Howard Bondell is a Professor of Statistical Data Science at the School of Mathematics and Statistics, University of Melbourne, since 2018. He serves as Head of School since 2021, Co-Director of the Melbourne Centre for Data Science, and holds an ARC Future Fellowship (2020-2024). Ph.D. in Statistics, Rutgers University (2005) Academic Career: North Carolina State University (2005-2018) His research focuses on model selection , robust estimation , regularisation , Bayesian methods , and uncertainty quantification in statistical and machine learning. His publications emphasize applications in regression analysis, quantile modeling, variable selection for high-dimensional data, and genetic data analysis. Scientific awards include: Fellow of the American Statistical Association (2017) ARC Future Fellow (2020-2024)
Stephen T. Wong holds the John S. Dunn Presidential Distinguished Chair in Biomedical Engineering and serves as Professor of Radiology and Medicine with Tenure and Chief of Medical Physics at Houston Methodist. He maintains professorships across multiple prestigious institutions including Weill Cornell Medicine (Radiology, Neurosciences, Pathology and Laboratory Medicine), Texas A&M University, Baylor College of Medicine, University of Texas MD Anderson Cancer Center, Rice University, University of Texas Health Houston, and University of Houston. Weill Cornell Medicine: Professor of Computer Science and Bioengineering in Radiology (since 2008), Pathology and Laboratory Medicine (since 2010), and Neuroscience (since 2012) Houston Methodist: John S. Dunn Presidential Distinguished Chair in Biomedical Engineering Academic leadership: Director of multiple research centers including Ting Tsung and Wei Fong Chao Center for BRAIN and AI in Innovative Medicine lab Dr. Wong's research employs a systems-based approach integrating engineering with biology and medicine to elucidate disease mechanisms. His laboratory focuses on discovering novel drugs and biomarkers while developing advanced diagnostic and therapeutic devices, with particular emphasis on cancer, neurological disorders, and metabolic diseases. Current projects target micro- and macroenvironments of cancer and Alzheimer's disease, apply spatial and systems biology methods for drug discovery, create label-free point-of-care molecular diagnostics, and develop AI applications for stroke triage and treatment. His publication portfolio demonstrates consistent growth over three decades, with over 500 peer-reviewed papers and five books. Recent work shows strong emphasis on artificial intelligence applications in medical imaging, cancer therapeutics, and neurological diagnostics, with multiple 2025 publications featuring multimodal AI approaches for hepatocellular carcinoma, lung cancer interventions, tumor evolution, brain imaging, and thyroid nodule characterization. Fellowships: IEEE, AIMBE, IAMBE, ACMI, AMIA, Optica, and AAIA Honors: AIIA Fellow (2024), American College of Medical Informatics Fellow (2023), AAIA-Fellow (2021), AIMBE Fellow (2021) Professional: Registered Professional Engineer (PE), Executive education from Stanford, MIT, and Columbia Business Schools Dr. Wong has trained over 170 PhD, MD/PhD, and postdoctoral scholars, with four now holding endowed chairs. His research has received continuous NIH funding for three decades, supporting 35 active and completed projects including DeepStroke+ for AI stroke detection, Alzheimer's disease research, and cancer diagnostics. He has founded multiple research centers including the Division of Shared Resources at Houston Methodist Neal Cancer Center, Translational Biophotonics Lab, and Center for Modeling Cancer Development.
Hannes Hick is a Professor at Graz University of Technology , affiliated with the Institute of Machine Elements and Development Methodology . His research focuses on mechanical development, tribology, and systems engineering for automotive and industrial applications. He actively contributes to engineering education and methodology standardization. Research Interests Hydrogen internal combustion engines System modeling and digital twins Tribology in electric drivetrains Sustainable engineering practices MBSE (Model-Based Systems Engineering) Friction and wear analysis Article Trends His recent work emphasizes hydrogen propulsion systems, model-based approaches for interdisciplinary engineering challenges, tribological optimization for sustainable mobility, and integrating AI with mechanical design workflows. Labs and Teams He leads research at the Institute of Machine Elements, focusing on mechanical validation and development methodologies for advanced powertrain systems.
Dr. Barbara E. Jones serves as an Associate Professor in the Department of Internal Medicine at the University of Utah School of Medicine, with dual appointments in Pulmonary and Critical Care Medicine. Her clinical practice spans diverse healthcare settings within the Veterans Affairs system and academic medical centers, focusing on evidence-based adaptation of care to varied patient populations. Her educational background includes: M.D. from University of Washington School of Medicine B.A. in Philosophy from Dartmouth College Master of Science in Clinical Investigation (M.S.C.I) from University of Utah Postdoctoral Fellowship in Pulmonary and Critical Care Medicine at University of Utah Residency in Internal Medicine at University of Utah Dr. Jones' research centers on decision-making processes in pneumonia diagnosis and treatment, employing a tripartite informatics approach combining population analytics, cognitive behavior analysis, and clinical decision support systems. Her work specifically targets reducing diagnostic uncertainty and treatment variation across healthcare systems, with emphasis on equitable care delivery for diverse patient populations. Current projects investigate diagnostic discordance in community-acquired pneumonia, electronic surveillance for hospital-acquired infections, and machine learning applications for diagnostic error detection. Analysis of her 15 most recent publications reveals consistent focus on pneumonia management systems, with emerging emphasis on pandemic impacts on diagnostic practices and AI-driven quality improvement. Her work predominantly utilizes large VA healthcare datasets spanning 100+ medical centers, featuring mixed-methods approaches that integrate quantitative analytics with qualitative clinician experience assessment. Dr. Jones actively contributes to clinical guideline development and medical education through editorial work in major journals including Chest and Annals of Internal Medicine , where she frequently addresses controversies in pneumonia diagnosis and antibiotic stewardship. Her research program operates at the intersection of the University of Utah Health system and the Veterans Affairs national healthcare network, leveraging electronic clinical decision support implementations across diverse hospital settings including rural and critical access facilities. Current initiatives focus on real-time feedback systems for diagnostic performance improvement and automated surveillance for healthcare-associated infections.
Dr. Matteo Fasiolo is a Senior Lecturer in the School of Mathematics at the University of Bristol, specializing in Statistical Science. His research focuses on advanced statistical modeling with significant applications in electricity demand forecasting and medical statistics, leveraging Generalized Additive Models (GAMs) as a core methodology. His primary research interests span: Generalized Additive Models and their extensions for complex data structures Covariance matrix modeling for high-dimensional energy forecasting Probabilistic forecasting techniques for uncertainty quantification Statistical machine learning including variational inference and contrastive learning Applications in electricity grid management and medical diagnostics Recent publications (2023-2025) reveal a dual focus: developing scalable statistical methods for electricity net-demand prediction in Great Britain using additive covariance models, and applying distributional regression to medical challenges like kidney function decline and cardiovascular risk prediction. His work on SoftCVI demonstrates innovation in variational inference, while extensions to GAMs address both mean modeling and full distributional forecasting. Dr. Fasiolo has supervised at least one student as indicated by university records. His research outputs include 16 publications and 2 publicly available datasets, reflecting active contributions to methodological statistics and domain-specific applications in energy systems.
Stefania De Vincentis is an Associate Professor of History of Contemporary Art at the Department of Humanities, Ca' Foscari University of Venice. She teaches across multiple degree programs including History of Contemporary Art, Digital and Public Humanities, and Economics and Management of Arts and Cultural Activities. Her office is located at Malcanton Marcorà (VeDPH, second floor) where she holds student office hours on Wednesdays from 4:00 PM to 6:00 PM. Dr. De Vincentis earned her PhD with honors in Human Sciences from the University of Ferrara. Her educational background includes studies in video art and visual arts at the Academy of Fine Arts in Bologna, the IUAV-Institute of Architecture at the University of Venice, and a Master's Degree in Cultural Heritage Management (MuSeC) at the University of Ferrara. She was a Visiting Student at the Getty Research Institute in Los Angeles and a fellow at the Ermitage Italia Center for Art History. Her research focuses on the intersection of art history and digital technologies, particularly examining how digital tools transform museum experiences and art historical scholarship. Dr. De Vincentis investigates digital museum applications, virtual reality approaches to art collections, and the impact of technologies like IIIF (International Image Interoperability Framework) on art historical research. Her work explores how AI, VR, and other digital methodologies can enhance engagement with cultural heritage while raising critical questions about the nature of digital art history as a discipline. Analysis of her recent publications reveals a consistent focus on practical digital applications in museum contexts, particularly at institutions like the Galleria Borghese in Rome. Her work spans from theoretical reflections on digital art history methodology to hands-on implementations of virtual reality and AI technologies for museum collections. As a founding member of the DiDiART laboratory (Diagnostics and Digital for Art) at the University of Ferrara and a member of the Venice Center for Digital and Public Humanities (VeDPH), Dr. De Vincentis actively contributes to digital humanities initiatives. She has collaborated extensively with cultural institutions including the Ferrara Arte Foundation, Pinacoteca Nazionale di Ferrara, and Estense Castle, bridging academic research with practical museum applications. Her teaching portfolio demonstrates a commitment to preparing students for careers at the intersection of art, technology, and cultural heritage management. She offers specialized courses that equip students with both theoretical frameworks and practical skills for engaging with digital transformations in the cultural sector.
Elisabeth Wetzer is an Associate Professor in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. Her research bridges artificial intelligence with healthcare applications, focusing on multimodal image registration, bias mitigation in AI, and physics-informed learning models. Current Role: Associate Professor, Machine Learning Group Research Themes: AI ethics, medical imaging, cross-modal representations, algorithmic fairness Her recent work explores technical challenges in PET imaging analysis and societal implications of AI bias. Collaborative projects span medicine, mathematics, and computer science disciplines. Key scientific contributions include: Physics-informed deep learning for PET image data Studies on multi-task learning efficacy in medical classification Research on gender bias in algorithmic systems She actively participates in diversity initiatives and public outreach, including presentations at Nobel laureate conferences and media engagements on AI ethics.
Tetsuya Sakai is a Professor at the School of Fundamental Science and Engineering within Waseda University's Faculty of Science and Engineering. His work focuses on information access, retrieval, and natural language processing, with a particular emphasis on evaluation frameworks for search systems. Affiliations: Waseda University (Faculty of Science and Engineering, School of Fundamental Science and Engineering) Academic Rank: Professor Research Interests : Dr. Sakai's research spans four key areas: (1) Information Access —designing systems for direct and immediate information delivery, (2) Search Evaluation —developing metrics like Height-Biased Gain and hierarchical intent-based diversity measures, (3) Fairness in IR —pioneering frameworks for group fairness in conversational search, and (4) Statistical Reform —advocating Bayesian methods and robust experimental design. His work also addresses privacy inconsistencies in mobile apps and cognitive biases in LLMs. Scientific Awards : Notable recognitions include induction into the SIGIR Academy (2023) , ACM Distinguished Member (2018) , ACM Senior Member (2016) , and multiple DEIM/FIT/CSS Best Paper Awards . He has received teaching honors like the Waseda Presidential Teaching Award (2016) and WASEDA e-Teaching Award (2018) . Article Trends : Recent publications highlight: Advancements in LLM-assisted relevance assessments and hallucination diagnostics for tool-augmented models Conversational search fairness through multi-level evaluation frameworks and group diversity metrics Innovations in 3D medical reconstruction from clinical data and multimodal uncertainty modeling Statistical rigor via randomization tests , credible intervals , and topic set design Privacy analysis in mobile app descriptions and cognitive bias studies in search interaction
Dustin D. French, PhD, serves as Professor in Ophthalmology and Medical Social Sciences (Determinants of Health) at Northwestern University's Feinberg School of Medicine. His dual appointments bridge clinical ophthalmology with population health research, focusing on healthcare system optimization through economic and policy analyses. Dr. French's educational foundation includes: PhD from The Ohio State University (2001) His research program centers on health economics and outcomes research , with signature expertise in: Comparative and cost-effectiveness methodologies Health informatics and big data applications Health services research and policy evaluation Addressing health disparities in diabetic eye care and rural communities Recent publications reveal a strategic focus on AI-driven clinical decision support and resource optimization, with 2025 studies examining glaucoma identification algorithms, nursing home quality metrics, microhematuria diagnostic pathways, and pediatric obesity interventions. Dr. French's scholarly impact is recognized through prestigious awards: International Society for Pharmacoepidemiology Outstanding Reviewer Award (2016, 2010) Department of Veterans Affairs Distinguished Service Award (2016) International Society for Pharmacoepidemiology Distinguished Article Award (2006) His leadership in health services research extends to mentorship and grant-funded initiatives targeting healthcare quality improvement, particularly in diabetic eye care access for minority populations. Institutional affiliations include the Center for Diabetes and Metabolism, IPHAM's Center for Health Services & Outcomes Research, and NUCATS, where he contributes to translational research infrastructure.
Carl-Mikael Zetterling is a Professor and Head of Department at Kungliga Tekniska Högskolan (KTH) in Stockholm, Sweden, affiliated with the School of Electrical Engineering and Computer Science (ICT) and the Electronics and Embedded Systems department. His research focuses on process technology and device design for high-temperature, high-power silicon carbide (SiC) electronics, expanding into SiC-based analog and integrated circuits. He has authored over 300 publications, including books on SiC process technology and plagiarism prevention. Dr. Zetterling has held leadership roles such as Vice Dean of the School of ICT (2013–2017) and teacher representative on KTH's faculty board. He has collaborated internationally at Stanford University, Kyoto University, and Kyoto Institute of Technology. His work addresses applications in extreme environments, including Venus exploration and fusion reactor monitoring, with a focus on radiation tolerance and thermal resilience. The 15 most recent publications highlight trends in wide bandgap semiconductors, gamma irradiation effects on SiC devices, and high-temperature integrated circuits. His articles span structural health monitoring with machine learning, novel SiC diode designs, and radiation-hardened electronics. Key contributions include advancements in self-aligned contacts, trench MOSFETs, and compact modeling for extreme conditions. While no formal awards are listed, his roles in technical program committees (TMS Electronic Materials Conference, IEEE SISC Conference) and editorial work demonstrate significant academic service. He teaches courses ranging from digital design to high-temperature electronics, overseeing degree projects in embedded systems, communication, and nanotechnology.
Bo Markussen is a Professor at the University of Copenhagen within the Department of Mathematical Sciences . He is also a member of the Data Science Laboratory , where he contributes to statistical methodology and interdisciplinary collaborations. His academic journey began with a Cand.Scient (MSc) and PhD in Statistics from the University of Copenhagen, awarded in 1998 and 2002 respectively. 2012–present: Professor, Department of Mathematical Sciences, University of Copenhagen 2009–2012: Associate Professor, Department of Basic Sciences and Environment, University of Copenhagen 2006–2009: Assistant Professor, Department of Basic Sciences and Environment, University of Copenhagen Bo Markussen's research focuses on applied statistics , particularly in functional data analysis and multiple testing corrections in genetics . His work spans diverse domains including environmental science, agriculture, and public health. Recent research output highlights applications in Arctic climate data analysis, fire risk modeling, plant stress phenotyping, and nutritional biomarker prediction. His recent publications demonstrate a strong trend toward machine learning integration with statistical modeling , addressing challenges in high-dimensional data analysis and environmental risk assessment. Collaborations span institutions in Denmark and internationally, reflecting his engagement in pan-Arctic climate studies and tropical agricultural research. 2018–present: Associate Editor, Scandinavian Journal of Statistics 2017–2019: Chair, Danish Society for Theoretical Statistics 2015–2017: Board Member, Danish Society for Theoretical Statistics As a central figure in the Data Science Laboratory , Markussen leads statistical consultancy initiatives and contributes to methodological advancements. His expertise bridges theoretical statistics with real-world applications, particularly in handling complex datasets across biological and environmental domains.
Allon Guez is a Professor in the Department of Electrical and Computer Engineering at Drexel University. His research focuses on control systems, robotics, artificial intelligence, medical robotics, and automated decision making. He actively bridges academia and industry through high-tech entrepreneurship. Education PhD in Electrical Engineering, University of Florida MS in Electrical Engineering, University of Florida MBA in Finance, Drexel University BS in Electrical Engineering, Technion - Israel Institute of Technology His research portfolio spans medical robotics, automated decision making systems, and advanced control algorithms. Key areas include wearable safety devices, radiation control in imaging systems, and closed-loop brain stimulation technologies. Notable contributions include founding ControlRad (radiation reduction systems) and GraceFall (fall detection technology). His work demonstrates a strong emphasis on translating academic research into commercial medical devices. Recent publications highlight innovations in: Fetal brainwave monitoring Postural disturbance detection Seizure prediction algorithms Magnetic microrobotics Dynamic CT collimation Cardiac tissue modeling
Anna Vilanova is a Full Professor in Visual Analytics at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), and is associated with the Electrical Engineering department's Signal Processing Systems. Previously, she served as Associate Professor at TU Delft (2013-2019) and Assistant Professor at TU/e (2002-2013). Her research focuses on Visual Analytics for high-dimensional data , explainable AI , and biomedical applications including Diffusion Weighted Imaging, 4D Flow, and Pangenomics. Education: Doctorate in Computer Graphics & Visualization (2001) Master in Computer Science (1997), Universitat Politècnica de Catalunya Research Highlights: Vilanova leads work on Visual Analytics systems for biomedical data, with recent publications in Diffusion MRI modeling , Tractography visualization , Explainable AI frameworks , and Pangenomic variant analysis . Her work bridges dimensionality reduction , uncertainty visualization , and medical imaging applications. Scientific Contributions: NWO-Veni grant (2005): "Visualization of global tensor information for diffusion tensor imaging" NWO-Aspasia grant (2013) Best Poster Award EuroVis (2025) Best Demo/Poster Awards (2022) Leadership & Service: Vilanova serves on the IEEE VIS Steering Committee , was EUROGRAPHICS President (2019-2022), and contributes to conferences like IEEE Visualization and EG-EuroVis . She co-founded the EAISI Health research initiative at TU/e.
Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.
Bihuan Chen is an Associate Professor at the College of Computer Science and Artificial Intelligence, Fudan University, specializing in software engineering with focus on software supply chain security and trustworthy AI systems. His research spans multiple programming languages including JavaScript, Python, Java, and C/C++ across application and AI domains. Dr. Chen earned his B.Sc. and Ph.D. in Computer Science from Fudan University in 2009 and 2014 respectively, followed by postdoctoral research at Nanyang Technological University (2014-2017). His research interests include software supply chain risk assessment, trustworthy AI systems, and program analysis. His recent publications demonstrate strong focus on malicious package detection in NPM/PyPI ecosystems, vulnerability patch porting using LLMs, and safety verification for autonomous driving systems. The work shows increasing integration of machine learning techniques with traditional program analysis approaches, particularly evident in the 2024-2025 publications that leverage LLMs for vulnerability detection and code refinement. ACM SIGSOFT Distinguished Paper Award (FSE 2016, ASE 2018, ASE 2022, FSE 2025) IEEE TCSE Distinguished Paper Award (ICSME 2020, SANER 2023) CCF Prototype Competition Awards (2nd and 3rd Prizes) Dr. Chen has advised over 50 students including current PhD candidates and notable alumni now at Huawei, ByteDance, and other leading tech firms. His fuxi platform assesses security, legal, and maintenance risks across the software engineering lifecycle. He serves on program committees for major conferences including ICSE, FSE, ASE, and ISSTA, and as Associate Editor for the Journal of Software: Evolution and Process.
