Univ.-Prof. Torsten Möller, PhD is a Professor at the University of Vienna and serves as Head of the Research Group Visualization and Data Analysis and Head of the Research Network Data Science. His work spans data visualization, visual analytics, and human-computer interaction, with a focus on biomedical, environmental, and societal data applications. Academic rank: Professor Research group: Visualization and Data Analysis Network: Data Science Email: torsten.moeller@univie.ac.at Research interests include: Visual data analysis for complex systems Interdisciplinary applications in climate science and medicine Human-computer interaction in data exploration Image processing and computer graphics Recent publication trends show expertise in: Visualizing climate change and pandemic data Multi-volumetric and network analysis Algorithmic transparency and user-centered design Interdisciplinary collaborations (e.g., astrophysics, medical imaging) Statistical and uncertainty visualization Design frameworks for visualization recommendation Teaching includes courses in: Computer graphics and visualization Image processing and analysis Human-computer interaction Data analysis projects Doctoral research seminars
Narges Sharif Razavian is an Assistant Professor at NYU Grossman School of Medicine , holding appointments in both the Department of Population Health and Department of Radiology . She earned her PhD from Carnegie Mellon University and completed postdoctoral training at New York University's Courant Institute in Computer Science's Machine Learning group. Research focuses on applying Machine Learning and Artificial Intelligence to healthcare challenges, including Predictive Analytics for disease outcomes, Biomarker Discovery , and Medical Imaging analysis. Recent publications highlight her work on AI-driven diagnosis in oncology (lung and pancreatic cancer), hematoma expansion prediction in neurology, and real-time models for infectious disease outcomes (e.g., COVID-19). She utilizes Electronic Health Records (EHRs) and multimodal data to develop clinical decision support systems, with applications in public health surveillance and personalized medicine. Contact: Email | Phone: 212-263-2234 | Office: 227 East 30th Street, 6th Floor, Room 639, New York City
Madeleine Torcasso is an Assistant Professor in the Department of Medicine-Hematology and Oncology at the University of Chicago. She leads the Torcasso Lab, which investigates spatial patterns of disease within native tissue environments, bridging computational methods with clinical applications. Her research focuses on tumor-immune interactions using high-dimensional spatial proteomic and transcriptomic data to uncover mechanisms of disease progression and therapeutic resistance. Her lab integrates artificial intelligence with spatial omics to identify biomarkers and decode cellular communication in the tumor microenvironment. Key research interests include computational pathology, spatial biology, and translational oncology, with an emphasis on developing analytical tools for complex tissue data. Her sole recent publication (2024) introduces a computational method for classifying cells in multiplexed immunofluorescence images, advancing automated analysis of tissue microenvironments. No awards, students, or grants are detailed in available sources.
Dr. Andrew Hines is a Researcher at the School of Computer Science, University College Dublin, specializing in machine learning applications for signal processing in speech, audio, and video domains. His work focuses on Quality of Experience (QoE) modeling, speech quality assessment, and immersive media analysis. He has held leadership roles in European COST Actions like Qualinet and CryptoAction, and previously worked in industry as a Director of Engineering. University: University College Dublin Role: Director of Research, Innovation and Impact Key Collaborations: IEEE (Senior Member), Audio Engineering Society (Ireland) Research interests center on machine learning for QoE optimization, audio-visual integration, and healthcare applications like heart sound classification and stroke rehabilitation. His recent publications explore self-supervised learning, neural speech codecs, and contextual factors in speech/audio quality assessment. Scientific contributions include awards like IEEE Senior Membership, and his work spans both academic research and industrial engineering in finance and aviation sectors. He leads the QxLab research team at UCD and develops open-source platforms such as WARP-Q and AQP for quality metrics.
Dr. Thomas E. Doyle is an Associate Professor at the McMaster School of Biomedical Engineering and the Department of Electrical & Computer Engineering at McMaster University. His research focuses on biomedical signal processing, human-computer interfacing (HCI), and machine learning applications for healthcare augmentation, rehabilitation, and enhancement. He holds a Ph.D. from Western Ontario, Canada, and teaches courses like COMPENG 2DI4 (Logic Design). His work bridges cybernetics and clinical applications, emphasizing AI-driven solutions for medical diagnostics, patient monitoring, and space exploration. Education: B.E.Sc, B.Sc, M.E.Sc, Ph.D. from Western Ontario, Canada Recent Projects: Developed AI systems for remote healthcare diagnostics (2023) Collaborated with NASA on medical emergency simulators for deep space missions (2017–2023) Led ventilator development efforts for local hospitals during the pandemic (2020) His research interests span machine learning for mental health diagnostics, trust quantification in medical AI, and extended reality (XR) for medical training. He emphasizes interdisciplinary approaches, integrating computational methods with healthcare challenges. Recent publications highlight applications in pediatric emergency care, chronic pain management, and reliable medical device design. Dr. Doyle actively engages in educational initiatives, including first-year engineering pedagogy and experiential learning programs. He has received funding for projects such as the Educating the Engineer of 2025 (EtE-25) awards and contributes to initiatives like the Digital & Smart Systems and Health & Bio-innovation research clusters at McMaster.
Dr. Andrea Lecchini Visintini is an Associate Professor at the School of Electronics and Computer Science , University of Southampton. He specializes in systems modelling and control with applications in aerospace engineering and biomedical domains, utilizing Monte Carlo methods for stochastic optimization. Cyber-Physical Systems Research Group Institute for Life Sciences Research Focus: His work bridges computational methods with practical applications in: Neurovascular coupling and brain tissue pulsation analysis Advanced control strategies for aerospace systems Stochastic optimization in machine learning and fault detection Medical imaging and diagnostic protocol development Publication Trends: Recent work emphasizes interdisciplinary approaches combining computational neuroscience with engineering, focusing on brain hemodynamics, MIMO system control, and data augmentation techniques for imbalanced datasets. Supervision: Currently supervising PhD student Xuankun Cai in Computer Science.
Rui Li is an Associate Professor in the Ph.D. program at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. She directs the Lab for Use-inspired Computational Intelligence (LUCI), focusing on AI applications in computational biology and medical imaging. Education includes: B.Sc. in Computer Science, Harbin Institute of Technology M.Sc. in Computer Science, Tianjin University of Technology Ph.D. in Computing and Information Sciences, RIT Research integrates statistical machine learning with computational biology, medical image analysis, and human visual attention modeling. Current projects include deep learning for histopathology, multimodal medical image registration, and gene network inference. Publications demonstrate consistent focus on medical AI applications, with recent advances in unsupervised image registration, interactive segmentation, and multimodal fusion techniques. Key trends include self-supervised learning, uncertainty-aware models, and human-AI collaboration frameworks. Awards include the NSF CAREER Award for developing adaptive machine intelligence systems. Advises multiple PhD students on projects spanning deep learning architectures, biomedical image analysis, and biological network modeling. Leads several NSF-funded projects including human-centered image understanding systems and gene-protein network inference tools. Directs LUCI lab investigating machine learning for healthcare applications and teaches graduate courses in Statistical Machine Learning and Deep Learning.
Esteban G. Tabak is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. in Mathematics from MIT (1992) and a Hydraulic Engineer degree from the University of Buenos Aires (1988). His research spans fluid dynamics, data science, and optimization, with notable contributions to optimal transport theory, atmospheric and ocean modeling, and machine learning methodologies. He leads the Research and Training Group in Mathematical Modeling and Simulation at NYU. Research Interests include Data Analysis, Optimal Transport, Applied Mathematics, and Physics, particularly in fluid dynamics and geophysical flows. His work bridges theoretical advancements with practical applications, such as sea ice dynamics, internal waves, and turbulence modeling. Publications highlight innovations in density estimation, constrained optimization, and energy spectrum analysis of oceanic internal waves. Collaborations span disciplines, including biomedical applications (e.g., heart transplant diagnostics) and climate science. His methodologies, such as dual ascent algorithms and prototypal analysis, emphasize data-driven solutions to complex systems. Teaching includes courses on partial differential equations, fluid dynamics, and mathematical modeling. His work has been supported by grants addressing stratified flows, internal wave energy spectra, and turbulent mixing.
Stella Yu is a Professor specializing in computer vision, machine learning, and AI applications across medical imaging, robotics, and wildlife recognition. She emphasizes adaptive advising tailored to individual student strengths, with regular one-on-one and group meetings to discuss research progress and paper presentations. Yu's research group focuses on unsupervised learning, deep learning workflows, and interdisciplinary applications such as MRI reconstruction, meibography analysis, and aerial wildlife monitoring. Her work bridges theoretical advancements with practical tools like DeepInPy for inverse problems. She strongly advocates for teaching experience through GSI roles and ensures students have conference funding to present findings at top venues. Education Expectations: Regular literature review, lab presence for junior students, and clear authorship protocols. Key Research Themes: Feature learning, medical AI, computational optics, and wildlife population surveys. Her lab promotes a collaborative environment with structured feedback loops, emphasizing both technical rigor and creative problem-solving. Current projects include BatVision for 3D spatial navigation using audio signals, and AI-driven solutions for dry eye diagnosis and building information modeling.
Kambiz Ghazinour is a Professor and Chair of the Department of Cybersecurity at SUNY Canton, where he directs the Advanced Information Security and Privacy (AISP) Lab. He holds a PhD in Computer Science from the University of Calgary (2012) and a Postdoctoral Fellowship from the University of Ottawa and Children's Hospital of Eastern Ontario (2014). Previously, he served as an Assistant Professor at Kent State University (2015-2019) and earned a Master's in High Performance Scientific Computing from the University of New Brunswick (2007). Research Focus: Data Security and Privacy, Privacy Enhancing Technologies, Usable Security, Healthcare Systems, and Social Media. Key Projects: DigitalPASS—patented simulation-based privacy education tool for social media safety. His recent publications span Cybersecurity , Deep Learning , and Health Informatics , including works on cryptocurrency price prediction, Alzheimer's detection via eye tracking, and privacy-preserving surveillance. He has received multiple teaching awards such as the Best Teaching Award at the University of Calgary (2008). Faculty Recognition Award, Kent State University (2015, 2016) University Teaching Certificate (2009) Dr. Ghazinour's teaching portfolio includes graduate courses in Data Mining, Digital Forensics, Cryptography, and undergraduate instruction in cybersecurity fundamentals and programming.
Todd A. Alonzo is a Professor of Research in the Department of Preventive Medicine at the University of Southern California . As Group Statistician for the Children's Oncology Group , he focuses on statistical methods for biomarker analysis, medical diagnostic testing, and clinical trial design in pediatric acute myeloid leukemia (AML). Education: B.S. in Statistics, California State Polytechnic University (1994) MS and PhD in Biostatistics, University of Washington (1997, 2000) Research Interests include: Development of statistical frameworks for diagnostic accuracy Genomic and proteomic profiling in AML Pharmacogenomic score systems for chemotherapy response Non-inferiority trial design in low-event-rate settings Health disparities in pediatric oncology Scientific Awards : Fellow, American Statistical Association (2018) Outstanding Teacher Award, International Society for Magnetic Resonance in Medicine (2017) NIH Predoctoral Cardiovascular Biostatistics Training Grant (1995) ENAR Biometrics Society Distinguished Student Paper Award (1999) WNAR Biometrics Society Best Student Oral Presentation (1999) Leadership & Service includes editorial board memberships (Biometrics, Pediatric Blood & Cancer, Biometrical Journal), reviewer for 30+ scientific journals, and roles on multiple Data Safety and Monitoring Boards. He served as President of the International Biometric Society Western Northern America Region (WNAR) in 2009.
Timo Minssen is Professor of Law at the University of Copenhagen (UCPH) and the Founding Director of UCPH's Center for Advanced Studies in Bioscience Innovation Law (CeBIL). He also holds affiliations as an LML Research Affiliate at the University of Cambridge and an Inter-CeBIL Research Affiliate at Harvard Law School's Petrie-Flom Centre. With extensive expertise in Intellectual Property, Competition, and Regulatory Law, Minssen focuses on the legal aspects of emerging health and life science technologies, including genome editing, big data, artificial intelligence, and quantum technology. His educational background includes a German law degree (Staatsexamen) from Georg-August-University in Göttingen, and Swedish biotech & IPR related LL.M., LL.Lic., and LL.D. degrees from Lund University and Uppsala University. His PhD thesis on the patentability of biopharmaceutical technology in the US & Europe received the prestigious Swedish King Oscar award. 2024: TUM Global Visiting Professor, Technical University of Munich (Germany) 2016: Visiting Research Fellow, University of Cambridge (UK) 2014: Visiting Research Fellow, University of Oxford (UK) 2013-14: Visiting Scholar, Harvard Law School (US) 2012: LL.D. - Doctor of Laws (Swedish "juris doktor"), EU/US patent law, Lund University, Sweden Minssen's research spans AI & Big Data in Health & Life Sciences, Sustainable and responsible innovation & tech transfer, Pharmaceutical-, Life Science- & Biotech Law, Comparative European & US Patent Law, Intellectual Property Law & Open Innovation, and EU Competition- & US Antitrust Law. His work addresses legal issues throughout the lifecycle of health and life science products and processes, from R&D regulation to technology transfer and commercialization. His extensive publication record includes 7 books and over 200 articles and book chapters published in leading journals such as Science, Nature Biotechnology, JAMA, and Harvard Business Review. His research has been featured in The Economist, Financial Times, and other major media outlets. Minssen's recent work shows a strong focus on AI regulation, quantum technology law, and data governance in health contexts, reflecting the evolving landscape of technology and law. Scientific Awards and Recognition King Oscar award for best Jur. Dr. thesis (2014) Jorcks Fonds Forsknings Pris (Jorck's Foundation Research Prize) (2017) Awapatent Research Prize (2009) Max Planck Research Scholarship (2005) Visiting Scholar appointments at Harvard Law School, University of Oxford, and University of Cambridge Recipient of a Novo Nordisk Foundation Grant for a "Collaborative Research Program in Biomedical Innovation Law" (2018) As an advisor, Minssen serves international organizations including the WHO, WIPO, and EU Commission. He has supervised numerous PhD students in areas including pharmaceutical law, biotechnology patents, and antimicrobial resistance. His current research projects include the Novo Nordisk Foundation's International Collaborative Bioscience Innovation & Law (Inter-CeBIL) Programme (50 million DKK), CLASSICA: EU Horizon Project on AI-assisted surgery, and AI@Care: Law and Ethics and Algorithmic Bias in Healthcare. Minssen leads the Center for Advanced Studies in Bioscience Innovation Law (CeBIL), which serves as a hub for interdisciplinary research on the intersection of law, technology, and innovation in the health and life sciences. The center collaborates with institutions worldwide to address pressing legal challenges in emerging technologies.
Yang Song is an ARC Future Fellow and Scientia Associate Professor at the School of Computer Science and Engineering , University of New South Wales (UNSW) . She serves as Associate Head of School (Research) and Co-Director of iCinema , focusing on AI and Computer Vision applications for social good. Education: BEng in Computer Engineering (Nanyang Technological University, Singapore), PhD in Computer Science (UNSW, 2013) Research Areas: Biomedical image analysis, human-centred AI, graph data modeling, neuro-symbolic learning, and AI trustworthiness. Her work develops domain-specific deep learning models for radiological segmentation, histopathology cancer analysis, and 3D reconstruction. Recent projects address explainability in LLMs, fairness in AI, and human-robot interaction frameworks. With over 200 peer-reviewed publications in top venues like CVPR , MICCAI , and NeurIPS , her research spans biomedical imaging, robotics, and general multimodal AI. Scientific Awards include: 2024: ARC Industrial Transformation Research Hub for Human-Robot Teaming 2023: Google Inclusion Research Award 2022: NHMRC Ideas Grant for computational brain imaging 2021: UNSW Engineering Research Excellence Award 2020: Scientia Fellowship (UNSW) 2019: ARC Future Fellowship She supervises 24 current PhD/MPhil students and has graduated 15 advisees, including placements at Harvard University and Siemens Healthineers. Her grants include collaborations with Surf Life Saving Australia and industry partnerships for AI-driven solutions.
Aysegul Gunduz, Ph.D., is a Professor and Fixel Brain Mapping Professor at the University of Florida's Herbert Wertheim College of Engineering, Department of Biomedical Engineering. She leads the Brain Mapping Laboratory, focusing on neural networks and clinical translation for neurological disorders. Her work integrates electrophysiology, bioimaging, and neuromodulation to develop diagnostic and therapeutic systems for conditions like Parkinson’s disease, epilepsy, movement disorders, and stroke. Education: B.S., Electrical Engineering, Middle East Technical University (2001) M.S., Electrical Engineering, North Carolina State University (2003) Ph.D., Electrical Engineering, University of Florida (2008) Post-doctoral Fellowship in Neurology, Albany Medical College (2011) Research interests include human brain mapping, closed-loop deep brain stimulation (DBS), neuromodulation strategies for movement disorders, and wearable sensor technologies for neurological monitoring. Her lab emphasizes translational research, bridging basic science with clinical applications to improve patient outcomes. Awards include the BMES Fellowship (2024), AIMBE Fellowship (2022), and PECASE (2019), reflecting her leadership in neural engineering. Her articles explore cutting-edge topics like DBS efficacy, neural network dynamics, and ethical considerations in neural device research. Grants and collaborations focus on advancing adaptive DBS and brain-computer interfaces. She mentors students in neuroengineering and advocates for equitable participation in clinical research. The Brain Mapping Laboratory actively engages in multidisciplinary projects with neurologists, surgeons, and industry partners. Future work includes optimizing closed-loop systems for Tourette syndrome and Parkinson’s disease, developing open-source neuroimaging tools, and expanding wearable sensor applications for real-time neurological monitoring.
Dr. Shirley Coyle is an Assistant Professor in the School of Electronic Engineering at Dublin City University (DCU) and Programme Chair for the BSc Global Challenges. She holds a BEng in Electronic Engineering from DCU (2000) and a PhD in Biomedical Engineering from NUI Maynooth (2005). Her career includes roles as a Telecoms Engineer at Siemens, Research Fellow at the National Centre for Sensor Research, and Team Leader of Wearable Sensors in the INSIGHT Centre for Data Analytics. She also studied part-time at the Grafton Academy for Fashion Design and later founded a consultancy in wearable technologies. Her research focuses on smart garments, wearable sensors, and sustainable textiles, with applications in healthcare, sports performance, and S.T.E.A.M. integration. Key interests include developing wearable chemical sensors, energy-autonomous sensing systems, and IoT-enabled rehabilitation devices. She has pioneered work on wearable sensors for monitoring chronic diseases, athlete training, and home rehabilitation using VR. Dr. Coyle’s work spans interdisciplinary collaboration, combining biomedical engineering with textile design. Her contributions include innovations in electrospun textiles, self-powered sensors, and sensor integration with microfluidics. She has held leadership roles in DCU’s Governing Authority and promotes STEM education through design-focused initiatives.
