor at the Faculty of Medicine and Biological Sciences, Stefan cel Mare Suceava University. She is an MD, PhD with expertise in clinical microbiology, antimicrobial resistance, and molecular diagnostics. Her academic career spans teaching roles at multiple Romanian institutions, including junior instructor (1991-2004) and associate lecturer (2012-2020), culminating in her current professorship since 2025. Her research focuses on Antibiotic resistance mechanisms in Enterobacteriaceae Dynamics of Mycobacterium tuberculosis resistance Molecular biology applications in pandemics Microbiological computing Her work has produced 15+ publications spanning microbiology, immunology, and public health themes. Recent publications analyze SARS-CoV-2 complications, antimicrobial resistance patterns, and innovative diagnostic approaches. Key trends include interdisciplinary applications of informatics in medicine and biopolymer-based healthcare solutions. Scientific recognition includes Honorary Citizen of Suceava (2021) Anastasie Crimca Distinction (2022) She has served as abstract reviewer, trainer, and session chair at international conferences like ECCMID and ESCMID. As attending physician and head of Suceava Emergency County Hospital's Tuberculosis and Molecular Biology Laboratory since 2018, she bridges clinical practice with academic research. Her leadership extends to establishing molecular diagnostic labs during the COVID-19 pandemic.

Jacek CichoszView profile

Researcher

• System Identification
• Biomedical Engineering
• Signal Processing
• +3 more

Jacek Cichosz is an academic researcher affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology , specializing in the Department of Systems and Computer Networks . Holding a PhD, he focuses on system identification, biomedical engineering, and signal processing applications. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Department of Systems and Computer Networks His research spans system identification , medical imaging analysis , and fuzzy logic applications in diagnostics , particularly for laryngeal pathology and ECG interpretation. He has developed educational tools like the CARDIO system and contributed to computer-aided diagnosis frameworks. Recent publications highlight trends in recursive identification , image analysis , and fuzzy rule-based diagnostic systems , with interdisciplinary applications in healthcare and computer science. Keywords include system identification , medical imaging , and biomedical signal processing . Contact: jacek.cichosz@pwr.edu.pl

Paul N. StaatsView profile

Professor

• Cytopathology
• Gynecologic Pathology
• Breast Pathology
• +2 more

Dr. Paul N. Staats is a Professor of Pathology at the University of Maryland Medical Center, where he has practiced anatomic pathology since 2009. He holds multiple administrative roles including Assistant Director of Anatomic Pathology Laboratory Operations and Interim Chair of the Department of Pathology, while also serving as Director of the Cytopathology Service. His clinical focus spans cytopathology, gynecologic pathology, and breast pathology. Research Interests: Refinement of morphologic criteria for cancer diagnosis Implementation of ancillary testing in cytopathology Improving laboratory quality and efficiency Collaborative approaches in hormone-responsive cancers Publications Trends: His recent work emphasizes diagnostic accuracy in cervical and thyroid cytology, leveraging computational methods for nuclear analysis, and standardizing communication protocols in urgent pathology diagnoses. Education: B.S. in Chemistry from Yale University (1994-1998) M.D. from SUNY Upstate Medical University (1998-2003) Post-Sophomore Fellowship at SUNY Upstate (2000-2001) Residency at Massachusetts General Hospital (2003-2007) Specialized fellowships in gynecologic pathology and cytopathology (2007-2009)

• Machine Learning
• IoT Security
• Deep Learning
• +4 more

Ponnusamy Vijayakumar is a researcher affiliated with SRM University in Kanchipuram, India, within the College of Engineering and Department of Electrical & Computer Engineering . His work spans interdisciplinary domains including Machine Learning , IoT Security , and Deep Learning , with additional expertise in Blockchain , Augmented Reality , and Cyber-Physical Systems . Research Interests : Vijayakumar focuses on applying advanced machine learning techniques to real-world problems such as energy sector optimization , food safety , and medical diagnostics . His recent work explores federated learning for secure IoT environments, predictive analysis using stochastic methods, and computer vision for rehabilitation and security applications. Article Trends : Over the past five years, he has contributed to IoT security through anomaly detection frameworks, augmented reality for plant disease detection, and blockchain for credentialing systems. His publications also address deep learning applications in agricultural quality analysis and medical imaging for musculoskeletal disorders. Collaborations : Vijayakumar has collaborated extensively with experts in Serbia, India, and Germany, particularly with researchers like Nemanja Zdravkovic , Aman Kumar Mishra , and Sowmya Natarajan , across conferences such as BISEC and journals like IEEE Access .

• Osteoarthritis
• Magnetic Resonance Imaging
• Biomarkers
• +3 more

Jeffrey B Driban, PhD, AT Ret, CSCS, is a Professor in the Department of Population and Quantitative Health Sciences at UMass Chan Medical School. He holds secondary appointments in Epidemiology at the T.H. Chan School of Medicine and Population Health Sciences at the Morningside Graduate School of Biomedical Sciences. His academic background includes a BS in Athletic Training from the University of Delaware, and both a Master of Education and Doctor of Philosophy in Kinesiology with an Emphasis in Athletic Training from Temple University, followed by a post-doctoral fellowship in Rheumatology at Tufts Medical Center. Dr. Driban's research focuses on novel risk factors, biomarkers, and imaging markers for osteoarthritis. His work aims to optimize clinical trials and deepen understanding of osteoarthritis pathophysiology, phenotypes, and prevention strategies. He envisions a streamlined therapeutic pipeline using targeted population subsets and sensitive outcome measures to reduce the global burden of osteoarthritis. Key research areas include: Accelerated osteoarthritis progression mechanisms Imaging biomarkers for early detection Lifestyle interventions for primary/secondary prevention Environmental factors in hand osteoarthritis Machine learning applications in prognosis His publication portfolio demonstrates consistent focus on osteoarthritis pathophysiology, diagnostic innovations (especially MRI and radiomics), and non-pharmacological interventions. Recent work emphasizes deep learning applications for automated lesion segmentation, biomechanical influences of activities like cycling/swimming, and prognostic modeling for end-stage disease. Longitudinal cohort analyses (e.g., Osteoarthritis Initiative) underpin much of this research. Dr. Driban has secured multiple NIH grants as Principal Investigator, including R01AR076411 (2021-2026) on composite structural endpoints in knee osteoarthritis and R01AR081791 (2023-2026) investigating environmental factors in hand osteoarthritis. He chairs task groups for the Osteoarthritis Action Alliance and serves on editorial boards for the Journal of Rheumatology, BMC Musculoskeletal Disorders, Journal of Athletic Training, and Arthritis Care & Research.

Jinan FiaidhiView profile

Professor

• Thick Data Analytics
• Deep Learning
• Few Shots Learning
• +12 more

Dr. Jinan Fiaidhi is a Professor of Computer Science at Lakehead University, Canada since 2001. She served as Graduate Coordinator for the MSc and PhD programs in Computer Science and Biotechnology. She holds adjunct positions at the University of Western Ontario. Her academic journey includes degrees from Essex University (PgD, 1983) and Brunel University (PhD, 1986). She has held academic roles at institutions like Sultan Qaboos University and Philadelphia University prior to Lakehead. Her research focuses on Thick Data Analytics , Deep Learning , and Collaborative Learning , with applications in healthcare, medical imaging, and AI-driven diagnostics. She is a Professional Engineer (PEng) in Ontario and a Senior Member of IEEE. She chairs the IEEE Special Interest Group on Big and Thick Data for eHealth and founded the International Journal of Extreme Automation and Connectivity in Healthcare (IJEACH) as its Emeritus Editor-in-Chief. Her research is funded by NSERC and MITACS grants. Key projects include developing frameworks like QL4POMR for problem-oriented medical records and applying thick data analytics to Crohn’s disease, ulcerative colitis, and cataract severity analysis. Education: PhD in Computer Science, Brunel University (1986) PgD in Computer Science, Essex University (1983) Awards: None explicitly listed, but holds professional designations: PEng, IEEE Senior Member, ISP (CIPS), and MBCS (Chartered Computing). Grants: NSERC, MITACS (specific projects unspecified). Labs/Teams: Leads IEEE eHealth initiatives and collaborates on projects involving thick data analytics in healthcare interoperability and extreme automation.