Find Academic Supervisors

• Biomedical Engineering
• Neuroprosthetics
• Sensory Bionics
• +3 more

Professor Gregg Suaning is a leading academic in Biomedical Engineering at the University of Sydney, affiliated with the School of Biomedical Engineering, The University of Sydney Nano Institute, and the Brain and Mind Centre. He holds a PhD from UNSW and has over 25 years of experience in implantable neuroprosthetics, focusing on restoring sensory functions for the blind and deaf. His work spans academic and industry roles, including contributions to Cochlear's cochlear implant innovations and pioneering visual prosthetic research. Education: PhD (UNSW, 2003), MSc and BSc (California State University, 1986-1988). Research interests center on developing implantable bionics to restore vision and hearing. Notable projects include the Phoenix 99 suprachoroidal visual prosthesis and facial nerve paralysis interventions. He has authored over 150 peer-reviewed papers and secured $60M+ in research funding. Awards include the Bartimaeus Award for contributions to the blind community and multiple innovation awards. He leads IEEE's Engineering in Medicine and Biology Society committees and holds senior IEEE membership. Current projects focus on facial nerve paralysis treatments, high-density micro-electrode arrays, and improving cochlear implant efficacy. His lab collaborates with clinical partners to advance biocompatible neural interfaces and prosthetic systems.

Cesar RuizView profile

Assistant Professor

• Metal Additive Manufacturing
• Process-Informed Machine Learning
• Quality Control
• +3 more

Cesar Ruiz is an Assistant Professor in the Department of Industrial & Systems Engineering at The University of Oklahoma. His research focuses on integrating domain knowledge with machine learning and stochastic modeling for engineering applications, particularly in metal additive manufacturing, predictive maintenance, and quality control. He holds a Ph.D. and M.S. in Industrial Engineering from the University of Arkansas and a B.A. in Business Engineering from the Higher School of Economics and Business in El Salvador. His research domains include metal additive manufacturing processes, process-informed machine learning frameworks, and reliability evaluation in complex systems. Notable works address challenges in layer segmentation for quality assessment, predictive maintenance strategies, and degradation-based reliability analysis. Ruiz has received prestigious awards such as the RAMS Golomski Award (2020, 2022), ASQ Best Reliability Paper (2021), and IEEE CASE Best Conference Paper (2021). His affiliations include SRE, INFORMS, and ASME. His research spans aerospace and defense systems, with recent publications emphasizing advanced manufacturing techniques, functional data analysis, and Bayesian modeling for reliability growth. He collaborates on projects involving biosensor optimization and large-scale system maintenance frameworks.

Peter J. BasserView profile

Research Professor

• Quantitative Imaging
• Tissue Sciences
• Diffusion MRI
• +7 more

Peter J. Basser is a leading research scientist at the National Institutes of Health (NIH), specifically within the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), where he heads the Section on Quantitative Imaging and Tissue Sciences (SQITS). His work bridges physics, engineering, and neuroscience to develop non-invasive MRI methods for probing tissue microstructure, particularly in the brain. His educational background is not explicitly mentioned, but his scientific achievements reflect deep training in biophysics and medical imaging. He earned his Ph.D. and has built a career at NIH as a principal inventor of key neuroimaging technologies. Basser's research focuses on quantitative imaging and tissue sciences , especially using diffusion MRI to study brain structure and function. He pioneered Diffusion Tensor MRI (DTI) , Streamline Tractography , and advanced methods like MAP MRI , CHARMED , and AxCaliber , enabling in vivo measurement of axon diameters and microstructural features previously accessible only through histology. His work aims to translate these tools into clinical use for diagnosing developmental disorders, trauma, and neurodegeneration. The 15 most recent articles reflect a consistent focus on developing novel MRI biomarkers, particularly through diffusion and relaxometry methods. They explore water exchange, restriction, glymphatic clearance, latency connectomes, and cortical microstructure, demonstrating a trajectory toward in vivo MRI histology and precision imaging for pediatric and neurological applications. Scientific Awards: National Academy of Engineering (NAE), Inducted 2020 National Academy of Inventors (NAI) Fellow, 2024 Eduard Rhein Technology Award, 2021 ISMRM Gold Medal, 2008 ISMRM Lauterbur Lecturer, 2020 American Society of Neuroradiology Honorary Member, 2019 Victor M. Haughton Award, 2017 ISMRM Fellow, 2010 AIMBE Fellow Best Paper Award, Frontiers in Physics, 2023 Basser leads a dynamic research group that mentors postdoctoral fellows and trainees, many of whom have received prestigious awards. His lab has secured significant grants from the NIH BRAIN Initiative, NICHD, USUHS, and the Bill & Melinda Gates Foundation. The SQITS lab develops open-source software tools like TORTOISE , dmritool , and HI-SPEED , which are widely used in the neuroimaging community. The lab collaborates with institutions such as Uniformed Services University and participates in major initiatives like the Human Placenta Project and the Human Connectome Project. Basser’s vision is to transform clinical MRI scanners into quantitative scientific instruments for precision medicine and large-scale brain mapping. Labs and Teams: Section on Quantitative Imaging and Tissue Sciences (SQITS), NICHD, NIH Neuropathology-Neuroradiology Integration Core (with USUHS) Advanced Translational Neuroimaging Research & Development Core Diffusion – Data Processing Center (DPC)

Mian M. HamayunView profile

Associate Professor

• Computer Science
• Transaction Level Modeling (TLM)
• MPSoC Simulation
• +15 more

Mian M. Hamayun is an Associate Professor of Computer Science and Head of Subject Group at the University of Birmingham Dubai campus, part of the College of Engineering and Physical Sciences. He holds a PhD in System Modelling, Simulation & Virtualization from the University of Grenoble Alpes, France, and has academic qualifications from Joseph Fourier University and COMSATS Institute of Information Technology, Pakistan. PhD in System Modelling, Simulation & Virtualization, University of Grenoble Alpes, 2013 MS in Informatics (Parallel & Distributed Systems), Joseph Fourier University, 2009 MSE in Software Engineering (Gold Medalist), COMSATS, 2005 PGCHE, University of Birmingham, 2021 FHEA and SFHEA, University of Birmingham (2021, 2024) His research spans Transaction Level Modeling (TLM) , MPSoC simulation , cloud computing , virtualization , Internet of Things/Vehicles , edge AI , and TinyML . He focuses on performance estimation, binary translation, and system-level simulation for embedded and distributed systems. The recent publications reflect a strong trend in IoT and vehicular networks , secure telemedicine , cloud orchestration , and embedded system simulation . His work integrates virtualization with security, real-time constraints, and machine learning on edge devices. Senior Fellow of the Higher Education Academy (SFHEA), 2024 Fellow of the Higher Education Academy (FHEA), 2021 Dr. Hamayun has supervised multiple research students and contributed to projects involving fatigue detection, person re-identification, retinal image analysis, and secure surgery frameworks. He has taught core computer science courses including Operating Systems, Networks, Data Structures, Algorithms, and Functional Programming at both undergraduate and postgraduate levels. His prior roles include Assistant Professor at NUST SEECS, R&D Engineer in France, and Lecturer at IUT1. He leads research in virtual prototyping and edge intelligence at the Dubai campus, contributing to smart city and cognitive systems initiatives. His lab work involves hybrid simulation environments, secure automotive platforms, and distributed IoT systems.

Md Rasedul IslamView profile

Associate Professor

• Bio-robotics
• Ergonomic Mechanism
• Intelligent System and Control
• +1 more

Md Rasedul Islam is an Associate Professor in the Department of Mechanical Engineering at the University of Wisconsin-Green Bay, affiliated with the College of Science, Engineering and Technology. His research focuses on bio-robotics, ergonomic mechanisms, intelligent control systems, and automation. Ph.D. in Mechanical Engineering (2020), University of Wisconsin-Milwaukee B.Sc. in Mechanical Engineering (2012), Khulna University of Engineering & Technology, Bangladesh Islam's work bridges robotics with rehabilitation, emphasizing wearable systems like exoskeletons and humanoid robots. His studies explore hybrid control methodologies, adaptive algorithms, and automation of dynamic systems for therapeutic applications. His publications highlight advancements in exoskeleton robotics, EMG-based control, and real-time sensor systems. Notable awards include the Chancellor Graduate Student Award (2020), Distinguished Dissertation Fellowship (2018-2019), and Prime Minister Gold Medal (2012).

Luke KellyView profile

Associate Professor

• Biomechanics
• Human Locomotion
• Foot and Ankle Function
• +4 more

Luke Kelly is an Honorary Associate Professor at the School of Human Movement and Nutrition Sciences (The University of Queensland). Specializing in human foot biomechanics , his research explores the evolutionary, neuromuscular, and elastic tissue mechanisms underlying foot function during locomotion, with applications in health (osteoarthritis) , rehabilitation , and robotic/prosthetic design . Industry collaborations: Australian Sports Commission , Asics Oceania , Cricket Australia Research focus: Foot structure-function relationships , Muscle mechanics , Energy conservation His work employs advanced methodologies like biplanar videoradiography and markerless motion capture , addressing questions about foot adaptation to surfaces , diabetic foot morphology , and neuromechanical control . While not directly supervising students currently, he has guided research on foot fatigue , prosthetic optimization , and injury biomechanics . Luke Kelly’s contributions include: Key publications in Journal of Biomechanics , Royal Society Interface , and PNAS Dataset creation for foot shape modeling , muscle-tendon dynamics , and joint kinematics Grants from ARC Discovery , NHMRC , and Arthritis Foundation of Australia

Maurizio LeottaView profile

Assistant Professor

• Software Engineering
• Test Automation
• Empirical Software Engineering
• +7 more

Maurizio Leotta is an Assistant Professor in Computer Science at the University of Genova, Italy, where he has been employed since 2018. He is a member of the Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS) within the School of Mathematical, Physical and Natural Sciences. Additionally, he serves on the School Council and the Department Board of DIBRIS. Dr. Leotta received his PhD in Computer Science from the University of Genova in 2015, with a thesis on Automated Web Testing under the supervision of Prof. Filippo Ricca. His doctoral work was revised by Prof. Massimiliano di Penta (Università del Sannio, Italy) and Prof. Ali Mesbah (University of British Columbia, Canada). Before his academic career, he worked as an IT Technician in various companies and participated in research and industrial projects funded by organizations such as Finmeccanica S.p.A. and the Italian Space Agency. Dr. Leotta's primary research focuses on Software Engineering, with particular emphasis on Test Automation, which is his main research topic. He collaborates with Prof. Paolo Tonella from USI, Switzerland on this area. His other significant research interests include Empirical Software Engineering, Requirements Engineering, Business Process Modelling, and Model-Driven Software Engineering. His work often addresses practical challenges in web and mobile application testing, with a growing interest in applying AI and gamification techniques to improve software testing processes. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional software testing methodologies, particularly in end-to-end web testing. He has made significant contributions to improving test robustness, addressing flakiness in test execution, and developing tools for better test maintenance. His work also shows increasing interest in gamification approaches to engage developers and students in software testing activities, as well as applications of large language models to enhance test automation. Dr. Leotta has received multiple prestigious awards for his research, including: Best Paper Award at ICST 2025 (Short Papers, Vision and Emerging Results) Distinguished Paper Award at ICST 2023 (Industry Papers) Best Paper Award at QUATIC 2022 (Full Papers) Best Paper Award at ICST 2022 (Demo and Testing Tool Papers) Best Paper Award at QUATIC 2020 (Full Papers) Best Student Paper Award at ICWE 2016 (Full Papers) Dr. Leotta has advised numerous graduate students, including three PhD candidates who have completed their degrees (Andrea Fasciglione in 2024, Dario Olianas in 2023, and Diego Clerissi in 2020). He currently supervises multiple Master's students working on topics related to software testing, web accessibility, and AI applications in software engineering. He has also served as a postdoctoral advisor for researchers including Diego Clerissi and Dario Olianas. Beyond advising, Dr. Leotta has secured research funding through collaborations with industry partners and has been involved in multiple research projects focused on software testing and verification. He co-directs the Software Engineering for Healthcare (SEH) Laboratory, which has been partially supported by Janssen Italia (previously by Actelion Pharmaceuticals Italia). The lab focuses on applying software engineering techniques to healthcare applications, particularly in the areas of wearable technology for patient monitoring and medical data analysis. Dr. Leotta is also active in the Gamify research community, organizing workshops on gamification in software development, verification, and validation.

Soumyadipta AcharyaView profile

Assistant Professor

• Global health
• Instrumentation and medical devices
• Machine learning
• +1 more

Soumyadipta Acharya is an Assistant Professor of Biomedical Engineering at Johns Hopkins University and Graduate Program Director of the Center for Bioengineering Innovation and Design (CBID). His work focuses on developing medical technologies for global health challenges, particularly in maternal and neonatal mortality reduction. Education: PhD in Biomedical Engineering (Johns Hopkins, 2009), MSE in Biomedical Engineering (University of Akron, 2004), MBBS (University of Calcutta, 1999) Research interests include: Global health technology design Instrumentation and medical devices Machine learning applications in diagnostics Signal processing for physiological data Recent publications highlight innovations in mosquito surveillance, telemedicine platforms, and non-invasive diagnostics. Technologies like VectorCam and AI-driven digital assistants address malaria monitoring and rural healthcare gaps. Scientific awards: Olympus Emerging Educational Leader Award (2011) NASA Tech Briefs Award (2006) NASA Space Act Board Award (2008) His work has received funding from USAID and the Bill & Melinda Gates Foundation, with field studies in South Asia and East Africa. He directs a graduate program in bioengineering innovation, emphasizing local immersion and partnerships for impactful solutions.

• Biometrics
• Behavioral Biometrics
• Mobile Authentication
• +12 more

Ruben Tolosana is a researcher at the Biometrics and Data Pattern Analytics Lab (BiDA Lab) in the School of Engineering at Universidad Autónoma de Madrid. His work centers on biometrics, with emphasis on behavioral and mobile authentication, face recognition, privacy-enhancing technologies, and deep learning applications in human-computer interaction. He actively contributes to major research initiatives such as the FRCSyn-onGoing challenge and the ChildCI framework. PhD in Computer Science or related field (inferred from publication volume and role) Advanced training in machine learning, computer vision, and biometrics His research interests include behavioral biometrics, mobile device security, synthetic data for AI training, privacy in biometric systems, and the application of Transformer models to user authentication. Tolosana's work often involves the development of novel datasets and benchmarking platforms to advance the field. He has co-authored influential surveys on privacy vulnerabilities in mobile sensors and privacy-preserving techniques in biometric recognition. The most recent articles highlight trends in using synthetic data for face recognition, applying Transformers to behavioral biometrics (keystroke, touchscreen, gait), and developing frameworks for child age detection via interaction patterns. His work consistently addresses real-world challenges such as privacy, bias, and system generalization. Publications in journals like Information Fusion , Pattern Recognition , and ACM Computing Surveys reflect the high impact and interdisciplinary nature of his research. Second FRCSyn-onGoing: Winning solutions and post-challenge analysis to improve face recognition with synthetic data (2025) ChildCI framework: Analysis of motor and cognitive development in children-computer interaction for age detection (2024) FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems (2024) SwipeFormer: Transformers for mobile touchscreen biometrics (2024) An Overview of Privacy-Enhancing Technologies in Biometric Recognition (2024) Ruben Tolosana collaborates extensively with researchers in the BiDA Lab, including Rubén Vera-Rodríguez, Aythami Morales, Julian Fierrez, and others. He has contributed to the development of public databases such as mEBAL and ChildCIdb, supporting open science. His work is supported by ongoing research grants (inferred from project scope and publications), and he plays a key role in organizing workshops such as WAMWB to advance the mobile and wearable biometrics community. He is a core member of the Biometrics and Data Pattern Analytics Lab (BiDA Lab), a leading research group in biometric technologies, where he contributes to multiple projects involving mobile authentication, face recognition, and privacy-preserving AI systems.

Josien PluimView profile

Professor

• Medical Image Analysis
• Image Registration
• Image Segmentation
• +12 more

Josien Pluim is a Full Professor of Medical Image Analysis at Eindhoven University of Technology (TU/e), where she leads the Medical Image Analysis group and serves as vice-dean of the Department of Biomedical Engineering. She also holds a part-time professorship at the University Medical Center Utrecht. Her research is centered at the intersection of artificial intelligence and clinical medicine, with strong affiliations to EAISI (Eindhoven Artificial Intelligence Systems Institute) and the EAISI Health initiative. Her academic background includes a Master's in Computer Science from the University of Groningen (1996), specializing in Scientific Computing and Imaging, followed by a PhD (2001) from the Image Sciences Institute at UMC Utrecht on multimodality image registration using mutual information. She advanced from assistant to associate professor at UMC Utrecht before joining TU/e as a Full Professor in 2014, with a concurrent part-time appointment at UMC Utrecht since 2015. Pluim’s research interests span medical image analysis, including image registration, segmentation, detection, and deep learning, with clinical applications in neurology and oncology. She investigates both methodological development and real-world clinical translation. Recent work emphasizes generative AI for synthetic data, robustness in deep learning models, and super-resolution techniques for brain MRI. Her publications reveal a strong trend toward addressing data scarcity, generalization, and evaluation in medical AI, particularly through simulation and diffusion models. She has co-authored over 250 peer-reviewed papers and is recognized with prestigious fellowships: Fellow of the MICCAI Society IEEE Fellow Pluim has served in leadership roles across the academic community, including Associate Editor for journals such as IEEE Transactions on Medical Imaging , IEEE TBME , and Medical Image Analysis . She has chaired major conferences like WBIR 2006 and MICCAI 2010, and served on the Executive Board of the MICCAI Society. She actively supervises research and educational projects, including team challenges and capstone courses in medical image analysis. Her group is involved in significant collaborative research, such as the EU-funded openGTN project, which supports PhD training in generative models for medical imaging. She also contributes to scientific advisory boards, including the Hanarth Fonds.

Philippe PoignetView profile

Professor

• Surgical Robotics
• Medical Robotics
• Robotic Systems
• +7 more

Philippe Poignet is a Professor at the University of Montpellier, affiliated with the Institut Universitaire de Technologie (IUT) and conducting research at the LIRMM (Laboratory of Informatics, Robotics, and Microelectronics of Montpellier). He served as Director of LIRMM from July 2015 to October 2023 and co-heads the IRP with Stanford University since 2025. His work focuses on Surgical Robotics, with a particular emphasis on medical device development, control systems, and biomedical applications. Co-founder of startup ACUSURGICAL (retinal surgery robotics) Scientific collaborator with STERLAB (flexible ureteroscopy robotics) Co-organized Summer School on Surgical Robotics (SSSR) for 20 years His research spans medical robotics , control theory , and biomedical imaging , with applications in needle steering, tissue interaction, and surgical precision. Recent publications highlight advances in soft tensegrity design , model predictive control , and multi-modality imaging registration . Scientific recognition includes: Best Paper Award at ARK’22 Prix de l’Innovation de l’I-Site MUSE (2020) Chevalier des Palmes Académiques (2019) He supervises doctoral students in projects related to flexible robotics , bioimpression , and robotic shoulder surgery , with collaborations across Europe and industry partners like CARANX Medical and CEDRAT Technologies.

• Machine Learning
• Human-Computer Interaction
• 3D Graphics
• +8 more

Chiara Innocente is a PhD student in Management, Production, and Design at the Department of Management and Production Engineering (DIGEP) of Politecnico di Torino. She holds the academic rank of Consultant Lecturer and contributes to teaching in courses related to 3D graphics in biomedical engineering applications. Her research spans computational modeling, human-computer interaction, and machine learning, with a focus on rehabilitation engineering, cultural heritage preservation, and medical imaging. Education: PhD in Management, Production, and Design (2022-2025) Research interests include: Machine learning applications in healthcare and rehabilitation engineering Human-computer interaction for augmented/virtual reality 3D graphics and computational modeling for biometric and cultural heritage Convolutional neural networks in medical image analysis Her recent publications highlight interdisciplinary work bridging computer science with clinical applications and cultural preservation. She collaborates on national patents for athlete performance evaluation systems and participates in the Augmented Humans research framework.

• Information and Communication Technology
• Electrical and Electronic Engineering
• Artificial Intelligence
• +3 more

Paweł Tarnowski is a researcher at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems , Faculty of Electrical Engineering, Warsaw University of Technology. His work focuses on biomedical signal processing, emotion recognition, and machine learning applications in affective computing and human-computer interaction. Research areas include Information and Communication Technology (ICT) Electrical and Electronic Engineering Artificial Intelligence Neuroscience Human Factors His recent publications highlight trends in emotion recognition using multimodal physiological signals (EEG, EOG, GSR), driver fatigue detection, and deep learning techniques like 1D-CNN for medical diagnostics. Articles also address urban traffic monitoring and neuromarketing research via EEG analysis. At Warsaw University of Technology, he supervises thesis projects and contributes to interdisciplinary research in biomedical engineering and signal processing. Labs/Teams: Institute of the Theory of Electrical Engineering, Measurement and Information Systems .

Yongmin LiView profile

Professor

• Data Science
• Machine Learning
• Artificial Intelligence
• +12 more

Professor Yongmin Li is a Senior Member of the IEEE and Senior Fellow of the Higher Education Academy at the Department of Computer Science , Brunel University London , within the College of Engineering, Design and Physical Sciences . His work spans multiple domains including data science, artificial intelligence, medical imaging, and foveated rendering. He has consistently been ranked in the world's top 2% scientists by Elsevier's Standardized Citation Indicators since 2020. Research Interests include Data Science Medical Imaging Computer Vision Biomedical Engineering Natural Language Processing Set-Membership Filtering Scientific Awards include 1st Place, RETOUCH Challenge (Online), MICCAI 2023 2nd Place, FeTA Challenge, MICCAI 2022 Most Influential Paper over the Decade Award, MVA 2019 Best Paper Award, Bioimaging 2018 VC Prize, Brunel University 2015

Torsten ZeschView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Educational Technology
• +6 more

Prof. Dr.-Ing. Torsten Zesch serves as Deputy Scientific Director, Member of the Executive Board, and Head of the Research Professorship for Computational Linguistics at FernUniversität in Hagen since March 2022. He previously held W2 and W1 Professorships for Sprachtechnologie (Language Technology) at Universität Duisburg-Essen from 2014-2022. Zesch is also Spokesperson of the Advisory Board of the German Society for Computational Linguistics and Language Technology (GSCL) since 2024, having previously served as GSCL President from 2018-2024. Dr. Zesch completed his dissertation (Dr.-Ing.) in Computer Science at Technische Universität Darmstadt in 2009. His research spans robust language processing systems, analysis of non-standard language structures, and educational applications of language technology, with particular focus on automatic content scoring and spelling error correction for learner language. Zesch's recent publications reveal a strong focus on integrating language technology with educational applications. His work demonstrates expertise in developing practical NLP solutions for educational assessment, including transformer-based spelling error feedback systems, hierarchical automatic scoring methods, and leveraging LLMs for educational applications despite cold-start problems. He also maintains significant research in hate speech detection, particularly in multimodal contexts and visio-linguistic models. As leader of the Computational Linguistics research professorship within the CATALPA research center, Zesch directs a team investigating how language technology can support educational processes. His projects DAKODA and KISS-Pro focus on language acquisition and educational applications. He has contributed significantly to the field through numerous publications in top NLP and educational technology venues, with over 100 publications spanning from 2006 to present.