Alan Wassyng is a Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. With over four decades of academic contributions, he specializes in formal methods, safety-critical systems, and software assurance cases. Key research intersections: Automotive software safety Medical device software certification Cyber-physical systems engineering Model-driven development with domain experts His scholarly work emphasizes rigorous methodologies for software verification, particularly through tabular expressions and Workflow+ models. Recent projects include a $2M GM Canada partnership to advance automotive safety systems. Teaching leadership spans interdisciplinary capstone projects in biomedical engineering, covering software design, safety-critical development, and mechatronics applications.
Martin Stridh is an Associate Professor and Senior Lecturer at the Department of Biomedical Engineering, Lund University, specializing in biomedical signal processing and data-driven diagnostics. He teaches courses in biomedical engineering, signal processing, e-health, and machine learning for healthcare applications. His research focuses on leveraging signal processing and machine learning to improve diagnostics and treatment outcome prediction in cardiac and eye-tracking data. Notable projects include AI-based ECG screening, artifact-free ECG analysis, and event detection in cardiac signals. Recent publications highlight advancements in atrial fibrillation detection using ECG data, with an emphasis on reducing false alarms and improving accuracy. His work often involves collaboration with clinical and engineering teams, particularly in the MY-ATRIA network. Top 10 cited paper 2006-2008 in Medical Engineering and Physics Best teacher 2001 by the Computer Science program Svenska Cardiologföreningens och Knolls Competition Abstract Prize 1999 As founder of Cardiolund AB, he applies his research to automated ECG analysis for arrhythmia screening and patient prioritization. He supervises PhD students, including Ricardo Salinas Martinez, and contributes to cross-disciplinary workshops like Engineering Health Crossroads.
Lale Tükenmez Ergene is a Professor at Istanbul Technical University 's Department of Electrical Engineering, specializing in Electrical Machines and Energy Conversion . Her work bridges theoretical research and practical applications in motor design for electric vehicles and home appliances. Ph.D. in Electrical Engineering from Rensselaer Polytechnic Institute 20+ years of academic and administrative leadership Focus areas: Permanent Magnet Motors, Synchronous Reluctance Motors, and Sensorless Control Systems Her research explores: Optimization of traction motors for electric vehicles Advanced sensorless control algorithms for motor drives Reduction of voltage distortion in high-performance motors Integration of predictive diagnostics in motor systems Applications of neurofuzzy control systems in multicopters Recent publications highlight trends in PMaSynRM parameter estimation , flux weakening capabilities , and real-time motor diagnostics . Her work spans both traditional electrical engineering and cross-disciplinary innovations like VR-based language learning systems for EU workforce mobility. Scientific recognition includes: Best Poster Paper Award (2016) 2nd Prize in Graduation Design Competition (2015) Doctoral Thesis Excellence Award (2015) She leads projects such as: Pmasynrm's Innovative Real-Time Model Diagnostic System (2021-2024) Sensorless Magnet-Supported Motor Drive for Washing Machines (2019-2022) VR-based Business English Training for Engineers (2018-2022)
Dr. Stella Daskalopoulou is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) and a Professor in the Department of Medicine at McGill University's Faculty of Medicine and Health Sciences. She specializes in translational cardiovascular research with a focus on vascular health, atherosclerosis, and hypertension management. Senior Scientist, RI-MUHC Glen site Professor, Department of Medicine, McGill University Member, Cardiovascular Health Across the Lifespan Program Research Focus: Her work integrates biomedical technology with clinical investigation to identify early vascular impairment markers. Key areas include: Arterial stiffness and hemodynamic assessment Adiponectin signaling pathways in atherosclerosis Sex-specific cardiovascular risk stratification Biomarker discovery for plaque instability Pregnancy-related vascular complications Machine learning applications in plaque classification Scientific Engagement: Dr. Daskalopoulou contributes to hypertension guideline development (2024 ESC Guidelines) and explores environmental impacts on vascular health (household pollution studies). Her recent publications emphasize: Deep learning for plaque characterization Sex hormone receptor pathways in atherosclerosis Adipokine interactions with HDL metabolism Clinical decision tools for preeclampsia prediction Laboratory: Leads the Vascular Health Unit at RI-MUHC, combining histopathology, immunophenotyping, and advanced imaging for translational research.
Jonathan Voersaa Wenshøj is an academic researcher at the Department of Computer Science, University of Copenhagen. He contributes to the Machine Learning section's activities spanning theoretical foundations and applications in diverse domains like information retrieval, medical data analysis, remote sensing, sustainability, and biological modeling. The section participates in the SCIENCE AI Centre and collaborates with initiatives like TreeSense for global tree resource analysis. His research intersects machine learning with quantum computing, medical informatics, and sustainability. Recent publications highlight applications in environmental monitoring, healthcare diagnostics, and energy-efficient AI systems. The department provides advanced compute resources including a powerful cluster for intensive machine learning tasks. This researcher's work appears in diverse machine learning domains, with recent publications addressing quantum-inspired architectures, explainable AI in medical imaging, and sustainable computing practices. The section actively hosts events including seminars, conferences, and PhD defences related to machine learning advancements.
Yuta Sugiura is an Associate Professor in the Department of Information and Computer Science at Keio University's Faculty of Science and Technology. His research focuses on innovative human-computer interaction techniques, particularly in wearable computing, tangible interfaces, and novel input methods. Previously, he worked as a postdoctoral researcher at the National Institute of Advanced Industrial Science. Dr. Sugiura's research interests span Human-Computer Interaction, Wearable Computing, Augmented Reality, Tangible User Interfaces, Gesture Recognition, Ubiquitous Computing, Haptics, and Virtual Reality. His work often explores how everyday objects and environments can become interactive surfaces, with notable projects including the iRing (intelligent ring), SenSkin (skin as interface), and EarHover (mid-air gesture recognition for hearables). He has developed numerous novel interaction techniques that leverage physical properties of materials and human physiology for input and output. His recent publications indicate a strong focus on hearable computing, medical applications of HCI, edible interfaces, and novel authentication methods. The research shows a consistent pattern of exploring unconventional interaction surfaces and leveraging subtle physical phenomena for input sensing. His work has significant implications for healthcare applications, particularly in neurological disorder screening and rehabilitation. Best Paper Award Dr. Sugiura has advised numerous students who have gone on to publish significant work in top-tier HCI venues. His research has been supported by various grants enabling the development of novel interaction techniques and systems. He maintains strong collaborations with researchers across Japan and internationally, particularly in the fields of wearable computing and medical applications of HCI. His laboratory appears to focus on lifestyle computing, developing interfaces that integrate seamlessly into daily activities. Current projects include exploring edible displays, adaptive ear interfaces, and novel authentication methods using wearable devices. Future work seems to be heading toward more medical applications of HCI, particularly in neurological assessment and rehabilitation.
Professor Aoife Gowen is a leading academic at the UCD School of Biosystems & Food Engineering , specializing in hyperspectral imaging and its applications across medicine, food safety, and engineering. Her research, supported by prestigious European Research Council (ERC) funding, investigates water molecule interactions with surfaces to improve bone graft materials and develop innovative diagnostic tools for prostate cancer. She also leads Science Foundation Ireland (SFI)-funded projects on hyperspectral monitoring of bacterial growth for food safety. Beyond technical research, Professor Gowen has developed computational tools now integrated into commercial chemical analysis software. Her work spans interdisciplinary domains, including sustainable transport policy, critical thinking education, and promoting gender diversity in engineering. As a key figure in the Women on Walls initiative, she has enhanced visibility for women in STEM fields. Her recent publications focus on spectral technologies for food quality, microplastics characterization, and medical diagnostics, reflecting her commitment to addressing global challenges in health and sustainability. Scientific Awards: ERC Grant for water-surface interaction research Professor Gowen actively collaborates with European networks and industry partners, driving advancements in hyperspectral imaging applications. Her lab’s efforts to bridge computational science with real-world chemical analysis have positioned her as a pioneer in invisible chemistry visualization, impacting medicine, food, and environmental engineering.
Thomas J. Marini, M.D. , is an Assistant Professor in the Department of Imaging Sciences at the University of Rochester Medical Center. He specializes in diagnostic radiology and neuroradiology, with a focus on expanding imaging access through teleultrasound and AI integration. Education M.D., University of Rochester, 2017 Internship, Transitional Year, St. Josephs Hospital and Health Center, 2017–2018 Residency, Diagnostic Radiology, University of Rochester Medical Center, 2018–2022 Fellowship, Neuroradiology, University of Rochester Medical Center, 2022–2023 Research Interests Dr. Marini’s research spans medical imaging , artificial intelligence , and telemedicine , particularly in low-resource settings. His recent work explores AI-driven ultrasound segmentation, pulmonary vasculature imaging, and telediagnostic systems for obstetrics, thyroid, and breast imaging. His publications highlight AI integration in ultrasound, automated fetal biometry , and imaging accessibility improvements in rural Peru. This aligns with his focus on democratizing diagnostic tools through technology. Scientific Awards Best Fellow Teacher Award, 2023 Cum Laude Award (American Society of Pediatrics), 2022 University of Rochester Imaging Sciences Excellence Award, 2022 Roentgen Research Award, 2022 Holman Research Pathway Certificate, 2022 Phi Beta Kappa Honor Society Induction, 2013
Prof. Frank-Peter Schilling is a Senior Lecturer at Zurich University of Applied Sciences (ZHAW) School of Engineering and Deputy Director of the Centre for Artificial Intelligence (CAI). He leads the Intelligent Vision Systems group and coordinates the PhD Programme in Data Science with the University of Zurich. As an Adjunct Professor at Victoria University of Wellington, he specializes in AI, Machine Learning, and applications in healthcare and physical sciences. His research focuses on deep learning-based computer vision, MLOps, and trustworthy AI certification frameworks. Education: PhD in Physics (University of Heidelberg, 2001) Dipl.-Phys. (MSc equivalent in Physics, University of Heidelberg, 1998) CAS University Didactics (PH Zurich, 2024) Research Interests: Developing AI systems for medical imaging (e.g., CBCT artifact reduction) Certification schemes for AI trustworthiness (e.g., certAInty project) Applications of deep learning in particle physics and industrial vision Achievements: Recipient of the EPS HEP Prize (2013) for contributions to the Higgs boson discovery at CERN Lead author of over 20 peer-reviewed articles on AI, MLOps, and medical imaging Principal investigator for projects like AI-BRIDGE (responsible AI development) and GenAI4SKA (Square Kilometre Array simulations) Teaching: Courses in MLOps, Machine Learning Operations, and Computer Vision at BSc and MSc levels. Developed the CAS Advanced Machine Learning program. Labs & Networks: Active in ELLIS (European Lab for Learning and Intelligent Systems), CLAIRE (AI research), and ZHAW’s Digital Health/Datalab initiatives.
Meredith Broussard is an Associate Professor at the Arthur L. Carter Journalism Institute of New York University and serves as Research Director at the NYU Alliance for Public Interest Technology. She also sits on the advisory board for the Center for Critical Race and Digital Studies. Her work bridges computer science and sociology, focusing on exposing systemic biases in AI and advocating for equitable technological redesign. Her research examines how AI systems perpetuate societal inequalities through racial, gender, and ability biases. Notable examples include mortgage approval algorithms favoring white applicants, facial recognition inaccuracies for people of color, and biased medical diagnostic models. She argues that these systems often replicate historical inequities encoded in their training data. Broussard’s books, More Than A Glitch (2024) and Artificial Unintelligence (2018), critique AI’s limitations and societal impacts. She emphasizes the need for human-centered approaches to technology, urging vigilance against unchecked algorithmic systems. Her career spans journalism (Philadelphia Inquirer), software development (AT&T Bell Labs, MIT Media Lab), and public advocacy. Broussard combines technical expertise with critical social analysis to challenge technochauvinism and promote justice in tech.
Mohammad Alsharid is a Visiting Fellow at the University of Oxford's Institute of Biomedical Engineering and a postdoctoral research fellow at Khalifa University. He holds a DPhil in Engineering Science (2022) from the University of Oxford, where his thesis focused on automated textual captioning for ultrasound visuals. His research integrates machine learning, natural language processing, and computer vision to advance biomedical image analysis, particularly in fetal ultrasound. Alsharid’s work emphasizes multimodal interplay in medical imaging and automated workflow analysis. Education: DPhil in Engineering Science (2022), University of Oxford; BSc (exact discipline unspecified). Research Interests: Develop AI-driven solutions for medical imaging through multimodal learning, curriculum learning approaches for ultrasound captioning, and automated analysis of fetal echocardiography scans. His methods leverage gaze-assisted systems and self-supervised representation learning for improved diagnostic support. Publications Highlight Trends: Focus on automated captioning for medical videos, curriculum-based learning frameworks, and multimodal neural networks for biomedical applications. Lab Affiliations: Active in the Institute of Biomedical Engineering at Oxford, contributing to interdisciplinary projects in AI and healthcare.
Professor Minh N. Do is the Thomas and Margaret Huang Endowed Professor in Signal Processing & Data Science at the University of Illinois at Urbana-Champaign (UIUC), with primary appointment in the Department of Electrical and Computer Engineering. He holds multiple affiliate appointments across campus including with the Coordinated Science Laboratory, Beckman Institute for Advanced Science and Technology, Department of Bioengineering, Department of Computer Science, Institute for Genomic Biology, College of Medicine, and School of Computing and Data Science. Additionally, he serves as Director of the joint VinUni-Illinois Smart Health Center and holds an Honorary Vice-Provost position at VinUniversity. Professor Do received his B.Eng. in Computer Engineering (First Class Honors) from the University of Canberra, Australia in 1997, followed by his Dr.Sci. in Communication Systems from the Swiss Federal Institute of Technology Lausanne (EPFL) in 2001. His educational journey was marked by exceptional achievement, earning the University Medal from the University of Canberra and a Silver Medal from the 32nd International Mathematical Olympiad. Professor Do's research focuses on developing new multidimensional signal processing tools with applications across several domains. His primary research interests include smart health, data science, computational imaging, and signal processing. His work spans biomedical imaging, machine learning, computer vision, and robotics, with particular emphasis on geometric image representations, integrating image formation and processing, and image processing from multiple sensors. His research bridges theoretical investigations with practical applications, creating impactful solutions in healthcare, diagnostics, and AI systems. His recent publications demonstrate a consistent trajectory toward multimodal AI systems, robust learning frameworks, and healthcare applications. Professor Do's work increasingly integrates signal processing with deep learning approaches to address challenges in medical imaging, cross-modal transfer, and real-world deployment of AI systems. His research shows strong emphasis on practical applications with societal impact, particularly in healthcare diagnostics and smart health technologies. Professor Do's scientific achievements have been recognized with numerous prestigious awards: Member of the National Academy of Artificial Intelligence (2025) Fellow of Asia-Pacific Artificial Intelligence Association (2023) Thomas and Margaret Huang Endowed Professor, UIUC (2020-present) Fellow of IEEE (2014) Young Author Best Paper Award, IEEE Signal Processing Society (2008) CAREER award from the National Science Foundation (2003) Best Doctoral Thesis Award from EPFL (2001) As an educator, Professor Do has taught numerous courses spanning digital signal processing, probability, data science, and image processing. His teaching excellence has been recognized with multiple "Teachers Ranked as Excellent" awards at UIUC. He also maintains active industry connections through tech-transfer efforts, having co-founded Personify and served as Chief Scientist of Misfit. His leadership extends to administrative roles, having served as Vice-Provost for VinUniversity during 2020-2021. Professor Do leads research initiatives at the intersection of signal processing and healthcare applications, with particular focus on the Smart Health Center collaboration between UIUC and VinUniversity. His lab develops innovative solutions for medical diagnostics, point-of-care testing, and neurological assessment using advanced signal processing and AI techniques.
Daswin De Silva is a Full Professor of AI and Analytics at La Trobe University, Australia, and Deputy Director of the Centre for Data Analytics and Cognition (CDAC). He also holds an Adjunct Professor position at Lulea University of Technology, Sweden. His expertise spans AI ethics, algorithm development, and applications in healthcare, energy, and education. He leads major initiatives like the La Trobe Energy AI Platform for net-zero emissions and the OptusU AI Micro-credentials program. Education: PhD in AI (Monash University, 2011). Awards include the Australian Awards for University Teaching (2021), Vice-Chancellor’s Teaching Award (2019), and Mid-Career Research Excellence Award (2018). Editor of five journals including IEEE Transactions on Industrial Informatics and Springer Discover AI. Research focuses on generative AI, ethical AI systems, and vector symbolic architectures. Recent work includes AI applications in healthcare diagnostics, energy efficiency, and smart cities. He has secured AU$14M in research funding and supervised 15 PhD completions with 10 current students. Leadership roles include Deputy Chair of La Trobe’s Research & Graduate Studies Committee and chairmanship of IEEE committees on Responsible AI and Web/Information Systems. Keynote speaker at global conferences like IEEE HSI, INDIN, and ETFA. Media engagements include ABC News, Forbes, and The Conversation.
Prof. Athina Tzovara is a Professor at the University of Bern, leading the Cognitive Computational Neuroscience (CCN) research group within the Institute of Computer Science (Faculty of Science) with a dual affiliation in the Department of Neurology (Faculty of Medicine). Her research integrates computational modeling, machine learning, and neural recordings to study cognitive processes and neurological conditions. Key areas include sleep dynamics, coma prognosis, AI-driven healthcare solutions, and neural mechanisms underlying consciousness. Education: BEng in Electrical & Computer Engineering (National Technical University of Athens, 2009), PhD in Neuroscience (University Hospital Centre Lausanne, 2012). Postdoctoral roles at University of Zurich and University College London preceded her current position. Research focuses on: (1) Sleep-wake disorders using EEG and causal inference approaches, (2) Predictive neural coding models for auditory processing in coma patients, (3) Ethical AI frameworks for automated medical diagnostics, and (4) Data-driven phenotyping of neurological conditions. Recent work emphasizes translating computational neuroscience insights into clinical tools - such as EEG-based coma outcome prediction and bias-aware sleep scoring algorithms. Her lab collaborates internationally on initiatives like SPHYNCS (narcolepsy cohort study) and Brain Mappers for open neuroscience communication. Advocates for inclusive scientific practices through gender bias mitigation strategies and multilingual dissemination efforts. Active in Open Science initiatives like Open Humans platform development.
Dr. Yongxin (Leon) Zhao is an Associate Professor in Biomedical Engineering and Biological Sciences at Carnegie Mellon University's College of Engineering. He leads a research group developing tools for understanding biological systems at the biomolecular level, with a focus on expansion microscopy and optogenetic probes. His work has advanced nanoscale imaging and diagnostic methodologies, supported by grants from NIH, NSF, DoD, and others. Education: B.S., Sun Yat-Sen University (2009); Ph.D., University of Alberta (2014); Postdoctoral Research at University of Alberta (2014). Key achievements include the GECO calcium indicators and QuasAr voltage indicators, and foundational contributions to expansion microscopy (Nature Biotechnology 2017, 2023; Science 2022). Research interests span bioengineering, cell/tissue engineering, and nanotechnology-driven diagnostics. Zhao's lab pioneers applications in neuroscience, cancer, and infectious diseases. Awards include the NIH Director's New Innovator Award (2018–2023) and MIT Translational Fellowship (2015–2016). Collaborations emphasize global technology democratization, with projects ranging from nanofabrication to computational analysis using deep learning. His lab's tools are widely adopted in neurobiology and pathology labs worldwide.
