Dr. Zhenghao Chen is an Assistant Professor at the University of Newcastle. He holds a B.Eng. H1 and Ph.D. from the University of Sydney (2017 and 2022). His research focuses on Computer Vision, NLP, and Machine Learning, with expertise in Generative AI. He has published in top conferences like CVPR and journals such as IEEE T-IP. Awards include the Google Australia Prize and ACM SIGMM Outstanding Thesis Award. He previously worked at TikTok and Disney Research, and serves on program committees for major conferences. Research interests emphasize generative models and industrial applications. His publications span topics like image compression, facial recognition, and medical imaging. Awards highlight academic and industrial recognition. Teaching includes courses on visual signal understanding and video intelligence. Current roles include HDR recruitment and organizing international workshops.
Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.
Maria Chiara Fiorentino is a Research Fellow at the Department of Information Engineering, Polytechnic University of Marche, Italy. Her work focuses on applying deep learning techniques to medical image analysis, particularly in ultrasound, MRI, and CT imaging. Education Master’s in Biomedical Engineering, Università Politecnica delle Marche (Honors) Ph.D. in Information Engineering, Università Politecnica delle Marche (Laude) Research Interests: Dr. Fiorentino specializes in deep learning for medical imaging, with applications in diagnosing neurodegenerative diseases like Parkinson’s, cardiovascular conditions, and musculoskeletal disorders. Her recent work includes federated learning for fetal ultrasound analysis, AI-driven vocal fold pose estimation, and domain adaptation in MRI segmentation. Scientific Awards: Paolo Marziali Thesis Prize for her Master’s research Gruppo Nazionale di Bioingegneria award for her Ph.D. thesis Publications: Dr. Fiorentino’s work spans fetal brain image synthesis, zero-shot learning robustness, and machine learning for catheterization management and stenosis detection.
Vahid Behzadan is an Assistant Professor in Data Science and Computer Science at the University of New Haven's Tagliatela College of Engineering. He leads the SAIL Lab, focusing on AI safety and security, particularly in autonomous systems like driverless cars and smart cities. His work addresses adversarial attacks on machine learning and reinforcement learning, with applications in cybersecurity and healthcare. Behzadan has held prior positions at Kansas State University, University of Nevada Reno, and University of Birmingham, UK. He holds a Ph.D. in Computer Science and an M.S. from the University of Nevada Reno, and a B.Eng. from the University of Birmingham. His research spans AI ethics, cybersecurity, and complex systems. Behzadan advises the UNH hacking team and actively participates in policy initiatives, including Connecticut's AI Working Group and the Connecticut AI Alliance. He has contributed to over 30 peer-reviewed articles and frequently engages in media discussions on topics like AI safety, facial recognition, and cybersecurity threats. Key research areas include adversarial machine learning, AI forensics, and ethical AI design. His work bridges theoretical advancements with real-world applications in transportation, healthcare, and national security. Behzadan collaborates with organizations such as the Transportation Research Laboratory (TRL) and Open Web Application Security Project (OWASP).
Marina L. Gavrilova is a Professor at the University of Calgary, Canada. Her research focuses on biometric systems, computer vision, and machine learning with an emphasis on multimodal recognition and security applications. She has authored numerous publications in top journals and conferences, contributing to advancements in fields like emotion-aware de-identification, generative adversarial networks, and ethical AI frameworks in healthcare. Her work spans social behavioral biometrics, gait recognition, masked face recognition, and aesthetic-based person identification. Key contributions include frameworks for ethical AI in care systems, fusion algorithms for multi-biometric systems, and innovations in visual and audio signal processing. Collaborations with experts like Osvaldo Gervasi, Jon G. Rokne, and Padma Polash Paul highlight her interdisciplinary approach. Publications emphasize practical applications such as privacy-preserved biometrics, emotion detection from social media, and adaptive systems for template aging. Despite no explicit mention of grants or labs, her extensive co-author network and frequent citations indicate significant academic influence.
Donato Romano serves as Associate Professor at The BioRobotics Institute of Scuola Superiore Sant'Anna, Italy, where he coordinates the Bio-Robotic Ecosystems Lab and co-founded the spin-off company HUBILIFE srl. His interdisciplinary work bridges robotics, biology, and AI to develop biohybrid systems for biodiversity preservation, sustainable environmental management, and life support in extreme scenarios including space exploration. With over 90 publications and an H-index of 27 (Scopus, March 2025), he has established significant academic leadership through editorial roles across 12+ international journals. Romano's educational foundation includes advanced degrees with honors: an M.Sc. in Agriculture Science and Technologies (2014) and a PhD in BioRobotics (2018), both from Scuola Superiore Sant'Anna. His academic journey includes visiting scholar positions at Khalifa University and substantial industry-academia collaboration through HUBILIFE srl, which commercializes bioinspired devices for human daily life improvement. His research program focuses on bioinspired and biomimetic robotics with particular emphasis on animal-robot interaction, biohybrid systems, and natural intelligence. Key projects address critical global challenges: SENSORBEES develops biohybrid environmental surveillance for ecological monitoring; REGOLIFE investigates lunar soil-terrestrial organism interactions for space agriculture; and OCEAN ROBOCTO explores marine ecosystem solutions. This work demonstrates a strategic progression from fundamental behavioral studies toward applied ecological and extraterrestrial systems. Analysis of his recent publications reveals strong trends in AI-driven behavioral analysis, with deep learning increasingly applied to entomological studies and pest management. The research spans agricultural applications (precision monitoring traps, larval detection systems), ecological conservation (biodiversity surveillance), and extreme-environment adaptation (lunar regolith studies). A distinctive feature is the consistent integration of biohybrid approaches where living organisms and robotic systems create synergistic capabilities exceeding either component alone. Romano's scientific recognition includes election as Junior Fellow of the Italian Academy of Engineering and Technology (2025), the Lucani fuori dal Comune award (2024), and multiple best-thesis prizes. His editorial leadership spans high-impact journals including IEEE Transactions on Medical Robotics and Bionics and Pest Management Science, where he serves as Associate Editor. As principal investigator, Romano coordinates major international projects totaling over €15M in funding: HORIZON-EIC's SENSORBEES (2024-2029), ASI's REGOLIFE (2024-2027), National Geographic's OCEAN ROBOCTO (2024-2026), and PRIN's COSMIC (2023-2025). His teaching portfolio includes PhD courses in Biosystems for Biorobotics and M.Sc. instruction in Bionics Engineering at Scuola Superiore Sant'Anna and University of Pisa. The Bio-Robotic Ecosystems Lab under Romano's direction pioneers biohybrid technologies where living organisms and robotic systems create integrated solutions. Current initiatives include SENSORBEES' environmental monitoring swarms, REGOLIFE's moonworm colonization systems, and HUBILIFE's commercial vector-control devices. The lab maintains active collaborations with space agencies, agricultural institutes, and conservation organizations, positioning biohybrid systems as next-generation tools for planetary-scale challenges.
Paul J. Kennedy is a Professor at the University of Technology Sydney's Centre for Artificial Intelligence. He holds a PhD from the same institution (1999). His research focuses on machine learning applications in healthcare, bioinformatics, medical imaging, and data mining. Key areas include developing algorithms for genomic data analysis, healthcare pathway modeling, and edge-cloud frameworks for omics data. Education: PhD in Artificial Intelligence (1999, UTS). Research interests span machine learning, health informatics, and data compression. Notable work includes studies on administrative health records, lung nodule detection, and virtual reality-based cancer cohort analysis. He has co-authored over 100 publications across journals like BMC Bioinformatics, IEEE Transactions, and Artificial Intelligence in Medicine. Advising: Collaborates extensively with students/researchers but no explicit student list provided. Grants and labs: Active in interdisciplinary projects involving medical and computational teams, though specific grants are not detailed here.
Dana Brooks is a Research Professor in the Department of Electrical and Computer Engineering at Northeastern University, with affiliations in Bioengineering. He holds a PhD from Northeastern University (1991) and has received the Søren Buus Outstanding Research Award (2006). His primary research focuses on biomedical signal and image processing, medical imaging techniques (including MRI and electrocardiography), and neuromodulation technologies such as transcranial magnetic stimulation (TMS). He is also involved in protein conformation estimation using X-ray scattering and optimization algorithms for medical applications. Dr. Brooks leads the Biomedical Signals Processing Lab and collaborates with the Center for Integrative Biomedical Computing . His work bridges engineering and medicine, with recent grants including a $400K NSF MRI grant for advanced TMS systems and a $600K NSF grant for motor cortical organization studies. He has advised students like Setareh Ariafar (PhD’20) and contributed to innovations in image mosaicking for confocal microscopy and machine learning applications in dermatology. His publications span computational neuroscience, cardiac imaging, and uncertainty quantification in biomedical simulations. Notable achievements include developing algorithms for ECG imaging, optimizing TMS protocols, and creating tools like UncertainSCI for simulation reliability assessment.
Dr. Minglun Gong is a Professor and Director of the School of Computer Science at the University of Guelph (since 2019). Previously, he served as Professor and Head of the Department of Computer Science at Memorial University of Newfoundland. He holds a Ph.D. from the University of Alberta (2003), M.Sc. from Tsinghua University (1997), and B.Engr. from Harbin Engineering University (1994). His research focuses on visual computing, including computer graphics, computer vision, visualization, image processing, and pattern recognition. He has authored over 150 referred papers and holds patents in the field. He is an Associate Editor for Pattern Recognition and IEEE Signal Processing Letters , and has received awards such as the Izaak Walton Killam Memorial Award and multiple best paper awards. Dr. Gong has advised numerous students, including Ph.D./M.Sc. candidates and visiting scholars. His lab's recent work includes UAV path planning for urban reconstruction, image stylization techniques, and 3D human pose estimation. He actively participates in academic service, including editorial roles, conference program committees, and administrative roles at multiple institutions. His teaching spans courses in image processing, computational photography, and technical communication. He is also involved in administrative committees, such as Graduate Studies and Promotion at Memorial University. Key research contributions include advancements in transparent object modeling, underwater 3D reconstruction, and image-to-image translation. His work emphasizes practical applications in fields like medical imaging, autonomous systems, and environmental modeling.
Professor Dong Xu is a Tenured Professor in the Department of Computer Science at the University of Hong Kong (HKU), part of the School of Computing and Data Science. He holds a B.Eng. and Ph.D. from the University of Science and Technology of China (USTC). His career includes tenured roles at Nanyang Technological University and the University of Sydney, alongside postdoctoral research at Columbia University. His research focuses on Artificial Intelligence, Computer Vision, Multimedia, and Machine Learning , with applications in autonomous driving, AR/VR, medical image analysis, and video surveillance. Xu has authored over 150 papers in top journals and conferences, including CVPR, ICCV, and IEEE Transactions. He actively contributes to the academic community as an editorial board member for journals like ACM Computing Surveys and IEEE Transactions, and through leadership roles in conferences such as ACM Multimedia and ICME. Notable awards include Fellowships from IEEE and IAPR, and the IEEE Signal Processing Society Distinguished Lecturer title (2021–2022). Education: B.Eng. (USTC, 2001), Ph.D. (USTC, 2005) Professional Service: Program Coordinator of ACM Multimedia 2024, Guest Editor of over ten special issues.
Roziana Ramli is an academic affiliated with Northumbria University, holding a PhD in Computer Science. Her research focuses on medical imaging techniques, cybersecurity in healthcare systems, and bio-inspired optimization algorithms. She has contributed to advancements in retinal fundus image registration and IoT security protocols. Her work integrates computer vision with biomedical applications, addressing challenges in healthcare monitoring and assistive technologies. Key research areas include federated learning in healthcare networks, prosodic feature analysis for language recognition, and secure communication for drone networks. Her systematic literature reviews and algorithmic innovations highlight her interdisciplinary approach to solving technical and clinical problems. Though no formal awards are listed, her active publication record from 1999 to 2024 demonstrates sustained academic engagement.
David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.
Adam Woźniak is a Professor and Vice-Rector for Development at Warsaw University of Technology (WUT), holding positions at the Faculty of Mechatronics and the Institute of Metrology and Biomedical Engineering. He earned a PhD in 2002, D.Sc. (habilitation) in 2011, and was promoted to full professor in 2017. His research focuses on advanced geometrical measurement techniques, coordinate metrology, quality engineering, and reliability of mechatronic systems. He has authored 2 books and over 130 scientific publications, including work on probing accuracy, X-ray CT applications, and dynamic error analysis in manufacturing systems. Notably, he served as Director of the Institute of Metrology and Biomedical Engineering (2012–2020) and later as Dean of the Faculty of Mechatronics (2020). He received the Polish Prime Minister’s Prize for Scientific Achievements (2012) and multiple scholarships from the Foundation for Polish Science. Education: PhD (2002), D.Sc. (2011), Warsaw University of Technology; Visiting Professor at École Polytechnique de Montréal (2005–2006). Research interests include coordinate measuring machine (CMM) performance, probing system accuracy, and industrial CT applications. His work addresses dynamic error identification, probe error compensation, and precision measurement techniques. Recent projects involve high-density point cloud correction, scanning probe validation, and pediatric growth measurement systems. His articles analyze topics like probe reliability, CNC machine tool errors, and X-ray CT threshold optimization. Awards also include recognition for leadership in standardization bodies, including roles in Poland’s Council for Metrology and Standardization. He has supervised 6 PhD students and numerous master’s candidates, contributing to over a dozen funded research projects. His lab, the Virtual Manufacturing Research Laboratory, integrates metrology, mechatronics, and biomedical engineering for advanced measurement solutions.
Gökhan Alcan is an Assistant Professor in Robotics and Machine Learning at the Automation Technology and Mechanical Engineering Unit of Tampere University, Finland. He leads the Advanced Learning, Control and AutomatioN (ALCAN) Research Group, focusing on safe model predictive control, constrained optimal control theory, reinforcement learning, and their applications to dynamical systems. His research addresses challenges in robotic manipulation, safe navigation, and human-robot collaboration through projects like the Aurora initiative on automated and connected machines. Education: B.Sc., M.Sc., and Ph.D. in Mechatronics Engineering from Sabanci University (2008–2019). Postdoctoral research at Sabanci University (2019) and Aalto University (2020–2024). Research Interests: Robotics, control theory, system identification, autonomous systems, and machine learning applied to robotic manipulation, autonomous vehicles, and safety-critical systems. Notable work includes trajectory optimization for hybrid systems, magnetic manipulation for medical applications, and sim-to-real gap analysis in cloth manipulation. Awards: Third Place in Aalto Open Science Award 2023, Elginkan Foundation Technology Award (2016), and multiple scholarships. Advised Ph.D. student David Blanco Mulero, who defended his thesis on robotic manipulation of deformable objects. Labs/Teams: ALCAN Research Group, former roles in Aalto University's Intelligent Robotics Group and Sabanci University's Control, Vision, and Robotics (CVR) Group.
Mia Liljeström is a Staff Scientist at the Department of Neuroscience and Biomedical Engineering, Aalto University. She holds a Doctoral Degree in Engineering and Technology from Aalto University (2010) and a Master's Degree in Engineering and Technology from Helsinki University of Technology (2002). Doctoral Degree: Aalto University, 2010 Master's Degree: Helsinki University of Technology, 2002 Her research focuses on Magnetoencephalography (MEG) , Functional Connectivity , and Brain Networks . She explores Transcranial Magnetic Stimulation (TMS) , Functional MRI , and Neural Networks to map language-critical brain areas and study cortical dynamics. Recent work includes automated speech artefact removal from MEG data and test-retest reliability of brain connectivity metrics. Mia actively participates in conferences like MEG Nord and has presented invited talks on language processing and large-scale brain networks. Her publications emphasize MEG-informed TMS, cortical beta modulation, and brain stimulation precision. She contributes to the UN Sustainable Development Goal of Quality Education through neuroimaging research.
